OTA upgrade aging detection system and method based on big data analysis

Abstract

The invention discloses an OTA upgrade timeliness detection system and method based on big data analysis, and relates to the technical field of big data analysis. An intelligent terminal is in butt joint with a power grid equipment upgrade log, historical version upgrade efficiency data is extracted, association is performed according to a single event, and a retransmission analysis set is constructed by taking a start timestamp as a key; structured integration of historical data is achieved, and association and integrity of data such as network performance parameters and installation package size are guaranteed. A network performance detection window is set to collect key parameters, fluctuation characteristics are calculated, a mapping relation between packet loss rate and retransmission quantity is established in combination with a neural network, quantitative association of the two is realized, and retransmission prediction accuracy is greatly improved. The first transmission duration based on historical data before upgrading and the second transmission duration calculated in real time during upgrading are fused, the total duration is obtained and compared with the preset value, upgrading timeliness dynamic detection is achieved, timeout abnormity is recognized in time, early warning is conducted, and upgrading monitoring and intervention are effectively guaranteed.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, specifically to an OTA upgrade timeliness detection system and method based on big data analytics. Background Technology

[0002] As the core carrier of the entire power system—generation, transmission, transformation, distribution, and consumption—power grid equipment is the cornerstone of ensuring a continuous and stable power supply. Its performance and condition directly affect energy security, the continuity of industrial production, and the electricity needs of millions of residents. Against the backdrop of the accelerated construction of new power systems, power grid equipment must not only undertake traditional power transmission tasks but also adapt to complex scenarios such as high-proportion renewable energy integration and source-grid-load-storage coordination, supporting the optimized allocation and efficient utilization of energy across regions. The reliable operation and efficient management of power grid equipment have irreplaceable strategic significance for the stability of the national energy system.

[0003] In the timeliness monitoring of existing power grid equipment upgrades, the upgrade process is easily affected by factors such as network status fluctuations and expansion of the upgrade area, leading to a surge in data transmission packet loss and frequent retransmissions. This chain reaction directly makes it difficult to accurately measure the upgrade time of power grid equipment in various regions, making it impossible to identify abnormal situations such as upgrade timeouts in a timely manner, and consequently hindering the timely implementation of intervention and repair measures for equipment that has exceeded the upgrade timeout. Therefore, there is an urgent need for an OTA upgrade timeliness monitoring system and method based on big data analysis. Summary of the Invention

[0004] The purpose of this invention is to provide an OTA upgrade timeliness detection system and method based on big data analysis to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an OTA upgrade timeliness detection method based on big data analysis, the OTA upgrade timeliness detection method comprising the following steps: Step S1: Obtain historical version upgrade efficiency data of power grid equipment to construct a version upgrade retransmission analysis set. The version upgrade efficiency data includes network performance data, installation package size and retransmission data volume. Step S1-1: Connect to the upgrade log system of the power grid equipment through the intelligent terminal platform, and extract the version upgrade efficiency data generated during the historical version upgrade process of the power grid equipment. The version upgrade efficiency data is specifically log record information, including network performance data, installation package size, and retransmission data volume. The network performance data is represented as network performance parameters; the installation package size is represented as the number of bytes in the installation package; and the retransmission data volume is represented as the statistical value of retransmission data packets. Step S1-2: Perform field matching on the extracted log information, associate network performance parameters, installation packet byte count and retransmission data packet statistics by single upgrade event, select upgrade start timestamp as key and version upgrade efficiency data as value, and construct version upgrade retransmission analysis set; By connecting to the power grid equipment upgrade log system through an intelligent terminal platform, historical version upgrade efficiency data containing network performance parameters, installation packet byte count, and retransmission data packet statistics are extracted. Field matching is performed to associate data based on single upgrade events, and a version upgrade retransmission analysis set is constructed using the upgrade start timestamp as the key. This achieves structured integration of historical upgrade data, ensuring data correlation and integrity. It provides a reliable data foundation for subsequent analysis of the relationship between network performance and retransmission data volume, as well as upgrade timeliness detection. Furthermore, the timestamp key facilitates data retrieval and reuse.

[0006] Step S2: Obtain the network performance data of the current power grid equipment undergoing version upgrade in real time, and record it as real-time network performance data. Analyze the network performance data in the version upgrade retransmission analysis set to obtain the packet loss rate during each historical version upgrade. Analyze the packet loss rate and the retransmission data volume of the corresponding historical version upgrade to obtain the relationship between network performance data and retransmission data volume. Step S2-1: Before the current power grid equipment starts the version upgrade, set a network performance detection window with a duration of T, and collect network performance parameter sampling values ​​within the window at fixed time intervals. The network performance parameters include signal strength, transmission bandwidth and delay duration. Step S2-2: Calculate the arithmetic mean of the sampled values ​​for each network performance parameter, and record it as the baseline value of the corresponding network performance parameter; calculate the difference between the parameter value and the baseline value at each sampling time, sum the absolute values ​​of all differences and divide by the total number of sampling times to obtain the fluctuation coefficient of each network performance parameter. The fluctuation coefficient is represented by the value obtained by normalizing the calculation results. The real-time network performance fluctuation characteristics are obtained through weighted fusion calculation. The formulas for calculating the baseline values ​​of network performance parameters are as follows: ; In the formula, μ k Let x be the baseline value of the k-th network performance parameter; k，i This represents the sampled value of the k-th network performance parameter in the i-th sampling; n represents the total number of samplings for the network performance parameter; and k represents the number of the network performance parameter, including signal strength, transmission bandwidth, and latency. The formula for calculating the fluctuation coefficient of network performance parameters is as follows: ; In the formula, B kThe initial value of the fluctuation coefficient of the kth network performance parameter is represented as the initial value. After normalization, it is mapped to the interval [0, 1] to obtain the fluctuation coefficient of the kth network performance parameter. Step S2-3: Extract network performance parameter sampling data corresponding to each historical record from the version upgrade retransmission analysis set, and calculate the network performance fluctuation characteristics of each historical version upgrade according to the methods of steps S2-1 to S2-2; at the same time, retrieve the total number of data packets sent and the number of successfully received data packets in the historical record of the version upgrade process, and calculate the packet loss rate of each historical version upgrade by subtracting the number of successfully received data packets from the total number of data packets sent and then dividing it by the total number of data packets sent. Step S2-4: Select historical network performance fluctuation characteristics as input variables and select the packet loss rate corresponding to the historical network performance fluctuation characteristics as output variables. Construct a neural network training sample set and obtain the mapping relationship between network performance fluctuation characteristics and packet loss rate through model training. Step S2-5: Using the product of the total number of data packets sent and the packet loss rate in the historical version upgrade process, the amount of retransmitted data is obtained, that is, the mapping relationship between network performance fluctuation characteristics and the amount of retransmitted data is obtained. Before the current power grid equipment undergoes a version upgrade, a network performance monitoring window with a duration of T is set to collect sampled values ​​of network performance parameters such as signal strength, transmission bandwidth, and latency. The baseline values ​​and fluctuation coefficients of each parameter are calculated and weighted to obtain real-time network performance fluctuation characteristics. At the same time, historical network performance parameter sampled data are extracted from the version upgrade retransmission analysis set, and historical network performance fluctuation characteristics are calculated using the same method. The historical packet loss rate is calculated by combining the total number of historical data packets sent and the number of successfully received packets. A neural network training sample set is constructed to obtain the mapping relationship between network performance fluctuation characteristics and packet loss rate. Then, the retransmission data volume is obtained by multiplying the total number of historical data packets sent and the packet loss rate. The mapping relationship between network performance fluctuation characteristics and retransmission data volume is established, realizing the quantitative correlation between network performance and retransmission data volume, and providing an accurate model basis for predicting the retransmission data volume of the current upgrade.

