Functional module positioning method and system in power system based on single Beidou system

Abstract

The present application relates to the technical field of satellite navigation and positioning, and discloses a method and system for positioning functional modules in an electric power system based on a single Beidou system. The method comprises: obtaining environmental electromagnetic interference intensity data, matching positioning frequency data x according to its magnitude, and then obtaining satellite positioning data and calculating module positioning data based on this. When x≤a, the deviation value of the positioning data of the stationary functional module within a preset distance is used to correct the module positioning data to obtain the final positioning result; when a<x<b, the camera module is called to capture the environmental image, identify the first target object, calculate its target displacement to obtain image positioning data, and fuse it with the module positioning data to obtain the final positioning data; when x≥b, a second channel interoperability module is searched, and the functional module and the interoperability module respectively obtain positioning data based on different channels and calculate the final positioning data. The present application can reduce electromagnetic interference and improve the positioning accuracy of single Beidou in the electric power system.

Description

Technical Field

[0001] The present application relates to the technical field of satellite navigation and positioning, and in particular to a method and system for positioning functional modules in a power system based on a single Beidou system. Background Art

[0002] Beidou, my country's independently developed global satellite navigation system, has become a crucial component of its national strategy. In positioning technology, dual-frequency Beidou and single-frequency Beidou are two primary implementation options, each with distinct technical characteristics, application scenarios, and promotional significance.

[0003] Single BeiDou receives signals from only one frequency band (such as B1). It features simple hardware, high integration, and low power consumption, making it suitable for common civilian and industrial applications such as work badges, vehicle management, and agricultural navigation. However, single BeiDou suffers from low stability in complex environments. This is particularly true in power systems with harsh electromagnetic environments, where positioning data errors are significant, making it difficult to meet the high-precision positioning requirements of power systems.

[0004] Although dual-frequency Beidou can simultaneously receive signals from two frequency bands (such as B1 and B2), its positioning accuracy can reach centimeter level and it has strong anti-interference ability, making it suitable for high-precision professional scenarios, it has problems such as complex hardware design, high processing power requirements, high power consumption, and high manufacturing costs. Its large-scale application in scenarios such as power systems is limited in terms of cost and other aspects.

[0005] Therefore, there is an urgent need for a positioning technology solution that can improve positioning accuracy in power systems while taking into account factors such as cost and power consumption. Summary of the Invention

[0006] In order to improve the positioning accuracy of single Beidou in the power system, the present application provides a method and system for positioning functional modules in the power system based on a single Beidou system.

[0007] In a first aspect, the present application provides a method for positioning functional modules in a power system based on a single BeiDou system, which adopts the following technical solutions:

[0008] A method for positioning functional modules in a power system based on a single Beidou system comprises the following steps:

[0009] Obtaining interference intensity data of electromagnetic interference in the environment, and matching positioning frequency data x according to the interference intensity data. The greater the interference intensity data, the greater the positioning frequency data x.

[0010] Acquire multiple satellite positioning data of a single functional module according to the positioning frequency data x, and calculate the average value of the multiple satellite positioning data to obtain module positioning data;

[0011] If x≤a, obtaining the module positioning data of multiple stationary functional modules within a preset set distance range; calculating positioning deviation values ​​of the multiple module positioning data, correcting the module positioning data using the positioning deviation values, and using the corrected module positioning data as the final positioning data;

[0012] If a<x<b, calling the camera module to capture environmental image data, identifying a first target object from the environmental image data, calculating target displacement data of the first target object, calculating image positioning data based on the target displacement data, and calculating final positioning data based on the image positioning data and the module positioning data;

[0013] If x ≥ b, then search for an interoperable module that performs positioning based on the second frequency channel, obtain the interoperable module that is closest, establish a communication connection, and guide the interoperable module to the functional module. The functional module obtains first positioning data based on the first frequency channel, and obtains second positioning data based on the second frequency channel through the interoperable module. The functional module calculates the final positioning data based on the first positioning data and the second positioning data; wherein a and b are frequency range parameters used to distinguish the intensity levels of electromagnetic interference.

[0014] By adopting the above technical solution, it is possible to accurately obtain electromagnetic interference intensity data in the environment and cleverly match the positioning frequency based on this data, showing that the greater the interference intensity, the higher the positioning frequency. By calculating the average value of multiple satellite positioning data, the error is effectively reduced and the positioning accuracy is initially guaranteed. Under different levels of electromagnetic interference, this method shows strong adaptability. When the interference intensity is low, that is, the positioning frequency data, the module positioning data of multiple stationary functional modules within the preset distance range will be obtained. By calculating the positioning deviation value, the module positioning data itself is corrected to further improve the accuracy of positioning. When the interference is at a medium intensity, the camera module is called to capture the environmental image data, identify the first target object, and accurately calculate the target displacement data, and then obtain the image positioning data. Combined with the module positioning data, the multi-source data is used to enhance the reliability of positioning and make up for the shortcomings of satellite positioning under this interference level. When interference intensity is high, the system actively seeks out interoperable modules that perform positioning based on the second channel. It establishes a communication connection with the nearest interoperable module and guides it to the functional module. The functional module obtains first positioning data based on the first channel, and the interoperable module obtains second positioning data based on the second channel. The two are combined to calculate the final positioning data, significantly reducing the adverse effects of strong interference on positioning. This method fully adapts to the complex and changing electromagnetic environment of power systems and significantly improves the accuracy of functional module positioning.

[0015] Optionally, the step of obtaining interference intensity data of electromagnetic interference in the environment further includes the following sub-steps:

[0016] Acquiring magnetic field strength data of a plurality of magnetic field sensors within the set distance range;

[0017] Calculate the average value of the magnetic field strength data to obtain the average magnetic field strength, the formula is: ;

[0018] Calculate the fluctuation value of the magnetic field intensity data to obtain the fluctuation degree value, the formula is: ;

[0019] The interference intensity data I is calculated based on the average magnetic field intensity and the fluctuation degree value;

[0020] ;

[0021] Among them, α and β are the weight coefficients of the average magnetic field intensity and the fluctuation degree value, α+β=1; n is the number of magnetic field sensors, M i The magnetic field strength data obtained by the i-th magnetic field sensor.

[0022] By adopting this technical solution, the average magnetic field strength reflects the overall level of the magnetic field, while the fluctuation value reflects the severity of magnetic field fluctuations. In actual electromagnetic interference, strong interference can arise from both high-intensity magnetic fields and severe magnetic field fluctuations. By incorporating these two factors, the interference intensity data I can more comprehensively and accurately characterize the interference conditions in the electromagnetic environment. For example, near a substation, the average magnetic field strength may be high, while in areas where electrical equipment is frequently started and stopped, the magnetic field fluctuates significantly. This comprehensive calculation can accurately reflect the interference conditions in different scenarios.

[0023] Optionally, the method further comprises the following steps:

[0024] Calculating fluctuation curves of a plurality of the interference intensity data within a preset set time period;

[0025] Calculating the fluctuation amplitude data f according to the fluctuation curve;

[0026] Adjust the size of a according to the fluctuation amplitude data f, the larger the fluctuation amplitude data f, the larger a is, and the smaller the fluctuation amplitude data f, the smaller a is;

[0027] Adjust the size of b according to the fluctuation amplitude data f, the larger the fluctuation amplitude data f, the smaller b is, and the smaller the fluctuation amplitude data f, the larger b is;

[0028] Among them, the adjustment range of a is greater than the adjustment range of b.

