High-precision data acquisition, encryption and remote transmission system and method

Abstract

The invention relates to the technical field of data transmission, and particularly discloses a high-precision data acquisition, encryption and remote transmission system and method, and the method comprises the steps: S1, calculating a monitoring period based on the historical number of faults of a smart power grid, calculating the ratio of the monitoring period and the ratio change rate, and calculating the monitoring time based on an initial period; s2, calculating a voltage stable value of an initial period, calculating a voltage fluctuation range, presetting a fluctuation range threshold value, correcting the monitoring time based on the voltage fluctuation range to obtain correction time, and repeating execution until a satisfaction condition is met; and S3, calculating the duration time, and carrying out quantitative influence analysis based on the duration time and the maximum value of the voltage fluctuation range. According to the invention, a supervision strategy and an early warning mechanism for the intelligent power grid are designed, response and prevention are carried out in advance according to abnormal data characteristics of the power grid, and the operation stability of the power grid is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to a high-precision data acquisition, encryption, and remote transmission system and method. Background Art

[0002] Smart grid online management utilizes advanced information and communications technologies, the Internet of Things (IoT), big data analytics, and artificial intelligence to conduct real-time monitoring, optimize scheduling, and automate control of power generation, transmission, distribution, and consumption, thereby improving the safety, reliability, and sustainability of the power grid. By integrating various smart sensors, smart meters, and distributed energy management systems, this system enables digital and intelligent operation of the power system, providing critical support for efficient grid management and the integration of new energy sources.

[0003] With existing technologies, as the proportion of renewable energy generation increases, the challenges to grid stability also increase. Renewable energy generation equipment such as wind and photovoltaic power generation lack the rotational inertia of traditional synchronous generators, significantly weakening the grid's frequency regulation capabilities. The intermittent and fluctuating nature of renewable energy output leads to frequent voltage fluctuations in the grid, seriously impacting power quality. These fluctuations not only affect the normal operation of sensitive equipment but can also trigger more serious problems such as grid resonance. Due to the lack of effective regulatory strategies and early warning mechanisms in smart grid online management systems, anomalies in power data cannot be detected in a timely manner. Existing smart grid online management systems have incomplete monitoring and early warning mechanisms, and their ability to identify abnormal data features during power system operation is limited. They are often only able to passively handle faults after they occur, making it difficult to implement timely fault warnings and proactive defenses. Summary of the Invention

[0004] The purpose of the present invention is to provide a high-precision data acquisition, encryption, and remote transmission system and method to solve the above technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A high-precision data collection, encryption, and remote transmission method comprises the following steps: S1: Calculate a monitoring period T based on the number of faults N in the history of the smart grid, and obtain grid data during the monitoring period T. The grid data includes the electromotive force E of the generator set, the reactance X of the line, the reactive power Q, and the active power P; Calculate the ratio of the monitoring period B = P / Q and the rate of change of the ratio , if the ratio change rate S is greater than the preset change threshold S max , let the current monitoring period be the initial period, and calculate the monitoring time t based on the initial period, where B now Represents the ratio of the current monitoring period, B lastRepresents the ratio of the previous monitoring period to the current monitoring period; S2: Calculate the voltage stability value of the initial cycle , taking the end of the initial cycle as the starting point to obtain the voltage stability value V within the monitoring time t s , calculate the voltage fluctuation range , preset fluctuation range threshold U max , based on the voltage fluctuation range U, the monitoring time t is corrected, and the corrected monitoring time is recorded as the corrected time tx; Take the end of the monitoring time as the starting point to obtain the voltage stability value within the correction time tx, calculate the voltage fluctuation range, and repeat the above steps until the voltage fluctuation range U≤U max And the rate of change of the ratio within the corresponding correction time tx is S≤S max ; S3: Calculate duration , where I represents the number of correction times, tx i represents the i-th correction time; Get the maximum value Um of the voltage fluctuation range during the entire monitoring time, based on the duration T con And the maximum value Um of the voltage fluctuation range is quantitatively analyzed.

