A method, system and storage medium for analyzing operation data of power concentrator

Abstract

The present invention provides a method, system and storage medium for analyzing operation data of a power concentrator, and relates to the technical field of power data analysis. The method comprises: obtaining the real-time position and time data of the power concentrator device, and obtaining the multimodal time series data when the power concentrator is running; performing dynamic positioning and orbit calculation of the power concentrator according to the multimodal time series data to obtain the position information of the power concentrator; calculating the position of the power concentrator according to the position information of the power concentrator; calculating the speed of the power concentrator according to the position; analyzing the speed with the power consumption and operation time data to obtain the correlation analysis result; evaluating the operation state of the device according to the analysis result to obtain the evaluation result; optimizing and adjusting the real-time device state according to the evaluation result. The present invention can identify the abnormality of the device in advance and reduce the probability of failure through dynamic positioning and calculation.

Description

Technical Field

[0001] The present invention relates to the technical field of power data analysis, and in particular to a method, system and storage medium for analyzing operation data of a power concentrator. Background Art

[0002] As a core component of smart grid, power concentrator is responsible for centralized data management, real-time monitoring and fault diagnosis, as well as intelligent load management. It integrates data from different types of power equipment from different regions to provide a unified analysis, management and dispatching platform for grid operations. However, with the increasing complexity and demand of smart grid, power concentrator faces many technical challenges in operation. Among them, the difficulty of positioning and trajectory management is particularly prominent.

[0003] Since power concentrators often need to move between different areas, how to ensure their accurate positioning, trajectory calculation and status monitoring in a dynamic environment has become an urgent problem to be solved. In addition, error is also an issue that cannot be ignored. Traditional positioning methods, such as satellite navigation and base station positioning, may be affected by factors such as environmental interference and changes in signal strength, resulting in positioning errors. This error not only affects the positioning accuracy of the power concentrator, but may also have a negative impact on its key parameters such as speed and power consumption. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method, system and storage medium for analyzing the operation data of a power concentrator, which can identify equipment anomalies in advance and reduce the probability of failure through dynamic positioning and calculation.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, a method for analyzing operation data of a power concentrator is provided, the method comprising:

[0007] Acquire the real-time location and time data of the power concentrator equipment, and obtain the multi-modal time series data of the power concentrator during operation;

[0008] Based on the multi-modal time series data, the dynamic positioning and orbit calculation of the power concentrator are performed to obtain the location information of the power concentrator;

[0009] Calculating the location of the power concentrator according to the location information of the power concentrator;

[0010] Based on the location, the speed of the power concentrator is calculated;

[0011] Analyze the speed with the power consumption and runtime data to obtain correlation analysis results;

[0012] According to the analysis results, the operating status of the equipment is evaluated to obtain the evaluation results;

[0013] Optimize and adjust real-time equipment status based on the evaluation results.

[0014] Furthermore, according to the multimodal time series data, dynamic positioning and orbit calculation of the power concentrator are performed to obtain the location information of the power concentrator, including:

[0015] According to multimodal time series data, and Compute the predicted position on the track, where and are the horizontal and vertical coordinates of the predicted position on the track, is the index variable, is the semi-major axis, It is The eccentric anomaly angle corresponding to the observation time is is the eccentricity;

[0016] Based on the predicted position on the track, and Calculate the difference between the predicted position and the actual observed position, is the error in the horizontal axis, is the error of the ordinate;

[0017] Based on the difference between the predicted position and the actual observed position, Calculate the sum of squared errors, where is the sum of squared errors; It is The actual observation position of the observation point Coordinate values, It is The actual observation position of the observation point Coordinate value;

[0018] According to the sum of squared errors, the semi-major axis, eccentric anomaly angle and eccentricity are optimized to obtain the adjusted predicted position;

[0019] The location information of the power concentrator is determined according to the adjusted predicted position.

[0020] Furthermore, according to the sum of squared errors, the semi-major axis, the eccentric anomaly angle and the eccentricity are optimized to obtain the adjusted predicted position, including:

[0021] According to the sum of squared errors, the semi-major axis, the eccentric anomaly angle and the eccentricity are respectively derived to obtain the partial derivative of the semi-major axis, the partial derivative of the eccentricity and the partial derivative of the eccentric anomaly angle;

[0022] Optimize the semi-major axis using gradient descent The partial derivative and eccentricity of The partial derivatives and partial anomaly angles of The partial derivatives of to obtain the optimized orbital parameters, which include semi-major axis, eccentric anomaly angle and eccentricity;

[0023] The predicted position is recalculated using the optimized orbital parameters to obtain the adjusted predicted position.

[0024] Further, according to the location information of the power concentrator, calculating the location of the power concentrator includes:

[0025] According to the location information of the power concentrator, the longitude and latitude coordinate information of the power concentrator is obtained once in each measurement cycle;

[0026] According to the latitude and longitude coordinate information of the primary power concentrator in each measurement cycle, the standard deviation of a set of position data is calculated to obtain the positioning error range;

[0027] According to the positioning error range and error source, the horizontal, vertical and total error range of the position can be obtained, wherein the error source includes the multipath effect of satellite signals, base station density and signal strength, environmental interference and positioning error.

[0028] Further, according to the position, the speed of the power concentrator is calculated, including:

[0029] Based on the location, obtain the latitude and longitude data and timestamp of the adjacent location;

[0030] Based on the latitude and longitude data and timestamps of adjacent locations, Calculate the speed of the power concentrator, where and are the differences between longitude and latitude, respectively. is the time interval between adjacent positions, It's speed.

[0031] Furthermore, the speed is analyzed with the power consumption and the running time data to obtain correlation analysis results, including:

[0032] According to the position data, power consumption data and operation time data, the power consumption and position error in different time periods are obtained;

[0033] According to the power consumption and position error, The correlation coefficient between power consumption and position is calculated to obtain the correlation analysis result, where: and are the rankings of power consumption and position error, is the number of data points, is the correlation coefficient between power consumption and position, It is The measurement error of the data points is is the index.