[0029] By employing this technical solution and analyzing the interference intensity fluctuation curve, the system can dynamically track changes in electromagnetic interference. When interference fluctuations are large, the a value is adjusted more significantly, enabling faster positioning strategy changes to adapt to interference changes. When interference fluctuations are small, the b value is adjusted appropriately to ensure the appropriate timing of positioning strategy switching and avoid the additional resource consumption caused by frequent switching.

[0030] Optionally, identifying the first target object from the environmental image data includes the following sub-steps:

[0031] Preprocessing the environmental image data;

[0032] A feature extraction algorithm is used to extract specific features from the preprocessed image;

[0033] Matching the extracted specific features with a pre-stored feature template of the first target object, using a feature matching algorithm to find the feature points that best match the template features, and determining the position of the first target object in the image based on the best matching feature points;

[0034] Calculating a geometric characteristic value of the first target body, comparing the geometric characteristic value with a geometric reference value, and calculating a verification value;

[0035] If the verification value is within a set range, the first target is a correct first target; otherwise, it is an incorrect first target, and the first target is re-identified from the environmental image data.

[0036] By adopting this technical solution and implementing multiple verification mechanisms, encompassing both feature matching and geometric feature verification, recognition reliability is greatly enhanced and the probability of misidentification is reduced. Simultaneously, image preprocessing and optimization reduce the complexity of subsequent computations, while the rapidity of geometric feature verification allows for the timely elimination of erroneous results, avoiding wasted processing time and significantly improving recognition efficiency, ensuring the system can accurately, efficiently, and stably identify the primary target.

[0037] Optionally, the step of calculating the target displacement data of the first target body and calculating the image positioning data according to the target displacement data includes the following sub-steps:

[0038] In two adjacent frames of images, the position of the first target object is respectively identified;

[0039] Calculating target displacement data according to a position change of the first target object in two adjacent frames of images;

[0040] Integrating the target displacement data to obtain the cumulative displacement of the first target body over a period of time;

[0041] The initial position of the camera module is used as a reference point, and the current position of the first target object, ie, image positioning data, is calculated according to the accumulated displacement and the position of the reference point.

[0042] By adopting the above technical solutions, it is not only possible to adapt to various complex motion states of the target object, whether it is uniform speed, variable speed or irregular motion, but also to accurately calculate the position based on stable reference points when the environment changes, greatly enhancing the system's adaptability to different scenarios, ensuring the accuracy and reliability of the positioning results, and providing solid protection for applications that rely on the target object's position information.

[0043] Optionally, the step of calculating the image positioning data further includes the following sub-steps:

[0044] Acquiring environmental image data based on a single camera device on the mobile functional module;

[0045] Identify at least two first targets from the environmental image data, and calculate the angles formed by the two first targets closest to the camera device and the camera device;

[0046] Matching the preset positioning information corresponding to each first target object according to the two first targets closest to the camera device;

[0047] The image positioning data of the mobile function module is calculated based on the matched two preset positioning information and the angle.

[0048] By employing this technical solution, different targets have distinct positions and characteristics within the environment. Multiple targets can verify each other, reducing positioning errors caused by individual target recognition errors or positional fluctuations, making positioning results more accurate and reliable. When the preset positioning information for two targets is known, the angle between them can help determine the specific position of the camera device, i.e., the mobile module, relative to the two targets, thereby improving positioning accuracy.

[0049] Optionally, the step of calculating the image positioning data further includes the following sub-steps:

[0050] Based on the camera devices of multiple different angles on the mobile function module, multiple surrounding environment image data are obtained;

[0051] Identify at least two first targets from each of the environmental image data, and calculate the angles formed by the two first targets closest to the camera device and the camera device;

[0052] Matching the preset positioning information corresponding to each first target object according to the two first targets closest to the camera device;

[0053] Calculating the image positioning parameters of the mobile function module in each of the environmental image data according to the matched two preset positioning information and the included angle;

[0054] The average value of all the image positioning parameters is calculated as the final image positioning data.

[0055] By adopting the above technical solution, multiple environmental image data of the surrounding environment are obtained based on the camera devices at multiple different angles on the mobile functional module, which can fully capture the target body information from different perspectives and avoid the information loss caused by a single perspective. At least two first target bodies are identified from each environmental image data, and the angle formed by the two first target bodies closest to the camera device and the camera device is calculated, providing rich geometric information for positioning. According to the preset positioning information corresponding to the two target bodies, the image positioning parameters of the mobile functional module in each environmental image data are calculated in combination with the angle, and multiple sets of data are used to verify each other, effectively reducing the positioning deviation caused by a single target body or a single image error. By calculating the average value of all image positioning parameters as the final image positioning data, the data is further smoothed, the accuracy and stability of positioning are improved, and the positioning result can more accurately reflect the actual position of the mobile functional module, greatly enhancing the reliability of the system positioning in complex environments.

[0056] Optionally, the method further comprises the following steps:

[0057] In a preset working time period S, the time coordinate set of the current positioning frequency data x≤a is calculated as set A, the time coordinate set of a<x<b is calculated as set B, and the time coordinate set of b≤x is calculated as set C;

[0058] The time span for calculating set A is a1, the time span for calculating set B is b1, and the time span for calculating set C is c1;

[0059] According to the time span a1, time span b1 and time span c1, calculate the total time span d=a1+b1+c1-m, where m is the adjustment parameter;

[0060] Calculate the frequency correction value D=d / S according to the total time span d and the working time period S;

[0061] The next positioning frequency data x is corrected according to the frequency correction value D, and the next positioning frequency data x=D×the current positioning frequency data x.

[0062] By adopting the above technical solution, the distribution and duration of electromagnetic interference in different intensity ranges can be clearly understood by calculating time span a1, time span b1, and time span c1. This enables dynamic adjustment of positioning frequency, making it more adaptable to the actual electromagnetic interference environment.

[0063] Optionally, the method further comprises the following steps:

[0064] Calculate the average value of the plurality of positioning frequency data x within a set time period as the average positioning frequency x';

[0065] Calculating the difference between the average positioning frequency x' and the preset reference positioning frequency data x";

[0066] The size of the adjustment parameter m is adjusted according to the difference. The larger the difference is, the larger the adjustment parameter m is; the smaller the difference is, the smaller the adjustment parameter m is.

[0067] By employing this technical solution, the average positioning frequency can be viewed as a quantitative reflection of the electromagnetic interference conditions during a given period. When the interference conditions differ significantly from expectations, the system can more flexibly adjust the positioning strategy to adapt to the actual interference environment, improving positioning accuracy and efficiency. Conversely, when the difference is smaller, indicating that the actual interference conditions are closer to expectations, the adjustment parameter m can be reduced, resulting in relatively small adjustments to the positioning strategy and avoiding unnecessary resource consumption caused by over-adjustment.

[0068] In a second aspect, the present application provides a functional module positioning system in a power system based on a single Beidou system, which adopts the following technical solutions:

[0069] A functional module positioning system in an electric power system based on a single Beidou system includes a processor, wherein the processor executes the steps of any one of the above-mentioned functional module positioning methods in an electric power system based on a single Beidou system.

[0070] In summary, this application includes at least one of the following beneficial technical effects:

[0071] By calculating the average and fluctuation values ​​of multiple magnetic field sensor data, we can comprehensively and accurately characterize the interference conditions of the electromagnetic environment. The positioning frequency is dynamically matched based on the interference intensity. The stronger the interference, the higher the positioning frequency, effectively improving positioning accuracy.

[0072] In varying electromagnetic interference intensity ranges, the system flexibly utilizes various methods, including satellite positioning, image recognition, and interoperable module positioning. In low-interference situations, the system uses positioning data from surrounding stationary modules to correct its own positioning. In moderate interference situations, it combines image positioning data to enhance positioning reliability. In strong interference situations, the interoperable module achieves dual-channel positioning, minimizing interference impacts and ensuring comprehensive positioning accuracy.

