Method and system for positioning functional modules in power system based on single Beidou system

Abstract

The invention relates to the technical field of satellite navigation and positioning, and discloses a functional module positioning method and system in a power system based on a single Beidou system, and the method comprises the steps: obtaining environmental electromagnetic interference intensity data, matching positioning frequency data x according to the size of the environmental electromagnetic interference intensity data, obtaining satellite positioning data, and calculating module positioning data. When x is smaller than or equal to a, correcting module positioning data by using a deviation value of the positioning data of the static function module within a preset distance to obtain a final positioning result; when a < x < b, calling a camera module to shoot an environment image, identifying a first target body, calculating target displacement of the first target body to obtain image positioning data, and fusing the image positioning data with module positioning data to obtain final positioning data; and when x is greater than or equal to b, searching a second channel interoperability module, and obtaining positioning data based on different channels by the function module and the interoperability module and calculating to obtain final positioning data. According to the invention, electromagnetic interference can be reduced, and the positioning precision of the single Beidou in a power system is improved.

Description

Technical Field

[0001] This 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] As a global satellite navigation system independently developed in China, the Beidou satellite has become an important part of the national strategy. In positioning technology, dual-frequency Beidou and single-frequency Beidou are two main implementation schemes, and they have different technical characteristics, application scenarios, and promotion significances.

[0003] Single Beidou only receives signals of one frequency band (such as B1), has simple hardware, high integration, and low power consumption, and is suitable for ordinary civilian and industrial scenarios, such as work cards, vehicle management, agricultural navigation, etc. However, the stability of single Beidou is relatively low in complex environments. Especially in the power system with a harsh electromagnetic environment, its positioning data has a large error and it is difficult to meet the high-precision positioning requirements of the power system.

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

[0005] Therefore, there is an urgent need for a positioning technical solution that can improve the positioning accuracy in the power system 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, this application provides a method and system for positioning functional modules in a power system based on a single Beidou system.

[0007] In a first aspect, this application provides a method for positioning functional modules in a power system based on a single Beidou system, adopting the following technical solution:

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

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

[0010] Obtain 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 the module positioning data;

[0011] If x ≤ a, obtain the module positioning data of multiple stationary functional modules within a preset set distance range; calculate the positioning deviation values of the multiple module positioning data, correct the module positioning data with the positioning deviation values, and use the corrected module positioning data as the final positioning data;

[0012] If a < x < b, call the camera module to capture environmental image data, identify the first target object from the environmental image data, calculate the target displacement data of the first target object, calculate the image positioning data according to the target displacement data, and calculate the final positioning data according to the image positioning data and the module positioning data;

[0013] If x ≥ b, find the interoperable module that performs positioning based on the second channel, obtain the nearest interoperable module, establish a communication connection, guide the interoperable module to the functional module, the functional module obtains the first positioning data based on the first channel, the interoperable module obtains the second positioning data based on the second channel, and the functional module calculates the final positioning data based on the first positioning data and the second positioning data; where a and b are frequency range parameters used to distinguish the intensity levels of electromagnetic interference.

[0014] By adopting the above technical solutions, it is possible to accurately obtain the electromagnetic interference intensity data in the environment, and cleverly match the positioning frequencies based on this data, showing the characteristics 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 electromagnetic interference levels, this method shows extremely strong adaptability. When the interference intensity is low, that is, when the positioning frequency data is obtained, the module positioning data of multiple stationary functional modules within a preset set distance range will be obtained, and the positioning deviation value will be calculated to correct the module positioning data of its own module, further improving the accuracy of positioning. When the interference is at a medium intensity, the camera module is called to capture environmental image data, the first target object is identified, the target displacement data is accurately calculated, and then the image positioning data is obtained, which is combined with the module positioning data, and the reliability of positioning is enhanced by using multi-source data, making up for the deficiencies of satellite positioning at this interference level. When the interference intensity is high, the interoperable module that performs positioning based on the second channel will be actively searched for, a communication connection will be established with the nearest interoperable module and it will be guided to 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. The two are combined to calculate the final positioning data, greatly reducing the adverse impact of strong interference on positioning. This method fully adapts to the complex and changeable electromagnetic environment in the power system and significantly improves the accuracy of functional module positioning.

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

[0016] Obtain the magnetic field intensity data of multiple magnetic field sensors within the set distance range;

[0017] Calculate the average value of the magnetic field intensity data to obtain the average magnetic field intensity. The formula is ;

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

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

[0020] ;

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

[0022] By adopting the above technical solution, the average magnetic field intensity reflects the overall level of the magnetic field, and the fluctuation degree value reflects the severity of the magnetic field change. In actual electromagnetic interference, strong interference may stem from either a high-intensity magnetic field or a drastic fluctuation of the magnetic field. By incorporating both of these factors simultaneously, the interference intensity data I can more comprehensively and accurately characterize the interference situation of the electromagnetic environment. For example, near a substation, the average magnetic field intensity may be relatively high, while in areas where electrical equipment starts and stops frequently, the magnetic field fluctuation degree is large. The comprehensive calculation can accurately reflect the interference conditions in different scenarios.

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

[0024] Calculate the fluctuation curve of multiple pieces of the interference intensity data within a preset set time period;

[0025] Calculate the fluctuation amplitude data f based on the fluctuation curve;

[0026] Adjust the magnitude of a according to the fluctuation amplitude data f. The larger the fluctuation amplitude data f, the larger a; the smaller the fluctuation amplitude data f, the smaller a;

[0027] Adjust the magnitude of b according to the fluctuation amplitude data f. The larger the fluctuation amplitude data f, the smaller b; the smaller the fluctuation amplitude data f, the larger b;

[0028] where the adjustment amplitude of a is greater than that of b.

[0029] By adopting the above technical solution, through the analysis of the interference intensity fluctuation curve, the system can dynamically track the changes in electromagnetic interference. When the interference fluctuates greatly, the value of a is adjusted more significantly, enabling a more rapid change in the positioning strategy to adapt to the interference changes; when the interference fluctuates slightly, the value of b is moderately adjusted to ensure the rationality of the positioning strategy switching timing and avoid the additional resource consumption caused by frequent switching.

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

[0031] Preprocess the environmental image data;

[0032] Extract specific features from the preprocessed image using a feature extraction algorithm;

[0033] Match the extracted specific features with the feature template of the first target stored in advance, use a feature matching algorithm to find the feature points that best match the template features, and determine the position of the first target in the image based on the best-matching feature points;

[0034] Calculate the geometric feature values of the first target, compare the geometric feature values with geometric reference values, and calculate a verification value;

[0035] If the verification value is within the set range, the first target is the 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 the above technical solution, through a multiple verification mechanism covering feature matching and geometric feature verification, the recognition reliability is greatly enhanced and the misrecognition probability is reduced. At the same time, preprocessing optimizes the image to reduce the subsequent computational complexity, and the rapidity of geometric feature verification can promptly exclude incorrect results, avoiding the waste of invalid processing time, significantly improving the recognition efficiency, and ensuring that the system can accurately, efficiently, and stably identify the first target.