[0007] Step S3: Obtain the size of the installation package for the current power grid equipment to upgrade the version, and combine it with the analysis of the relationship between network performance data and retransmission data volume to obtain the retransmission data volume for the current power grid equipment to upgrade the version, which is denoted as the predicted retransmission data volume. Using real-time network performance fluctuation characteristics as input, the mapping relationship between network performance fluctuation characteristics and retransmission data volume is obtained, and the packet loss rate of the current connected power grid equipment upgrade is output. The installation package size of the current connected power grid equipment upgrade is obtained through the intelligent terminal platform. The packet loss rate of the current connected power grid equipment upgrade is multiplied by the installation package size of the current connected power grid equipment upgrade to obtain the retransmission data volume of the current power grid equipment for version upgrade, which is denoted as the predicted retransmission data volume.

[0008] By inputting real-time network performance fluctuation characteristics into the mapping relationship between network performance fluctuation characteristics and retransmission data volume, the packet loss rate of the current connected power grid equipment upgrade is obtained. The installation packet size of the current connected power grid equipment upgrade is obtained through the intelligent terminal platform. The predicted retransmission data volume is obtained by multiplying the packet loss rate by the installation packet size. This achieves accurate prediction of retransmission data volume based on real-time network status and current installation packet information, providing key retransmission data parameters for subsequent upgrade timeliness detection.

[0009] Step S4: Set the version upgrade timeliness sliding window to capture the version upgrade efficiency data analysis in the version upgrade retransmission analysis set, obtain the standard installation package transmission rate for the current power grid equipment to perform version upgrade, and calculate the theoretical transmission time of the installation package by the size of the installation package for the current power grid equipment to perform version upgrade and the standard installation package transmission rate. Step S4-1: Set the window size of the version upgrade timeout sliding window to M times the number of times the currently connected power grid equipment is upgraded, where M is a positive integer and the value of M is less than the total number of historical version upgrade events included in the version upgrade retransmission analysis set; set the sliding step size to 1 version upgrade event. Step S4-2: Select the historical version upgrade event with the latest timestamp in the version upgrade retransmission analysis set as the starting point, and extract the version upgrade efficiency data corresponding to M consecutive historical version upgrade events to form the initial version upgrade timeliness analysis window dataset. Step S4-3: For each historical version upgrade efficiency data in the initial version upgrade timeliness analysis window dataset, extract the installation package size and actual transmission time of the corresponding historical version upgrade, calculate the single transmission rate, i.e., the ratio of the installation package size to the actual transmission time; calculate the arithmetic mean of all single transmission rates to obtain the standard installation package transmission rate. Step S4-4: Obtain the size of the installation package for the current power grid equipment version upgrade, divide the size of the installation package by the standard installation package transmission rate to obtain the theoretical transmission time of the installation package. The theoretical transmission time of the installation package is the estimated time required for the installation package for the current power grid equipment version upgrade to complete the transmission at the standard installation package transmission rate without retransmission. By setting a sliding window for version upgrade timeliness, selecting the latest timestamp of M consecutive historical version upgrade efficiency data from the version upgrade retransmission analysis set, calculating the arithmetic mean of the single transmission rate to obtain the standard installation package transmission rate, and then dividing the size of the current power grid equipment version upgrade installation package by the standard rate to obtain the theoretical transmission time of the installation package, it is possible to determine the standard transmission parameters based on recent historical upgrade data, providing a transmission time benchmark under no-retransmission conditions for upgrade timeliness detection, and ensuring the timeliness and accuracy of the parameters.