[0073] Preprocessing of environmental image data, combined with steps such as feature extraction, matching, and geometric feature verification, can effectively improve the accuracy and reliability of first target object recognition, reduce the probability of misidentification, and at the same time reduce computational complexity and improve recognition efficiency.

[0074] The displacement is calculated based on the change in the target's position between two adjacent frames of images. The cumulative displacement is obtained through integration, and the image positioning data is calculated with the initial position of the camera module as the reference point. This can keenly capture the subtle movements of the target, adapt to complex motion states and environmental changes, and ensure that the positioning results are accurate and reliable.

[0075] Image data is acquired based on a single or multiple cameras at different angles, and positioning data is calculated using information on multiple targets and angles. This can comprehensively capture target information from multiple perspectives, reduce positioning deviations, and improve positioning accuracy and stability through mutual verification of multiple sets of data and calculation of average values.

[0076] The time spans of different interference intensity intervals are calculated and the positioning frequency correction value is adjusted accordingly to achieve dynamic positioning frequency adjustment. Dynamic adjustment parameters are adjusted based on the difference between the average positioning frequency and the reference positioning frequency, enabling the system to better adapt to different electromagnetic interference environments. While ensuring positioning accuracy, it also improves system operating efficiency, avoids resource waste, and enhances the overall positioning performance of the mobile functional module of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a step diagram of the method for positioning functional modules in a power system based on a single BeiDou system.

[0078] Figure 2 This is a diagram of the steps of identifying a first target object from the environmental image data.

[0079] Figure 3 This is a step diagram for calculating target displacement data of the first target body and calculating image positioning data based on the target displacement data.

[0080] Figure 4 This is a diagram of the steps for calculating image positioning data based on a single camera device on a mobile functional module.

[0081] Figure 5 This is a diagram of the steps for calculating image positioning data based on multiple camera devices on a mobile functional module. DETAILED DESCRIPTION

[0082] Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings.

[0083] Throughout this specification, reference to the terms "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0084] The present application embodiment discloses a method for positioning a functional module in a power system based on a single Beidou system. Figure 1 , including the following steps:

[0085] In real-world power systems, electromagnetic interference (EMI) can arise from a wide range of sources, including substation equipment operation and current fluctuations in transmission lines. Various electromagnetic monitoring devices, such as electric and magnetic field sensors, deployed around the mobile functional module collect relevant data. This data is then analyzed using a specific algorithm to determine the EMI intensity. Positioning frequency data x is then matched against the interference intensity data. A clear correlation exists: the greater the interference intensity data, the greater the positioning frequency x. This is because in environments with strong EMI, satellite signals are susceptible to interference, causing fluctuations and reducing positioning accuracy. Therefore, increasing the positioning frequency is necessary to obtain more accurate positioning information. For example, near a large substation, where EMI intensity is high, the interference intensity data measured by monitoring equipment and calculated by the algorithm is high. In this case, the matching positioning frequency data x increases accordingly, for example, from one positioning per minute under normal conditions to three positioning per minute.

[0086] Multiple satellite positioning data for a single functional module is obtained based on positioning frequency data x. Taking a single Beidou system as an example, the Beidou positioning terminal on the mobile functional module continuously receives signals from Beidou satellites at a predetermined positioning frequency, thereby obtaining multiple satellite positioning data. Because each received satellite signal may be affected by various factors and contain certain errors, the module positioning data is calculated by averaging multiple satellite positioning data. This reduces random errors and initially improves positioning accuracy. For example, at a certain moment, the positioning frequency data x is 2 positionings per minute. Within two consecutive minutes, the positioning terminal obtains four satellite positioning data: (100.1, 20.2), (100.3, 20.1), (99.9, 20.3), and (100.2, 20.0). By calculating the average of these four data points, the module positioning data is (100.125, 20.15).

[0087] If x≤a, this means that the electromagnetic interference intensity is relatively low at this time, and satellite positioning is less affected by the interference. In this case, module positioning data for multiple stationary functional modules within a preset set distance range is obtained. In the power system, there are many relatively stationary functional modules, such as fixed distribution cabinets and transformer monitoring modules. Assuming that the set distance range is a 50-meter radius area centered on the mobile functional module, module positioning data for three stationary functional modules is obtained within this area, namely (100.0, 20.0), (100.2, 20.1), and (99.8, 20.2). The positioning deviation values ​​of the multiple module positioning data are then calculated. For example, using the preliminary module positioning data (100.125, 20.15) of the mobile functional module as a reference, the deviation from the positioning data of these three stationary functional modules is calculated, and the average positioning deviation value is obtained as (-0.1, -0.1). This positioning deviation value is used to correct the module positioning data, and the corrected module positioning data (100.025, 20.05) is used as the final positioning data to further improve positioning accuracy.

[0088] If a<x<b, it indicates that the electromagnetic interference intensity is at a medium level, and the accuracy of satellite positioning is affected to a certain extent. At this time, the camera module is called to capture environmental image data. In actual application scenarios, the mobile function module can be equipped with a high-definition camera, which will capture the surrounding environment at a set shooting frequency. Identifying the first target from the environmental image data requires the use of advanced image recognition technology, such as a target detection algorithm based on deep learning. For example, at an electric equipment maintenance site, the image recognition algorithm successfully identified an electric equipment with a specific shape and logo as the first target in the environmental image captured by the camera. The target displacement data of the first target is then calculated by comparing the position change of the first target in two adjacent frames of images. Assuming that in the two frames of images, the first target has moved 5 pixels horizontally and 3 pixels vertically, the actual target displacement data is obtained after conversion. Image positioning data is calculated based on the target displacement data, and then combined with the previously obtained module positioning data. Through a specific data fusion algorithm, such as the weighted average method, assuming that the image positioning data weight is 0.4 and the module positioning data weight is 0.6, the final positioning data is calculated. In this way, multi-source data is used to enhance the reliability of positioning and make up for the shortcomings of satellite positioning under this interference level.

[0089] If x ≥ b, it indicates high electromagnetic interference intensity, severely disrupting satellite positioning. In this case, an interoperable module is sought for positioning based on the second channel. In power systems, there may be some backup positioning devices with different positioning channels, serving as interoperable modules. The nearest interoperable module is located by searching for surrounding signals. For example, an interoperable module is located within 200 meters of the mobile functional module. A communication connection is established, guiding the interoperable module to the vicinity of the functional module. The functional module obtains first positioning data based on the first channel, and the interoperable module obtains second positioning data based on the second channel. For example, if the positioning data obtained by the functional module via the first channel is (101.0, 21.0), and the positioning data obtained by the interoperable module via the second channel is (100.8, 20.9), the functional module uses an appropriate algorithm, such as the Kalman filter, based on these two positioning data to calculate the final positioning data, significantly reducing the adverse effects of strong interference on positioning.

[0090] By acquiring electromagnetic interference intensity data in the environment, a dynamic correlation is established between interference intensity and positioning frequency. The greater the interference intensity, the higher the positioning frequency. This approach addresses the challenges posed by complex electromagnetic environments. In the satellite positioning process, averaging the positioning data from multiple satellites effectively filters out random errors, providing a preliminary guarantee for positioning accuracy.

