Intelligent deicing method and system for overhead line system

Abstract

The invention relates to the technical field of overhead line system deicing, and discloses an intelligent deicing method and system for an overhead line system. The method comprises the following steps: firstly, acquiring ice layer monitoring data such as ice layer thickness distribution information, environment temperature information and wind speed information of a contact network area in real time; inputting the data into a simulation modeling tool, and constructing a three-dimensional ice layer distribution model of the overhead line system; judging whether deicing is needed or not according to matching of ice layer thickness distribution information in the model and a preset ice layer thickness threshold value mapping table; if yes, deicing equipment position parameters and deicing agent type parameters are extracted from a preset parameter database, and the deicing agent applying process is simulated in the three-dimensional model; and finally, outputting an ice layer fracture path, an ice melting range and deicing efficiency data, analyzing rationality, matching degree and effectiveness, and generating a parameter optimization instruction to adjust subsequent deicing parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of catenary deicing, and in particular to a method and system for intelligent deicing of a catenary. Background Art

[0002] Catenary icing is a common operational challenge in cold regions. In winter, ice easily forms on the surface of the catenary in low temperatures. Over time, the ice gradually increases in thickness, potentially adversely affecting the structural stability and electrical conductivity of the catenary. Traditional catenary de-icing methods rely heavily on manual inspections and operations, which not only consume significant manpower and resources but are also limited by inspectors' experience and judgment, making it difficult to fully and accurately understand ice conditions in real time. Failure to promptly detect excessive ice can lead to catenary failures, disrupting normal train operations. Some existing mechanical de-icing equipment lacks accurate understanding of ice distribution during use, leading to a degree of blind de-icing. This can lead to incomplete de-icing, leaving residual ice and posing safety risks; or excessive de-icing, resulting in wasted energy and de-icing agents. Furthermore, environmental factors such as temperature and wind speed significantly influence ice formation and melting, and traditional de-icing methods fail to fully account for these factors, resulting in inconsistent de-icing results. With the rapid development of rail transit, the safety and reliability requirements for contact network operation are becoming increasingly higher. Traditional de-icing methods can no longer meet actual operational needs. A more intelligent and efficient de-icing method is needed to deal with the problem of contact network icing. Summary of the Invention

[0003] The object of the present invention is to provide a method and system for intelligent deicing of a contact network to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides a method for intelligent deicing of a contact network, the method comprising: Real-time collection of ice monitoring data in the overhead line area, including ice thickness distribution information, ambient temperature information, and wind speed information; Using the ice layer monitoring data as input information, a three-dimensional ice layer distribution model of the contact network is constructed through a simulation modeling tool; Based on the ice thickness distribution information in the three-dimensional ice distribution model, matching it with a preset ice thickness threshold mapping table to determine whether a de-icing operation is triggered; When it is determined that a de-icing operation is required, de-icing equipment location parameters and de-icing agent type parameters are extracted from a preset parameter database; simulating a deicing agent application process in the three-dimensional ice layer distribution model using the deicing equipment location parameters and the deicing agent type parameters; Output the ice layer rupture path, ice melting range and deicing efficiency data during the deicing process, analyze the rationality of the ice layer rupture path, the matching degree of the deicing equipment position parameters and the validity of the deicing efficiency data, and generate parameter optimization instructions to adjust subsequent deicing parameters.

[0005] Preferably, the specific process of constructing the three-dimensional ice distribution model of the contact network includes: Performing format conversion processing on the ice layer monitoring data to convert it into a standardized format recognizable by numerical calculation software; Using geographic information system tools, generating a continuous ice distribution surface based on the ice monitoring data in the standardized format; In the simulation modeling software, different ice layer area blocks are divided according to the continuous ice layer distribution surface; The physical property data of the ice layer is assigned to each ice area block, and local refined modeling is performed to form a three-dimensional ice layer distribution model of the contact network.

[0006] Preferably, the specific process of determining the de-icing operation requirement includes: extracting ice thickness values ​​at various locations in the contact network area from the ice thickness distribution information; Comparing each ice thickness value with a safe ice thickness threshold in the preset ice thickness threshold mapping table; If the number of locations where the ice thickness exceeds the safe ice thickness threshold reaches a preset ratio, it is determined that a de-icing operation is required; Otherwise, it is determined that no de-icing operation is required, and a maintain status quo instruction is output.

[0007] Preferably, the preset parameter database includes de-icing equipment location parameter confirmation logic and de-icing agent type parameter confirmation logic, wherein the de-icing equipment location parameter confirmation logic is: According to the ice distribution density data of the contact network area, the position correction coefficient is matched with the position correction coefficient corresponding to the ice distribution density stored in the database to obtain the position correction coefficient; Multiplying the position correction coefficient by the preset reference equipment position, and calculating the result as the de-icing equipment position parameter; Based on the ambient temperature information, the temperature-corresponding equipment deployment pattern is extracted from the database to determine the specific installation direction of the de-icing equipment; Based on the wind speed information, the safe operating distance data of the de-icing equipment is adjusted to obtain the final de-icing equipment position parameters.