[0037] Optionally, in the step of calculating the target displacement data of the first target and calculating the image positioning data based on the target displacement data, the following sub-steps are included:

[0038] In two adjacent frames of images, respectively identify the positions of the first target;

[0039] Calculate the target displacement data based on the position changes of the first target in two adjacent frames of images;

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

[0041] Taking the initial position of the camera module as a reference point, calculate the current position of the first target object, i.e., the image positioning data, based on the cumulative displacement and the position of the reference point.

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

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

[0044] Based on a single camera device on the mobile function module, obtain environmental image data;

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

[0046] According to the two first target objects closest to the camera device, match the preset positioning information corresponding to each first target object;

[0047] Calculate the image positioning data of the mobile function module according to the two matched preset positioning information and the angle.

[0048] By adopting the above technical solution, different target objects have different positions and characteristics in the environment. Multiple target objects can corroborate each other, reducing the positioning deviation caused by the recognition error or position change of a single target object, making the positioning result more accurate and reliable. Given the preset positioning information of two target objects, the angle can help determine the specific position of the camera device, i.e., the mobile function module, relative to these two target objects, thereby improving the positioning accuracy.

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

[0050] Based on multiple camera devices with different angles on the mobile function module, obtain multiple surrounding environmental image data;

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

[0052] According to the two first target objects closest to the camera device, match the preset positioning information corresponding to each first target object;

[0053] Calculate the image positioning parameters of the mobile function module in each of the environmental image data based on the two preset positioning information and the included angle that are matched;

[0054] Calculate the average value of all the image positioning parameters as the final image positioning data.

[0055] By adopting the above technical solution, multiple environmental image data around are obtained based on the imaging devices at multiple different angles on the mobile function module, and the information of the target object can be comprehensively captured from different perspectives, avoiding information loss caused by a single perspective. At least two first target objects are identified from each environmental image data, and the included angle formed by the two first target objects closest to the imaging device and the imaging device is calculated, providing rich geometric information for positioning. According to these two target objects, the corresponding preset positioning information is matched, and the image positioning parameters of the mobile function module in each environmental image data are calculated in combination with the included angle. By using multiple sets of data to confirm each other, the positioning deviation caused by a single target object or a single image error is effectively reduced. By calculating the average value of all the image positioning parameters as the final image positioning data, the data is further smoothed, the accuracy and stability of the positioning are improved, the positioning result can more accurately reflect the actual position of the mobile function module, and the reliability of the system in positioning in a complex environment is greatly enhanced.

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

[0057] Within the preset working time period S, calculate that the time coordinate set when the current positioning frequency data x ≤ a is set A, the time coordinate set when a < x < b is set B, and the time coordinate set when b ≤ x is set C;

[0058] Calculate the time span of set A as a1, calculate the time span of set B as b1, and calculate the time span of set C as c1;

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

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

[0061] Correct the next positioning frequency data x 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, by calculating the time span a1, the time span b1, and the time span c1, the distribution situation and duration of electromagnetic interference in different intensity intervals can be clearly grasped. The dynamic adjustment of the positioning frequency is realized, enabling it to better adapt to the actual electromagnetic interference environment.

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

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

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

[0066] Adjust the magnitude of the adjustment parameter m according to the difference. The larger the difference, the larger the adjustment parameter m; the smaller the difference, the smaller the adjustment parameter m.

[0067] By adopting the above technical solution, the average positioning frequency can be regarded as a quantitative manifestation of the electromagnetic interference situation during this period. When the interference situation is significantly different from the expectation, the system can adjust the positioning strategy more flexibly to adapt to the actual interference environment and improve the positioning accuracy and efficiency. On the contrary, when the difference is smaller, it indicates that the actual interference situation is closer to the expectation. At this time, reducing the adjustment parameter m makes the adjustment range of the positioning strategy relatively small, avoiding unnecessary resource consumption caused by excessive adjustment.

[0068] In a second aspect, the present application provides a function module positioning system in a power system based on a single Beidou system, adopting the following technical solution:

[0069] A function module positioning system in a power system based on a single Beidou system includes a processor, and the processor executes the steps of the function module positioning method in a power system based on a single Beidou system as described in any one of the above.

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

[0071] By calculating the average value and fluctuation value of multiple magnetic field sensor data, the interference situation of the electromagnetic environment can be comprehensively and accurately characterized. Dynamically matching the positioning frequency based on the interference intensity, the stronger the interference, the higher the positioning frequency, effectively improving the positioning accuracy.

[0072] In different electromagnetic interference intensity intervals, flexibly use multiple means such as satellite positioning, image recognition, and mutual use module positioning. When the interference is low, use the positioning data of surrounding stationary function modules to correct its own positioning; when the interference is medium, combine the image positioning data to enhance the positioning reliability; when the interference is strong, use the mutual use module to achieve dual-channel positioning, reduce the interference impact, and comprehensively ensure the positioning accuracy.

[0073] Preprocess the environmental image data, combine steps such as feature extraction, matching, and geometric feature verification, effectively improve the accuracy and reliability of the first target body recognition, reduce the probability of misrecognition, and at the same time reduce the computational complexity and improve the recognition efficiency.

[0074] Calculate the displacement by recognizing the position change of the target object based on two adjacent frames of images, obtain the cumulative displacement through integration, and calculate the image positioning data with the initial position of the camera module as the reference point, which can sensitively capture the subtle movement of the target object, adapt to complex motion states and environmental changes, and ensure the accuracy and reliability of the positioning result.

[0075] Obtain image data based on a single or multiple camera devices at different angles, and calculate the positioning data using the information of multiple target objects and included angles, which can comprehensively capture target information from multiple perspectives, reduce positioning deviation, and improve the positioning accuracy and stability through mutual verification of multiple groups of data and average value calculation.

[0076] Calculate the time span of different interference intensity intervals, adjust the positioning frequency correction value accordingly, and achieve dynamic adjustment of the positioning frequency. Dynamically adjust the parameters based on the difference between the average positioning frequency and the reference positioning frequency, so that the system can better adapt to different electromagnetic interference environments, improve the system operation efficiency while ensuring the positioning accuracy, avoid resource waste, and enhance the overall performance of the positioning of the mobile function module in the power system. Description of the Drawings

[0077] Figure 1 It is a step diagram of the positioning method for the function module in the power system based on the single Beidou system.