[0006] As a further solution of the present invention: in the step S4, based on the duration T con The methods for quantitative impact analysis of the maximum value Um of the voltage fluctuation range include: If duration 10≤T con ≤60, then it is recorded as second-level fluctuation. If T con >60, it is recorded as minute-level fluctuation; When the fluctuation is at the second level; If Um is in [-U sta ,U sta ], it is recorded as normal fluctuation and no processing is performed; If Um is in [-U sta ,U sta ], it is recorded as a type of abnormal fluctuation. When a type of abnormal fluctuation exists, the staff is prompted to check the transformer; When the price fluctuates at the minute level; If Um is in [-U sta ,U sta ], it is recorded as a type of abnormal fluctuation. When a type of abnormal fluctuation exists, the staff is prompted to check the transformer; If Um is in [-U sta ,U sta ], it is recorded as a Class II abnormal fluctuation, prompting the staff to carry out explosion-proof treatment on the capacitor bank; Among them, U sta Represents the preset standard fluctuation value.

[0007] As a further solution of the present invention: in the step S1, the method for calculating the monitoring period T based on the number of faults N in the fault history of the smart grid includes: Obtain the mean ΔT of the fault time interval of the smart grid ave , calculate the monitoring period .

[0008] As a further solution of the present invention: in the step S1, if the time interval between two smart grid failures is less than a preset determination threshold, it is recorded as one smart grid failure.

[0009] As a further solution of the present invention: in the step S1, the monitoring time t=T / N is calculated based on the initial cycle.

[0010] As a further solution of the present invention: in step S2, the method for correcting the monitoring time t based on the voltage fluctuation range U includes: Calculate correction time , where λ represents the preset unit correction coefficient.

[0011] As a further solution of the present invention: in step S3, if the duration T con If it is less than 10, it is recorded as normal fluctuation.

[0012] A high-precision data acquisition, encryption, and remote transmission system, comprising: Data acquisition module: Calculates monitoring period T based on the number of faults N in the fault history of the smart grid, and acquires grid data in the monitoring period T. The grid data includes the electromotive force E of the generator set, the reactance X of the line, the reactive power Q, and the active power P; Calculate the ratio of the monitoring period B = P / Q and the rate of change of the ratio , if the ratio change rate S is greater than the preset change threshold S max , let the current monitoring period be the initial period, and calculate the monitoring time t based on the initial period, where B now Represents the ratio of the current monitoring period, B last Represents the ratio of the previous monitoring period to the current monitoring period; Data processing module: calculate the voltage stability value of the initial cycle , taking the end of the initial cycle as the starting point to obtain the voltage stability value V within the monitoring time t s , calculate the voltage fluctuation range , preset fluctuation range threshold U max, based on the voltage fluctuation range U, the monitoring time t is corrected, and the corrected monitoring time is recorded as the corrected time tx; Take the end of the monitoring time as the starting point to obtain the voltage stability value within the correction time tx, calculate the voltage fluctuation range, and repeat the above steps until the voltage fluctuation range U≤U max And the rate of change of the ratio within the corresponding correction time tx is S≤S max ; Data analysis module: Calculate duration , where I represents the number of correction times, tx i represents the i-th correction time; Get the maximum value Um of the voltage fluctuation range during the entire monitoring time, based on the duration T con And the maximum value Um of the voltage fluctuation range is quantitatively analyzed.

[0013] The beneficial effects of the present invention are as follows: first, the monitoring period is calculated based on the historical number of failures of the smart grid. The more historical failures there are, the shorter the failure interval is, the smaller the monitoring period is, and the tighter the data acquisition of the smart grid is, so that the abnormality of the smart grid can be discovered more quickly, and a response method can be made. By periodically acquiring grid data, the grid data includes the electromotive force of the generator set, the reactance of the line, the reactive power and the active power, the stability of the current grid can be roughly judged, and then the ratio of active power to reactive power is calculated. This ratio result is used for grid stability assessment, and then the grid stability change is calculated by the ratio change rate. Because the stability of different grids is different, a reasonable judgment can only be made based on different times of the same grid, and a specific measurement standard cannot be given. When the ratio change rate is greater than the preset change threshold, it means that the current grid change is more drastic and further monitoring should be carried out.