[0010] Step S5: Obtain the predicted retransmission duration by combining the predicted retransmission data volume with the standard installation package transmission rate. Add the theoretical transmission duration of the installation package to the predicted retransmission duration to obtain the first predicted installation package transmission duration. Real-time statistics are collected on the packet loss rate of the current power grid equipment undergoing version upgrade. Combined with the analysis of the transmission data volume of the current power grid equipment undergoing version upgrade, the second predicted installation package transmission duration is obtained. Based on the first and second predicted installation package transmission durations, the timeliness of the power grid equipment version upgrade is detected. Step S5-1: Divide the predicted retransmission data amount obtained in step S3 by the standard installation packet transmission rate obtained in step S4 to obtain the predicted retransmission duration; sum the theoretical transmission duration of the installation packet obtained in step S4 with the predicted retransmission duration to obtain the first predicted installation packet transmission duration. Step S5-2: During the current power grid equipment version upgrade process, real-time network performance parameters are collected at fixed time intervals. The real-time network performance parameters include signal strength, transmission bandwidth and delay duration. At the same time, the total number of data packets sent and the number of data packets successfully received are recorded. Step S5-3: Calculate the real-time packet loss rate based on real-time network performance parameters; extract the amount of data that has been transmitted, compare the amount of data that has been transmitted with the total size of the installation package to obtain the transmission progress ratio, where the transmission progress ratio is specifically the proportion of the amount of data that has been transmitted to the total size of the installation package, and obtain the remaining untransmitted proportion based on the transmission progress ratio. Step S5-4: Subtract the number of successfully received data packets from the total number of data packets sent to obtain the amount of data retransmitted in real time; and calculate the actual average transmission rate, specifically by using the ratio of the amount of data transmitted to the duration of transmission. Step S5-5: Divide the real-time retransmission data volume by the proportion of the remaining untransmitted data to obtain the retransmission data volume that needs to be retransmitted when the current power grid equipment is upgraded. This is recorded as the real-time retransmission data volume. Steps S5-6: Divide the real-time retransmission data volume by the actual average transmission rate to obtain the real-time retransmission duration; multiply the total size of the installation package by the proportion of the remaining untransmitted data to obtain the remaining untransmitted data volume; divide the remaining untransmitted data volume by the actual average transmission rate to obtain the remaining data transmission duration. Step S5-7: Add the real-time retransmission duration, the remaining data transmission duration, and the current transmission duration to obtain the second predicted installation package transmission duration; perform a weighted summation operation on the first predicted installation package transmission duration and the second predicted installation package transmission duration to obtain the total transmission duration of the current power grid equipment version upgrade; compare the total transmission duration of the current power grid equipment version upgrade with the preset installation package transmission duration. The specific process is as follows: When the total transmission time exceeds the preset transmission time of the installation package, it is judged as an abnormal version upgrade timeliness and a warning signal is issued. When the total transmission time does not exceed the preset installation package transmission time, it is determined that the version upgrade is timely and the monitoring of the power grid equipment version upgrade continues. The weighted sum of the transmission time of the first predicted installation package and the transmission time of the second predicted installation package is calculated using the following formula: ; In the formula, T total ω represents the total transmission time for the current power grid equipment version upgrade; ω1 represents the weighting coefficient for the transmission time of the first predicted installation packet; t perd1 ω1 represents the transmission duration of the first predicted installation packet; ω2 represents the weighting coefficient for the transmission duration of the second predicted installation packet; t perd2 This represents the transmission duration of the second predicted installation package; it satisfies ω1+ω2=1.

[0011] The predicted retransmission duration is obtained by comparing the predicted retransmission data volume with the standard installation package transmission rate. The first predicted installation package transmission duration is obtained by summing the theoretical transmission duration of the installation package. During the upgrade process, network performance parameters and transmission data are collected in real time to calculate the real-time packet loss rate, the proportion of data already transmitted, the proportion of data remaining untransmitted, the real-time retransmission data volume, and the actual average transmission rate. This yields the real-time retransmission duration and the remaining data transmission duration. The second predicted installation package transmission duration is obtained by summing the current transmission duration. The total transmission duration is obtained by weighted summation of the two predicted durations and compared with the preset installation package transmission duration. This enables dynamic detection of the timeliness of the power grid equipment version upgrade, timely identification of anomalies and issuance of early warnings, and ensures effective monitoring of the upgrade process.

[0012] Furthermore, an OTA upgrade timeliness detection system based on big data analysis is provided, which includes a historical upgrade data processing module, a network performance correlation analysis module, a retransmission data volume prediction module, a standard transmission parameter calculation module, and an upgrade timeliness detection module. The historical upgrade data processing module is used to acquire historical version upgrade efficiency data of power grid equipment and construct a version upgrade retransmission analysis set; the network performance correlation analysis module is used to determine the packet loss rate and the relationship between network performance data and retransmission data volume through historical network performance data analysis; the retransmission data volume prediction module is used to predict the retransmission data volume of the current upgrade by combining the size of the current upgrade installation package and the relationship between network performance and retransmission data volume; the standard transmission parameter calculation module is used to analyze historical upgrade data through a sliding window to calculate the standard installation package transmission rate and the theoretical transmission time of the installation package; the upgrade timeliness detection module is used to detect the timeliness of power grid equipment version upgrades through fusion analysis of predicted transmission time. The output of the historical upgrade data processing module is electrically connected to the input of the network performance correlation analysis module; the output of the network performance correlation analysis module is electrically connected to the input of the retransmission data volume prediction module; the output of the retransmission data volume prediction module is electrically connected to the input of the standard transmission parameter calculation module; and the output of the standard transmission parameter calculation module is electrically connected to the upgrade timeliness detection module. The historical upgrade data processing module includes a historical data acquisition unit and a retransmission analysis set construction unit. The historical data acquisition unit is used to extract network performance data, installation package size and retransmission data volume during the historical version upgrade process from the power grid equipment upgrade log system. The retransmission analysis set construction unit is used to associate the extracted historical data according to a single upgrade event and construct a version upgrade retransmission analysis set with the upgrade start timestamp as the key. The network performance correlation analysis module includes a historical packet loss rate analysis unit and a performance retransmission relationship modeling unit. The historical packet loss rate analysis unit is used to calculate the packet loss rate of each historical upgrade based on the total number of data packets sent and the number of successfully received data packets in the historical version upgrade. The performance retransmission relationship modeling unit is used to establish the relationship between network performance data and retransmission data volume through the mapping relationship between historical network performance fluctuation characteristics and packet loss rate. The retransmission data volume prediction module includes a current installation package information acquisition unit and a predicted retransmission volume calculation unit; the current installation package information acquisition unit is used to acquire the size of the installation package for the current power grid equipment version upgrade; the predicted retransmission volume calculation unit is used to calculate the predicted retransmission data volume using the packet loss rate of the current upgrade and the installation package size. The standard transmission parameter calculation module includes a sliding window data analysis unit and a theoretical transmission duration determination unit; the sliding window data analysis unit is used to extract historical upgrade data and calculate the standard installation package transmission rate; the theoretical transmission duration determination unit is used to calculate the theoretical transmission duration of the installation package based on the current installation package size and the standard transmission rate; The upgrade timeliness detection module includes a predicted duration fusion unit and a timeliness status determination unit. The predicted duration fusion unit is used to perform a weighted summation of the first predicted installation package transmission time and the second predicted installation package transmission time to obtain the total transmission time of the current upgrade. The timeliness status determination unit is used to compare the total transmission time of the current upgrade with the preset installation package transmission time, determine the upgrade timeliness status, and issue a corresponding signal.