[0008] Preferably, the specific contents of the deicing agent type parameter confirmation logic include: Matching the ice layer thickness distribution information with deicing agent type and unit area dosage data corresponding to ice layer thickness stored in a database to obtain deicing agent type data and unit area dosage data; Calculating the total deicing agent requirement based on the surface area data of the catenary area and the unit area dosage data; Combined with the equipment coverage data in the de-icing equipment location parameters, the single-point de-icing agent distribution amount is calculated to form the de-icing agent type parameter.

[0009] Preferably, the specific analysis method for the rationality of the ice layer rupture path includes: Extract ice breakup path data generated during simulated de-icing; If the ice layer rupture path data covers all locations with high ice thickness and forms a continuous rupture network, the rationality of the ice layer rupture path is marked as qualified; If the ice layer rupture path data has local fractures or does not cover key locations, the rationality of the ice layer rupture path will be marked as unqualified and a re-simulation process will be triggered.

[0010] Preferably, the specific analysis method of the matching degree of the de-icing equipment position parameters includes: extracting actual ice distribution data of the catenary area from the ice monitoring data; Performing difference calculation on the actual ice layer distribution data and the expected ice melting range data during the simulated deicing process to obtain a position deviation value; If the position deviation value is less than a preset tolerance threshold, marking the de-icing equipment position parameter matching degree as a match; Otherwise, the de-icing equipment position parameter matching degree is marked as mismatched, and an equipment position adjustment instruction is generated.

[0011] Preferably, the specific analysis method of the effectiveness of the de-icing efficiency data includes: Calculate the ratio of actual ice melt volume data to expected ice melt volume data during the simulated deicing process; If the ratio is above the lower limit of the preset efficient ice melting range, the validity of the deicing efficiency data is marked as efficient; If the ratio is within a preset low-efficiency ice melting range, marking the effectiveness of the de-icing efficiency data as low-efficiency; If the ratio is lower than a preset lower limit of the inefficient ice melting range, the validity of the deicing efficiency data is marked as invalid.

[0012] Preferably, after executing the parameter optimization instruction, the method further includes: Converting the optimized de-icing equipment position parameters and de-icing agent type parameters into an equipment control instruction set; The deicing actuator in the catenary area is driven by the device control instruction set to adjust the operating position and deicing agent injection parameters; Real-time collection of actual operation trajectory data of de-icing actuators and de-icing agent coverage data; Comparing the actual operation trajectory data with the expected operation trajectory during the simulated deicing process to generate an equipment positioning error value; When the equipment positioning error value exceeds a preset operation tolerance threshold, the equipment position recalibration process is triggered and the de-icing equipment position parameters are updated.

[0013] Preferably, the present invention further includes a catenary intelligent deicing system for implementing the above-mentioned catenary intelligent deicing method, the system comprising: A data acquisition module is used to collect ice layer monitoring data in the contact network area in real time, wherein the ice layer monitoring data includes ice layer thickness distribution information, ambient temperature information, and wind speed information; a three-dimensional modeling module, connected to the data acquisition module, for taking the ice layer monitoring data as input information and constructing a three-dimensional ice layer distribution model of the catenary through a simulation modeling tool; a de-icing determination module, connected to the three-dimensional modeling module, configured to determine whether a de-icing operation is triggered based on matching the ice thickness distribution information in the three-dimensional ice distribution model with a preset ice thickness threshold mapping table; a parameter extraction module, connected to the deicing determination module, for extracting deicing equipment location parameters and deicing agent type parameters from a preset parameter database when it is determined that a deicing operation is required; a deicing simulation module, connected to the three-dimensional modeling module and the parameter extraction module, respectively, for simulating a deicing agent application process in the three-dimensional ice layer distribution model using the deicing equipment position parameters and the deicing agent type parameters; An analysis and optimization module is connected to the deicing simulation module and is used to output the ice layer rupture path, ice melting range and deicing efficiency data during the deicing process, analyze the rationality of the ice layer rupture path, the matching degree of the deicing equipment position parameters and the validity of the deicing efficiency data, and generate parameter optimization instructions to adjust subsequent deicing parameters.