[0014] Then, the voltage stability value is calculated to judge the voltage situation of the entire power grid at this time. The voltage is chosen as the reference value because it has a better reference. First of all, the voltage represents the effective value of the phase voltage of a node in the power grid. Its physical essence is the electromotive force intensity that drives the movement of charge. In the formula, it can play the role of voltage drop warning. When QX / P increases, it represents a sudden increase in reactive power demand or line overload, which will cause V to drop, indicating the risk of voltage imbalance. Therefore, the change of V in the formula directly reflects the energy balance state of the entire power grid system. However, it is not enough to provide a reason for subsequent judgment based solely on voltage fluctuations. If the judgment is not based on the actual voltage fluctuation range, it lacks rationality. Therefore, it is necessary to calculate the voltage fluctuation range to determine the actual situation of the power grid. The purpose of correcting the monitoring time is to be able to detect and warn of dangerous situations in a timely manner.

[0015] The duration is then calculated, which is used to determine the magnitude of the voltage change and serves as a basis for the final judgment. The maximum value of the voltage fluctuation range is used to reflect the intensity of the change. Combining these two points, the actual impact on the power grid can be determined and corresponding countermeasures can be taken. The present invention implements a method for high-precision acquisition of power grid data, thereby achieving efficient detection of abnormal power grid conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below with reference to the accompanying drawings.

[0017] Figure 1 The present invention is a flowchart of a high-precision data acquisition, encryption, and remote transmission system and method. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, the present invention is a high-precision data collection, encryption, and remote transmission method, comprising the following steps: S1: Calculate a monitoring period T based on the number of faults in the history of the smart grid N, and obtain grid data during the monitoring period T. The grid data includes the electromotive force E of the generator set, the reactance X of the line, the reactive power Q, and the active power P; Calculate the ratio of the monitoring period B = P / Q and the rate of change of the ratio , if the ratio change rate S is greater than the preset change threshold S max , let the current monitoring period be the initial period, and calculate the monitoring time t based on the initial period, where B now Represents the ratio of the current monitoring period, B last Represents the ratio of the previous monitoring period to the current monitoring period; S2: Calculate the voltage stability value of the initial cycle , taking the end of the initial cycle as the starting point to obtain the voltage stability value V within the monitoring time t s , calculate the voltage fluctuation range , preset fluctuation range threshold U max , based on the voltage fluctuation range U, the monitoring time t is corrected, and the corrected monitoring time is recorded as the corrected time tx; Take the end of the monitoring time as the starting point to obtain the voltage stability value within the correction time tx, calculate the voltage fluctuation range, and repeat the above steps until the voltage fluctuation range U≤U max And the rate of change of the ratio within the corresponding correction time tx is S≤S max ; S3: Calculate duration , where I represents the number of correction times, tx i represents the i-th correction time; Get the maximum value Um of the voltage fluctuation range during the entire monitoring time, based on the duration T con And the maximum value Um of the voltage fluctuation range is quantitatively analyzed.

[0020] It should be noted that during the operation of a smart grid, to ensure stable power supply, the monitoring period needs to be calculated based on the number of historical failures of the smart grid. Specifically, when the number of historical failures of a smart grid is high, it means that the grid has experienced relatively high frequency of failures over a period of time, and the failure intervals tend to be shortened accordingly.

[0021] The monitoring cycle is adjusted based on the number of historical faults. A shorter monitoring cycle allows for more timely data acquisition for the smart grid. More frequent data acquisition provides a more comprehensive understanding of the grid's operating status. Any abnormalities can be detected more quickly, buying time to implement effective countermeasures.