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

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

[0080] Figure 4 It is a step diagram of calculating the image positioning data based on a single camera device on the mobile function module.

[0081] Figure 5 It is a step diagram of calculating the image positioning data based on multiple camera devices on the mobile function module. Detailed Embodiments

[0082] The following details the embodiments of the present application, and the examples of the embodiments are shown in the drawings.

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

[0084] An embodiment of the present application discloses a method for positioning functional modules in a power system based on a single Beidou system. Referring to Figure 1 , the method includes the following steps:

[0085] In the actual power system environment, the sources of electromagnetic interference are extensive. For example, the operation of substation equipment, the change of current in transmission lines, etc. will all generate electromagnetic interference. Through various electromagnetic monitoring devices arranged around the mobile functional module, such as electric field sensors, magnetic field sensors, etc., relevant data are collected, and a specific algorithm is used to comprehensively analyze these data, so as to obtain the interference intensity data of electromagnetic interference. Subsequently, the positioning frequency data x is matched according to the interference intensity data. There is a clear correlation here, that is, the greater the interference intensity data, the greater the positioning frequency data x. This is because in a strong electromagnetic interference environment, satellite signals are easily interfered and fluctuate, and the positioning accuracy is reduced. Therefore, it is necessary to increase the positioning frequency to obtain more accurate positioning information as much as possible. For example, near a large substation, the electromagnetic interference intensity is relatively high. After measurement by monitoring devices and calculation by algorithms, the interference intensity data is obtained as a relatively large value. At this time, the matched positioning frequency data x increases accordingly. For example, from positioning once per minute in a normal environment, it is increased to positioning three times per minute.

[0086] Multiple satellite positioning data of a single functional module are obtained according to the positioning frequency data x. Taking the single Beidou system as an example, the Beidou positioning terminal on the mobile functional module continuously receives signals from Beidou satellites according to the determined positioning frequency, so as to obtain multiple satellite positioning data. Since the satellite signals received each time may have certain errors due to various factors, the average value of the multiple satellite positioning data is calculated to obtain the module positioning data, thereby reducing random errors and initially improving the positioning accuracy. For example, at a certain moment, the positioning frequency data x is two times per minute. In two consecutive minutes, the positioning terminal obtains 4 satellite positioning data, which are (100.1, 20.2), (100.3, 20.1), (99.9, 20.3), (100.2, 20.0) respectively. By calculating the average value of these 4 data, the module positioning data is obtained as (100.125, 20.15).

[0087] If \(x\leq a\), this means that the electromagnetic interference intensity is relatively low at this time, and the satellite positioning is less affected by interference. In this case, obtain the module positioning data of multiple stationary functional modules within a preset set distance range. In the power system, there are many relatively stationary functional modules, such as fixed power distribution cabinets, transformer monitoring modules, etc. Assume that the set distance range is within a 50-meter radius area centered on the mobile functional module. Within this area, the module positioning data of 3 stationary functional modules are obtained, which are (100.0, 20.0), (100.2, 20.1), and (99.8, 20.2) respectively. Then calculate the positioning deviation values of the multiple module positioning data. For example, taking the preliminary module positioning data (100.125, 20.15) of the mobile functional module as a reference, calculate the deviation from the positioning data of these 3 stationary functional modules, and obtain an average positioning deviation value of (-0.1, -0.1). Use this positioning deviation value to correct the module positioning data, and take the corrected module positioning data (100.025, 20.05) as the final positioning data to further improve the 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 the actual application scenario, the mobile functional module can be equipped with a high-definition camera, which will capture the surrounding environment according to the set shooting frequency. Identify the first target object from the environmental image data, which requires the use of advanced image recognition technologies, such as object detection algorithms based on deep learning. For example, at a power equipment maintenance site, in the environmental image captured by the camera, a power equipment with a specific shape and identification is successfully identified as the first target object through the image recognition algorithm. Then calculate the target displacement data of the first target object, which is calculated by comparing the position changes of the first target object in two adjacent frames of images. Assume that in two frames of images, the first target object moves 5 pixels horizontally and 3 pixels vertically. After conversion, the actual target displacement data is obtained. Calculate the image positioning data according to the target displacement data, and then combine it with the previously obtained module positioning data. Through a specific data fusion algorithm, such as the weighted average method, assume that the weight of the image positioning data is 0.4 and the weight of the module positioning data is 0.6, and calculate the final positioning data, so as to use multi-source data to enhance the reliability of positioning and make up for the deficiencies of satellite positioning under this interference level.

[0089] If x≥b, it indicates that the electromagnetic interference intensity is relatively high and satellite positioning is severely interfered. At this time, an interoperable module for positioning based on the second channel is searched for. In the power system, there may be some standby positioning devices with different positioning channels as interoperable modules. By searching for surrounding signals, the nearest interoperable module is obtained. For example, an interoperable module is found within a range of 200 meters around the mobile function module. A communication connection is established to guide the interoperable module near the function module. The function 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. For example, the positioning data obtained by the function module through the first channel is (101.0, 21.0), and the positioning data obtained by the interoperable module through the second channel is (100.8, 20.9). Based on these two positioning data, the function module uses an appropriate algorithm, such as the Kalman filtering algorithm, to calculate the final positioning data, greatly reducing the adverse impact of strong interference on positioning.

[0090] By obtaining the electromagnetic interference intensity data in the environment, a dynamic correlation between the interference intensity and the positioning frequency is constructed, that is, the greater the interference intensity, the higher the positioning frequency, so as to cope with the challenges of positioning in a complex electromagnetic environment. In the satellite positioning link, by averaging multiple satellite positioning data, random errors are effectively filtered out, providing a preliminary guarantee for positioning accuracy.

[0091] In the face of different degrees of electromagnetic interference, this method adopts different strategies. When the interference intensity is relatively low, that is, when the positioning frequency data is relatively small, the system will automatically obtain the positioning data of multiple stationary function modules within a preset set distance range. For example, with the mobile function module as the center, within a radius of 50 meters, the positioning information of surrounding stationary function modules such as power distribution cabinet and transformer monitoring modules is collected. By calculating the deviation values between these data and the positioning data of its own module, its own positioning is corrected to further improve the positioning accuracy.

[0092] When the interference is at a medium intensity, the system will quickly call the camera module to collect image data of the environment. Using advanced image recognition technology, the first target body is accurately identified. By analyzing the position changes of the target body in adjacent image frames, the target displacement data is accurately calculated, and then the image positioning data is obtained. Subsequently, the image positioning data and the module positioning data are organically combined to give full play to the advantages of multi-source data, enhance the positioning reliability, and effectively make up for the deficiencies of satellite positioning at this interference level.