[0034] Further, based on the analysis results, the operating status of the equipment is evaluated to obtain evaluation results, including:

[0035] Based on historical data, set the range, scope and operation duration of power consumption;

[0036] According to the analysis results, power consumption data, location data and duration data of the same timestamp are obtained;

[0037] Compare the power consumption data, location data and duration data of the same timestamp with the set power consumption range, range and operation duration to obtain a comparison result;

[0038] According to the comparison results, the equipment status scoring system is set up to evaluate the operating status of the equipment to obtain the evaluation results.

[0039] In a second aspect, a power concentrator operation data analysis system includes:

[0040] An acquisition module is used to acquire the real-time position and time data of the power concentrator device, and obtain the multimodal time series data when the power concentrator is running; based on the multimodal time series data, the power concentrator is dynamically positioned and tracked to obtain the position information of the power concentrator; and the position of the power concentrator is calculated based on the position information of the power concentrator;

[0041] A processing module, used to calculate the position of the power concentrator according to the position information of the power concentrator; and calculate the speed of the power concentrator according to the position;

[0042] The analysis module is used to analyze the speed, power consumption and operation time data to obtain correlation analysis results; based on the analysis results, the operation status of the equipment is evaluated to obtain evaluation results; based on the evaluation results, the real-time equipment status is optimized and adjusted.

[0043] According to a third aspect, a computing device includes:

[0044] one or more processors;

[0045] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0046] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0047] The above solution of the present invention includes at least the following beneficial effects:

[0048] The location data of the power concentrator is obtained in real time, and dynamic positioning and orbit calculation are performed based on multimodal time series data to ensure that the equipment is always in a controllable state; by dynamically adjusting the orbit parameters and performing orbit prediction, the positioning information can be continuously optimized during the operation of the equipment, and the speed can be obtained by calculating the location information of the power concentrator, so that the operating status of the equipment can be comprehensively evaluated to ensure that the equipment works stably within the predetermined orbit range. By combining the position, power consumption and running time data for analysis, the optimal state of equipment operation can be identified. Comprehensive consideration of multiple data such as position, speed, power consumption, etc. can identify potential failure risks in advance. According to the historical data and real-time monitoring results of the equipment, the system automatically evaluates the operating status of the equipment by comparing with the set power consumption range, range, and running time, timely discovers equipment operation deviations, and reduces losses caused by equipment failures. By combining multiple time series data such as power consumption, position error, and running time, the overall operating status of the equipment can be deeply analyzed. By calculating the correlation coefficient between power consumption and position error, the intrinsic relationship between equipment operating efficiency and position error can be revealed. The system automatically adjusts the operating status of the equipment according to the evaluation results, optimizes the power consumption and position of the equipment, and avoids unnecessary energy waste and errors. Dynamically adjust the operating parameters of the equipment to improve the operating efficiency of the power concentrator. Through accurate dynamic positioning and calculation, it is possible to identify equipment anomalies in advance and reduce the probability of failure. Through comprehensive analysis of the results and other indicators of equipment operation (such as power consumption, operating time, etc.), a comprehensive equipment evaluation system can be established to provide data support for equipment maintenance, upgrades, and replacements. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of a method for analyzing operation data of a power concentrator provided in an embodiment of the present invention.

[0050] Figure 2 It is a schematic diagram of a power concentrator operation data analysis system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in a form and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0052] like Figure 1 As shown, an embodiment of the present invention provides a method for analyzing operation data of a power concentrator, the method comprising the following steps:

[0053] Step 11, obtaining real-time location and time data of the power concentrator device, and obtaining multimodal time series data when the power concentrator is running;

[0054] Step 12, performing dynamic positioning and orbit calculation of the power concentrator according to the multimodal time series data to obtain location information of the power concentrator;

[0055] Step 13, calculating the position of the power concentrator according to the position information of the power concentrator;

[0056] Step 14, calculating the speed of the power concentrator according to the position;

[0057] Step 15, analyzing the speed, power consumption and running time data to obtain a correlation analysis result;

[0058] Step 16, evaluating the operating status of the equipment according to the analysis result to obtain an evaluation result;

[0059] Step 17: Optimize and adjust the real-time device status based on the evaluation results.

[0060] In the embodiment of the present invention, by acquiring the position and time data of the power concentrator in real time, a comprehensive time series database can be established, which helps to ensure real-time understanding of the operating status of the power concentrator and provides the possibility for fault prevention and timely processing; through dynamic positioning and orbit calculation, the real-time position information of the power concentrator can be accurately obtained, which is of great significance for monitoring the operating status of the equipment and timely discovering the deviation or abnormal movement of the equipment, and helps to ensure the safe operation of the equipment. Further accurate calculation of the position of the power concentrator can provide accurate positioning information for the maintenance, inspection and troubleshooting of the equipment, and reduce the waste of resources and time delays caused by inaccurate position. By calculating the speed of the power concentrator, the moving state of the equipment can be understood, which is important for evaluating the operating efficiency of the equipment and discovering abnormal movement or stagnation; correlation analysis of speed with other key parameters (such as power consumption and operating time) can reveal the inherent connection and law between them, which helps to deeply understand the operating status of the equipment; by evaluating the operating status of the equipment, abnormalities or potential problems of the equipment can be discovered in time, providing decision support for the maintenance and management of the equipment, helping to ensure the stable operation of the equipment and reduce the possibility of failure. Optimizing and adjusting the equipment status based on the evaluation results can ensure that the equipment is always in the best operating state, improve the equipment's operating efficiency and reliability, help reduce operation and maintenance costs, extend equipment life, and provide strong guarantees for the stable operation of the power system.

[0061] In a preferred embodiment of the present invention, the above step 11, obtaining the real-time position and time data of the power concentrator device to obtain multi-modal time series data when the power concentrator is running, includes:

[0062] The longitude, latitude and altitude data of each collection are obtained, and the collection time is recorded to achieve the correspondence between each location data point and the timestamp, and obtain the multimodal time series data when the power concentrator is running.