[0013] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention connects to the upgrade log system through a smart terminal platform, extracts historical version upgrade efficiency data, associates it with single upgrade events and constructs a version upgrade retransmission analysis set with the upgrade start timestamp as the key, realizes the structured integration of historical data, ensures the correlation and integrity of data such as network performance parameters and installation package byte count, and provides a reliable data foundation for subsequent analysis of the relationship between network performance and retransmission data volume and upgrade timeliness detection.

[0014] 2. This invention collects parameters by setting a network performance detection window, calculates fluctuation characteristics, and constructs a neural network model by combining historical data. It obtains the mapping relationship between network performance fluctuation characteristics and packet loss rate and retransmission data volume, realizes the quantitative correlation between network performance and retransmission data volume, provides accurate model support for the current upgraded prediction of retransmission data volume calculation, and improves the accuracy of retransmission data volume prediction.

[0015] 3. This invention integrates the transmission time of the first predicted installation package based on the pre-upgrade prediction with the transmission time of the second predicted installation package calculated in real time during the upgrade process to obtain the total transmission time and compares it with the preset time. This enables dynamic detection of the timeliness of power grid equipment version upgrades, timely identification of anomalies such as timeouts and issuance of early warnings, and ensures effective monitoring and timely intervention in the upgrade process. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an OTA upgrade timeliness detection method based on big data analysis according to the present invention. Figure 2 This is a schematic diagram of the OTA upgrade timeliness detection system based on big data analysis according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1: As Figure 1 As shown, this invention provides a technical solution: an OTA upgrade timeliness detection method based on big data analysis. The OTA upgrade timeliness detection method includes the following steps: Step S1: Obtain historical version upgrade efficiency data of power grid equipment to construct a version upgrade retransmission analysis set. The version upgrade efficiency data includes network performance data, installation package size and retransmission data volume. Step S1-1: Connect to the upgrade log system of the power grid equipment through the intelligent terminal platform, and extract the version upgrade efficiency data generated during the historical version upgrade process of the power grid equipment. The version upgrade efficiency data is specifically log record information, including network performance data, installation package size, and retransmission data volume. The network performance data is represented as network performance parameters; the installation package size is represented as the number of bytes in the installation package; and the retransmission data volume is represented as the statistical value of retransmission data packets. Step S1-2: Perform field matching on the extracted log information, associate network performance parameters, installation packet byte count and retransmission data packet statistics by single upgrade event, select upgrade start timestamp as key and version upgrade efficiency data as value, and construct version upgrade retransmission analysis set; In practice, a reliable connection is established between the intelligent terminal platform and the power grid equipment upgrade log system. Historical upgrade efficiency data, including network performance parameters, installation packet byte count, and retransmission data packet statistics, is accurately extracted from the logs. The core is to match and associate these scattered log information fields according to a single upgrade event, and construct an analysis set with the upgrade start timestamp as the key. This process must ensure that the data corresponding to each timestamp is accurate and avoid data mixing of different upgrade events, so as to provide a structured and highly correlated foundation for subsequent analysis based on historical data.

[0019] Step S2: Obtain the network performance data of the current power grid equipment undergoing version upgrade in real time, and record it as real-time network performance data. Analyze the network performance data in the version upgrade retransmission analysis set to obtain the packet loss rate during each historical version upgrade. Analyze the packet loss rate and the retransmission data volume of the corresponding historical version upgrade to obtain the relationship between network performance data and retransmission data volume. Step S2-1: Before the current power grid equipment starts the version upgrade, set a network performance detection window with a duration of T, and collect network performance parameter sampling values ​​within the window at fixed time intervals. The network performance parameters include signal strength, transmission bandwidth and delay duration. Step S2-2: Calculate the arithmetic mean of the sampled values ​​for each network performance parameter, and record it as the baseline value of the corresponding network performance parameter; calculate the difference between the parameter value and the baseline value at each sampling time, sum the absolute values ​​of all differences and divide by the total number of sampling times to obtain the fluctuation coefficient of each network performance parameter. The fluctuation coefficient is represented by the value obtained by normalizing the calculation results. The real-time network performance fluctuation characteristics are obtained through weighted fusion calculation. Step S2-3: Extract network performance parameter sampling data corresponding to each historical record from the version upgrade retransmission analysis set, and calculate the network performance fluctuation characteristics of each historical version upgrade according to the methods of steps S2-1 to S2-2; at the same time, retrieve the total number of data packets sent and the number of successfully received data packets in the historical record of the version upgrade process, and calculate the packet loss rate of each historical version upgrade by subtracting the number of successfully received data packets from the total number of data packets sent and then dividing it by the total number of data packets sent. Step S2-4: Select historical network performance fluctuation characteristics as input variables and select the packet loss rate corresponding to the historical network performance fluctuation characteristics as output variables. Construct a neural network training sample set and obtain the mapping relationship between network performance fluctuation characteristics and packet loss rate through model training. Step S2-5: Using the product of the total number of data packets sent and the packet loss rate in the historical version upgrade process, the amount of retransmitted data is obtained, that is, the mapping relationship between network performance fluctuation characteristics and the amount of retransmitted data is obtained. In the specific implementation, firstly, based on the network performance detection window set before the current upgrade starts, continuously collect parameters such as signal strength, transmission bandwidth, and latency. By calculating the baseline value and fluctuation coefficient of each parameter and weighting and fusing them, the real-time fluctuation characteristics are obtained. At the same time, corresponding data is extracted from the historical set and processed in the same way. Combined with the historical data packet transmission and reception, the packet loss rate is calculated. In this way, a neural network sample set is constructed for training to obtain the mapping relationship. During the process, it is necessary to ensure that the feature calculation methods of real-time and historical data are consistent to ensure that the model can accurately reflect the actual correlation between network performance fluctuations and packet loss rate and retransmission data volume.