[0093] If the interference intensity is relatively high, the system will actively search for an interoperable module for positioning based on the second channel, lock the nearest interoperable module and establish a communication connection, and guide it close to the function module. The function 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, the final positioning result is calculated, greatly reducing the adverse impact of strong interference on positioning.

[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 also includes the following sub-steps:

[0096] Calculate multiple magnetic field sensors within a set distance range to obtain multiple magnetic field strength data. Set a specific distance range around the mobile function module, arrange multiple magnetic field sensors within this range, sense the changes in the surrounding magnetic field in real time, and obtain multiple magnetic field strength data. For example, in a scene near a power transmission line, with the mobile function module as the center, 5 magnetic field sensors are evenly distributed within a radius of 10 meters, and they continuously collect 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, 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. The average magnetic field strength can be calculated by the formula as (1 / 5)×(10+12+8+11+9)=10.

[0098] Calculate the fluctuation value of the magnetic field strength data to get the fluctuation degree value. The formula is: ; The larger the fluctuation value, the more dramatic the change of 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] Among them, α and β are the weight coefficients of the average magnetic field strength and the fluctuation value, respectively, and α+β=1. In practical applications, the setting of the weight coefficient will be adjusted according to the specific characteristics of the electromagnetic environment. For example, in some environments where the magnetic field is relatively stable but the overall strength is high, the value of α may be appropriately increased; while in areas where the magnetic field strength changes frequently, the value of β will be increased.

[0102] The actual electromagnetic interference situation is very complex. Strong interference may stem from high-intensity magnetic fields or from drastic fluctuations in the magnetic field. For example, near large substations, due to the operation of numerous electrical devices, a relatively strong magnetic field will be generated, and the average magnetic field intensity may be high at this time. In some areas where electrical devices start and stop frequently, such as workshops in factories, the start and stop of the devices will cause rapid changes in the magnetic field intensity and a large degree of magnetic field fluctuation. By simultaneously incorporating these two factors, namely the average magnetic field intensity and the degree of fluctuation value, the interference intensity data I can more comprehensively and accurately characterize the interference situation of the electromagnetic environment, providing an accurate basis for subsequent adjustment of the positioning strategy based on the interference intensity.

[0103] The method further includes the following steps:

[0104] Calculate the fluctuation curve of multiple interference intensity data within a preset set time period; during the operation of the power system, the system continuously monitors the electromagnetic interference intensity data in the environment. Taking a certain substation area as an example, within a preset set time period, such as one day (24 hours), at regular time intervals (assumed to be 10 minutes), the interference intensity data is recorded. Through these discrete data points, using a professional curve fitting algorithm, the fluctuation curve of multiple interference intensity data is calculated and plotted, intuitively showing the change trend of the interference intensity over time.

[0105] Calculate the fluctuation amplitude data f according to the fluctuation curve; the fluctuation amplitude data f is an important indicator to measure the severity of electromagnetic interference changes. It is determined by analyzing the undulation degree of the data points on the curve, such as calculating a certain statistic (such as the average difference, etc.) of the difference between the peak and trough of the curve. Suppose within a certain period of time, the peak interference intensity of the fluctuation curve is 80 and the trough interference intensity is 20, and the fluctuation amplitude data f is obtained as 60 through a specific calculation method.

[0106] After obtaining the fluctuation amplitude data f, the system will dynamically adjust the two key parameters a and b based on it. Specifically, the larger the fluctuation amplitude data f, the more drastic the change in electromagnetic interference. At this time, increase the value of a. For example, if the original value of a is 30, when f increases to a certain extent, the value of a may increase to 40. This means that under relatively high interference intensity, the positioning correction strategy based on the surrounding stationary function modules is triggered earlier. Just like during the centralized start-up or fault repair of equipment in the substation, when the electromagnetic interference changes drastically, by increasing the value of a, the system can timely utilize the positioning information of relatively stable surrounding stationary function modules such as distribution cabinets to enhance the stability and accuracy of positioning. At the same time, decrease the value of b, such as from 60 to 50, so that the system can more quickly enable more complex but interference-adaptive strategies such as image data-assisted positioning or mutual use module collaborative positioning to cope with the rapid change of interference intensity.

[0107] On the contrary, when the fluctuation amplitude data f is smaller, that is, the change in interference intensity is relatively stable. At this time, reducing the value of a, for example, from 30 to 25, can make more full use of the preliminary calculation results of satellite positioning data at a lower interference intensity and reduce unnecessary complex positioning processes. Because when the interference is stable, satellite positioning is relatively reliable and there is no need to enable complex strategies prematurely. At the same time, increasing the value of b, for example, from 60 to 70, can make the system switch to a more complex positioning strategy only when the interference intensity truly rises to a higher level, improving the positioning efficiency.

[0108] It should be particularly noted that the adjustment range of a is greater than that of b. This design further strengthens the flexible response of the system to different interference fluctuation situations. When the interference fluctuation is large, adjusting the value of a more significantly can change the positioning strategy more quickly to adapt to the interference change. For example, in the event of a strong interference event such as the sudden start of a large device in the power system, increasing the value of a significantly can quickly call the positioning information of the surrounding stationary modules for correction. When the interference fluctuation is small, moderately adjusting the value of b can ensure the rationality of the positioning strategy switching timing and avoid the additional resource consumption caused by frequent switching. Through this series of operations, the adaptive ability of the positioning system to complex electromagnetic interference environments has been significantly improved, the selection timing of the positioning strategy has been optimized, the high efficiency and accuracy of the function module positioning under different interference dynamic changes have been guaranteed, and the positioning function in the power system can always operate stably and accurately in the complex and changeable electromagnetic environment.

[0109] The analysis of the interference intensity fluctuation curve enables the system to dynamically track the changes in electromagnetic interference. By adjusting a and b accordingly with the change of the fluctuation amplitude data f, the dynamic optimization of the switching conditions of the positioning strategy can be achieved. When the fluctuation amplitude data f is larger, it indicates that the electromagnetic interference changes more drastically. At this time, increasing the a value means that under relatively high interference intensity, the positioning correction strategy based on the surrounding static function module is triggered earlier, and the positioning stability and accuracy are enhanced by using the relatively stable positioning information of the surrounding modules; at the same time, reducing the b value allows the system to enable more complex but strong interference-adaptive strategies such as image data assisted positioning or interoperable module collaborative positioning more quickly to cope with the rapid changes in interference intensity. On the contrary, when the fluctuation amplitude data f is smaller, that is, the interference intensity changes relatively smoothly, reducing the a value can make better use of the preliminary calculation results of the satellite positioning data under lower interference intensity, reducing unnecessary complex positioning processes; increasing the b value allows the system to switch to a more complex positioning strategy only when the interference intensity rises to a higher level, thereby improving positioning efficiency. In addition, the adjustment amplitude of a is greater than the adjustment amplitude of b, which further enhances the system's flexible response to different interference fluctuations. When the interference fluctuation is large, the a value is adjusted more significantly, and the positioning strategy can be changed more quickly to adapt to the interference change; when the interference fluctuation is small, the b value is adjusted appropriately to ensure the rationality of the timing of switching the positioning strategy and avoid the additional resource consumption caused by frequent switching. This significantly improves the positioning system's ability to adapt to complex electromagnetic interference environments, optimizes the timing of selecting positioning strategies, and ensures the efficiency and accuracy of functional module positioning under different interference dynamic changes.