[0022] When periodically acquiring grid data, the electromotive force (EMF) of a generator set is a parameter that reflects its ability to generate electrical energy. Line reactance directly affects the loss and stability of electrical energy during transmission. Reactive power and active power are key indicators of grid operation, representing the portion of power within the grid that does not generate external work but affects voltage stability, and the portion of power that actually generates external work. By collecting and analyzing this data, we can roughly assess the current grid stability.

[0023] Further analysis is then required to calculate the ratio of active power to reactive power. This ratio reflects the balance between active and reactive power under different operating conditions of the power grid. However, simply knowing the magnitude of the ratio is not enough to accurately determine changes in power grid stability. Due to the varying structures and load characteristics of different power grids, their stability also varies. A single, universally applicable metric cannot be simply established. Instead, assessments should be made using the same power grid at different points in time, focusing on how the ratio changes over time, particularly the rate of change.

[0024] When the rate of change of the ratio exceeds the preset threshold, it indicates that the current grid operation status has undergone a significant change. This change may be caused by a sudden increase or decrease in grid load, a failure of power equipment, or other external factors. In this case, to ensure the safe and stable operation of the grid, further monitoring should be carried out.

[0025] The voltage stability value is then calculated to assess the voltage status of the entire grid at that moment. Voltage is chosen as a reference value because it provides a good indicator of grid operating conditions. First, voltage represents the effective value of the phase voltage at a node in the grid. Physically, it represents the electromotive force (EMF) that drives charge movement, which is directly related to the transmission and distribution of electrical energy within the grid. Within complex grid equations, voltage also plays an important role in providing early warning of voltage drops. In this equation, when QX / P increases, Q represents reactive power, X represents line reactance, and P represents active power. A rising ratio of this value may indicate a sudden increase in reactive power demand or an overloaded line. This situation causes a drop in voltage (V). A voltage drop indicates the risk of voltage imbalance in the grid, as voltage stability is crucial for ensuring the proper operation of grid equipment and a stable power supply.

[0026] Voltage fluctuations act as a barometer of the grid's operating status; any change could indicate adjustments or anomalies in the energy flow and distribution within the grid. However, voltage fluctuations alone are insufficient to provide sufficient and reasonable justification for subsequent judgments. Short-term, normal fluctuations may occur during grid operation. Failure to base judgments on the actual voltage fluctuation range can lead to erroneous assessments of the grid's operating status, resulting in inaccurate and inappropriate judgments. Therefore, after observing voltage fluctuations, it is necessary to further calculate the voltage fluctuation range to determine the actual state of the grid. This allows for differentiation between normal voltage fluctuations and abnormal fluctuations that could impact the safe and stable operation of the grid.

[0027] Furthermore, it's necessary to adjust the monitoring time. Properly setting the monitoring time ensures accurate grid data is acquired at appropriate intervals, allowing for timely detection of changes in grid operating conditions. This allows for early detection of anomalies, enabling proactive response and preventing further escalation and deterioration of the problem, thereby ensuring safe and reliable grid operation.

[0028] Calculating the duration is a key factor in accurately determining the magnitude of the voltage fluctuation and forms an important basis for comprehensive assessment and final decision-making. When a voltage fluctuation occurs in a power grid, simply knowing the range of the fluctuation is not enough; the duration of the fluctuation is also crucial. This is because varying durations indicate varying degrees of impact on grid equipment and the entire system. Furthermore, the maximum value of the voltage fluctuation range effectively reflects the magnitude of the voltage change. This maximum value, like the peak value of the voltage fluctuation, intuitively demonstrates the extent to which the voltage deviates from the normal range. A large maximum value indicates a significant voltage fluctuation, which can significantly impact sensitive equipment within the grid and even affect the stable operation of the entire grid.

[0029] Combining the above two points, namely, determining the magnitude of voltage changes by determining their duration, and understanding the intensity of changes by analyzing the maximum value of the voltage fluctuation range, if the voltage change is small in magnitude and short in duration, simple monitoring and recording may be sufficient to observe its subsequent developments. However, if the voltage change is large in magnitude, lasts for a long time, or has a high intensity, more proactive measures must be taken promptly, such as adjusting the grid's operating parameters and arranging equipment maintenance, to ensure the grid's safe and stable operation. This enables efficient inspection of abnormal grid conditions, improves the reliability and safety of grid operation, and provides strong technical support for ensuring the stability and quality of power supply.