[0020] Step S3: Obtain the size of the installation package for the current power grid equipment to upgrade the version, and combine it with the analysis of the relationship between network performance data and retransmission data volume to obtain the retransmission data volume for the current power grid equipment to upgrade the version, which is denoted as the predicted retransmission data volume. Using real-time network performance fluctuation characteristics as input, the mapping relationship between network performance fluctuation characteristics and retransmission data volume is obtained, and the packet loss rate of the current connected power grid equipment upgrade is output. The installation package size of the current connected power grid equipment upgrade is obtained through the intelligent terminal platform. The packet loss rate of the current connected power grid equipment upgrade is multiplied by the installation package size of the current connected power grid equipment upgrade to obtain the retransmission data volume of the current power grid equipment for version upgrade, which is denoted as the predicted retransmission data volume.

[0021] In specific implementation, step S3 relies on the mapping relationship between network performance fluctuation characteristics and retransmission data volume obtained in step S2. The real-time network performance fluctuation characteristics are input into the model to obtain the packet loss rate of the current upgrade. Then, combined with the current installation packet size obtained by the smart terminal platform, the retransmission data volume is predicted by multiplying the packet loss rate and the installation packet size. During implementation, attention should be paid to the timeliness of real-time network performance data collection and the accuracy of installation packet size acquisition to ensure that the prediction results can truly reflect the retransmission requirements of the current upgrade.

[0022] Step S4: Set the version upgrade timeliness sliding window to capture the version upgrade efficiency data analysis in the version upgrade retransmission analysis set, obtain the standard installation package transmission rate for the current power grid equipment to perform version upgrade, and calculate the theoretical transmission time of the installation package by the size of the installation package for the current power grid equipment to perform version upgrade and the standard installation package transmission rate. Step S4-1: Set the window size of the version upgrade timeout sliding window to M times the number of times the currently connected power grid equipment is upgraded, where M is a positive integer and the value of M is less than the total number of historical version upgrade events included in the version upgrade retransmission analysis set; set the sliding step size to 1 version upgrade event. Step S4-2: Select the historical version upgrade event with the latest timestamp in the version upgrade retransmission analysis set as the starting point, and extract the version upgrade efficiency data corresponding to M consecutive historical version upgrade events to form the initial version upgrade timeliness analysis window dataset. Step S4-3: For each historical version upgrade efficiency data in the initial version upgrade timeliness analysis window dataset, extract the installation package size and actual transmission time of the corresponding historical version upgrade, calculate the single transmission rate, i.e., the ratio of the installation package size to the actual transmission time; calculate the arithmetic mean of all single transmission rates to obtain the standard installation package transmission rate. Step S4-4: Obtain the size of the installation package for the current power grid equipment version upgrade, divide the size of the installation package by the standard installation package transmission rate to obtain the theoretical transmission time of the installation package. The theoretical transmission time of the installation package is the estimated time required for the installation package for the current power grid equipment version upgrade to complete the transmission at the standard installation package transmission rate without retransmission. In practice, by setting a sliding window for version upgrade timeliness, the latest consecutive upgrade data from the historical set is selected to form an analysis window. The average value of the single transmission rate within the window is calculated as the standard transmission rate. The theoretical transmission time is then obtained by dividing the current installation package size by the standard rate. The key is that the window size needs to be reasonably set according to the total amount of historical data, so as to ensure that there are enough samples to reflect the recent transmission patterns, while avoiding the introduction of outdated data due to an excessively large window, which would affect the timeliness of the standard rate.