[0063] In an embodiment of the present invention, GNSS or RTK-GPS can provide more accurate longitude, latitude and altitude data; combined with the multi-dimensional information of longitude, latitude and altitude, it is possible to obtain an accurate position in three-dimensional space, thereby improving the positioning capability of the device in complex environments. High-precision navigation systems can maintain higher signal quality in more complex environments (such as urban canyons or forest areas). Each acquisition of location data will be matched with the corresponding timestamp, which helps to ensure the timing and synchronization of the data. Through accurate time records, the device status and position changes at different time points can be traced in subsequent analysis. The association of location data with timestamps ensures the integrity of the data and avoids data loss or inconsistency. By recording the location and timestamp of the device in real time, the dynamic trajectory of the device can be drawn and the movement pattern of the device in different time periods can be analyzed; after the positioning data and timestamp are combined, the movement path, speed, acceleration, etc. of the device can be accurately analyzed to help evaluate the operating efficiency of the device. It is not only the location data (longitude, latitude and altitude), but also combined with other sensor data (such as power consumption, ambient temperature, etc.) for comprehensive analysis. Through time series analysis, the operating trend of the device in different time periods can be revealed; the precise matching of timestamp and location data helps to quickly identify the time and place of the problem when the device deviates or is abnormal, and to make timely intervention or fault warning. By obtaining and recording the location information and timestamp of the device in real time, the equipment operator can monitor the location and operating status of the equipment at any time to ensure the normal operation of the equipment.

[0064] In a specific embodiment of the present invention, a GNSS receiver can obtain positioning information such as longitude, latitude and altitude of the location data; after the receiver establishes communication with the satellite, it will continuously output real-time longitude, latitude and altitude data. Each time new location data is received, the receiver will record the current position and improve the positioning through differential GPS. In order to ensure the correspondence between each location data point and the collection time, an accurate timestamp is recorded for each collected positioning data through the internal clock of the receiver or the external synchronization clock. Each time the location data is collected, the corresponding timestamp is recorded at the same time to form a correspondence between the position and time. By continuously collecting longitude, latitude and altitude information, and adding a timestamp to each collection point, a set of time series data containing time and location information can be obtained. The data structure is multimodal, including geographic coordinate position and time.

[0065] In a preferred embodiment of the present invention, the above step 12, performing dynamic positioning and orbit calculation of the power concentrator according to the multimodal time series data to obtain the position information of the power concentrator includes:

[0066] Step 121, based on the multimodal time series data, and Compute the predicted position on the track, where and are the horizontal and vertical coordinates of the predicted position on the track, is the index variable, is the semi-major axis, It is The eccentric anomaly angle corresponding to the observation time is is the eccentricity;

[0067] Step 122, based on the predicted position on the track, and Calculate the difference between the predicted position and the actual observed position, is the error in the horizontal axis, is the error of the ordinate;

[0068] Step 123, based on the difference between the predicted position and the actual observed position, Calculate the sum of squared errors, where is the sum of squared errors; It is The actual observation position of the observation point Coordinate values, It is The actual observation position of the observation point Coordinate value;

[0069] Step 124, optimizing the semi-major axis, the eccentric anomaly angle and the eccentricity according to the sum of squared errors to obtain an adjusted predicted position;

[0070] Step 125, determining the location information of the power concentrator according to the adjusted predicted location.

[0071] In an embodiment of the present invention, by using multimodal time series data to dynamically locate the power concentrator, the position of the power concentrator can be continuously updated to ensure real-time and accurate positioning information. By calculating the difference between the predicted position and the actual observed position, the orbit parameter set of the power concentrator (such as the semi-major axis, the anomaly angle, etc.) can be optimized, which can make the positioning of the power concentrator more accurate, reduce errors, and improve positioning. According to the difference between the predicted position and the actual observed position (such as the horizontal coordinate error and the vertical coordinate error), the positioning error of the power concentrator can be discovered and corrected in real time. By iteratively updating the orbit parameters, the error can be gradually reduced to ensure the orbit and position. As the observation data increases, the orbit parameter set will be updated and adjusted according to the actual observation results; through the continuous accumulation of observation data and difference correction, a more accurate orbit model can be established. Dynamic positioning can not only provide an accurate position of the power concentrator, but also predict its future position. By dynamically updating the orbit parameters, it is ensured that the orbit model is more stable over time; through accurate dynamic positioning, the path planning and scheduling of the power concentrator can be more accurate, and dynamic positioning enables the system to monitor the motion state of the power concentrator in real time, and adjust its working mode or perform fault warning according to the actual position. Dynamic positioning can not only provide the current location of the power concentrator, but also predict its future trajectory through orbit calculation, which is very important for power companies to conduct load forecasting, equipment monitoring and energy scheduling, and helps to refine management. Accurate positioning and orbit calculation can avoid safety issues caused by position errors, such as the power concentrator entering a dangerous area or failing to reach the target location on time. By dynamically adjusting the track and position, it can ensure that the power concentrator always operates within a safe range.

[0072] In a specific embodiment of the present invention, a set of observation data is obtained based on multimodal time series data, including observation positions at multiple times. Each observation data point includes actual horizontal and vertical coordinates, and obtains corresponding timestamps, orbital parameters, etc., including the periapsis angle, which is a parameter that describes the angle between an object and the periapsis on an elliptical orbit. The orbital parameters include the semi-major axis of the orbit. Based on the known orbital parameters and the periapsis angle at each time point, the predicted position on the orbit is calculated by a formula, wherein the horizontal coordinate prediction formula calculates the predicted target position on the orbit based on the current periapsis angle and the known orbital parameters. x The vertical coordinate prediction formula is to calculate the position of the object on the y Position on the axis.