[0091] This method employs different strategies to address varying degrees of electromagnetic interference. When interference intensity is low, meaning the positioning frequency is relatively low, the system automatically acquires positioning data for multiple stationary modules within a preset distance range. For example, within a 50-meter radius around the mobile module, it collects positioning information for surrounding stationary modules, such as power distribution cabinets and transformer monitoring modules. By calculating the deviation between this data and the module's own positioning data, it corrects its own positioning, further improving positioning accuracy.

[0092] When interference reaches moderate levels, the system rapidly activates the camera module to capture image data of the environment. Using advanced image recognition technology, it accurately identifies the primary target. By analyzing the target's positional changes in adjacent image frames, it precisely calculates the target's displacement and generates image positioning data. This image positioning data is then organically combined with the module's positioning data, leveraging the advantages of multi-source data to enhance positioning reliability and effectively compensate for the shortcomings of satellite positioning under these interference levels.

[0093] If the interference intensity is high, the system actively searches for an interoperable module using the second channel for positioning. It locks onto the closest interoperable module, establishes a communication connection, and guides it toward the functional module. The functional module obtains the first positioning data based on the first channel, and the interoperable module obtains the second positioning data based on the second channel. By fusing these two sets of data to calculate the final positioning result, the adverse effects of strong interference on positioning are greatly reduced.

[0094] In summary, this method can fully adapt to the complex and changeable electromagnetic environment in the power system, improve the accuracy of functional module positioning from multiple dimensions and levels, and provide strong support for the stable operation of the power system.

[0095] The step of obtaining interference intensity data of electromagnetic interference in the environment further includes the following sub-steps:

[0096] Calculate multiple magnetic field strength data points from multiple magnetic field sensors within a set distance range. Set a specific distance range around the mobile functional module and deploy multiple magnetic field sensors within this range to sense changes in the surrounding magnetic field in real time and acquire multiple magnetic field strength data points. For example, in a scenario near a power transmission line, five magnetic field sensors are evenly distributed within a 10-meter radius around the mobile functional module, continuously collecting magnetic field strength data at their location.

[0097] Calculate the average value of the magnetic field strength data to get the average magnetic field strength. The formula is: n is the number of magnetic field sensors, and Mi is the magnetic field strength data obtained by the i-th magnetic field sensor. The magnetic field strength data obtained by the five magnetic field sensors are M1=10, M2=12, M3=8, M4=11, and M5=9, respectively. The average magnetic field strength is calculated as (1 / 5)×(10+12+8+11+9)=10.

[0098] Calculate the fluctuation value of the magnetic field intensity data to obtain the fluctuation degree value. The formula is: ; The larger the fluctuation value, the more drastic the change in magnetic field intensity at different locations and times.

[0099] The interference intensity data I is calculated based on the average magnetic field intensity and the fluctuation degree value;

[0100] ;

[0101] Here, α and β are weighting factors for the average magnetic field strength and fluctuation, respectively, with α + β = 1. In practice, the weighting factors will be adjusted based on the specific electromagnetic environment. For example, in environments with relatively stable magnetic fields but high overall strength, the value of α may be appropriately increased; whereas in areas with frequent fluctuations in magnetic field strength, the value of β may be increased.

[0102] Actual electromagnetic interference situations are very complex. Strong interference can arise from both high-intensity magnetic fields and dramatic fluctuations in magnetic fields. For example, near large substations, the operation of numerous electrical devices generates a strong magnetic field, resulting in a high average magnetic field strength. In areas where electrical equipment frequently starts and stops, such as in factory workshops, the start-up and shutdown of equipment can cause rapid changes in magnetic field strength and significant fluctuations. By incorporating both average magnetic field strength and fluctuation values, the interference intensity data I can more comprehensively and accurately characterize the interference state of the electromagnetic environment, providing an accurate basis for subsequent adjustments to interference intensity-based positioning strategies.

[0103] The method further comprises the steps of:

[0104] Calculates fluctuation curves for multiple interference intensity data points within a preset time period. During power system operation, the system continuously monitors electromagnetic interference intensity data in the environment. For example, for a specific substation area, interference intensity data is recorded at regular intervals (assuming 10 minutes) over a preset time period, such as a 24-hour day. Using these discrete data points, a specialized curve-fitting algorithm is used to calculate and plot fluctuation curves for multiple interference intensity data points, visually demonstrating the changing trends of interference intensity over time.

[0105] The fluctuation amplitude data f is calculated based on the fluctuation curve. The fluctuation amplitude data f is an important indicator of the severity of electromagnetic interference changes. It is determined by analyzing the fluctuations of the data points on the curve. For example, a statistical measure (such as the average difference) is calculated for the difference between the peaks and troughs of the curve. Assume that during a certain period of time, the peak interference intensity of the fluctuation curve is 80 and the trough interference intensity is 20. Using a specific calculation method, the fluctuation amplitude data f is 60.

[0106] After obtaining the fluctuation amplitude data f, the system dynamically adjusts two key parameters, a and b, based on it. Specifically, a larger fluctuation amplitude data f indicates more dramatic changes in electromagnetic interference. In this case, the value of a is increased. For example, if a is originally 30, it may increase to 40 when f increases to a certain level. This means that under relatively high interference intensity, positioning correction strategies based on surrounding static functional modules are triggered earlier. For example, during centralized equipment startup or troubleshooting within a substation, when electromagnetic interference fluctuates dramatically, increasing the value of a allows the system to promptly utilize positioning information from relatively stable static functional modules in the surrounding area, such as distribution cabinets, to enhance positioning stability and accuracy. Simultaneously, reducing the value of b, for example from 60 to 50, allows the system to more quickly initiate more complex but interference-adaptive strategies, such as image data-assisted positioning or collaborative positioning using interoperable modules, to cope with rapid changes in interference intensity.

[0107] Conversely, when the fluctuation amplitude f is smaller, the interference intensity changes relatively steadily. In this case, reducing the value of a, for example from 30 to 25, allows for more efficient utilization of the initial calculation results of satellite positioning data at lower interference intensities, reducing unnecessary complex positioning processes. Because satellite positioning is relatively reliable when interference is stable, there's no need to prematurely activate complex strategies. Simultaneously increasing the value of b, for example from 60 to 70, allows the system to switch to more complex positioning strategies only when interference intensity reaches a high level, improving positioning efficiency.

[0108] It is particularly important to note that the adjustment range of a is greater than that of b. This design further enhances the system's flexible response to varying interference fluctuations. When interference fluctuates significantly, a more pronounced adjustment of the a value enables more rapid changes in positioning strategies to adapt to interference changes. For example, in the event of a strong interference event, such as the sudden startup of large-scale equipment in the power system, a significant increase in the a value allows for rapid correction by invoking positioning information from surrounding static modules. When interference fluctuations are less severe, the b value is appropriately adjusted to ensure the rationality of the timing of switching positioning strategies and avoid the additional resource consumption caused by frequent switching. This series of operations significantly enhances the positioning system's adaptability to complex electromagnetic interference environments, optimizes the timing of positioning strategy selection, and ensures the efficient and accurate positioning of functional modules under varying interference dynamics. This ensures that positioning functions in power systems can always operate stably and accurately in complex and changing electromagnetic environments.

