Side-based power transmission equipment monitoring method and system

Abstract

The embodiment of the invention discloses a side-based power transmission equipment monitoring method and system. The method comprises the following steps: monitoring indexes of target power transmission equipment through a sensor, and obtaining operation data of the target power transmission equipment; determining a standard score of the operation data, and determining a comprehensive health score of the target power transmission equipment according to the standard score of the operation data, the adaptive deviation correction item of the operation data and the proportionality coefficient; according to the comprehensive health score, the standardization score and the trend factor of the target power transmission equipment, determining a comprehensive score index of the target power transmission equipment; and determining the equipment operation state of the target power transmission equipment according to the comprehensive scoring index of the target power transmission equipment. According to the scheme, the equipment operation state of the target power transmission equipment can be accurately evaluated based on the operation data of the target power transmission equipment, accurate evaluation is performed by considering the relevance of the operation data between different sampling periods, and the accuracy and reliability of the operation state evaluation of the target power transmission equipment are improved.

Description

Technical Field

[0001] The present application relates to the technical field of power equipment monitoring, and in particular to a side-based power transmission equipment monitoring method and system. Background Art

[0002] Transmission equipment refers to equipment responsible for transmission, protection, control and other functions in the transmission system, mainly including substations, transmission lines, transformers, circuit breakers, disconnectors, etc. Transmission equipment plays a vital role in the power system. They carry the transmission of electricity, protect the power system and control the flow of current. Substations transform high-voltage electricity into low-voltage electricity suitable for the power supply system, transmission lines transmit current from power plants to different places, transformers adjust voltage and current, circuit breakers protect the safety of equipment and personnel, and disconnectors cut off power lines during maintenance. The status monitoring of transmission equipment is crucial to the safe operation of the power system. Edge-based transmission equipment monitoring refers to real-time monitoring and data analysis of transmission equipment to evaluate its operating status and potential failures to ensure the stable operation and safety of the equipment.

[0003] In the current edge-based transmission equipment monitoring scheme, whether the operating status of the transmission equipment is normal is judged only based on whether the voltage and current of the transmission equipment exceed certain thresholds. This is somewhat one-sided and cannot accurately and comprehensively evaluate the operating status of the transmission equipment, and has low accuracy. Summary of the invention

[0004] The embodiments of the present application provide a method and system for monitoring power transmission equipment based on the edge, so as to comprehensively determine the operating status of the target power transmission equipment.

[0005] According to one aspect of the present application, a method for monitoring power transmission equipment based on a side is provided, the method comprising:

[0006] Monitoring the indicators of the target power transmission equipment through sensors to obtain the operating data of the target power transmission equipment; wherein the operating data includes at least one of the current, voltage, temperature and vibration of the target power transmission equipment;

[0007] Determine a standardized score of the operating data, and determine a comprehensive health score of the target power transmission equipment according to the standardized score of the operating data, the adaptive deviation correction item of the operating data, and the proportionality coefficient; wherein the standardized score reflects the degree of deviation of the operating data of each sampling period from the average value; and the adaptive deviation correction item reflects the degree of deviation of the operating data of the current sampling period from the operating data of each previous sampling period;

[0008] Determining a comprehensive score index of the target power transmission equipment according to the comprehensive health score, the standardized score, and the trend factor of the target power transmission equipment;

[0009] The device operating state of the target power transmission device is determined according to the comprehensive scoring index of the target power transmission device.

[0010] According to one aspect of the present application, a side-based power transmission equipment monitoring system is provided, the system comprising:

[0011] A server, a data acquisition module, a side processing module, and an evaluation response module; the data acquisition module, the side processing module, and the evaluation response module are connected to the server respectively;

[0012] The data acquisition module acquires the operating data of the target power transmission equipment, the side processing module performs local processing on the operating data transmitted by the data acquisition module, so as to perform real-time analysis on the operating status of the target power transmission equipment and generate a health status report of the target power transmission equipment, the evaluation response module evaluates the health status report of the power transmission equipment to form an evaluation result, and generates a maintenance strategy for the target power transmission equipment according to the evaluation result:

[0013] Among them, the side processing module includes a data receiving unit, a status analysis unit, and a local alarm unit. The data receiving unit receives the operation data from the data acquisition module, and the status analysis unit processes and analyzes the received operation data to form an analysis result to obtain the health status report of the target power transmission equipment. The local alarm unit triggers an early warning according to the health status report and prompts the maintenance personnel to handle it.