[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, b=45.

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

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

[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 an actual power system scenario, for example, when taking pictures of the environment around a transmission line, the acquired environmental image data may be disturbed by various factors. For example, due to poor weather conditions (such as haze, heavy rain, etc.), the clarity of the image is reduced, or due to the electronic noise of the shooting device itself, there are many noise points in the image. Through preprocessing operations, these problems can be effectively improved. Common preprocessing methods include filtering, such as using Gaussian filtering to smooth the image and reduce noise interference; and image enhancement processing, for example, enhancing the contrast of the image through histogram equalization to make the feature information in the image more prominent. The preprocessed image is like well-organized data, providing a better foundation for subsequent feature extraction.

[0117] Use a feature extraction algorithm to extract specific features from the preprocessed image; In a power scenario, the first target object may be devices such as power poles and transformers. Taking a power pole as an example, the feature extraction algorithm will focus on extracting its unique shape features (such as a slender columnar structure, the distribution of cross arms, etc.), color features (if the pole surface has a specific painted color), and texture features (the material texture of the pole surface, etc.). Currently, there are many mature feature extraction algorithms to choose from, such as the SIFT (Scale-Invariant Feature Transform) algorithm, which can stably extract key feature points and descriptors in the image at different scales and rotation angles, and these features accurately reflect the essential features of the target object.

[0118] Match the extracted specific features with the feature template of the first target object stored in advance, use a feature matching algorithm to find the feature points that best match the template features, and determine the position of the first target object in the image based on the best-matched feature points. Assume that a standard feature template of a power pole is stored in advance, and this template contains the feature information of the pole under various typical angles and lighting conditions. Through a feature matching algorithm, such as a matching algorithm based on Euclidean distance, calculate the similarity between the extracted features and the template features, and find the feature points with the highest similarity. For example, in a captured transmission line image, a set of feature points that best match the pole template features are found through the matching algorithm, and these feature points form the approximate outline of the pole in the image, thus accurately determining the position of the pole in the image. This positioning method based on feature matching effectively avoids positioning errors caused by changes in the image perspective (such as shooting the pole from different angles) and lighting changes (such as different lighting intensities and angles in the morning and evening).

[0119] Calculate the geometric characteristic values of the first target object, compare the geometric characteristic values with the geometric reference values, and calculate the verification value. Continuing with the example of a power pole, its geometric characteristic values may include height, distance between cross arms, diameters at the bottom and top of the pole, etc. We have preset the reference values of these geometric parameters, and by calculating the difference between the actually extracted geometric characteristic values of the pole and the reference values, we obtain the verification value. For example, calculate the deviation ratio between the actually measured height of the pole and the reference height as part of the verification value.

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

[0121] This multiple verification mechanism, covering feature matching and geometric feature verification, greatly enhances the reliability of identification and reduces the probability of misidentification. At the same time, preprocessing to optimize the image reduces the complexity of subsequent calculations such as feature extraction and matching, just like clearing obstacles on the road to make the calculation process smoother; and the rapidity of geometric feature verification can promptly exclude incorrect results, avoiding wasting ineffective processing time on incorrect identifications, significantly improving the identification efficiency, ensuring that the system can accurately, efficiently and stably identify the first target object, and providing a reliable basis for subsequent operations such as positioning calculations based on the position of the target object.

[0122] Refer to Figure 3 In the step of calculating the target displacement data of the first target object and calculating the image positioning data according to the target displacement data, the following sub-steps are included:

[0123] In two adjacent frames of images, respectively identify the positions of the first target object. In the inspection scenario of the power system, a mobile function module is used to carry a camera module to inspect the transmission line, and the camera module continuously captures environmental images at a certain frame rate. For two adjacent frames of images, use an advanced image recognition algorithm (such as a target detection algorithm based on deep learning) to identify the position of the first target object. For example, the first target object is a shock absorber, equipment number plate or street lamp pole number plate on the transmission line, etc. For example, in the first frame of image, through algorithm analysis, it is determined that the pixel coordinates of the shock absorber in the image are (x1, y1); in the adjacent next frame of image, the same algorithm identifies that the pixel coordinates of the shock absorber become (x2, y2).

[0124] Calculate the target displacement data based on the position change of the first target object in two adjacent frames of images. Calculate its position change based on the positions of the identified first target object in two adjacent frames of images, so as to obtain the target displacement data. Continuing with the above example of the shock absorber hammer, assuming that each pixel represents an actual distance of d meters, then the displacement Δx in the horizontal direction = (x2 - x1) × d, and the displacement Δy in the vertical direction = (y2 - y1) × d. In this way, the subtle movement of the target object can be keenly captured, the error caused by single-frame image analysis can be effectively reduced, and the displacement calculation accuracy can be significantly improved. If only a single frame of image is analyzed, it is very difficult to accurately judge whether the target object has moved and the direction and distance of the movement.

[0125] Integrate the target displacement data to obtain the cumulative displacement of the first target object over a period of time. As the camera module continuously captures images, a series of target displacement data between two adjacent frames of images will be obtained. Integrating these displacement data, that is, adding them up, the cumulative displacement of the first target object over a period of time can be obtained. Assuming that in n adjacent frame pairs, the horizontal displacements obtained in sequence are Δx1, Δx2, ⋯, Δx n , and the vertical displacements are Δy1, Δy2, ⋯, Δy n , then during this period, the cumulative displacement in the horizontal direction , and the cumulative displacement in the vertical direction . This integration operation synthesizes multi-frame displacement information, further eliminates the random error of single-frame calculation, and enables the displacement data to more truly reflect the actual movement status of the target object over a period of time.

[0126] Take the initial position of the camera module as a reference point, and calculate the current position of the first target object, that is, the image positioning data, based on the cumulative displacement and the position of the reference point. When starting to shoot, record the initial position (X0, Y0) of the camera module and use it as a reference point. According to the cumulative displacement (X, Y) calculated above, the current position of the first target object can be calculated. For the shock absorber hammer, its current position (image positioning data) is (X0 + X, Y0 + Y). This method can not only adapt to various complex motion states of the target object, whether it is uniform motion, variable motion or irregular motion, but also accurately calculate the position based on a stable reference point when the environment changes. For example, when the drone encounters shaking due to air flow during inspection or the environmental light conditions change, since the initial position of the camera module is used as a reference point, the current position of the shock absorber hammer can still be accurately calculated.