[0030] Furthermore, it's important to note that data encryption and transmission security are crucial aspects of smart grid data acquisition and transmission. Since grid data contains a wealth of core information related to power system operation, any leakage or malicious tampering of this data could pose a serious threat to the safe and stable operation of the grid. To ensure data security, encryption algorithms are used at the data acquisition end to encrypt the raw data, converting sensitive grid data into ciphertext. This prevents unauthorized users from directly accessing and understanding the data's true content. Even if intercepted by criminals during data transmission, the data cannot be easily decrypted, effectively protecting the security and privacy of grid data.

[0031] Regarding data transmission, a secure and reliable communication channel is established to ensure that encrypted data is accurately transmitted to the data processing center. This communication channel utilizes a virtual private network (VPN) and firewall to prevent external network attacks and intrusions. Furthermore, the transmission process is monitored and verified in real time to ensure data integrity and consistency. If any anomalies or signs of data tampering are detected during transmission, the system will immediately issue an alarm and take appropriate measures, such as retransmitting the data or initiating emergency response procedures.

[0032] Furthermore, to further enhance data security, encryption keys are regularly updated and managed. The frequency of encryption key updates is adjusted based on actual security needs to ensure key security and effectiveness. Furthermore, strict security measures are implemented for key storage and management to prevent data security risks caused by key leaks.

[0033] The implementation of the above data collection, encryption and transmission measures provides a solid security guarantee for the data acquisition and analysis of the smart grid, ensures the accurate judgment of the grid operation status and the timely handling of abnormal situations, and effectively maintains the stable operation of the smart grid and the safe and reliable power supply.

[0034] In another preferred embodiment of the present invention, based on the duration T con The methods for quantitative impact analysis of the maximum value Um of the voltage fluctuation range include: Based on the duration T con The methods for quantitative impact analysis of the maximum value Um of the voltage fluctuation range include: If duration 10≤T con ≤60, then it is recorded as second-level fluctuation. If T con >60, it is recorded as minute-level fluctuation; When the fluctuation is at the second level; If Um is in [-U sta ,U sta ], it is recorded as normal fluctuation and no processing is performed; If Um is in [-U sta ,U sta ], it is recorded as a type of abnormal fluctuation. When a type of abnormal fluctuation exists, the staff is prompted to check the transformer; When the price fluctuates at the minute level; If Um is in [-U sta ,U sta ], it is recorded as a type of abnormal fluctuation. When a type of abnormal fluctuation exists, the staff is prompted to check the transformer; If Um is in [-U sta ,U sta ], it is recorded as a Class II abnormal fluctuation, prompting the staff to carry out explosion-proof treatment on the capacitor bank; Among them, U sta Represents the preset standard fluctuation value.

[0035] In another preferred embodiment of the present invention, a method for calculating a monitoring period T based on the number of faults N in the smart grid fault history includes: Obtain the mean ΔT of the fault time interval of the smart grid ave , calculate the monitoring period .

[0036] It can be understood that the mean value of the fault time interval of the smart grid ΔT can be seen from the formula ave The smaller the value, the shorter the monitoring period and the more frequent the monitoring. The more faults occur in the smart grid, the shorter the monitoring period and the more frequent the monitoring.

[0037] In another preferred embodiment of the present invention, if the time interval between two smart grid failures is less than a preset determination threshold, it is recorded as one smart grid failure.

[0038] It's important to note that in actual smart grid operation, failures may not be completely independent events. In some cases, the occurrence of a first failure may have a certain impact on the grid system, causing changes in the grid's operating state and, in turn, triggering a second failure. For example, a first failure may cause performance degradation of some equipment, alter the load distribution of a line, or cause anomalies in the system's protection devices. These changes may trigger a second failure within a short period of time. If the time interval between two failures is short, less than a preset judgment threshold, then the two failures can be considered closely related, representing different stages or manifestations of the same fault event, and therefore it is reasonable to record them as a single smart grid failure.