[0014] Compared with the prior art, the present invention has the following beneficial effects: Real-time ice monitoring data allows operators to promptly understand the ice thickness distribution, ambient temperature, and wind speed in the catenary area. This information provides a comprehensive reference for subsequent de-icing operations. A three-dimensional ice distribution model constructed based on this data intuitively presents the specific distribution of the ice, allowing operators to clearly understand the overall ice situation and avoid de-icing omissions that were previously caused by incomplete information. When determining the need for de-icing, the system matches the thresholds against a pre-set ice thickness mapping table, making de-icing triggering more objective and standardized, eliminating over-reliance on subjective judgment. When de-icing is required, parameters for the de-icing equipment location and de-icing agent type are extracted from a pre-set parameter database, ensuring rational and targeted de-icing parameter selection and reducing arbitrariness. Using a three-dimensional ice distribution model to simulate the deicing agent application process allows for a preview of deicing effectiveness before actual deicing operations, identifying potential problems in advance. The output data on ice breakup paths, ice melting range, and deicing efficiency provides a detailed basis for analyzing the deicing process. Analysis of this data can generate parameter optimization instructions to adjust subsequent deicing parameters, ensuring that deicing operations are continuously optimized to meet actual needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a working principle diagram of the contact network intelligent deicing method according to the present invention; Figure 2 Flowchart constructed for the three-dimensional ice distribution model; Figure 3 Flowchart for confirmation of de-icing equipment location parameters; Figure 4 Flowchart for de-icing equipment location parameter matching analysis; Figure 5 Flowchart of instruction execution and calibration for parameter optimization. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] See also Figure 1 The present invention provides a method for intelligent deicing of a contact network, the method comprising: Real-time ice monitoring data is collected from the catenary area. This data includes ice thickness distribution, ambient temperature, and wind speed information. Using this data as input, a three-dimensional ice distribution model of the catenary is constructed using simulation modeling tools. The ice thickness distribution information in the three-dimensional ice distribution model is matched against a preset ice thickness threshold mapping table to determine whether de-icing is required. If de-icing is determined to be necessary, the de-icing equipment location parameters and de-icing agent type parameters are extracted from a preset parameter database. Using these parameters, the de-icing agent application process is simulated within the three-dimensional ice distribution model. The de-icing process outputs the ice breakage path, ice melting range, and de-icing efficiency data. The rationality of the ice breakage path, the matching of the de-icing equipment location parameters, and the validity of the de-icing efficiency data are analyzed, generating parameter optimization instructions to adjust subsequent de-icing parameters.

[0018] Example 1: See Figure 2 During the construction of the three-dimensional ice distribution model for the catenary, ice monitoring data is converted to a standardized format recognizable by numerical calculation software. The raw ice monitoring data may come from different types of sensors, and their data formats may vary. For example, some sensors output data in XML format, while others in TXT format. Using a format conversion tool, this data is uniformly converted to a format compatible with numerical calculation software, such as .mat for MATLAB or .dat for ANSYS, allowing the data to be directly read and processed by the software.

[0019] Using Geographic Information System (GIS) tools, a continuous ice distribution surface is generated based on standardized ice monitoring data. GIS tools, equipped with spatial analysis and interpolation capabilities, take discrete monitoring point data as input and process it using an interpolation algorithm to fill in the gaps between monitoring points, forming a continuous surface covering the entire catenary area. During the interpolation process, the interpolation accuracy is automatically adjusted based on the distribution density of monitoring points. Higher interpolation accuracy is used in areas with dense monitoring points, while lower accuracy is applied in areas with sparse monitoring points, balancing surface accuracy and computational efficiency.

[0020] In the simulation modeling software, the continuous ice distribution surface is divided into different ice area blocks. This division takes into account the structural characteristics of the catenary system, using key structures such as the catenary support and suspension system as demarcation points to divide the continuous surface into several independent blocks. Each block corresponds to a specific section of the catenary system, such as the conductor section between two supports or the section connecting the support and suspension system. The size of the block is determined by the uniformity of the ice distribution. Areas with large variations in ice distribution are divided into smaller blocks, while areas with more uniform distribution are divided into larger blocks to facilitate subsequent refined modeling.

[0021] Each ice area block is assigned ice physical property data, and local refined modeling is performed to form a three-dimensional ice distribution model of the contact network. Ice physical property data include but are not limited to ice density, elastic modulus, Poisson's ratio, thermal conductivity, etc. These data are obtained through long-term experimental accumulation and historical data statistics, stored in a database, and called up according to the ice characteristics of the block during modeling. During the local refined modeling process, the contact interface between the ice layer and the contact network is refined, simulating the adhesion state of the ice layer on the contact network surface. At the same time, the stress distribution within the ice layer, such as the stress generated by the ice layer's own weight and the adhesion between the ice layer and the contact network, is considered, so that the model can accurately reflect the actual physical state of the ice layer.

[0022] When determining the need for de-icing operations, ice thickness values ​​at various locations within the catenary are extracted from the ice thickness distribution information. These values ​​are derived from the thickness parameters of each grid node in the three-dimensional ice distribution model. The model uses a gridding process to divide the catenary area into a large number of grids, each corresponding to a specific location. Thickness values ​​are accurate to the millimeter level, ensuring that the ice thickness at all locations in the catenary is fully reflected.

[0023] Each ice thickness value is compared with the safe ice thickness threshold in the preset ice thickness threshold mapping table. The preset ice thickness threshold mapping table is developed based on the design standards, operating experience, and relevant industry specifications of the contact network. The table sets corresponding safe ice thickness thresholds for different parts of the contact network. For example, the catenary of the contact network has a lower safe ice thickness threshold because it needs to bear greater tension; while the return line of the contact network has a relatively higher threshold because it is less stressed. During the comparison process, the ice thickness value of each grid node is compared with the threshold of the corresponding position in the table one by one, and the location information where the threshold is exceeded is recorded.

[0024] If the number of locations where ice thickness exceeds the safe ice thickness threshold reaches a preset ratio, de-icing is deemed necessary. This ratio is determined based on the importance of the catenary, the operating environment, and potential risk assessment. For busy mainline catenary systems, to ensure train safety, the ratio might be 20%; for branch lines or temporary lines, it might be 40%. The system calculates the ratio of locations exceeding the threshold to the total number of locations. When this ratio reaches or exceeds the preset ratio, the system determines that de-icing is necessary.