[0127] In summary, by accurately identifying the position of the first target object in two adjacent frames of images, calculating the target displacement data using the position change, integrating the target displacement data to obtain the cumulative displacement, and finally calculating the image positioning data in combination with the initial position of the camera module, the adaptability of the system to different scenarios is greatly enhanced, ensuring the accuracy and reliability of the positioning result, and providing a solid guarantee for applications that rely on the position information of the target object. In the power system, accurate target object positioning information is of great significance for fault detection, equipment maintenance, etc. For example, by precisely grasping the position change of the vibration damper, it is possible to timely detect whether there is abnormal vibration in the transmission line, so as to take measures in advance to avoid faults.

[0128] Referring to Figure 4 , in the step of calculating the image positioning data, the following sub-steps are further included:

[0129] Based on a single camera device on the mobile function module, environmental image data is acquired. In an actual power system scenario, for example, in a large substation, the mobile function module is, for example, a robot for inspection. During its movement within the station, the camera device continuously captures images of the surrounding environment. These images contain rich information, such as various power equipment, buildings, etc., providing basic data for subsequent target object recognition and positioning.

[0130] At least two first target objects are identified from the environmental image data, and the angle formed by the two first target objects closest to the camera device and the camera device is calculated. In the example of the substation, the first target objects can be different power poles, transformers and other equipment. Suppose two first target objects closest to the camera device are successfully identified, such as two power poles with specific numbers. Next, through computer vision algorithms and geometric calculation methods, the angle formed by these two first target objects and the camera device is calculated. Specifically, based on the pixel positions of the target objects in the image, the imaging principle of the camera device, and known camera parameters (such as focal length, etc.), knowledge such as trigonometric functions can be used to calculate the angle. For example, by measuring the relative position and angular relationship of the two target objects in the image and combining the internal parameter matrix of the camera, the actual angle value is calculated. This angle is one of the important parameters for subsequent positioning calculations.

[0131] Based on the two first target objects closest to the imaging device, the corresponding preset positioning information for each first target object is matched. Each first target object has its corresponding positioning information pre-stored in the system, which is determined during system initialization or previous measurement processes. For the two identified first target objects, the system will match them according to their characteristics (such as appearance, number, etc.) to find the corresponding preset positioning information. In the scenario of a substation, each power pole has its precise geographical coordinates as the preset positioning information, and the system finds the corresponding coordinate information from the database by comparing the characteristics of the poles identified in the image.

[0132] Based on the two matched preset positioning information and the included angle, the image positioning data of the mobile function module is calculated. After knowing the preset positioning information of two target objects and the included angles formed by them and the imaging device, the geometric positioning principle can be used to calculate the image positioning data of the mobile function module. The positions of the two target objects can be regarded as two known points on a plane, and the position of the imaging device (i.e., the mobile function module) can be determined by these two points and the included angle. For example, using knowledge such as the sine theorem and cosine theorem of a triangle, combined with the known coordinates of the target objects and the included angle, a mathematical model is established to solve the coordinate position of the mobile function module on the plane. In this way, the image positioning data of the mobile function module is obtained.

[0133] Different target objects have different positions and characteristics in the environment. Using multiple target objects for positioning can corroborate each other and reduce the positioning deviation caused by the recognition error or position change of a single target object. If only relying on one target object for positioning, once the recognition of this target object is incorrect or its position undergoes an unexpected change, the positioning result will have a large error. However, through the comprehensive calculation of the information of multiple target objects, the reliability of positioning can be improved. For example, when there is a certain error in the recognition of one power pole, the information of another pole can play a supplementary and verification role to make the final positioning result more accurate. In the case of knowing the preset positioning information of two target objects, the included angle can help determine the specific position of the imaging device, that is, the mobile function module, relative to these two target objects. The included angle reflects the relative orientation of the mobile function module in the plane formed by the two target objects, providing an additional constraint condition for positioning calculation. Through the included angle and the coordinate information of the target objects, the position of the mobile function module can be determined more precisely.

[0134] In summary, by the method of obtaining environmental image data based on a single imaging device, identifying multiple first target objects, calculating the included angle and combining with preset positioning information for calculation, the positioning accuracy and reliability of the mobile function module can be effectively improved, providing more accurate position information for applications such as power system inspection and monitoring.

[0135] Refer to Figure 5, in another implementation, the step of calculating the image positioning data further includes the following sub-steps:

[0136] Based on multiple camera devices at different angles on the mobile function module, obtain multiple surrounding environment image data. Installing multiple camera devices at different angles on the mobile function module can comprehensively observe the surrounding environment from different perspectives. Taking the power inspection robot as an example, four cameras at different angles, namely front, rear, left, and right, are installed at different positions on its body. When the robot moves in the substation, these cameras work simultaneously and continuously obtain multiple surrounding environment image data. Images taken from different angles can capture the characteristics and position information of the target object from different perspectives, avoiding 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 capture it completely, providing more comprehensive information for subsequent target object recognition.

[0137] Identify at least two first target objects from each environment image data, and calculate the angle formed by the two first target objects closest to the camera device and the camera device. From each environment image data, use advanced image recognition algorithms to identify at least two first target objects. In the substation scenario, these first target objects can be different power poles, switch cabinets and other equipment. For the image taken by each camera device, calculate the angle formed by the two first target objects closest to the camera device and the camera device. Taking the image taken by the front camera of the robot as an example, identify the two closest power poles, and through computer vision technology and geometric calculation methods, combined with the imaging principle and known parameters of the camera (such as focal length, pixel size, etc.), calculate the angle formed by these two power poles and the camera. This angle is an important geometric information for positioning calculation, which reflects the relative position and direction of the camera device relative to the target object.

[0138] According to the two first target objects closest to the camera device, match the preset positioning information corresponding to each first target object. Each first target object has its corresponding positioning information pre-stored in the system, and these information are accurately determined during system initialization or previous measurement processes. For each identified target object, the system will match according to its characteristics (such as appearance, number, unique identifier, etc.) to find the corresponding preset positioning information. For example, for a power pole with a specific number identified, the system will retrieve its accurate geographical coordinates from the database. Through this matching operation, an accurate reference point is provided for subsequent positioning calculation.