[0109] Analyzing the interference intensity fluctuation curve enables the system to dynamically track changes in electromagnetic interference. By adjusting a and b accordingly as the fluctuation amplitude data f changes, the positioning strategy switching conditions can be dynamically optimized. A larger fluctuation amplitude data f indicates more drastic electromagnetic interference fluctuations. Increasing a in this case triggers the positioning correction strategy based on the surrounding static functional modules earlier under relatively high interference intensities, leveraging the relatively stable positioning information of surrounding modules to enhance positioning stability and accuracy. Simultaneously, decreasing b allows the system to more quickly employ more complex, but interference-adaptive, strategies such as image data-assisted positioning or collaborative positioning with interoperable modules to address rapid fluctuations in interference intensity. Conversely, when the fluctuation amplitude data f is smaller, meaning that interference intensity fluctuations are relatively stable, decreasing a allows for more efficient utilization of preliminary satellite positioning data calculations under lower interference intensities, reducing unnecessary complexity in the positioning process. Increasing b allows the system to switch to more complex positioning strategies only when interference intensity reaches higher levels, improving positioning efficiency. Furthermore, the adjustment range of a is greater than that of b, further enhancing the system's flexible response to varying interference fluctuations. When interference fluctuates significantly, a more pronounced adjustment to the a value allows for more rapid positioning strategy changes to adapt to interference fluctuations. When interference fluctuations are smaller, b value is adjusted appropriately to ensure the rationality of positioning strategy switching timing and avoid the additional resource consumption caused by frequent switching. This significantly improves the positioning system's adaptability to complex electromagnetic interference environments, optimizes the timing of positioning strategy selection, and ensures efficient and accurate positioning of functional modules under varying interference dynamics.

[0110] Thresholds a and b are used to distinguish the intensity levels of electromagnetic interference (low, medium, and high). The specific initial values ​​are determined based on historical data or experimental tests. In the substation field test, when a=20 and b=50,

[0111] When the fluctuation amplitude f≥40, adjust to a=25 and b=45.

[0112] When f≤20, adjust to a=15 and b=55.

[0113] The probability of controlling the positioning error within 5 meters is 90%; after dynamic adjustment, it is increased to 95%.

[0114] Similarly, the initial setting of α and β is 0.5 to ensure the balance between the two.

[0115] Reference Figure 2 , identifying a first target object from the environmental image data, including the following sub-steps:

[0116] Preprocess the environmental image data. In real-world power system scenarios, such as capturing images of the environment surrounding transmission lines, the captured environmental image data may be subject to interference from various factors. For example, poor weather conditions (such as haze and heavy rain) can reduce image clarity, or the image may contain numerous noise points due to the inherent electronic noise of the capture device. Preprocessing can effectively improve these issues. Common preprocessing methods include filtering, such as using Gaussian filtering to smooth the image and reduce noise, and image enhancement, such as using histogram equalization to enhance image contrast and highlight feature information. Preprocessed images are like carefully organized data, providing a superior foundation for subsequent feature extraction.

[0117] Feature extraction algorithms are used to extract specific features from the preprocessed image. In power plant scenarios, the first target object may be equipment such as power poles and transformers. For example, a power pole, for example, will focus on extracting its unique shape (such as its slender columnar structure and the distribution of its crossarms), color (if the pole has a specific paint color), and texture (such as the texture of the material on the pole surface). Many mature feature extraction algorithms are currently available, such as the SIFT (Scale-Invariant Feature Transform) algorithm, which can stably extract key feature points and descriptors from images at different scales and rotation angles. These features accurately reflect the essential characteristics of the target object.

[0118] The extracted specific features are matched against a pre-stored feature template of the first target object. A feature matching algorithm is used to identify the feature points that best match the template features. The first target object's position in the image is then determined based on these best-matched feature points. For example, a standard feature template for power towers is pre-stored. This template contains information about the tower's characteristics under various typical angles and lighting conditions. A feature matching algorithm, such as one based on Euclidean distance, calculates the similarity between the extracted features and the template features, identifying the feature points with the highest similarity. For example, in an image of a power transmission line, the matching algorithm identifies a set of feature points that best match the tower template features. These feature points form the approximate outline of the tower in the image, accurately determining the tower's position in the image. This feature matching-based positioning method effectively mitigates positioning errors caused by changes in image perspective (e.g., capturing the tower from different angles) and lighting variations (e.g., varying light intensity and angle between morning and evening).

[0119] The geometric characteristic values ​​of the first target object are calculated and compared with the geometric reference values ​​to calculate the verification value. Continuing with the example of a power tower, its geometric characteristic values ​​may include height, distance between crossarms, and diameters at the base and top of the tower. Reference values ​​for these geometric parameters are pre-set, and the verification value is calculated by calculating the difference between the actual geometric characteristic values ​​extracted for the tower and the reference values. For example, the deviation ratio between the actual measured tower height and the reference height is calculated as part of the verification value.

[0120] If the verification value is within the set range, the first target object is considered correct; otherwise, it is considered incorrect and the first target object is re-identified from the environmental image data. For example, the tolerance range of the verification value is set to ±5%. If the calculated tower height verification value deviation is within this range, the identified target object can be confirmed to be the correct power tower. However, if the deviation exceeds this range, the identification is considered incorrect and the system will re-identify the target object from the environmental image data.

[0121] This multi-verification mechanism, encompassing both feature matching and geometric feature verification, greatly enhances recognition reliability and reduces the probability of misidentification. Simultaneously, preprocessing and optimizing the image reduces the complexity of subsequent feature extraction and matching calculations, much like clearing obstacles from the road, allowing for a smoother computational process. The rapidity of geometric feature verification eliminates erroneous results promptly, avoiding wasted processing time on misidentifications and significantly improving recognition efficiency. This ensures the system can accurately, efficiently, and stably identify the primary target, providing a reliable foundation for subsequent operations such as positioning calculations based on the target's position.

[0122] Reference Figure 3 , calculating the target displacement data of the first target body, and calculating the image positioning data according to the target displacement data, including the following sub-steps:

[0123] The location of the first target is identified in each of two adjacent image frames. In a power system inspection scenario, a mobile function module equipped with a camera module is used to inspect transmission lines. The camera module continuously captures images of the environment at a specific frame rate. Advanced image recognition algorithms (such as deep learning-based object detection algorithms) are used to identify the location of the first target in these two adjacent image frames. For example, the first target could be a shock absorber, equipment number plate, or streetlight pole number plate on a transmission line. For example, in the first image frame, algorithmic analysis determines the pixel coordinates of the shock absorber to be (x1, y1). In the next adjacent image frame, the same algorithm identifies the pixel coordinates of the shock absorber as (x2, y2).

[0124] The target displacement data is calculated based on the position change of the first target object in two adjacent image frames. Based on the identified position of the first target object in two adjacent image frames, its position change is calculated to obtain the target displacement data. Continuing with the shock-absorbing hammer example mentioned above, assuming that each pixel represents an actual distance of d meters, the horizontal displacement Δx = (x² − x¹) × d, and the vertical displacement Δy = (y² − y¹) × d. This method can keenly capture the subtle movements of the target object, effectively reducing the errors caused by single-frame image analysis and significantly improving the accuracy of displacement calculations. If only a single-frame image is analyzed, it is difficult to accurately determine whether the target object has moved, as well as the direction and distance of movement.

[0125] The target displacement data is integrated to obtain the cumulative displacement of the first target over a period of time. As the camera module continuously captures images, a series of target displacement data between two adjacent frames will be obtained. By integrating these displacement data, that is, adding them up, the cumulative displacement of the first target over a period of time can be obtained. Assume that in n adjacent frame pairs, the horizontal displacements obtained are Δx1, Δx2, ⋯, Δx n , the vertical displacements are Δy1, Δy2, ⋯, Δy n , then during this period, the cumulative displacement in the horizontal direction is , the cumulative displacement in the vertical direction This integration operation integrates the displacement information of multiple frames, further eliminating the random errors of single-frame calculations, and making the displacement data more realistically reflect the actual movement status of the target over a period of time.