[0025] Otherwise, it determines that de-icing is not necessary and issues a maintain status command. After issuing the maintain status command, the system does not activate the de-icing equipment but continues to monitor the ice layer in the catenary area, collecting ice layer data according to the set monitoring period, and repeating the above judgment process until de-icing is detected.

[0026] Example 2: See Figure 3 The preset parameter database stores de-icing equipment location parameter confirmation logic and de-icing agent type parameter confirmation logic, which are used to provide parameter support when determining that a de-icing operation is required.

[0027] During the implementation of the de-icing equipment position parameter confirmation logic, a matching process is first performed based on the ice distribution density data for the catenary area. This ice distribution density data is calculated using a three-dimensional ice distribution model. It is determined by the ratio of the ice volume in each region to the corresponding spatial volume in the model, reflecting the density of ice in different areas. A table of position correction coefficients corresponding to ice distribution density is pre-stored in the database. This table is compiled from historical de-icing operation records and simulation results. Different ice distribution density ranges correspond to different position correction coefficients. For example, areas with higher ice distribution density have correspondingly larger position correction coefficients, while areas with lower ice distribution density have correspondingly smaller position correction coefficients. By matching the calculated ice distribution density data with the data in the table, the corresponding position correction coefficients can be obtained.

[0028] Multiplying the position correction coefficient by the preset reference equipment position yields the initial value of the de-icing equipment position parameter. The preset reference equipment position is based on the overhead contact network's structural layout and equipment installation specifications, encompassing common operating points along the network, including coordinate information such as horizontal distance and vertical height. For example, for a conductor section of the overhead contact network, the reference position might be set 0.8 meters to the side of the conductor; for a connecting component of the overhead contact network, the reference position might be set 0.5 meters in front. The reference position is adjusted using the position correction coefficient to ensure that the equipment position more closely matches the actual ice distribution.

[0029] Based on ambient temperature information, the corresponding equipment deployment pattern is extracted from the database. The database stores equipment deployment patterns corresponding to different temperature ranges. These patterns are developed based on the influence of temperature on the diffusion of deicing agents and the rate of ice melting. For example, when the ambient temperature is between -5°C and 0°C, the deicing agent has good fluidity, and the equipment deployment pattern is horizontal spraying. When the ambient temperature is between -15°C and -10°C, to prevent the deicing agent from solidifying too quickly during spraying, the equipment deployment pattern is tilted upward at a 30-degree angle. When the ambient temperature is below -20°C, the equipment deployment pattern is a circular, multi-directional spraying pattern to increase the contact time between the deicing agent and the ice. Based on the real-time collected ambient temperature information, the corresponding deployment pattern is matched to determine the specific installation direction of the deicing equipment.

[0030] Based on wind speed information, the safe operating distance (SAD) data for de-icing equipment is adjusted. The SAD refers to the minimum distance between the de-icing equipment and the catenary. The initial SAD is set based on equipment performance and the catenary voltage level. For example, for a 25kV catenary, the initial SAD is 1.5 meters. Wind speed information is collected in real time by wind speed sensors. A database stores the relationship between wind speed and SAD adjustment coefficients. Higher wind speeds increase the adjustment coefficient. For example, when the wind speed is level 3 (3.4-5.4 m / s), the adjustment coefficient is 1.2, and the SAD is adjusted to 1.8 meters. When the wind speed is level 6 (10.8-13.8 m / s), the adjustment coefficient is 1.8, and the SAD is adjusted to 2.7 meters. By adjusting the SAD based on wind speed information, the final de-icing equipment location parameters are obtained, which include the equipment's three-dimensional coordinates, installation orientation, and SAD.

[0031] During the implementation of the de-icing agent type parameter confirmation logic, ice thickness distribution information is matched with de-icing agent type and unit area dosage data stored in the database. The database divides ice thickness into multiple intervals, each corresponding to a de-icing agent type and unit area dosage. For example, when the ice thickness is 1-3 mm, the corresponding de-icing agent type is Class A solution (calcium chloride concentration of 20%), with a unit area dosage of 15 g / m²; when the ice thickness is 3-7 mm, the corresponding de-icing agent type is Class B solution (calcium chloride concentration of 35%), with a unit area dosage of 30 g / m²; when the ice thickness is 7-15 mm, the corresponding de-icing agent type is Class C mixture (calcium chloride and potassium acetate in a 4:1 ratio), with a unit area dosage of 50 g / m²; and when the ice thickness exceeds 15 mm, the corresponding de-icing agent type is Class D paste, with a unit area dosage of 80 g / m². The corresponding de-icing agent type and unit area dosage data are matched based on the ice thickness in each area of ​​the 3D ice distribution model.