[0073] After obtaining the predicted position, the predicted position is compared with the actual observed position to calculate the errors of the horizontal and vertical coordinates respectively; the horizontal and vertical coordinate errors of each observation point are squared and summed, and these errors are accumulated to obtain a total amount that measures the difference between the predicted position and the actual position to obtain the error sum (minimization target). The error sum represents the sum of the errors between the predicted position and the actual position of all observation points. The goal is to minimize the error sum by adjusting the orbit parameters. By minimizing the error sum, an initial set of orbit parameters can be obtained, including the semi-major axis and eccentricity. These two parameters are the results of preliminary calculations and represent the basic characteristics of the orbit under given observation data. Through dynamic optimization or iterative algorithms, the orbit parameters can be gradually adjusted to minimize the error sum. As the number of iterations increases, the orbit parameters will gradually converge to a value that can more accurately reflect the actual orbit. According to each new observation data point, the orbit parameters are updated in a timely manner. The updated orbit parameters are used to continue to predict future orbital positions and keep real-time tracking of the target position.

[0074] In a preferred embodiment of the present invention, step 124, optimizing the semi-major axis, the eccentric anomaly angle and the eccentricity according to the sum of squared errors to obtain the adjusted predicted position, includes:

[0075] Step 1241, according to the sum of squared errors, the semi-major axis, the eccentric anomaly angle and the eccentricity are respectively derived to obtain the partial derivative of the semi-major axis, the partial derivative of the eccentricity and the partial derivative of the eccentric anomaly angle;

[0076] Step 1242, using gradient descent method to optimize the semi-major axis The partial derivative and eccentricity of The partial derivatives and partial anomaly angles of The partial derivatives of to obtain the optimized orbital parameters, which include semi-major axis, eccentric anomaly angle and eccentricity;

[0077] Step 1243, recalculate the predicted position using the optimized orbital parameters to obtain an adjusted predicted position.

[0078] In the embodiment of the present invention, the semi-major axis, the anomaly angle and the eccentricity can be adjusted more accurately by deriving and optimizing the orbit parameters, so that the error between the predicted position and the actual observed position is minimized, thereby improving the accuracy and reliability of the orbit prediction. The optimization process gradually approaches the optimal solution by iteratively adjusting the parameters, which not only improves the accuracy of the prediction, but also enhances the stability of the orbit calculation. For power concentrators or other equipment that requires precise positioning, the optimized orbit parameters can provide more accurate positioning information; accurate orbit prediction and positioning information help optimize the allocation and scheduling of resources.

[0079] In a specific embodiment of the present invention, the error sum of squares in the above steps includes two parts: one is x The error of the coordinates is y The error of the coordinates is calculated by adding the squares of these errors and then summing them for all observation points to get the total error sum of squares. The optimization of the semi-major axis, eccentric anomaly and eccentricity is to calculate the partial derivative of the error sum of squares with respect to each orbital parameter, where the partial derivative represents the rate of change of the error sum of squares with each parameter.

[0080] Step 1241, based on the error square sum, the semi-major axis, the eccentric anomaly angle and the eccentricity are respectively derived to obtain the partial derivative of the semi-major axis, the partial derivative of the eccentricity and the partial derivative of the eccentric anomaly angle, specifically including:

[0081] Step 12411:

[0082] pass Semi-major axis To perform derivation;

[0083] Step 12412:

[0084] pass Eccentricity To perform derivation;

[0085] Step 12413:

[0086] pass Angle of approach To perform derivation;

[0087] According to the above formula, after taking the derivative of the semi-major axis, we get an expression in which each term represents x Axis and y The influence of the error in the axial direction on the semi-major axis can be achieved by adjusting the semi-major axis to make the predicted orbital position closer to the actual observed position; according to the above formula, the partial derivative of the eccentricity shows the influence of the eccentricity on the sum of squared errors. According to the derivative result, the eccentricity is adjusted to reduce the total error; for the eccentric anomaly angle at each observation moment, according to the above formula, the partial derivative of the eccentric anomaly angle is taken to adjust the angle to reduce the sum of squared errors.

[0088] In another specific embodiment of the present invention, step 1242 uses a gradient descent method to optimize the semi-major axis. The partial derivative and eccentricity of The partial derivatives and partial anomaly angles of The partial derivatives of are used to obtain the optimized orbital parameters, including the semi-major axis, eccentric anomaly and eccentricity. The specific process includes: first, determining the initial semi-major axis, eccentric anomaly and eccentricity parameters; based on the partial derivatives of the semi-major axis, eccentric anomaly and eccentricity, subtracting the product of the partial derivative and a learning rate from each parameter, where the learning rate is a small positive number; by continuously iterating this process, gradually approaching the parameter value that can minimize the sum of squared errors, when the preset number of iterations is reached, stopping the iteration process, and considering that the optimal orbital parameter combination has been found.

[0089] Step 1243, use the optimized orbital parameters to recalculate the predicted position to obtain the adjusted predicted position. The specific process includes: according to the optimized orbital parameters, through the above-mentioned prediction position calculation formula, recalculate the predicted position of the object at a specific time point, the specific time point includes a certain moment in the future and a certain moment in the past. The predicted position obtained by the optimized parameters is closer to the actual observed position and has a smaller error. Therefore, the adjusted predicted position can be obtained.

[0090] In a preferred embodiment of the present invention, the above step 13, calculating the position of the power concentrator according to the position information of the power concentrator, includes:

[0091] Step 131, obtaining the longitude and latitude coordinate information of the power concentrator once in each measurement cycle according to the location information of the power concentrator;

[0092] Step 132, calculating a standard deviation of a set of position data according to the latitude and longitude coordinate information of the primary power concentrator in each measurement cycle to obtain a positioning error range;

[0093] Step 133, according to the positioning error range and error source, to obtain the horizontal, vertical and total error range of the position, wherein the error source includes the multipath effect of the satellite signal, base station density and signal strength, environmental interference and positioning error.