[0039] In another preferred embodiment of the present invention, the monitoring time t=T / N is calculated based on the initial period.

[0040] It should be noted that, because it is necessary to further divide the monitoring time into smaller ones, the initial monitoring period is divided again to obtain the monitoring time. Consistent with common sense, the more historical faults there are, the smaller the divided monitoring time.

[0041] In another preferred embodiment of the present invention, the method for correcting the monitoring time t based on the voltage fluctuation range U includes: Calculate correction time , where λ represents the preset unit correction coefficient.

[0042] It can be understood that the larger the voltage fluctuation range U is, the shorter the correction time is.

[0043] In another preferred embodiment of the present invention, if the duration T con If it is less than 10, it is recorded as normal fluctuation.

[0044] It's worth noting that in the actual operation of smart grids, short-term voltage fluctuations are often caused by temporary, minor interference factors, such as the normal startup and shutdown of power equipment and transient changes in small loads. In these cases, the voltage fluctuations are typically small and short-lived, with minimal impact on the overall stability of the grid and the safe operation of equipment.

[0045] A high-precision data acquisition, encryption, and remote transmission system, comprising: Data acquisition module: Calculates monitoring period T based on the number of faults N in the fault history of the smart grid, and acquires grid data in the monitoring period T. The grid data includes the electromotive force E of the generator set, the reactance X of the line, the reactive power Q, and the active power P; Calculate the ratio of the monitoring period B = P / Q and the rate of change of the ratio , if the ratio change rate S is greater than the preset change threshold S max , let the current monitoring period be the initial period, and calculate the monitoring time t based on the initial period, where B now Represents the ratio of the current monitoring period, B last Represents the ratio of the previous monitoring period to the current monitoring period; Data processing module: calculate the voltage stability value of the initial cycle , taking the end of the initial cycle as the starting point to obtain the voltage stability value V within the monitoring time t s , calculate the voltage fluctuation range , preset fluctuation range threshold U max , based on the voltage fluctuation range U, the monitoring time t is corrected, and the corrected monitoring time is recorded as the corrected time tx; Take the end of the monitoring time as the starting point to obtain the voltage stability value within the correction time tx, calculate the voltage fluctuation range, and repeat the above steps until the voltage fluctuation range U≤U max And the rate of change of the ratio within the corresponding correction time tx is S≤S max ; Data analysis module: Calculate duration , where I represents the number of correction times, tx i represents the i-th correction time; Get the maximum value Um of the voltage fluctuation range during the entire monitoring time, based on the duration T con And the maximum value Um of the voltage fluctuation range is quantitatively analyzed.

[0046] The above is a detailed description of an embodiment of the present invention. However, the content is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A high-precision data acquisition, encryption, and remote transmission method, characterized in that: The following steps are involved: S1: Calculate a monitoring period T based on the number of faults N in the history of the smart grid, and obtain grid data during the monitoring period T. The grid data includes the electromotive force E of the generator set, the reactance X of the line, the reactive power Q, and the active power P; Calculate the ratio of the monitoring period B = P / Q and the rate of change of the ratio , if the ratio change rate S is greater than the preset change threshold S max , let the current monitoring period be the initial period, and calculate the monitoring time t based on the initial period, where B now Represents the ratio of the current monitoring period, B last Represents the ratio of the previous monitoring period to the current monitoring period; S2: Calculate the voltage stability value of the initial cycle , taking the end of the initial cycle as the starting point to obtain the voltage stability value V within the monitoring time t s , calculate the voltage fluctuation range , preset fluctuation range threshold U max , based on the voltage fluctuation range U, the monitoring time t is corrected, and the corrected monitoring time is recorded as the corrected time tx; Take the end of the monitoring time as the starting point to obtain the voltage stability value within the correction time tx, calculate the voltage fluctuation range, and repeat the above steps until the voltage fluctuation range U≤U max And the rate of change of the ratio within the corresponding correction time tx is S≤S max ; S3: Calculate duration , where I represents the number of correction times, tx i represents the i-th correction time; Get the maximum value Um of the voltage fluctuation range during the entire monitoring time, based on the duration T con And the maximum value Um of the voltage fluctuation range is quantitatively analyzed.