[0032] The total deicing agent requirement is calculated based on the surface area data and unit area dose data for the catenary area. The surface area data for the catenary area is obtained through a 3D modeling module. This data covers the external surface area of ​​all catenary components (such as conductors, load-bearing cables, insulators, and connecting fittings). It is calculated by integrating the surface area of ​​the 3D model. The total deicing agent requirement is the sum of the surface area of ​​each area multiplied by the corresponding unit area dose. For example, if the surface area of ​​a region is 50 m² and the unit area dose is 30 g / m², the deicing agent requirement for that region is 1500 g. The total deicing agent requirement is calculated by summing the requirements of all regions.

[0033] The single-point deicing agent distribution amount is calculated by combining the equipment coverage data in the deicing equipment location parameters. The equipment coverage data is determined by the equipment's spray angle and pressure parameters. Each device corresponds to a coverage area, which may be fan-shaped, cone-shaped, etc., and is defined by three-dimensional coordinates. The single-point distribution amount of each device is calculated based on the area ratio of each device's coverage area, the ice thickness in the area, and the total deicing agent demand. For example, if the total demand is 10kg, the area coverage area of ​​a certain device accounts for 15%, and the ice thickness in the area is 1.2 times the average thickness, then the single-point distribution amount of the device is 10kg×15%×1.2=1.8kg. The deicing agent type parameter is formed by integrating information such as the deicing agent type, total demand, and the single-point distribution amount of each device.

[0034] Example 3: See Figure 4 During the analysis of the plausibility of ice rupture paths, we extracted ice rupture path data generated during the simulated deicing process. This data, derived from the dynamic simulation of ice stress changes under the action of deicing agents using simulation modeling software, includes information such as the rupture start coordinates, extension direction vector, and end boundary coordinates. This data is exported into a structured data format through a data interface, covering all simulated rupture trajectories within the catenary area.

[0035] A full coverage check is performed on the extracted ice rupture path data to determine whether it covers all high ice thickness locations. High ice thickness locations refer to areas in the three-dimensional ice distribution model where the ice thickness exceeds the safety threshold. These areas are marked with a coordinate set in the model. Through spatial topology analysis, each high ice thickness location is compared one by one to see whether there is at least one rupture path passing through its spatial range, and the coordinates of the uncovered locations are recorded. At the same time, the continuity of the rupture path is verified, and whether there are intersections or overlapping areas between the rupture paths to form an interconnected network structure. The continuous rupture network requires that the maximum spacing between any two adjacent rupture paths does not exceed the set connectivity threshold, which is determined based on the physical properties of the ice layer and the structural dimensions of the contact network.

[0036] If the ice breakup path data covers all locations with high ice thickness and forms a continuous breakup network, the ice breakup path plausibility is marked as acceptable. If the ice breakup path data contains localized gaps—that is, the spacing between breakup paths in some areas exceeds the connectivity threshold, or if there are uncovered locations with high ice thickness—the ice breakup path plausibility is marked as unacceptable, triggering a resimulation. During the resimulation, the deicing agent injection pressure and application time parameters are adjusted, and the deicing process simulation is run again until the breakup path plausibility meets the acceptable criteria.

[0037] During the analysis of the matching of de-icing equipment location parameters, actual ice distribution data in the catenary area is extracted from ice monitoring data. This data is collected by multispectral sensors and lidar installed along the catenary. After noise reduction and filtering, it is converted into three-dimensional point cloud data that contains the ice thickness and distribution range at each location. The density of the point cloud data is determined by the sampling frequency of the monitoring equipment to ensure coverage of all key areas of the catenary.

[0038] The spatial difference between the actual ice distribution data and the expected ice melt extent data during the simulated de-icing process was calculated. The expected ice melt extent data was generated by the simulation model based on de-icing equipment parameters and de-icing agent properties and represented as a three-dimensional grid, with each grid cell labeled "melted" or "unmelted." During the calculation, the actual ice distribution data and the expected ice melt extent data were converted to the same spatial coordinate system, using the same grid division criteria, and the status of each grid cell was compared.

[0039] The position deviation value is calculated using the following formula:

[0040] in, Indicates the position deviation value, Indicates the area where the ice is not actually melted but is expected to melt. represents the total area of ​​expected ice melt, It represents the average distance between the actual melting area and the expected melting area. Indicates the maximum spatial distance of the contact network area, It represents the area deviation weight coefficient, with a value range of 0 to 1, and is determined according to the spatial accuracy requirements of the de-icing operation.

[0041] If the position deviation is less than a preset tolerance threshold, the de-icing equipment position parameter match is marked as matched. The preset tolerance threshold is determined based on the mechanical control accuracy of the de-icing equipment and the structural clearance of the catenary. For example, for a robotic de-icing device, the threshold might be set to 0.3; for a jet de-icing device, the threshold might be set to 0.5. If the position deviation is greater than or equal to the preset tolerance threshold, the de-icing equipment position parameter match is marked as mismatched, and a device position adjustment instruction is generated.