[0139] Calculate the image positioning parameters of the mobile function module in each environmental image data based on the two preset positioning information and the included angle obtained by matching. Calculate the image positioning parameters of the mobile function module in each environmental image data by applying the geometric positioning principle based on the two preset positioning information and the calculated included angle. The positions of the two target bodies can be regarded as two known points on a plane, and the position of the imaging device (i.e., the mobile function module) can be determined by these two points and the included angle. For example, using mathematical knowledge such as the sine theorem and cosine theorem of a triangle, combined with the coordinates and included angle of the target body, establish a mathematical model to solve the image positioning parameters of the mobile function module in this perspective. By performing such calculations on the images captured by each imaging device, multiple sets of image positioning parameters are obtained.

[0140] Calculate the average value of all image positioning parameters as the final image positioning data. To further improve the accuracy and stability of positioning, calculate the average value of all image positioning parameters as the final image positioning data. Since there may be certain errors in the image positioning parameters under different perspectives, the errors can be smoothed by taking the average value, so that the final positioning result can more accurately reflect the actual position of the mobile function module. For example, average the image positioning parameters calculated by four cameras at different angles to obtain a comprehensive and more accurate positioning result. This method uses multiple sets of data to corroborate each other, effectively reducing the positioning deviation caused by the error of a single target body or a single image.

[0141] In summary, the method of obtaining multiple surrounding environmental image data based on multiple imaging devices at different angles on the mobile function module has significant advantages. It can comprehensively capture the information of the target body from different perspectives and provide rich geometric information for positioning. By corroborating multiple sets of data and calculating the average value, the positioning deviation is effectively reduced, and the accuracy and stability of positioning are improved. In a complex power system environment, such as the complex equipment layout and occlusion in a substation, this method can greatly enhance the reliability of system positioning, provide more accurate position information for applications such as power inspection and equipment monitoring, and ensure the safe and stable operation of the power system.

[0142] In the power system, the intensity of electromagnetic interference changes continuously over time. To enable the positioning system of the mobile function module to work accurately and efficiently, it is necessary to dynamically adjust the positioning frequency according to the actual situation of electromagnetic interference. The method further includes the following steps:

[0143] Within 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. According to the magnitude relationship between the positioning frequency data x and the thresholds a and b, the time coordinates are classified. Assume that the working time period S is one day (24 hours), from 0:00 to 24:00, and the positioning system records the positioning frequency data x every certain period (such as 1 minute). During this day, all time points where the positioning frequency data x ≤ a are recorded to form set A; the time points where a < x < b form set B; and the time points where b ≤ x form set C. For example, between 8:00 and 10:00 on a certain working day, the electromagnetic interference intensity is relatively low, and the positioning frequency data x is always less than or equal to a, so all time points within these two hours will be included in set A.

[0144] Calculate that the time span of set A is a1, the time span of set B is b1, and the time span of set C is c1. The time span reflects the duration of electromagnetic interference in different intensity intervals. 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; if the time points in set C are from 14:00 to 16:00, then c1 = 2 hours.

[0145] Based on the time span a1, the time span b1, and the time span c1, calculate the total time span d = a1 + b1 + c1 - m, where m is an adjustment parameter. The setting of the adjustment parameter m is to fine-tune the total time span to better adapt to different working scenarios and system requirements. For example, in some cases, to pay more attention to the impact of stronger electromagnetic interference periods, the value of m can be appropriately increased; in other cases, to comprehensively consider the impact of each intensity interval, the value of m can be decreased. Assume m = 0.5 hours, then 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 working time period S, calculate the frequency correction value D = d / S. The frequency correction value D reflects the degree of influence of the time distribution of different intensity intervals of electromagnetic interference on the positioning frequency within the entire working time period. In the above example, the working time period S = 24 hours, then the frequency correction value D = 5.5 / 24 ≈ 0.23.

[0147] Correct the next positioning frequency data x according to the frequency correction value D, and the next positioning frequency data x = D × the current positioning frequency data x. Assume that the current positioning frequency data x is 5 times per minute, then the next positioning frequency data x = 0.23 × 5 ≈ 1 time / minute.

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

[0149] If the electromagnetic interference intensity is relatively low for most of a working period (i.e., a1 is large), then the frequency correction value D will be relatively small, and the next positioning frequency will also be correspondingly reduced. For example, at night, the load of the power system is relatively low, and the electromagnetic interference is also weak. At this time, a1 is large, and reducing the positioning frequency can reduce unnecessary positioning operations, lower the energy consumption of the system and the consumption of computing resources, and improve the operating efficiency of the system.

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

[0151] In the power system, the electromagnetic interference conditions may vary greatly in different seasons and at different times. For example, in summer, due to the extensive use of high-power devices such as air conditioners, the electromagnetic interference may be stronger than in other seasons; during the day on weekdays, the electricity load is large, and the electromagnetic interference is also more serious than at night. This technical solution can enable the positioning system to operate stably in various situations, dynamically adjust the positioning frequency according to the actual electromagnetic interference conditions, reduce positioning errors caused by interference changes, improve the overall performance of functional module positioning in the power system, and ensure the safe and stable operation of the power system.

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

[0153] Within a set time period, the system continuously records multiple positioning frequency data x. For example, the set time period is one week, from 8 am to 8 pm every day, and the positioning frequency data x is recorded every half hour. Within this week, a total of 24×7 = 168 positioning frequency data are recorded. These data are summarized and the formula for calculating the average value is used: (where x iis the positioning frequency data for the i-th record, and n is the total number of records), the average positioning frequency x' is calculated. This operation is of great significance as it reflects the comprehensive impact of electromagnetic interference on the positioning frequency during this time period from a macroscopic perspective. Since the positioning frequency is closely related to the intensity of electromagnetic interference, the stronger the interference, the higher the positioning frequency. Therefore, the average positioning frequency x' actually becomes a quantitative manifestation of the electromagnetic interference situation during this period. Suppose that within this week, the calculated average positioning frequency x' is 8 times per hour for positioning.

[0154] A reference positioning frequency data x” is preset. This data is usually determined comprehensively based on various factors such as the electromagnetic interference situation under normal operation of the power system and the positioning accuracy requirements. For example, based on past experience and system tests, the reference positioning frequency data x” is determined to be 6 times per hour for positioning. Then, the difference between the average positioning frequency x' and the preset reference positioning frequency data x” is calculated, i.e., Δx = x' - x”. In the above example, the difference Δx = 8 - 6 = 2 times / hour. This difference can intuitively reflect the degree of difference between the actual electromagnetic interference situation and the expectation.