[0023] Step S5: Obtain the predicted retransmission duration by combining the predicted retransmission data volume with the standard installation package transmission rate. Add the theoretical transmission duration of the installation package to the predicted retransmission duration to obtain the first predicted installation package transmission duration. Real-time statistics are collected on the packet loss rate of the current power grid equipment undergoing version upgrade. Combined with the analysis of the transmission data volume of the current power grid equipment undergoing version upgrade, the second predicted installation package transmission duration is obtained. Based on the first and second predicted installation package transmission durations, the timeliness of the power grid equipment version upgrade is detected. Step S5-1: Divide the predicted retransmission data amount obtained in step S3 by the standard installation packet transmission rate obtained in step S4 to obtain the predicted retransmission duration; sum the theoretical transmission duration of the installation packet obtained in step S4 with the predicted retransmission duration to obtain the first predicted installation packet transmission duration. Step S5-2: During the current power grid equipment version upgrade process, real-time network performance parameters are collected at fixed time intervals. The real-time network performance parameters include signal strength, transmission bandwidth and delay duration. At the same time, the total number of data packets sent and the number of data packets successfully received are recorded. Step S5-3: Calculate the real-time packet loss rate based on real-time network performance parameters; extract the amount of data that has been transmitted, compare the amount of data that has been transmitted with the total size of the installation package to obtain the transmission progress ratio, where the transmission progress ratio is specifically the proportion of the amount of data that has been transmitted to the total size of the installation package, and obtain the remaining untransmitted proportion based on the transmission progress ratio. Step S5-4: Subtract the number of successfully received data packets from the total number of data packets sent to obtain the amount of data retransmitted in real time; and calculate the actual average transmission rate, specifically by using the ratio of the amount of data transmitted to the duration of transmission. Step S5-5: Divide the real-time retransmission data volume by the proportion of the remaining untransmitted data to obtain the retransmission data volume that needs to be retransmitted when the current power grid equipment is upgraded. This is recorded as the real-time retransmission data volume. Steps S5-6: Divide the real-time retransmission data volume by the actual average transmission rate to obtain the real-time retransmission duration; multiply the total size of the installation package by the proportion of the remaining untransmitted data to obtain the remaining untransmitted data volume; divide the remaining untransmitted data volume by the actual average transmission rate to obtain the remaining data transmission duration. Step S5-7: Add the real-time retransmission duration, the remaining data transmission duration, and the current transmission duration to obtain the second predicted installation package transmission duration; perform a weighted summation operation on the first predicted installation package transmission duration and the second predicted installation package transmission duration to obtain the total transmission duration of the current power grid equipment version upgrade; compare the total transmission duration of the current power grid equipment version upgrade with the preset installation package transmission duration. The specific process is as follows: When the total transmission time exceeds the preset transmission time of the installation package, it is judged as an abnormal version upgrade timeliness and a warning signal is issued. When the total transmission time does not exceed the preset installation package transmission time, it is determined that the version upgrade is timely and the monitoring of the power grid equipment version upgrade continues. In practice, the predicted retransmission duration is first obtained by predicting the amount of retransmitted data and the standard rate, and then added to the theoretical transmission duration to obtain the first predicted duration. During the upgrade process, network parameters and transmission data are collected in real time to calculate the real-time packet loss rate, transmission progress, actual rate, etc., and then the real-time retransmission duration and the remaining transmission duration are obtained. These are added together to obtain the second predicted duration. The two are weighted and summed and compared with the preset duration to determine the timeliness. In the implementation, it is necessary to balance the frequency of real-time data collection and the system load to ensure that the prediction results can be updated in a timely manner and that a warning can be issued quickly when the total duration exceeds the limit.

[0024] Example 2, as Figure 2 As shown, the present invention provides an OTA upgrade timeliness detection system based on big data analysis. The OTA upgrade timeliness detection system includes a historical upgrade data processing module, a network performance correlation analysis module, a retransmission data volume prediction module, a standard transmission parameter calculation module, and an upgrade timeliness detection module. The historical upgrade data processing module is used to acquire historical version upgrade efficiency data of power grid equipment and construct a version upgrade retransmission analysis set; the network performance correlation analysis module is used to determine the packet loss rate and the relationship between network performance data and retransmission data volume through historical network performance data analysis; the retransmission data volume prediction module is used to predict the retransmission data volume of the current upgrade by combining the size of the current upgrade installation package and the relationship between network performance and retransmission data volume; the standard transmission parameter calculation module is used to analyze historical upgrade data through a sliding window to calculate the standard installation package transmission rate and the theoretical transmission time of the installation package; the upgrade timeliness detection module is used to detect the timeliness of power grid equipment version upgrades through fusion analysis of predicted transmission time. The output of the historical upgrade data processing module is electrically connected to the input of the network performance correlation analysis module; the output of the network performance correlation analysis module is electrically connected to the input of the retransmission data volume prediction module; the output of the retransmission data volume prediction module is electrically connected to the input of the standard transmission parameter calculation module; and the output of the standard transmission parameter calculation module is electrically connected to the upgrade timeliness detection module. The historical upgrade data processing module includes a historical data acquisition unit and a retransmission analysis set construction unit. The historical data acquisition unit is used to extract network performance data, installation package size and retransmission data volume during the historical version upgrade process from the power grid equipment upgrade log system. The retransmission analysis set construction unit is used to associate the extracted historical data according to a single upgrade event and construct a version upgrade retransmission analysis set with the upgrade start timestamp as the key. The network performance correlation analysis module includes a historical packet loss rate analysis unit and a performance retransmission relationship modeling unit. The historical packet loss rate analysis unit is used to calculate the packet loss rate of each historical upgrade based on the total number of data packets sent and the number of successfully received data packets in the historical version upgrade. The performance retransmission relationship modeling unit is used to establish the relationship between network performance data and retransmission data volume through the mapping relationship between historical network performance fluctuation characteristics and packet loss rate. The retransmission data volume prediction module includes a current installation package information acquisition unit and a predicted retransmission volume calculation unit; the current installation package information acquisition unit is used to acquire the size of the installation package for the current power grid equipment version upgrade; the predicted retransmission volume calculation unit is used to calculate the predicted retransmission data volume using the packet loss rate of the current upgrade and the installation package size. The standard transmission parameter calculation module includes a sliding window data analysis unit and a theoretical transmission duration determination unit; the sliding window data analysis unit is used to extract historical upgrade data and calculate the standard installation package transmission rate; the theoretical transmission duration determination unit is used to calculate the theoretical transmission duration of the installation package based on the current installation package size and the standard transmission rate; The upgrade timeliness detection module includes a predicted duration fusion unit and a timeliness status determination unit. The predicted duration fusion unit is used to perform a weighted summation of the first predicted installation package transmission time and the second predicted installation package transmission time to obtain the total transmission time of the current upgrade. The timeliness status determination unit is used to compare the total transmission time of the current upgrade with the preset installation package transmission time, determine the upgrade timeliness status, and issue a corresponding signal.