[0042] Equipment position adjustment instructions include three-dimensional coordinate correction values, angle adjustment amounts, and operating radius correction coefficients. The three-dimensional coordinate correction values ​​are calculated based on the spatial distribution of position deviation values ​​and are used to align the de-icing equipment's operating base point. The angle adjustment amount is determined based on the azimuth deviation between the actual and expected ice melting ranges and is used to rotate the de-icing equipment's operating direction. The operating radius correction coefficient is calculated based on the area deviation ratio and is used to expand or reduce the de-icing equipment's coverage area. The adjustment instructions are transmitted as digital signals to the de-icing equipment's control system, where the position correction operation is executed via the servo motor and hydraulic system. The corrected equipment position parameters are stored in a preset parameter database for use in the next de-icing simulation.

[0043] After completing the position parameter adjustment, the de-icing process simulation and position deviation value calculation are performed again until the position deviation value is less than the preset tolerance threshold, ensuring that the actual operating range of the de-icing equipment is consistent with the expected ice melting range.

[0044] Example 4: During the analysis of de-icing efficiency data validity, the ratio of the actual ice melt volume data during the simulated de-icing process to the expected ice melt volume data is first calculated. The actual ice melt volume data is obtained using a three-dimensional ice layer distribution model. Specifically, it is the initial ice layer volume before simulated de-icing minus the residual ice layer volume after simulated de-icing. The initial ice layer volume is calculated by summing the volumes of all ice layer blocks in the model, with each block's volume calculated based on its three-dimensional spatial coordinates and geometric shape. The residual ice layer volume is calculated by performing the same volume calculation on the remaining ice layer blocks after the simulated de-icing is completed, and then summing them. The expected ice melt volume data is predetermined based on parameters such as de-icing agent type, unit area dosage, and exposure time. The correlation between these parameters and the expected ice melt effect is established through historical simulation data and analysis of the de-icing agent's physical properties. For example, for the same exposure time, a higher concentration of de-icing agent will result in a higher expected ice melt volume.

[0045] The calculated ratio is compared with the preset ice melting interval to determine the validity of the de-icing efficiency data. The preset ice melting interval includes a high-efficiency ice melting interval and a low-efficiency ice melting interval. The interval division is based on the design parameters of the de-icing equipment, the cost of the de-icing agent, and the de-icing requirements of the catenary.

[0046] The following are the specific division standards for the preset ice melting zones: Ratio range Validity Mark 0.8 and above Efficient 0.5 to 0.8 Inefficiency 0.5 or less invalid If the ratio is above the lower limit of the preset efficient ice melting range, that is, the ratio reaches or exceeds 0.8, the deicing efficiency data validity is marked as efficient. This indicates that the current deicing parameter combination can achieve good ice melting results in the simulated environment, the type and dosage of deicing agent are reasonably coordinated with the equipment location parameters, and the ice melting range and speed during the deicing process meet expectations.

[0047] If the ratio falls within the preset low-efficiency ice melting range (between 0.5 and 0.8), the de-icing efficiency data validity is marked as low. In this case, while the de-icing process achieved some ice melting, it was not ideal. This could be due to factors such as insufficient de-icing agent dosage, inappropriate matching between the equipment's location and the ice distribution, or a short duration of action. In this case, de-icing parameters may need to be adjusted, such as increasing the de-icing agent dosage per unit area, adjusting the de-icing equipment's spray angle to expand coverage, or extending the contact time between the de-icing agent and the ice.

[0048] If the ratio falls below the preset lower limit of the inefficient ice melting range (i.e., the ratio falls below 0.5), the de-icing efficiency data validity is marked as invalid. This means that the current de-icing parameter combination cannot meet basic de-icing requirements. Simulation results show that most of the ice layer has not been effectively melted. This may be due to a mismatch between the de-icing agent type and the ice thickness, such as using a low-concentration de-icing agent to treat thicker ice layers; or a large deviation in the position of the de-icing equipment, resulting in the de-icing agent not being sprayed into the target ice area. In this case, the de-icing parameters need to be significantly adjusted, such as switching to a higher concentration or different type of de-icing agent, re-planning the location parameters of the de-icing equipment, or increasing the number of de-icing equipment to improve coverage.

[0049] During the analysis, the spatial distribution characteristics of ice breakup paths and ice melt ranges must be combined to further refine the reasons behind the ratio. For example, if the ratio is in the efficient range but there are localized areas with unmelted ice, the deicing agent dosage or equipment location in that area must be fine-tuned while maintaining the overall parameters. If the ratio is in the inefficient range but the ice melt is concentrated in areas with high ice thickness, the deicing agent distribution in areas with low ice thickness can be appropriately reduced, and the saved dosage can be concentrated in areas with high ice thickness. This multi-dimensional analysis ensures the targeted and rationality of parameter adjustments, providing specific direction for subsequent deicing parameter optimization.

[0050] Example 5: See Figure 5 After executing the parameter optimization instructions, the optimized de-icing equipment position parameters and de-icing agent type parameters are converted into a set of equipment control instructions. This conversion process involves parameter parsing and signal encoding. The de-icing equipment position parameters include three-dimensional coordinates, operating angles, and motion trajectories. These parameters are parsed into pulse signals for the servo motor and pressure regulation signals for the hydraulic device. The de-icing agent type parameters include the agent number, injection flow rate, and pressure. These parameters are encoded into on-off signals for the solenoid valve and speed control signals for the metering pump. The format of the control instruction set complies with the de-icing equipment's communication protocol, ensuring that the instructions can be accurately recognized and executed by the equipment controller.