[0126] Using the camera module's initial position as a reference point, the current position of the first target, or image positioning data, is calculated based on the accumulated displacement and the reference point's position. At the start of filming, the camera module's initial position (X0, Y0) is recorded and used as a reference point. The current position of the first target can be calculated based on the previously calculated accumulated displacement (X, Y). For the shock absorber, its current position (image positioning data) is (X0 + X, Y0 + Y). This method not only adapts to various complex motion states of the target, whether uniform, variable, or irregular, but also accurately calculates its position based on a stable reference point even when the environment changes. For example, when a drone encounters airflow and sways during an inspection, or when ambient lighting conditions change, the current position of the shock absorber can still be accurately calculated using the camera module's initial position as a reference point.

[0127] In summary, by accurately identifying the position of the first target in two adjacent image frames, calculating the target displacement data based on the position change, integrating the target displacement data to obtain the cumulative displacement, and finally calculating the image positioning data based on the initial position of the camera module, the system greatly enhances its adaptability to different scenarios, ensuring the accuracy and reliability of the positioning results, and providing a solid foundation for applications that rely on target position information. In power systems, accurate target positioning information is crucial for fault detection, equipment maintenance, and other tasks. For example, by precisely understanding the position changes of a shock-absorbing hammer, abnormal vibrations in a transmission line can be detected promptly, allowing proactive measures to prevent failures.

[0128] Reference Figure 4 , the step of calculating the image positioning data also includes the following sub-steps:

[0129] Environmental image data is captured using a single camera on the mobile functional module. In a real-world power system scenario, such as a large substation, the mobile functional module might be a patrol robot. As it moves within the station, the camera continuously captures images of the surrounding environment. These images contain rich information, such as various power equipment and buildings, providing the foundation for subsequent object recognition and positioning.

[0130] At least two first targets are identified from the environmental image data, and the angle formed between the two first targets closest to the camera and the camera is calculated. In the substation example, the first targets can be different power poles, transformers, and other equipment. Assuming that the two first targets closest to the camera are successfully identified, such as two specifically numbered power poles, the angle formed between these two first targets and the camera is calculated using computer vision algorithms and geometric calculation methods. Specifically, the angle can be calculated using trigonometric functions based on the pixel positions of the targets in the image, the imaging principle of the camera, and known camera parameters (such as focal length). For example, by measuring the relative position and angular relationship of the two targets in the image and combining it with the camera's intrinsic parameter matrix, the actual angle value can be calculated. This angle is a key parameter in the subsequent positioning calculation.

[0131] Based on the two first targets closest to the camera, the system matches the preset positioning information corresponding to each first target. Each first target has its corresponding positioning information pre-stored in the system, which was determined during system initialization or a previous measurement process. For each identified first target, the system matches them based on their features (such as appearance and serial number) to find the corresponding preset positioning information. In the substation scenario, each power tower has its precise geographic coordinates as preset positioning information. The system compares the tower features identified in the image and finds the corresponding coordinate information from the database.

[0132] The image positioning data for the mobile functional module is calculated based on the matched two preset positioning information and the included angle. Once the preset positioning information for the two targets and the angle between them and the camera device are known, the image positioning data for the mobile functional module can be calculated using geometric positioning principles. The positions of the two targets can be considered as two known points on a plane. The position of the camera device (i.e., the mobile functional module) can be determined using these two points and the included angle. For example, by utilizing the sine and cosine theorems of triangles, combined with the known target coordinates and included angle, a mathematical model can be established to determine the coordinate position of the mobile functional module on the plane. In this way, the image positioning data for the mobile functional module is obtained.

[0133] Different targets have different positions and characteristics in the environment. Using multiple targets for positioning can verify each other and reduce positioning errors caused by single-target identification errors or position fluctuations. If positioning relies solely on a single target, significant errors can occur in the positioning result if the target's identification is incorrect or its position changes unexpectedly. However, by combining information from multiple targets, positioning reliability can be improved. For example, if there is a certain error in the identification of one power tower, information from another tower can supplement and verify the final positioning result, making the final positioning result more accurate. Given the preset positioning information of two targets, the angle between them can help determine the specific position of the camera device, or the mobile module, relative to the two targets. This angle reflects the relative orientation of the mobile module in the plane formed by the two targets, providing an additional constraint for positioning calculations. The angle, combined with the target coordinates, allows for more precise determination of the mobile module's position.

[0134] To sum up, the method of acquiring environmental image data based on a single camera device, identifying multiple first target objects, calculating the angle and combining it with preset positioning information can effectively improve the positioning accuracy and reliability of the mobile function module, and provide more accurate location information for inspection, monitoring and other applications of the power system.

[0135] Reference Figure 5In another embodiment, the step of calculating the image positioning data further includes the following sub-steps:

[0136] Based on the multiple cameras at different angles on the mobile functional module, multiple image data of the surrounding environment are obtained. Installing multiple cameras at different angles on the mobile functional module enables a comprehensive observation of the surrounding environment from different perspectives. Taking the power inspection robot as an example, four cameras with different angles are installed at different positions on its body: front, rear, left, and right. When the robot moves within the substation, these cameras will work simultaneously and continuously obtain multiple image data of the surrounding environment. Images taken from different angles can capture the characteristics and position information of the target object from different perspectives, avoiding the information loss caused by a single perspective. For example, a large transformer may be partially blocked by other equipment when viewed from the front, but the camera from the side perspective can fully capture it, thus providing more comprehensive information for subsequent target object recognition.

[0137] At least two first targets are identified from each environmental image data set, and the angle between the two first targets closest to the camera and the camera is calculated. Advanced image recognition algorithms are used to identify at least two first targets from each environmental image data set. In a substation scenario, these first targets can be various power towers, switchgear, and other equipment. For each image captured by a camera, the angle between the two first targets closest to that camera and the camera is calculated. For example, using the image captured by the robot's front camera, the two closest power towers are identified. Using computer vision technology and geometric calculation methods, combined with the camera's imaging principles and known parameters (such as focal length and pixel size), the angle between these two towers and the camera is calculated. This angle is crucial geometric information for positioning calculations, reflecting the relative position and orientation of the camera with respect to the target.

[0138] Based on the two first targets closest to the camera, the system matches the preset positioning information corresponding to each first target. The system pre-stores the positioning information for each first target, which is precisely determined during system initialization or a previous measurement process. For each identified target, the system matches it based on its characteristics (such as appearance, number, unique identifier, etc.) to find the corresponding preset positioning information. For example, for a power tower with a specific number, the system retrieves its precise geographic coordinates from a database. This matching operation provides an accurate reference point for subsequent positioning calculations.

[0139] Based on the matched two preset positioning information and the included angle, the image positioning parameters of the mobile functional module in each environmental image data are calculated. Based on the matched two preset positioning information and the calculated included angle, the image positioning parameters of the mobile functional module in each environmental image data are calculated using geometric positioning principles. The positions of the two targets can be considered as two known points on a plane. The position of the camera device (i.e., the mobile functional module) can be determined using these two points and the included angle. For example, mathematical knowledge such as the sine and cosine theorems of triangles, combined with the coordinates of the target objects and the included angle, can be used to establish a mathematical model to solve for the image positioning parameters of the mobile functional module at that viewing angle. By performing this calculation for each image captured by the camera device, multiple sets of image positioning parameters are obtained.

[0140] The average of all image positioning parameters is calculated as the final image positioning data. To further improve positioning accuracy and stability, the average of all image positioning parameters is calculated as the final image positioning data. Since image positioning parameters from different perspectives may have certain errors, taking the average value can smooth these errors, making the final positioning result more accurately reflect the actual position of the mobile functional module. For example, the image positioning parameters calculated by four cameras at different angles are averaged to obtain a comprehensive and more accurate positioning result. This method uses multiple sets of data to verify each other, effectively reducing positioning deviations caused by errors in a single target or a single image.