[0094] In an embodiment of the present invention, by accurately calculating the longitude and latitude coordinates of the power concentrator and obtaining its standard deviation, the positioning error can be accurately quantified, so that the power concentrator can significantly reduce the operational deviation caused by the positioning error when performing tasks, especially in dynamic positioning and resource allocation. Accurate positioning evaluation helps the system to track the trajectory of the power concentrator more accurately, thereby ensuring that the device can perform tasks according to the predetermined trajectory and position. By considering factors such as satellite signal multipath effects, base station density, signal strength, and environmental interference, the source of the error can be clearly identified and targeted optimization can be performed. By calculating the standard deviation of each measurement cycle, the error range can be quantified and dynamically adjusted. Horizontal refers to the positioning error of the power concentrator in the horizontal direction. Vertical evaluation helps the power concentrator to accurately control the positioning near terrain or buildings with large height differences. By measuring the vertical, the accurate positioning of the power concentrator at different heights can be ensured, avoiding the error or position offset of the power concentrator caused by the height error. By analyzing the source of the error, the optimization of the positioning algorithm can be guided, and accurate positioning can reduce the additional energy consumption caused by the orbit error. By reducing the positioning error, the power concentrator can reach the specified location in the shortest time, reducing unnecessary operations and time waste. By calculating and evaluating the positioning error of power concentrators in real time, the overall management efficiency of the power system can be optimized.

[0095] In a specific embodiment of the present invention, step 131, based on the location information of the power concentrator, obtains the longitude and latitude coordinate information of the power concentrator once in each measurement cycle. The specific process includes: setting a fixed time period (such as every hour, every day or every minute), during which the power concentrator periodically obtains its current position, and during each measurement cycle, the power concentrator obtains a longitude and latitude coordinate through its positioning device (such as GPS), and obtains the location information of multiple cycles (that is, the longitude and latitude coordinates obtained by multiple measurements).

[0096] Step 132, according to the longitude and latitude coordinate information of the primary power concentrator in each measurement cycle, calculate the standard deviation of a set of position data to obtain the positioning error range. The specific process includes: according to the position information of multiple cycles obtained in step 131, the longitude and latitude coordinate information of the primary power concentrator in each measurement cycle can be obtained, and the average value of the longitude and latitude coordinates in all measurement cycles is calculated. For a set of longitude and latitude coordinates, the average values ​​of latitude and longitude are calculated respectively to obtain an average longitude and latitude, and the difference between each measurement point and the average position is calculated. For each measured longitude and latitude coordinate, the deviation of latitude and longitude is calculated respectively, and then the square of all deviations is calculated and averaged, and then the square root is taken to calculate the standard deviation of latitude and longitude respectively. The standard deviation represents the error range of positioning. For example, if the standard deviation is large, it means that the measurement points are widely distributed and the positioning is low. After calculating the standard deviation, the error range of a set of data is obtained, where the error range reflects the accuracy of the power concentrator positioning, and the standard deviation value represents the positioning error range of the power concentrator during the measurement period. The smaller the error, the more accurate the positioning of the concentrator, and the larger the error range, the lower the positioning.

[0097] Step 133, according to the positioning error range and the error source, the horizontal, vertical and total error ranges of the position are obtained, wherein the error sources include the multipath effect of satellite signals, base station density and signal strength, environmental interference and positioning error. The specific process includes: according to the calculated error range and error source, the positioning error is analyzed, the positioning error is divided into horizontal, vertical and total error ranges, the horizontal error is the error in the longitude and latitude directions, the positioning system of the power concentrator not only obtains the longitude and latitude information, but also can measure the height and calculate the vertical, the vertical refers to the error range in the vertical direction, and the overall error range can be obtained by combining the standard deviation and the deviation. The size of the positioning error is not only affected by the system, but also by a variety of external factors. Common error sources include: the multipath effect of satellite signals is that when the satellite positioning system receives the signal, the multipath effect will be generated due to the reflection of the signal (such as reflection from buildings or the ground), which will cause the measured signal to be inaccurate, thereby increasing the positioning error. The density and signal strength of the base station will affect the positioning, and a higher base station density will generally provide more accurate positioning because triangulation can be performed through multiple base stations; when the signal strength is strong, the positioning error is small; and when the signal is weak, the error will increase. Environmental interference, such as buildings, trees, and atmospheric conditions, may affect signal transmission. Environmental changes (such as weather, physical obstacles, etc.) will lead to increased positioning errors. Positioning errors are errors in the positioning algorithm itself, such as error accumulation in the algorithm calculation. By comprehensively considering the above error sources, the positioning within each measurement cycle can be corrected. For example, in practical applications, if it is known that the multipath effect is strong, the error range can be appropriately increased during positioning estimation; if the signal strength is weak, a larger positioning error can be predicted. Finally, based on these error sources and corrections, combined with the comprehensive standard deviation and deviation, the overall error range is obtained, and the overall error range is used as an overall indicator of the power concentrator positioning system.

[0098] In a preferred embodiment of the present invention, the above step 14, calculating the speed of the power concentrator according to the position, includes:

[0099] Step 141, according to the location, obtain the latitude and longitude data and timestamp of the adjacent location;

[0100] Step 142, based on the data of the latitude and longitude of the adjacent location and the timestamp, Calculate the speed of the power concentrator, where and are the differences between longitude and latitude, respectively. is the time interval between adjacent positions, It's speed.

[0101] In the embodiment of the present invention, accurate speed can help the system monitor the mobile state of the power concentrator in real time, avoid position prediction errors caused by speed errors, and accurate speed information helps to optimize the mobile path of the power concentrator, so that it can complete tasks more efficiently and reduce resource waste. Accurate speed calculation enables the power dispatching system to grasp the dynamic changes of the concentrator in real time and adjust the power transmission path and load in time; when the power concentrator moves, through accurate speed data, the system can predict the arrival time of the concentrator, so as to distribute or adjust power in advance, reduce response time, and optimize power supply. Based on speed and position data, the system can predict the movement pattern and potential risks of the power concentrator. Through continuous speed data analysis, the future position change trend of the power concentrator can be predicted, and the foresight of power management can be improved. As the number of power concentrators increases, the system needs to process more dynamic data. Through accurate speed and position calculation, the system can be better expanded to large-scale power networks and improve the overall system performance.