[0051] The de-icing actuator in the catenary area is driven by a set of device control instructions to adjust the operating position and de-icing agent spray parameters. The de-icing actuator consists of a robotic arm, a spray device, and a spray storage unit. The robotic arm adjusts its joint angle and extension length based on the position parameters to direct the spray device to the designated operating position. The spray device switches the spray channel based on the de-icing agent type parameters, adjusting the nozzle pressure and spray angle to ensure that the de-icing agent is applied to the ice surface in the desired form and range. During this adjustment process, the actuator's motion status is fed back in real time via a built-in encoder to ensure that the movement accuracy meets the control instruction requirements.

[0052] Real-time data collection of the de-icing actuator's actual operating trajectory and de-icing agent coverage. This data is obtained via angle sensors installed at the arm's joints and positioning devices at the end, recording the actuator's position changes and movement speed during operation. The data sampling frequency is set based on operational accuracy requirements and is typically no less than 100 Hz. De-icing agent coverage data is collected via high-definition cameras and infrared thermal imagers deployed around the contact network. The cameras capture the de-icing agent's spray pattern and landing location, while the infrared thermal imagers detect temperature changes on the ice surface caused by the de-icing agent. The fusion of these two data forms the three-dimensional coordinate boundary of the coverage area.

[0053] The actual trajectory data is compared with the expected trajectory during the simulated de-icing process. The expected trajectory, generated by the simulation model, contains the theoretical position and motion of the actuator at each time point, stored as a time-coordinate sequence. During the comparison, the coordinates of the actual trajectory data and the expected trajectory at the same time points are interpolated to obtain the position deviation at each node. An interpolation algorithm is then used to generate a continuous deviation curve. The maximum deviation, average deviation, and deviation fluctuation frequency are extracted from this curve. These indicators are then combined to generate the device positioning error value.

[0054] When the equipment positioning error exceeds the preset operational tolerance threshold, the equipment position recalibration process is triggered and the de-icing equipment position parameters are updated. The preset operational tolerance threshold is determined based on the mechanical accuracy of the de-icing actuator and the structural clearance of the catenary. For example, for an articulated manipulator, the position deviation threshold is set at ±5mm, and the angular deviation threshold is set at ±1°. After the recalibration process is initiated, the de-icing actuator stops its current operation and enters calibration mode. It scans the contact network reference markers using a laser locator to obtain the actual coordinates of the markers. These actual coordinates are compared with the reference coordinates stored in a database to calculate the overall position correction. The manipulator's zero-point coordinates and joint parameters are adjusted based on the correction, and the nozzle position of the spray device is recalibrated. After calibration is complete, the de-icing equipment position parameters are updated, and the deviation data during the calibration process is recorded in the database for subsequent iterative updates of the parameter optimization logic. The calibrated actuator then resumes de-icing operations according to the updated parameters while continuing to collect and compare operational data, forming a closed-loop control loop until the positioning error stabilizes within the preset tolerance threshold.

[0055] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0056] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent deicing of a contact network, characterized by: Real-time collection of ice monitoring data in the overhead line area, including ice thickness distribution information, ambient temperature information, and wind speed information; Using the ice layer monitoring data as input information, a three-dimensional ice layer distribution model of the contact network is constructed through a simulation modeling tool; Based on the ice thickness distribution information in the three-dimensional ice distribution model, matching it with a preset ice thickness threshold mapping table to determine whether a de-icing operation is triggered; When it is determined that a de-icing operation is required, de-icing equipment location parameters and de-icing agent type parameters are extracted from a preset parameter database; simulating a deicing agent application process in the three-dimensional ice layer distribution model using the deicing equipment location parameters and the deicing agent type parameters; Outputting ice layer rupture path, ice melting range, and de-icing efficiency data during the de-icing process, analyzing the rationality of the ice layer rupture path, the matching degree of the de-icing equipment position parameters, and the validity of the de-icing efficiency data, and generating parameter optimization instructions to adjust subsequent de-icing parameters; The specific construction process of the three-dimensional ice distribution model of the contact network includes: Performing format conversion processing on the ice layer monitoring data to convert it into a standardized format recognizable by numerical calculation software; Using geographic information system tools, generating a continuous ice distribution surface based on the ice monitoring data in the standardized format; In the simulation modeling software, different ice layer area blocks are divided according to the continuous ice layer distribution surface; The physical property data of the ice layer is assigned to each ice area block, and local refined modeling is performed to form a three-dimensional ice layer distribution model of the contact network.

2. The method for intelligent deicing of contact network according to claim 1, characterized in that: The specific process of determining the de-icing operation requirement includes: extracting ice thickness values ​​at various locations in the contact network area from the ice thickness distribution information; Comparing each ice thickness value with a safe ice thickness threshold in the preset ice thickness threshold mapping table; If the number of locations where the ice thickness exceeds the safe ice thickness threshold reaches a preset ratio, it is determined that a de-icing operation is required; Otherwise, it is determined that no de-icing operation is required, and a maintain status quo instruction is output.