[0141] In summary, the method of acquiring multiple images of the surrounding environment using multiple cameras at different angles on a mobile functional module offers significant advantages. It can comprehensively capture target information from different perspectives, providing rich geometric information for positioning. By verifying and averaging multiple sets of data, positioning deviations are effectively reduced, improving positioning accuracy and stability. In complex power system environments, such as those with complex equipment layouts and obstructions within substations, this method can greatly enhance the reliability of system positioning, providing more accurate location information for applications such as power inspections and equipment monitoring, and ensuring the safe and stable operation of the power system.

[0142] In the power system, the intensity of electromagnetic interference will change over time. In order to make the positioning system of the mobile function module work accurately and efficiently, it is necessary to dynamically adjust the positioning frequency according to the actual situation of electromagnetic interference. The method also includes the following steps:

[0143] During the preset working time period S, the positioning system continuously monitors the intensity of electromagnetic interference and matches the corresponding positioning frequency data x based on the interference intensity data. Time coordinates are classified based on the relationship between the positioning frequency data x and thresholds a and b. Assuming the working time period S is one day (24 hours), from 0:00 to 24:00, the positioning system records the positioning frequency data x every certain period (e.g., 1 minute). During this day, all time points with positioning frequency data x ≤ a are recorded and form Set A; time points with a < x < b form Set B; and time points with b ≤ x form Set C. For example, between 8:00 and 10:00 on a certain weekday, the electromagnetic interference intensity is low, and the positioning frequency data x is always less than or equal to a. In this case, all time points within these two hours are included in Set A.

[0144] The time span of Set A is calculated as a1, the time span of Set B is calculated as b1, and the time span of Set C is calculated as c1. The time span reflects the duration of the electromagnetic interference at different intensity levels. Continuing with the above example, if the time points in Set A cover the two hours from 8:00 to 10:00, then a1 = 2 hours; if the time points in Set B are from 10:00 to 12:00, then b1 = 2 hours; and if the time points in Set C are from 2:00 to 16:00, then c1 = 2 hours.

[0145] Based on time spans a1, b1, and c1, calculate the total time span d = a1 + b1 + c1 - m, where m is an adjustment parameter. Adjustment parameter m is set to fine-tune the total time span to better suit different operating scenarios and system requirements. For example, in some cases, to prioritize the impact of periods of high electromagnetic interference, the value of m can be increased; in other cases, to comprehensively consider the impact of various intensity ranges, the value of m can be decreased. Assuming m = 0.5 hours, in the above example, the total time span d = 2 + 2 + 2 - 0.5 = 5.5 hours.

[0146] Based on the total time span d and the operating time period S, calculate the frequency correction value D = d / S. The frequency correction value D reflects the degree to which the temporal distribution of electromagnetic interference intensity intervals within the entire operating time period affects the positioning frequency. In the above example, the operating time period S = 24 hours, so the frequency correction value D = 5.5 / 24 ≈ 0.23.

[0147] Correct the next positioning frequency data x based on the frequency correction value D. The next positioning frequency data x = D × the current positioning frequency data x. Assuming the current positioning frequency data x is 5 positioning times per minute, the next positioning frequency data x = 0.23 × 5 ≈ 1 time / minute.

[0148] By calculating the time span a1, time span b1 and time span c1, the distribution and duration of electromagnetic interference in different intensity ranges can be clearly understood, thereby realizing dynamic adjustment of the positioning frequency to better adapt to the actual electromagnetic interference environment.

[0149] If electromagnetic interference intensity is low for most of the working time (i.e., a1 is large), the frequency correction value D will be relatively small, and the frequency of the next positioning will be reduced accordingly. For example, at night, when the power system load is relatively low and electromagnetic interference is weak, a1 is large. Reducing the positioning frequency can reduce unnecessary positioning operations, lower the system's energy consumption and computing resource consumption, and improve system operating efficiency.

[0150] Conversely, if high-intensity electromagnetic interference persists for a long time during the working hours (i.e., c1 is large), the frequency correction value D will be relatively large, and the next positioning frequency will increase. For example, during peak daytime electricity consumption, a large number of electrical devices are running simultaneously, generating strong electromagnetic interference. Increasing the positioning frequency in this situation can improve positioning accuracy, timely capture the position changes of functional modules, and reduce positioning errors caused by interference.

[0151] In power systems, electromagnetic interference can vary significantly across seasons and time periods. For example, in summer, due to the heavy use of high-power equipment like air conditioners, electromagnetic interference can be stronger than in other seasons. During the daytime on weekdays, when electricity loads are high, electromagnetic interference can be more severe than at night. This technical solution enables the positioning system to operate stably in all conditions, dynamically adjusting positioning frequency based on actual electromagnetic interference conditions, reducing positioning errors caused by interference fluctuations, improving the overall positioning performance of functional modules in the power system, and ensuring the safe and stable operation of the power system.

[0152] In the power system mobile functional module positioning method, in order to dynamically optimize the positioning strategy according to the electromagnetic interference condition, the dynamic adjustment steps for the adjustment parameter m are as follows:

[0153] During a set time period, the system will continuously record multiple positioning frequency data points x. For example, if the set time period is one week, from 8:00 AM to 8:00 PM every day, positioning frequency data points x will be recorded every half hour. During this week, a total of 24 × 7 = 168 positioning frequency data points will be recorded. This data is aggregated and the average value is calculated using the following formula: (where x iThe average positioning frequency x' is calculated based on the value of the i-th recorded positioning frequency data and n as the total number of recorded data. This operation is of great significance, as it reflects, from a macro perspective, the comprehensive impact of electromagnetic interference on positioning frequency during that time period. Since positioning frequency is closely related to electromagnetic interference intensity—the stronger the interference, the higher the positioning frequency—the average positioning frequency x' effectively quantifies the electromagnetic interference situation during that period. Assume that over the course of a week, the calculated average positioning frequency x' is 8 positioning times per hour.

[0154] A reference positioning frequency x" is preset. This frequency is typically determined based on a combination of factors, including the electromagnetic interference conditions under normal power system operation and positioning accuracy requirements. For example, based on past experience and system testing, the reference positioning frequency x" is set to 6 positioning times per hour. Next, the difference between the average positioning frequency x' and the preset reference positioning frequency x" is calculated: Δx = x' - x". In the above example, the difference Δx = 8 - 6 = 2 times / hour. This difference intuitively reflects the degree of difference between the actual electromagnetic interference conditions and the expected ones.

[0155] The adjustment parameter m is dynamically adjusted based on the calculated difference Δx. There's a clear correlation here: the larger the difference, the larger the adjustment parameter; the smaller the difference, the smaller the adjustment parameter. A large difference Δx indicates a significant deviation from expected electromagnetic interference. For example, if the difference Δx is 4 times / hour, this is significantly greater than the previous difference of 2 times / hour, indicating a significant difference between the actual electromagnetic interference and expected conditions. In this case, the adjustment parameter m is increased. For example, if m = 0.5, then m = 1. When calculating the total time span d = a1 + b1 + c1 - m, increasing m will reduce the total time span d. This is because the total time span d affects the frequency correction value D = d / S (where S is the working time period), which in turn affects the next positioning frequency data x = D × the current positioning frequency data x. This means that when interference conditions deviate significantly from expected conditions, the system can more flexibly adjust the positioning strategy. For example, during periods of strong electromagnetic interference, the positioning frequency can be significantly increased to improve positioning accuracy and efficiency.