[0014] The technical solution of the embodiment of the present application monitors the indicators of the target power transmission equipment through a sensor to obtain the operating data of the target power transmission equipment; wherein the operating data includes at least one of the current, voltage, temperature and vibration of the target power transmission equipment; determines the standardized score of the operating data, and determines the comprehensive health score of the target power transmission equipment according to the standardized score of the operating data, the adaptive deviation correction item of the operating data and the proportional coefficient; wherein the standardized score reflects the degree of deviation of the operating data of each sampling period from the average value; the adaptive deviation correction item reflects the degree of deviation of the operating data of the current sampling period from the operating data of each previous sampling period; determines the comprehensive score index of the target power transmission equipment according to the comprehensive health score, standardized score and trend factor of the target power transmission equipment; determines the equipment operating state of the target power transmission equipment according to the comprehensive score index of the target power transmission equipment. The above scheme can accurately evaluate the equipment operating state of the target power transmission equipment based on the operating data of the target power transmission equipment, and accurately evaluates the correlation between the operating data of different sampling periods, thereby improving the accuracy and reliability of the evaluation of the operating state of the target power transmission equipment.

[0015] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A flow chart of a side-based power transmission equipment monitoring method provided in an embodiment of the present application;

[0018] Figure 2 A schematic diagram of the structure of a side-based power transmission equipment monitoring system provided in an embodiment of the present application;

[0019] Figure 3 A schematic diagram of the connection of the local alarm unit provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0021] It should be noted that the terms "first", "second", "third", "fourth", "actual", "preset", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] Figure 1A flowchart of a method for monitoring power transmission equipment based on the edge provided in an embodiment of the present application is applicable to the case of determining the operating status of the power transmission equipment. The method can be executed by a power transmission equipment monitoring device based on the edge, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0023] S110. Monitor indicators of a target power transmission device through a sensor to obtain operating data of the target power transmission device; wherein the operating data includes at least one of current, voltage, temperature, and vibration of the target power transmission device.

[0024] The sensor is a sensor corresponding to the operation data, for example, when the operation data is voltage, the sensor is a voltage sensor, when the operation data is current, the sensor is a current sensor, when the operation data is temperature, the sensor is a temperature sensor, and when the operation data is vibration, the sensor is a vibration sensor. The target power transmission equipment is the power transmission equipment that needs to be monitored, and the operation data includes at least one of current, voltage, temperature and vibration.

[0025] Exemplarily, during the operation of the target power transmission equipment, the indicators of the target power transmission equipment may be monitored by sensors to obtain the operation data of the target power transmission equipment, so as to evaluate the operation status of the target power transmission equipment based on the operation data.

[0026] S120. Determine a standardized score of the operating data, and determine a comprehensive health score of the target power transmission equipment according to the standardized score of the operating data, the adaptive deviation correction item of the operating data, and the proportional coefficient; wherein the standardized score reflects the degree of deviation of the operating data of each sampling period from the average value; and the adaptive deviation correction item reflects the degree of deviation of the operating data of the current sampling period from the operating data of each previous sampling period.

[0027] Among them, the standardized score of the operating data reflects the degree of deviation of the operating data of each sampling period relative to the average value, which can be determined by the average value of the operating data obtained in the current sampling period and the operating data obtained in the historical sampling period. The adaptive deviation correction term reflects the degree of deviation of the operating data of the current sampling period relative to the operating data of the previous sampling periods, which can be determined by the operating data obtained in the current sampling period and the operating data obtained in the previous sampling periods. The proportional coefficient can be a coefficient used for adjustment and is a constant term.

[0028] In the present application, the standardized score of the operating data obtained in the current sampling period can be determined, and the comprehensive health score of the target power transmission equipment can be determined based on the standardized score of the operating data, the adaptive deviation correction term and the proportional coefficient to reflect the operating status of the target power transmission equipment.

[0029] S130: Determine a comprehensive score index of the target power transmission equipment according to the comprehensive health score, the standardized score, and the trend factor of the target power transmission equipment.

[0030] Among them, the trend factor reflects the changing trend of the comprehensive health score with the sampling period. The comprehensive score index of the target power transmission equipment can be determined based on the comprehensive health score, standardized score and trend factor, so as to accurately reflect the comprehensive score index of the target power transmission equipment, and combine the current comprehensive health score of the target power transmission equipment, the degree of deviation of the operating data of each sampling period from the average value, and the changing trend of the comprehensive health score in each sampling period, and more comprehensively consider the data of all aspects of the target power transmission equipment to evaluate the operating status of the target power transmission equipment.

[0031] S140: Determine the equipment operating state of the target power transmission equipment according to the comprehensive scoring index of the target power transmission equipment.

[0032] Exemplarily, the comprehensive scoring index of the target power transmission equipment is quantitative data reflecting the operating status of the target power transmission equipment, and the equipment operating status of the target power transmission equipment can be determined according to the comprehensive scoring index of the target power transmission equipment.