[0102] In a specific embodiment of the present invention, step 141, according to the position, obtains the data and timestamp of the latitude and longitude of the adjacent position, and the specific process includes: the power concentrator records the current latitude and longitude (longitude and latitude) and the corresponding timestamp through the positioning device. Each time the power concentrator measures, it will obtain these three data: longitude and latitude and timestamp. When obtaining two adjacent measurement points: the longitude and longitude and timestamp of the first measurement point, and the longitude and longitude and timestamp of the second measurement point.

[0103] Step 142, based on the data of the latitude and longitude of the adjacent location and the timestamp, Calculate the speed of the power concentrator, where and are the differences between longitude and latitude, respectively. is the time interval between adjacent positions, It's speed.

[0104] In a preferred embodiment of the present invention, the above step 15, analyzing the speed, power consumption and running time data to obtain a correlation analysis result, includes:

[0105] Step 151, obtaining power consumption and position error in different time periods according to the position data, power consumption data and operation time data;

[0106] Step 152, based on the power consumption and position error, The correlation coefficient between power consumption and position is calculated to obtain the correlation analysis result, where: and are the rankings of power consumption and position error, is the number of data points, is the correlation coefficient between power consumption and position, It is The measurement error of the data points is is the index.

[0107] In an embodiment of the present invention, if there is a positive correlation between power consumption and position error, it indicates that when the power concentrator consumes high power in some cases, it may be accompanied by a high positioning error, which can prompt the optimization of the energy management strategy of the power concentrator to reduce energy waste; if a negative correlation is found between power consumption and position error, it indicates that when the power consumption is low, the concentrator is high, and the system can adjust the strategy to maintain low power during operation. Through the analysis of the correlation coefficient, the dispatching system can adjust the operation mode of the power concentrator according to the real-time power consumption and error data. With power consumption, position and operation time data, the dispatching system can more accurately predict the behavior of the power concentrator and reduce errors and energy waste in scheduling. By analyzing the time series of power consumption and position error, the system can monitor abnormal fluctuation trends. Through these signals, the system can intervene early to prevent the equipment from running in an inefficient state for a long time. Through time series analysis, it can help the system formulate energy-saving strategies. Combined with power consumption, error and operation time data, the operation mode of the power concentrator is optimized, and an intelligent energy-saving control strategy is implemented to reduce the overall energy consumption of the power system. If the correlation between power consumption and position is low, the system can tolerate certain power fluctuations without affecting the position, which can improve the stability and fault tolerance of the power concentrator system.

[0108] In a specific embodiment of the present invention, step 151, based on the position data, power consumption data and running time data, to obtain the power consumption and position error of different time periods, the specific process includes: according to the position data obtained in the above steps 11 to 14, it can be known that the position data is the positioning error at each time point, represented by the standard deviation or other measurement error indicators, the power consumption data is the power consumption of the power concentrator in each time period, and the running time data is the running time of the power concentrator in each time period, and the running time can affect the calculation of power consumption; the power consumption, position and duration data are all from the same time period or time window. The power consumption and position error of each time period are ranked. According to the power consumption data in each time period, its ranking in the entire data set is calculated, and the time period with higher power consumption ranks first, and the time period with lower power consumption ranks last. Similarly, according to the position error in each time period, its ranking in the entire data set is calculated, and the time period with smaller (i.e. higher) position error ranks first, and the time period with larger position error ranks last.

[0109] Step 152, based on the power consumption and position error, The correlation coefficient between power consumption and position is calculated to obtain the correlation analysis result, where: and are the rankings of power consumption and position error, is the number of data points, is the correlation coefficient between power consumption and position, It is The measurement error of the data points is is an index. The specific process includes: according to the power consumption and the position error, the correlation coefficient between the power consumption and the position error is calculated by the formula. The ranking correlation coefficient is used to measure the correlation between two variables (here, power consumption and position error) to obtain the correlation coefficient. Among them. If the correlation coefficient is close to 1, it means that there is a strong positive correlation between power consumption and position error. If the correlation coefficient is close to -1, it means that there is a strong negative correlation between power consumption and position error. If the correlation coefficient is close to 0, it means that there is no significant correlation between power consumption and position error. Through the calculated correlation coefficient, the correlation between power consumption and position error is obtained to determine whether there is a certain relationship between power consumption and position. Positive correlation: If the time period with higher power consumption is often accompanied by a larger position error (or vice versa), it means that there is a positive correlation between power consumption and positioning; negative correlation: If the time period with higher power consumption is often accompanied by a smaller positioning error, or the time period with lower power consumption is accompanied by a larger positioning error, it implies that in some cases lower power consumption may lead to higher positioning; no correlation: If the calculated correlation coefficient is close to 0, it means that there is no obvious relationship between power consumption and position error.

[0110] In a preferred embodiment of the present invention, the above step 16, based on the analysis result, evaluates the operating status of the device to obtain an evaluation result, including:

[0111] Step 161, setting the range, scope and operation duration of power consumption based on historical data;

[0112] Step 162, acquiring power consumption data, location data and duration data of the same timestamp according to the analysis result;

[0113] Step 163, comparing the power consumption data, location data and duration data of the same timestamp with the set power consumption range, range and operation duration to obtain a comparison result;

[0114] Step 164, according to the comparison result, set the equipment status scoring system to evaluate the operating status of the equipment to obtain the evaluation result.

[0115] In an embodiment of the present invention, the range, scope and operation time of power consumption are set according to historical data. By monitoring and comparing power consumption, location and operation time, if the score of the device at a certain point in time is lower than the preset threshold, the system can immediately issue an alarm to prevent the device from running in an abnormal state for a long time. By continuously monitoring the operating status of the device, the abnormal behavior of the device can be predicted and identified in advance, sudden failure or damage of the device can be prevented, and the stability and reliability of the device can be ensured. According to the device score, it can be accurately determined whether the device needs maintenance, overhaul or replacement. By evaluating the operating status of the device, the service life of the device is optimized, unnecessary maintenance and replacement are reduced, thereby reducing the overall operating cost. Regular evaluation of the device status helps to maintain the normal operation of the device, reduce unexpected downtime, and improve the overall reliability of the system. Through continuous status evaluation, it can ensure that the device operates in the optimal performance state and improve the stability of the system. The stable operation of the device not only helps to improve the overall system efficiency, but also ensures the quality of customer service. Through the device status scoring system, the system can automatically monitor the health of the equipment, predict maintenance, and intelligently schedule the device status.