[0156] Conversely, when the difference Δx is smaller, the actual interference situation is closer to the expected one. For example, if the difference Δx is 0.5 times / hour, the adjustment parameter m is reduced, for example, from m=0.5 to m=0.2. This way, when calculating the total time span d, the change in d is relatively small, resulting in a relatively small adjustment range for the positioning strategy, thus avoiding unnecessary resource consumption caused by excessive adjustment. In practical applications, this method of dynamically adjusting the parameter m based on the difference can enable the positioning system to better adapt to complex and changing electromagnetic interference environments, while ensuring positioning accuracy, optimizing system resource utilization, and improving the overall performance of the positioning of the mobile functional module of the power system.

[0157] An embodiment of the present application also discloses a functional module positioning system in an electric power system based on a single Beidou system, including a processor, wherein the processor executes the steps of the functional module positioning method in an electric power system based on a single Beidou system as described in any one of the above.

[0158] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for positioning functional modules in a power system based on a single BeiDou system, characterized in that: The steps include: Obtaining interference intensity data of electromagnetic interference in the environment, and matching positioning frequency data x according to the interference intensity data. The greater the interference intensity data, the greater the positioning frequency data x. Acquire multiple satellite positioning data of a single functional module according to the positioning frequency data x, and calculate the average value of the multiple satellite positioning data to obtain module positioning data; If x≤a, obtaining the module positioning data of multiple stationary functional modules within a preset set distance range; calculating positioning deviation values ​​of the multiple module positioning data, correcting the module positioning data using the positioning deviation values, and using the corrected module positioning data as the final positioning data; If a<x<b, calling the camera module to capture environmental image data, identifying a first target object from the environmental image data, calculating target displacement data of the first target object, calculating image positioning data based on the target displacement data, and calculating final positioning data based on the image positioning data and the module positioning data; If x ≥ b, then search for an interoperable module that performs positioning based on the second frequency channel, obtain the interoperable module that is closest, establish a communication connection, and guide the interoperable module to the functional module. The functional module obtains first positioning data based on the first frequency channel, and obtains second positioning data based on the second frequency channel through the interoperable module. The functional module calculates the final positioning data based on the first positioning data and the second positioning data; wherein a and b are frequency range parameters used to distinguish the intensity levels of electromagnetic interference.

2. The method for positioning functional modules in a power system based on a single BeiDou system according to claim 1, characterized in that: The step of obtaining interference intensity data of electromagnetic interference in the environment further includes the following sub-steps: Acquiring magnetic field strength data of a plurality of magnetic field sensors within the set distance range; Calculate the average value of the magnetic field strength data to obtain the average magnetic field strength, the formula is: ; Calculate the fluctuation value of the magnetic field intensity data to obtain the fluctuation degree value, the formula is: ; The interference intensity data I is calculated based on the average magnetic field intensity and the fluctuation degree value; ； Among them, α and β are the weight coefficients of the average magnetic field intensity and the fluctuation degree value, α+β=1; n is the number of magnetic field sensors, M i The magnetic field strength data obtained by the i-th magnetic field sensor.

3. The method for positioning functional modules in a power system based on a single BeiDou system according to claim 2, characterized in that: The method further comprises the steps of: Calculating fluctuation curves of a plurality of the interference intensity data within a preset set time period; Calculating the fluctuation amplitude data f according to the fluctuation curve; Adjust the size of a according to the fluctuation amplitude data f, the larger the fluctuation amplitude data f, the larger a is, and the smaller the fluctuation amplitude data f, the smaller a is; Adjust the size of b according to the fluctuation amplitude data f, the larger the fluctuation amplitude data f, the smaller b is, and the smaller the fluctuation amplitude data f, the larger b is; Among them, the adjustment range of a is greater than the adjustment range of b.

4. The method for positioning functional modules in a power system based on a single BeiDou system according to claim 1, characterized in that: The step of identifying the first target object from the environmental image data comprises the following sub-steps: Preprocessing the environmental image data; A feature extraction algorithm is used to extract features from the preprocessed image; Matching the extracted features with a pre-stored feature template of the first target object, using a feature matching algorithm to find the feature points that best match the template features, and determining the position of the first target object in the image based on the best matching feature points; Calculating a geometric characteristic value of the first target body, comparing the geometric characteristic value with a geometric reference value, and calculating a verification value; If the verification value is within the set range, the first target is a correct first target; Otherwise, it is an erroneous first target object, and the first target object is re-identified from the environmental image data.

5. The method for positioning functional modules in a power system based on a single BeiDou system according to claim 1, characterized in that: The step of calculating the target displacement data of the first target body and calculating the image positioning data according to the target displacement data includes the following sub-steps: In two adjacent frames of images, the position of the first target object is respectively identified; Calculating target displacement data according to a position change of the first target object in two adjacent frames of images; Integrating the target displacement data to obtain the cumulative displacement of the first target body over a period of time; The initial position of the camera module is used as a reference point, and the current position of the first target object, ie, image positioning data, is calculated according to the accumulated displacement and the position of the reference point.

6. The method for positioning functional modules in a power system based on a single BeiDou system according to claim 1, characterized in that: The step of calculating the image positioning data further includes the following sub-steps: Acquiring environmental image data based on a single camera device on the mobile functional module; Identify at least two first targets from the environmental image data, and calculate the angles formed by the two first targets closest to the camera device and the camera device; Matching the preset positioning information corresponding to each first target object according to the two first targets closest to the camera device; The image positioning data of the mobile function module is calculated based on the matched two preset positioning information and the angle.

7. The method for positioning functional modules in a power system based on a single BeiDou system according to claim 1, characterized in that: The step of calculating the image positioning data further includes the following sub-steps: Based on the camera devices of multiple different angles on the mobile function module, multiple surrounding environment image data are obtained; Identify at least two first targets from each of the environmental image data, and calculate the angles formed by the two first targets closest to the camera device and the camera device; Matching the preset positioning information corresponding to each first target object according to the two first targets closest to the camera device; Calculating the image positioning parameters of the mobile function module in each of the environmental image data according to the matched two preset positioning information and the included angle; The average value of all the image positioning parameters is calculated as the final image positioning data.

8. The method for positioning functional modules in a power system based on a single BeiDou system according to claim 1, characterized in that: The method further comprises the steps of: In a preset working time period S, the time coordinate set of the current positioning frequency data x≤a is calculated as set A, the time coordinate set of a<x<b is calculated as set B, and the time coordinate set of b≤x is calculated as set C; The time span for calculating set A is a1, the time span for calculating set B is b1, and the time span for calculating set C is c1; According to the time span a1, time span b1 and time span c1, calculate the total time span d=a1+b1+c1-m, where m is the adjustment parameter; Calculate the frequency correction value D=d / S according to the total time span d and the working time period S; The next positioning frequency data x is corrected according to the frequency correction value D, and the next positioning frequency data x=D×the current positioning frequency data x.

9. The method for positioning functional modules in a power system based on a single BeiDou system according to claim 8, characterized in that: The method further comprises the steps of: Calculate the average value of the plurality of positioning frequency data x within a set time period as the average positioning frequency x'; Calculating the difference between the average positioning frequency x' and the preset reference positioning frequency data x"; Adjust the value of the adjustment parameter m according to the difference, the larger the difference is, the larger the adjustment parameter m is; The smaller the difference is, the smaller the adjustment parameter m is.

10. A functional module positioning system in a power system based on a single Beidou system, characterized in that: It includes a processor, which executes the steps of the method for positioning a functional module in a power system based on a single Beidou system as described in any one of claims 1 to 9.

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