3. The method for intelligent deicing of contact network according to claim 2, characterized in that: The preset parameter database includes de-icing equipment location parameter confirmation logic and de-icing agent type parameter confirmation logic, wherein the de-icing equipment location parameter confirmation logic is: According to the ice distribution density data of the contact network area, the position correction coefficient is matched with the position correction coefficient corresponding to the ice distribution density stored in the database to obtain the position correction coefficient; Multiplying the position correction coefficient by the preset reference equipment position, and calculating the result as the de-icing equipment position parameter; Based on the ambient temperature information, the temperature-corresponding equipment deployment pattern is extracted from the database to determine the specific installation direction of the de-icing equipment; Based on the wind speed information, the safe operating distance data of the de-icing equipment is adjusted to obtain the final de-icing equipment position parameters.

4. The method for intelligent deicing of the contact network according to claim 3, characterized in that: The specific contents of the deicing agent type parameter confirmation logic include: Matching the ice layer thickness distribution information with deicing agent type and unit area dosage data corresponding to ice layer thickness stored in a database to obtain deicing agent type data and unit area dosage data; Calculating the total deicing agent requirement based on the surface area data of the catenary area and the unit area dosage data; Combined with the equipment coverage data in the de-icing equipment location parameters, the single-point de-icing agent distribution amount is calculated to form the de-icing agent type parameter.

5. The method for intelligent deicing of contact network according to claim 1, characterized in that: The specific analysis methods for the rationality of the ice rupture path include: Extract ice breakup path data generated during simulated de-icing; If the ice layer rupture path data covers all locations with high ice thickness and forms a continuous rupture network, the rationality of the ice layer rupture path is marked as qualified; If the ice layer rupture path data has local fractures or does not cover key locations, the rationality of the ice layer rupture path will be marked as unqualified and a re-simulation process will be triggered.

6. The method for intelligent deicing of contact network according to claim 1, characterized in that: The specific analysis method of the matching degree of the de-icing equipment position parameters includes: extracting actual ice distribution data of the catenary area from the ice monitoring data; Performing difference calculation on the actual ice layer distribution data and the expected ice melting range data during the simulated deicing process to obtain a position deviation value; If the position deviation value is less than a preset tolerance threshold, marking the de-icing equipment position parameter matching degree as a match; Otherwise, the de-icing equipment position parameter matching degree is marked as mismatched, and an equipment position adjustment instruction is generated.

7. The method for intelligent deicing of contact network according to claim 1, characterized in that: The specific analysis methods for the validity of the de-icing efficiency data include: Calculate the ratio of actual ice melt volume data to expected ice melt volume data during the simulated deicing process; If the ratio is above the lower limit of the preset efficient ice melting range, the validity of the deicing efficiency data is marked as efficient; If the ratio is within a preset low-efficiency ice melting range, marking the effectiveness of the de-icing efficiency data as low-efficiency; If the ratio is lower than a preset lower limit of the inefficient ice melting range, the validity of the deicing efficiency data is marked as invalid.

8. The method for intelligent deicing of contact network according to claim 1, characterized in that: After executing the parameter optimization instruction, the method further includes: Converting the optimized de-icing equipment position parameters and de-icing agent type parameters into an equipment control instruction set; The deicing actuator in the catenary area is driven by the device control instruction set to adjust the operating position and deicing agent injection parameters; Real-time collection of actual operation trajectory data of de-icing actuators and de-icing agent coverage data; Comparing the actual operation trajectory data with the expected operation trajectory during the simulated deicing process to generate an equipment positioning error value; When the equipment positioning error value exceeds a preset operation tolerance threshold, the equipment position recalibration process is triggered and the de-icing equipment position parameters are updated.

9. An intelligent deicing system for a contact network, used to implement the intelligent deicing method for a contact network according to any one of claims 1 to 8, characterized in that: The system comprises: A data acquisition module is used to collect ice layer monitoring data in the contact network area in real time, wherein the ice layer monitoring data includes ice layer thickness distribution information, ambient temperature information, and wind speed information; a three-dimensional modeling module, connected to the data acquisition module, for taking the ice layer monitoring data as input information and constructing a three-dimensional ice layer distribution model of the catenary through a simulation modeling tool; a de-icing determination module, connected to the three-dimensional modeling module, configured to determine whether a de-icing operation is triggered based on matching the ice thickness distribution information in the three-dimensional ice distribution model with a preset ice thickness threshold mapping table; a parameter extraction module, connected to the deicing determination module, for extracting deicing equipment location parameters and deicing agent type parameters from a preset parameter database when it is determined that a deicing operation is required; a deicing simulation module, connected to the three-dimensional modeling module and the parameter extraction module, respectively, for simulating a deicing agent application process in the three-dimensional ice layer distribution model using the deicing equipment position parameters and the deicing agent type parameters; An analysis and optimization module is connected to the deicing simulation module and is used to output the ice layer rupture path, ice melting range and deicing efficiency data during the deicing process, analyze the rationality of the ice layer rupture path, the matching degree of the deicing equipment position parameters and the validity of the deicing efficiency data, and generate parameter optimization instructions to adjust subsequent deicing parameters.

Images