[0155] The adjustment parameter m is dynamically adjusted according to the calculated difference Δx. There is a clear corresponding relationship, that is, the larger the difference, the larger the adjustment parameter; the smaller the difference, the smaller the adjustment parameter. When the difference Δx is large, it indicates that the actual electromagnetic interference deviates significantly from the expectation. For example, if the difference Δx = 4 times / hour, which is significantly larger than the previous difference of 2 times / hour, this shows that the actual electromagnetic interference situation differs significantly from the expectation. At this time, the adjustment parameter m is increased. Suppose originally m = 0.5, and after increasing, m = 1. When calculating the total time span d = a1 + b1 + c1 - m, increasing m will make the total time span d smaller. Since the total time span d affects the frequency correction value D = d / S (S is the working time period), and further affects the next positioning frequency data x = D × the current positioning frequency data x. This means that when the interference situation differs greatly from the expectation, the system can adjust the positioning strategy more flexibly. For example, significantly increasing the positioning frequency during strong electromagnetic interference periods to improve the accuracy and efficiency of positioning.

[0156] On the contrary, when the difference Δx is smaller, it indicates that the actual interference situation is closer to the expectation. For example, if the difference Δx = 0.5 times / hour, at this time the adjustment parameter m is decreased. Suppose it is decreased from m = 0.5 to m = 0.2. In this way, when calculating the total time span d, the change in d is relatively small, making the adjustment range of the positioning strategy relatively small, thus avoiding unnecessary resource consumption caused by over-adjustment. In practical applications, this method of dynamically adjusting the parameter m according to the difference can enable the positioning system to better adapt to the complex and changeable electromagnetic interference environment, while ensuring the positioning accuracy, optimizing the utilization of system resources, and improving the overall performance of the positioning of the mobile function module of the power system.

[0157] The embodiment of the present application also discloses a functional module positioning system in a power system based on a single Beidou system, including a processor, and the processor executes the steps of the functional module positioning method in the 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 should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to 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, It includes the following steps: Obtain the interference intensity data of electromagnetic interference in the environment, and match the positioning frequency data x according to the interference intensity data. The greater the interference intensity data, the greater the positioning frequency data x; Obtain 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 the module positioning data; If x ≤ a, obtain the module positioning data of multiple stationary functional modules within a preset set distance range; calculate the positioning deviation values of the multiple module positioning data, correct the module positioning data with the positioning deviation values, and use the corrected module positioning data as the final positioning data; If a < x < b, call the camera module to capture environmental image data, identify the first target from the environmental image data, calculate the target displacement data of the first target, calculate the image positioning data according to the target displacement data, and calculate the final positioning data according to the image positioning data and the module positioning data; If x ≥ b, find the mutual use module that performs positioning based on the second channel, obtain the nearest mutual use module, establish a communication connection, guide the mutual use module to the functional module. The functional module obtains the first positioning data based on the first channel, and the mutual use module obtains the second positioning data based on the second channel. The functional module calculates the final positioning data based on the first positioning data and the second positioning data; where 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, wherein In the step of obtaining the interference intensity data of electromagnetic interference in the environment, the following sub-steps are further included: Obtain the magnetic field intensity data of multiple magnetic field sensors within the set distance range; The average magnetic field strength is obtained by calculating the average value of the magnetic field strength data, and the formula is ; Calculating the fluctuation value of the magnetic field strength data to obtain a fluctuation degree value, the formula is ; Calculate the interference intensity data I according to 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 respectively, and α + β = 1; n is the number of magnetic field sensors, and M i is the magnetic field intensity 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 includes the following steps: Calculate the fluctuation curve of multiple interference intensity data within a preset set time period; Calculate the fluctuation amplitude data f according to the fluctuation curve; Adjust the size of a according to the fluctuation amplitude data f. The greater the fluctuation amplitude data f, the greater a; the smaller the fluctuation amplitude data f, the smaller a; Adjust the size of b according to the fluctuation amplitude data f. The greater the fluctuation amplitude data f, the smaller b; the smaller the fluctuation amplitude data f, the greater b; 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, wherein Identifying the first target from the environmental image data includes the following sub-steps: Preprocess the environmental image data; Extract features from the preprocessed image using a feature extraction algorithm; Match the extracted features with the feature template of the first target stored in advance, use the feature matching algorithm to find the feature points that best match the template features, and determine the position of the first target in the image according to the best-matched feature points; Calculate the geometric feature value of the first target, compare the geometric feature value with the geometric reference value, and calculate the verification value; If the verification value is within the set range, the first target is the correct first target; Otherwise, for the incorrect first target object, re-identify the first target object 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, wherein In the step of calculating the target displacement data of the first target object and calculating the image positioning data according to the target displacement data, the following sub-steps are included: In two adjacent frames of images, respectively identify the positions of the first target object; According to the position change of the first target object in two adjacent frames of images, calculate the target displacement data; Integrate the target displacement data to obtain the cumulative displacement of the first target object over a period of time; Take the initial position of the camera module as a reference point, and calculate the current position of the first target object, that is, the image positioning data, according to the cumulative 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, In the step of calculating the image positioning data, the following sub-steps are further included: Based on a single camera device on the mobile function module, obtain environmental image data; Identify at least two first target objects from the environmental image data, and calculate the angles formed by the two first target objects closest to the camera device and the camera device; According to the two first target objects closest to the camera device, match the preset positioning information corresponding to each first target object; According to the two matched preset positioning information and the angle, calculate the image positioning data of the mobile function module.

7. The method for positioning functional modules in a power system based on a single Beidou system according to claim 1, wherein In the step of calculating the image positioning data, the following sub-steps are further included: Based on multiple camera devices with different angles on the mobile function module, obtain multiple surrounding environmental image data; Identify at least two first target objects from each environmental image data, and calculate the angles formed by the two first target objects closest to the camera device and the camera device; According to the two first target objects closest to the camera device, match the preset positioning information corresponding to each first target object; According to the two matched preset positioning information and the angle, calculate the image positioning parameters of the mobile function module in each environmental image data; Calculate the average value of all the image positioning parameters 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, wherein The method further includes the following steps: Within the preset working time period S, calculate that the time coordinate set where the current positioning frequency data x ≤ a is set A, the time coordinate set where a < x < b is set B, and the time coordinate set where b ≤ x is set C; Calculate the time span of set A as a1, calculate the time span of set B as b1, and calculate the time span of set C as c1; According to the time span a1, the time span b1, and the time span c1, calculate the total time span d = a1 + b1 + c1 - m, where m is an adjustment parameter; According to the total time span d and the working time period S, calculate the frequency correction value D = d / S; Correct the next positioning frequency data x 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, wherein The method further includes the following steps: Calculate the average value of multiple positioning frequency data x within the set time period as the average positioning frequency x'; Calculate the difference between the average positioning frequency x' and the preset reference positioning frequency data x"; Adjust the magnitude of the adjustment parameter m according to the difference value, the greater the difference value, the greater the adjustment parameter m; The smaller the difference value, the smaller the adjustment parameter m.

10. A functional module positioning system in a power system based on a single Beidou system, characterized in that, It includes a processor, and the processor executes the steps of the function module positioning method in the power system based on the single Beidou system according to any one of claims 1-9.

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