2. A high-precision data acquisition, encryption, and remote transmission method according to claim 1, characterized in that: In step S4, based on the duration T con The methods for quantitative impact analysis of the maximum value Um of the voltage fluctuation range include: If duration 10≤T con ≤60, then it is recorded as second-level fluctuation. If T con >60, it is recorded as minute-level fluctuation; When the fluctuation is at the second level; If Um is in [-U sta ,U sta ], it is recorded as normal fluctuation and no processing is performed; If Um is in [-U sta ,U sta ], it is recorded as a type of abnormal fluctuation. When a type of abnormal fluctuation exists, the staff is prompted to check the transformer; When the price fluctuates at the minute level; If Um is in [-U sta ,U sta ], it is recorded as a type of abnormal fluctuation. When a type of abnormal fluctuation exists, the staff is prompted to check the transformer; If Um is in [-U sta ,U sta ], it is recorded as a Class II abnormal fluctuation, prompting the staff to carry out explosion-proof treatment on the capacitor bank; Among them, U sta Represents the preset standard fluctuation value.

3. A high-precision data acquisition, encryption, and remote transmission method according to claim 1, characterized in that: In step S1, the method for calculating the monitoring period T based on the number of faults N in the smart grid fault history includes: Obtaining the mean time interval between smart grid failures , calculate the monitoring period .

4. A high-precision data acquisition, encryption, and remote transmission method according to claim 1, characterized in that: In the step S1, if the time interval between two smart grid failures is less than a preset determination threshold, it is recorded as one smart grid failure.

5. A high-precision data acquisition, encryption, and remote transmission method according to claim 1, characterized in that: In step S1 , a monitoring time t=T / N is calculated based on an initial period.

6. A high-precision data acquisition, encryption, and remote transmission method according to claim 1, characterized in that: In step S2, the method for correcting the monitoring time t based on the voltage fluctuation range U includes: Calculate correction time , where λ represents the preset unit correction coefficient.

7. A high-precision data acquisition, encryption, and remote transmission method according to claim 1, characterized in that: In step S3, if the duration T con If it is less than 10, it is recorded as normal fluctuation.

8. A high-precision data acquisition, encryption, and remote transmission system, characterized in that: include: Data acquisition module: Calculates monitoring period T based on the number of faults N in the fault history of the smart grid, and acquires grid data in the monitoring period T. The grid data includes the electromotive force E of the generator set, the reactance X of the line, the reactive power Q, and the active power P; Calculate the ratio of the monitoring period B = P / Q and the rate of change of the ratio , if the ratio change rate S is greater than the preset change threshold S max , let the current monitoring period be the initial period, and calculate the monitoring time t based on the initial period, where B now Represents the ratio of the current monitoring period, B last Represents the ratio of the previous monitoring period to the current monitoring period; Data processing module: calculate the voltage stability value of the initial cycle , taking the end of the initial cycle as the starting point to obtain the voltage stability value V within the monitoring time t s , calculate the voltage fluctuation range , preset fluctuation range threshold U max , based on the voltage fluctuation range U, the monitoring time t is corrected, and the corrected monitoring time is recorded as the corrected time tx; Take the end of the monitoring time as the starting point to obtain the voltage stability value within the correction time tx, calculate the voltage fluctuation range, and repeat the above steps until the voltage fluctuation range U≤U max And the rate of change of the ratio within the corresponding correction time tx is S≤S max ; Data analysis module: Calculate duration , where I represents the number of correction times, tx i represents the i-th correction time; Get the maximum value Um of the voltage fluctuation range during the entire monitoring time, based on the duration T con And the maximum value Um of the voltage fluctuation range is quantitatively analyzed.

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