[0116] In a specific embodiment of the present invention, step 161 sets the range, scope and operating duration of power consumption based on historical data. The specific process includes: determining the power consumption range based on historical data. For example, the power consumption may fluctuate between the maximum power and the minimum power of a certain device; the position refers to the error range of the device positioning, which is set based on historical data; the operating duration refers to the operating duration of the device in a specific time period, and the duration range is determined based on historical data.

[0117] Step 162, based on the analysis results, obtain the power consumption data, location data and duration data of the same timestamp. The specific process includes: when monitoring data in real time, the correlation analysis results can determine the abnormality of the device status in a certain period of time. For example: if the power consumption in the real-time data is obviously beyond the historical range, but the position error is consistent with the expected range, it may indicate an energy efficiency problem of the device (such as power failure or overload), and there is no need to worry too much about the location. If both exceed the standard at the same time, it may indicate that there is a systemic problem in the operation of the device, which may be a simultaneous failure of the positioning system or the power management system, so as to more accurately locate the root cause of the problem. After determining the root cause of the problem, extract specific values ​​from the real-time data. The real-time data contains data of multiple timestamps of the device operation, and each timestamp contains the following content: power consumption data, location data, and operation duration data.

[0118] Step 163, compare the power consumption data, location data and duration data of the same timestamp with the set power consumption range, range and running time to obtain the comparison result, and the specific process includes: compare the power consumption data, location data and duration data of the same timestamp with the set standard range, wherein, for each timestamp, compare its power consumption with the set power consumption range, for example, according to the set power consumption range, check whether the power consumption is within the range, if the power consumption exceeds this range, it may indicate that the device has an overload, failure or efficiency problem; for each timestamp, compare its position error with the set range, for example, according to the set range, if the positioning error of the device exceeds this range, it indicates that there is a problem with the positioning system or sensor of the device; for each timestamp, compare its running time with the set time range, for example, according to the set normal operation time range, if the time exceeds this range, it indicates that the operation mode of the device is abnormal. Through these comparisons, the comparison result of each time point can be obtained, that is, whether the power consumption, location and running time at the time point meet the predetermined range.

[0119] Step 164, according to the comparison results, set the equipment status scoring system, evaluate the operating status of the equipment, and obtain the evaluation results. The specific process includes: setting different scoring standards according to the comparison results. For example, if the power consumption is within the range, 10 points are scored; if it exceeds the range, points are gradually deducted according to the excess range; similarly, the position and running time have similar scoring rules. The score of each dimension can be weighted according to the importance; for example, assuming that the scoring system is as follows: power consumption is within the normal range, 10 points are scored, and points are deducted if it exceeds the range; the position error is within the range, 10 points are scored, and when it exceeds the range, the greater the error, the more points are deducted; the running time is within the normal range, 10 points are scored, and if it is too long or too short, points are deducted according to the difference. The scores of power consumption, position and running time at each time point are combined into a comprehensive score. The comprehensive score can be the weighted average of these individual scores, and the power consumption, position and running time account for 40%, 30% and 30% of the comprehensive score respectively. Through the scoring system, the running status score of the equipment is obtained. The higher the score, the more normal the device is running, and the lower the score, the more likely there is a problem or abnormality with the device. By scoring each time point, the changing trend of the device's running status over a period of time can be obtained.

[0120] In a preferred embodiment of the present invention, the above step 17, optimizing and adjusting the real-time device status according to the evaluation result, includes:

[0121] Step 171, based on the evaluation result, identifying the abnormality in the operation of the equipment, and performing fault diagnosis to obtain a diagnosis result;

[0122] Step 172, determining an optimization target based on the diagnosis result, and optimizing based on the optimization target to adjust the real-time device status.

[0123] In the embodiments of the present invention, by identifying anomalies, performing fault diagnosis, setting optimization goals and implementing optimization, potential problems of the equipment are discovered early, and sudden failures are prevented through precise optimization measures to ensure efficient and stable operation of the equipment. The power consumption and working mode of the equipment are optimized to ensure the rational use of energy and reduce waste. By reducing failures and extending the life of the equipment, the cost of repairing and replacing equipment is reduced. By continuously adjusting and optimizing the status of the equipment, it is always in the best working state, enhancing the overall stability of the system and ensuring long-term reliable operation.

[0124] In a specific embodiment of the present invention, through the analysis of the above steps, the abnormality in the operation of the equipment is identified. Once the abnormality of the equipment is identified, a set of rule bases is constructed according to the working principle of the equipment and common fault conditions. For example, if the power consumption of the equipment is abnormal and the position is too poor, it may be that the sensor is damaged or the power supply is unstable; the system automatically identifies the fault type of the equipment according to these rules to analyze the historical operation data of the equipment, sensor feedback, operation log and other information, combined with the equipment design and operation logic, to determine the cause of the abnormality of the equipment. After completing the fault diagnosis, the optimization goal is set according to the fault type. If the diagnosis result shows that the power consumption is too high, the optimization goal may be to reduce power consumption, improve energy efficiency, or repair the overload problem in the equipment; if the positioning is abnormal, the optimization goal may be to recalibrate the positioning system or replace the positioning sensor; if the running time of the equipment is abnormal, the optimization goal may be to adjust the operating cycle of the equipment, save energy, or optimize the operation scheduling. Based on the fault diagnosis results and the current state of the equipment, the system will automatically adjust the working parameters of the equipment and set specific optimization goals, such as: reducing power consumption by 10%, improving positioning to within 5 meters, and adjusting the running time to a reasonable range. According to the determined optimization target, the system starts to perform optimization operations and optimizes the power consumption of the equipment by adjusting the working mode of the equipment (such as reducing power demand, adopting energy-saving mode, etc.).