[0025] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for detecting the timeliness of OTA upgrades based on big data analysis, characterized in that: The OTA upgrade timeliness detection method includes the following steps: Step S1: Obtain historical version upgrade efficiency data of power grid equipment to construct a version upgrade retransmission analysis set. The version upgrade efficiency data includes network performance data, installation package size and retransmission data volume. Step S2: Obtain the network performance data of the current power grid equipment undergoing version upgrade in real time, and record it as real-time network performance data. Analyze the network performance data in the version upgrade retransmission analysis set to obtain the packet loss rate during each historical version upgrade. Analyze the packet loss rate and the retransmission data volume of the corresponding historical version upgrade to obtain the relationship between network performance data and retransmission data volume. Step S3: Obtain the size of the installation package for the current power grid equipment to upgrade the version, and combine it with the analysis of the relationship between network performance data and retransmission data volume to obtain the retransmission data volume for the current power grid equipment to upgrade the version, which is denoted as the predicted retransmission data volume. Step S4: Set the version upgrade timeliness sliding window to capture the version upgrade efficiency data analysis in the version upgrade retransmission analysis set, obtain the standard installation package transmission rate for the current power grid equipment to perform version upgrade, and calculate the theoretical transmission time of the installation package by the size of the installation package for the current power grid equipment to perform version upgrade and the standard installation package transmission rate. Step S5: Obtain the predicted retransmission duration by combining the predicted retransmission data volume with the standard installation package transmission rate. Add the theoretical transmission duration of the installation package to the predicted retransmission duration to obtain the first predicted installation package transmission duration. Real-time statistics are collected on the packet loss rate of the current power grid equipment undergoing version upgrades. Combined with the analysis of the transmission data volume of the current power grid equipment undergoing version upgrades, the second predicted installation package transmission duration is obtained. Based on the first and second predicted installation package transmission durations, the timeliness of the power grid equipment version upgrade is checked.

2. The OTA upgrade timeliness detection method based on big data analysis according to claim 1, characterized in that: The specific steps of step S1 are as follows: Step S1-1: Connect to the upgrade log system of the power grid equipment through the intelligent terminal platform, and extract the version upgrade efficiency data generated during the historical version upgrade process of the power grid equipment. The version upgrade efficiency data is specifically log record information, including network performance data, installation package size, and retransmission data volume. The network performance data is represented as network performance parameters; the installation package size is represented as the number of bytes in the installation package; and the retransmission data volume is represented as the statistical value of retransmission data packets. Step S1-2: Perform field matching on the extracted log information, associate network performance parameters, installation package byte count, and retransmission data packet statistics by single upgrade event, select the upgrade start timestamp as the key, select version upgrade efficiency data as the value, and construct a version upgrade retransmission analysis set.

3. The OTA upgrade timeliness detection method based on big data analysis according to claim 2, characterized in that: The specific steps of step S2 are as follows: Step S2-1: Before the current power grid equipment starts the version upgrade, set a network performance detection window with a duration of T, and collect network performance parameter sampling values ​​within the window at fixed time intervals. The network performance parameters include signal strength, transmission bandwidth and delay duration. Step S2-2: Calculate the arithmetic mean of the sampled values ​​for each network performance parameter collected, and record it as the baseline value of the corresponding network performance parameter; The difference between the parameter value and the baseline value at each sampling time is calculated. The absolute values ​​of all differences are summed and divided by the total number of sampling times to obtain the fluctuation coefficient of each network performance parameter. The fluctuation coefficient is represented by the value obtained by normalizing the calculation results. The real-time network performance fluctuation characteristics are obtained by weighted fusion calculation. Step S2-3: Extract network performance parameter sampling data corresponding to each historical record from the version upgrade retransmission analysis set, and calculate the network performance fluctuation characteristics of each historical version upgrade according to the methods of steps S2-1 to S2-2; at the same time, retrieve the total number of data packets sent and the number of successfully received data packets in the historical record of the version upgrade process, and calculate the packet loss rate of each historical version upgrade by subtracting the number of successfully received data packets from the total number of data packets sent and then dividing it by the total number of data packets sent. Step S2-4: Select historical network performance fluctuation characteristics as input variables and select the packet loss rate corresponding to the historical network performance fluctuation characteristics as output variables. Construct a neural network training sample set and obtain the mapping relationship between network performance fluctuation characteristics and packet loss rate through model training. Step S2-5: Using the product of the total number of data packets sent and the packet loss rate in the historical version upgrade process, the amount of retransmitted data is obtained, that is, the mapping relationship between network performance fluctuation characteristics and the amount of retransmitted data is obtained.

4. The OTA upgrade timeliness detection method based on big data analysis according to claim 3, characterized in that: In step S3, the real-time network performance fluctuation characteristics are used as the input for the mapping relationship between network performance fluctuation characteristics and retransmitted data volume, and the packet loss rate of the current connected power grid equipment upgrade is output. The size of the installation package for upgrading the current connected power grid equipment is obtained through the intelligent terminal platform. The packet loss rate of the current connected power grid equipment upgrade is multiplied by the size of the installation package for the current connected power grid equipment upgrade to obtain the amount of retransmitted data for the current power grid equipment to perform version upgrade, which is denoted as the predicted retransmitted data amount.

5. The OTA upgrade timeliness detection method based on big data analysis according to claim 4, characterized in that: The specific steps of step S4 are as follows: Step S4-1: Set the window size of the version upgrade timeout sliding window to M times the number of times the currently connected power grid equipment is upgraded, where M is a positive integer and the value of M is less than the total number of historical version upgrade events included in the version upgrade retransmission analysis set; set the sliding step size to 1 version upgrade event. Step S4-2: Select the historical version upgrade event with the latest timestamp in the version upgrade retransmission analysis set as the starting point, and extract the version upgrade efficiency data corresponding to M consecutive historical version upgrade events to form the initial version upgrade timeliness analysis window dataset. Step S4-3: For each historical version upgrade efficiency data in the initial version upgrade timeliness analysis window dataset, extract the installation package size and actual transmission time of the corresponding historical version upgrade, calculate the single transmission rate, i.e., the ratio of the installation package size to the actual transmission time; calculate the arithmetic mean of all single transmission rates to obtain the standard installation package transmission rate. Step S4-4: Obtain the size of the installation package for the current power grid equipment version upgrade, divide the size of the installation package by the standard installation package transmission rate to obtain the theoretical transmission time of the installation package. The theoretical transmission time of the installation package is the estimated time required for the installation package for the current power grid equipment version upgrade to complete the transmission at the standard installation package transmission rate without retransmission.