[0125] like Figure 2 As shown, an embodiment of the present invention further provides a power concentrator operation data analysis system 20, comprising:

[0126] The acquisition module 21 is used to acquire the real-time position and time data of the power concentrator device, and obtain the multimodal time series data when the power concentrator is running; perform dynamic positioning and orbit calculation of the power concentrator according to the multimodal time series data to obtain the position information of the power concentrator; calculate the position of the power concentrator according to the position information of the power concentrator;

[0127] The processing module 22 is used to calculate the position of the power concentrator according to the position information of the power concentrator; and calculate the speed of the power concentrator according to the position;

[0128] The analysis module 23 is used to analyze the speed, power consumption and operation time data to obtain a correlation analysis result; based on the analysis result, evaluate the operation status of the equipment to obtain an evaluation result; based on the evaluation result, optimize and adjust the real-time equipment status.

[0129] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for analyzing operation data of a power concentrator, characterized in that: The method comprises: Acquire the real-time location and time data of the power concentrator equipment, and obtain the multi-modal time series data of the power concentrator during operation; Based on the multi-modal time series data, the dynamic positioning and orbit calculation of the power concentrator are carried out to obtain the location information of the power concentrator, including: According to multimodal time series data, and Compute the predicted position on the track, where and are the horizontal and vertical coordinates of the predicted position on the track, is the index variable, is the semi-major axis, It is The eccentric anomaly angle corresponding to the observation time is is the eccentricity; based on the predicted position on the orbit, and Calculate the difference between the predicted position and the actual observed position, is the error in the horizontal axis, is the error in the ordinate; based on the difference between the predicted position and the actual observed position, Calculate the sum of squared errors, where is the sum of squared errors; It is The actual observation position of the observation point Coordinate values, It is The actual observation position of the observation point coordinate values; optimizing the semi-major axis, the eccentric anomaly angle and the eccentricity according to the sum of squared errors to obtain the adjusted predicted position; determining the location information of the power concentrator according to the adjusted predicted position; Calculating the location of the power concentrator according to the location information of the power concentrator; Based on the location, the speed of the power concentrator is calculated; Analyze the speed with the power consumption and runtime data to obtain correlation analysis results; According to the analysis results, the operating status of the equipment is evaluated to obtain the evaluation results; Optimize and adjust real-time equipment status based on the evaluation results.

2. The power concentrator operation data analysis method according to claim 1, characterized in that: According to the sum of squared errors, the semi-major axis, eccentric anomaly angle and eccentricity are optimized to obtain the adjusted predicted position, including: According to the sum of squared errors, the semi-major axis, the eccentric anomaly angle and the eccentricity are respectively differentiated to obtain the partial derivative of the semi-major axis, the partial derivative of the eccentricity and the partial derivative of the eccentric anomaly angle; Optimize the semi-major axis using gradient descent The partial derivative and eccentricity of The partial derivatives and partial anomaly angles of The partial derivatives of to obtain the optimized orbital parameters, which include semi-major axis, eccentric anomaly angle and eccentricity; The predicted position is recalculated using the optimized orbital parameters to obtain the adjusted predicted position.

3. The power concentrator operation data analysis method according to claim 2, characterized in that: According to the location information of the power concentrator, the location of the power concentrator is calculated, including: According to the location information of the power concentrator, the longitude and latitude coordinate information of the power concentrator is obtained once in each measurement cycle; According to the latitude and longitude coordinate information of the primary power concentrator in each measurement cycle, the standard deviation of a set of position data is calculated to obtain the positioning error range; According to the positioning error range and error source, the horizontal, vertical and total error range of the position can be obtained, wherein the error source includes the multipath effect of satellite signals, base station density and signal strength, environmental interference and positioning error.

4. The method for analyzing the operation data of a power concentrator according to claim 3, characterized in that: Calculate the speed of the power concentrator based on the location, including: According to the location, obtain the latitude and longitude data and timestamp of the adjacent location; according to the latitude and longitude data and timestamp of the adjacent location, through Calculate the speed of the power concentrator, where and are the differences between longitude and latitude, respectively. is the time interval between adjacent positions, It's speed.

5. The power concentrator operation data analysis method according to claim 4, characterized in that: Speed ​​is analyzed against power consumption and runtime data to provide correlation results, including: According to the position data, power consumption data and operation time data, the power consumption and position error in different time periods are obtained; According to the power consumption and position error, The correlation coefficient between power consumption and position is calculated to obtain the correlation analysis result, where: and are the rankings of power consumption and position error, is the number of data points, is the correlation coefficient between power consumption and position, It is The measurement error of the data points is is the index.

6. The method for analyzing the operation data of a power concentrator according to claim 5, characterized in that: Based on the analysis results, the operating status of the equipment is evaluated to obtain evaluation results, including: Based on historical data, set the range, scope and operation duration of power consumption; According to the analysis results, power consumption data, location data and duration data of the same timestamp are obtained; Compare the power consumption data, location data and duration data of the same timestamp with the set power consumption range, range and operation duration to obtain a comparison result; According to the comparison results, the equipment status scoring system is set up to evaluate the operating status of the equipment to obtain the evaluation results.

7. A power concentrator operation data analysis system, characterized in that: The system implements the method according to any one of claims 1 to 6, including: An acquisition module is used to acquire the real-time position and time data of the power concentrator device, and obtain the multimodal time series data when the power concentrator is running; based on the multimodal time series data, the power concentrator is dynamically positioned and tracked to obtain the position information of the power concentrator; and the position of the power concentrator is calculated based on the position information of the power concentrator; A processing module, used to calculate the position of the power concentrator according to the position information of the power concentrator; and calculate the speed of the power concentrator according to the position; The analysis module is used to analyze the speed, power consumption and operation time data to obtain correlation analysis results; based on the analysis results, the operation status of the equipment is evaluated to obtain evaluation results; based on the evaluation results, the real-time equipment status is optimized and adjusted.

8. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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