6. The OTA upgrade timeliness detection method based on big data analysis according to claim 5, characterized in that: The specific steps of step S5 are as follows: Step S5-1: Divide the predicted retransmission data amount obtained in step S3 by the standard installation packet transmission rate obtained in step S4 to obtain the predicted retransmission duration; sum the theoretical transmission duration of the installation packet obtained in step S4 with the predicted retransmission duration to obtain the first predicted installation packet transmission duration. Step S5-2: During the current power grid equipment version upgrade process, real-time network performance parameters are collected at fixed time intervals. The real-time network performance parameters include signal strength, transmission bandwidth and delay duration. At the same time, the total number of data packets sent and the number of data packets successfully received are recorded.

7. The OTA upgrade timeliness detection method based on big data analysis according to claim 6, characterized in that: Step S5 also includes: Step S5-3: Calculate the real-time packet loss rate based on real-time network performance parameters; extract the amount of data that has been transmitted, compare the amount of data that has been transmitted with the total size of the installation package to obtain the transmission progress ratio, where the transmission progress ratio is specifically the proportion of the amount of data that has been transmitted to the total size of the installation package, and obtain the remaining untransmitted proportion based on the transmission progress ratio. Step S5-4: Subtract the number of successfully received data packets from the total number of data packets sent to obtain the amount of data retransmitted in real time; and calculate the actual average transmission rate, specifically by using the ratio of the amount of data transmitted to the duration of transmission. Step S5-5: Divide the real-time retransmission data volume by the proportion of the remaining untransmitted data to obtain the retransmission data volume that needs to be retransmitted when the current power grid equipment is upgraded. This is recorded as the real-time retransmission data volume. Steps S5-6: Divide the real-time retransmission data volume by the actual average transmission rate to obtain the real-time retransmission duration; multiply the total size of the installation package by the proportion of the remaining untransmitted data to obtain the remaining untransmitted data volume; divide the remaining untransmitted data volume by the actual average transmission rate to obtain the remaining data transmission duration. Step S5-7: Add the real-time retransmission duration, the remaining data transmission duration, and the current transmission duration to obtain the second predicted installation package transmission duration; perform a weighted summation operation on the first predicted installation package transmission duration and the second predicted installation package transmission duration to obtain the total transmission duration of the current power grid equipment version upgrade; compare the total transmission duration of the current power grid equipment version upgrade with the preset installation package transmission duration. The specific process is as follows: When the total transmission time exceeds the preset transmission time of the installation package, it is judged as an abnormal version upgrade timeliness and a warning signal is issued. If the total transmission time does not exceed the preset installation package transmission time, it is determined that the version upgrade is timely and the monitoring of the power grid equipment version upgrade continues.

8. An OTA upgrade timeliness detection system based on big data analysis, which is applied to the OTA upgrade timeliness detection method based on big data analysis as described in any one of claims 1-7, characterized in that: The OTA upgrade timeliness detection system includes a historical upgrade data processing module, a network performance correlation analysis module, a retransmission data volume prediction module, a standard transmission parameter calculation module, and an upgrade timeliness detection module. The historical upgrade data processing module is used to acquire historical version upgrade efficiency data of power grid equipment and construct a version upgrade retransmission analysis set. The network performance correlation analysis module is used to determine the packet loss rate and the relationship between network performance data and retransmission data volume through historical network performance data analysis; the retransmission data volume prediction module is used to predict the retransmission data volume of the current upgrade by combining the size of the current upgrade installation package and the relationship between network performance and retransmission data volume; the standard transmission parameter calculation module is used to calculate the standard installation package transmission rate and the theoretical transmission time of the installation package by analyzing historical upgrade data through a sliding window. The upgrade timeliness detection module is used to detect the timeliness of power grid equipment version upgrades through fusion analysis of predicted transmission duration.

9. The OTA upgrade timeliness detection system based on big data analysis according to claim 8, characterized in that: The historical upgrade data processing module includes a historical data acquisition unit and a retransmission analysis set construction unit. The historical data acquisition unit is used to extract network performance data, installation package size and retransmission data volume during the historical version upgrade process from the power grid equipment upgrade log system. The retransmission analysis set construction unit is used to associate the extracted historical data according to a single upgrade event and construct a version upgrade retransmission analysis set with the upgrade start timestamp as the key. The network performance correlation analysis module includes a historical packet loss rate analysis unit and a performance retransmission relationship modeling unit. The historical packet loss rate analysis unit is used to calculate the packet loss rate of each historical upgrade based on the total number of data packets sent and the number of successfully received data packets in the historical version upgrade. The performance retransmission relationship modeling unit is used to establish the relationship between network performance data and retransmission data volume through the mapping relationship between historical network performance fluctuation characteristics and packet loss rate. The retransmission data volume prediction module includes a current installation package information acquisition unit and a predicted retransmission volume calculation unit; the current installation package information acquisition unit is used to acquire the size of the installation package for the current power grid equipment version upgrade; the predicted retransmission volume calculation unit is used to calculate the predicted retransmission data volume using the packet loss rate of the current upgrade and the installation package size.

10. The OTA upgrade timeliness detection system based on big data analysis according to claim 8, characterized in that: The standard transmission parameter calculation module includes a sliding window data analysis unit and a theoretical transmission duration determination unit; the sliding window data analysis unit is used to extract historical upgrade data and calculate the standard installation package transmission rate; the theoretical transmission duration determination unit is used to calculate the theoretical transmission duration of the installation package based on the current installation package size and the standard transmission rate; The upgrade timeliness detection module includes a prediction duration fusion unit and a timeliness status determination unit; the prediction duration fusion unit is used to perform a weighted summation of the first predicted installation package transmission duration and the second predicted installation package transmission duration to obtain the total transmission duration of the current upgrade. The timeliness status determination unit is used to compare the total transmission time of the current upgrade with the preset installation package transmission time, determine the upgrade timeliness status, and issue a corresponding signal.

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