[0033] The technical solution of the embodiment of the present application monitors the indicators of the target power transmission equipment through a sensor to obtain the operating data of the target power transmission equipment; wherein the operating data includes at least one of the current, voltage, temperature and vibration of the target power transmission equipment; determines the standardized score of the operating data, and determines the comprehensive health score of the target power transmission equipment according to the standardized score of the operating data, the adaptive deviation correction item of the operating data and the proportional coefficient; wherein the standardized score reflects the degree of deviation of the operating data of each sampling period from the average value; the adaptive deviation correction item reflects the degree of deviation of the operating data of the current sampling period from the operating data of each previous sampling period; determines the comprehensive score index of the target power transmission equipment according to the comprehensive health score, standardized score and trend factor of the target power transmission equipment; determines the equipment operating state of the target power transmission equipment according to the comprehensive score index of the target power transmission equipment. The above scheme can accurately evaluate the equipment operating state of the target power transmission equipment based on the operating data of the target power transmission equipment, and accurately evaluates the correlation between the operating data of different sampling periods, thereby improving the accuracy and reliability of the evaluation of the operating state of the target power transmission equipment.

[0034] As a non-limiting implementation, the method further includes:

[0035] For each existing power transmission device, determine key data of each existing power transmission device according to the device type and / or usage scenario of the existing power transmission device;

[0036] Dividing the key data into different data intervals, and determining the corresponding relationship between the data intervals and the proportional coefficients; wherein the key data in different data intervals are positively correlated with the proportional coefficients;

[0037] The process of determining the proportionality coefficient includes:

[0038] The key data of the target power transmission device is determined, and the proportional coefficient corresponding to the data interval where the key data of the target power transmission device is located is determined from the corresponding relationship as the proportional coefficient of the target power transmission device.

[0039] In the embodiment of the present application, the proportionality coefficient can be adaptively selected according to the actual situation, and specifically, it can be determined according to the actual type and application scenario of the target power transmission equipment. The critical data of each existing power transmission equipment can be determined in advance for each existing power transmission equipment according to the equipment type and / or usage scenario of the existing power transmission equipment, reflecting the criticality of the operating status of the existing power transmission equipment. The critical data is divided into different data intervals, and the corresponding relationship between each data interval and the proportionality coefficient is determined, so that the critical data of the different data intervals is positively correlated with the proportionality coefficient. Specifically, assuming that the existing power transmission equipment is a main transformer of a substation, and / or the existing power transmission equipment is used in a scenario with a high risk of failure, the critical data of the existing power transmission equipment is large. Assuming that the existing power transmission equipment is a regional power transmission line, and / or is used in a scenario with a moderate risk of failure, the critical data of the existing power transmission equipment is moderate. Assuming that the existing power transmission equipment is an indoor substation or standby equipment, and / or is used in an environment with a stable environment and a stable state, the critical data of the existing power transmission equipment is minimized. Different proportionality coefficients are set for critical data intervals of different sizes. For example, for the data interval with the largest critical data, the maximum proportional coefficient can be set. In this scenario, the stability of the existing power transmission equipment is crucial to the operation of the entire system. It is necessary to have a high sensitivity to the comprehensive health score, detect deviations in time and make maintenance responses, for example, the proportional coefficient is set to 18-20. Among them, a higher proportional coefficient makes the comprehensive health score more sensitive to the fluctuation of each deviation, and can quickly reflect the state changes of the existing power transmission equipment. For example, if there is a slight fluctuation in vibration or current, the comprehensive health score will drop significantly, reminding the monitoring personnel to take inspection measures quickly to prevent the risk of failure. For the data interval with moderate critical data, the proportional coefficient can be set moderately. For the transmission lines monitored daily, the operating environment of the existing power transmission equipment is relatively stable, but it is still necessary to maintain moderate sensitivity in order to detect abnormalities early and not be too sensitive to short-term fluctuations. For example, the proportional coefficient is set to 14-17. Among them, the moderate proportional coefficient can ensure a certain degree of sensitivity without fluctuating too frequently. The comprehensive health score of the existing power transmission equipment will not change frequently due to small short-term fluctuations, but when there are significant deviations in temperature, current, etc., the comprehensive health score will drop significantly, reminding maintenance personnel to pay attention. For the smallest data interval of the key data source, the proportional coefficient can be set to the minimum. In this case, the operating environment of the existing power transmission equipment is basically not affected by external factors, the fluctuation is small, the sensitivity requirement for the comprehensive health score is low, and more attention is paid to the long-term changes in the overall state of the existing power transmission equipment. For example, the proportional coefficient is set to 10-13. Among them, the smaller proportional coefficient makes the comprehensive health score less responsive to small deviations, which helps to maintain the stability of the score.When long-term data changes are small, the score will not change frequently due to short-term small fluctuations, but when there are large deviations in the status of existing transmission equipment, the comprehensive health score can still clearly reflect the changes.

[0040] In addition, in scenarios with significant seasonal load fluctuations, such as existing transmission equipment with large seasonal load fluctuations, the load of existing transmission equipment changes with the seasons. The comprehensive health score needs to adapt to the periodic fluctuations of the load, while avoiding frequent fluctuations caused by small seasonal fluctuations. The proportional coefficient is set to 15-18. The value of this proportional coefficient can take into account both load fluctuations and equipment stability requirements. The comprehensive health score of existing transmission equipment will not drop significantly during peak load periods, but it can still maintain a high sensitivity to severe fluctuations.

[0041] Exemplarily, for the target power transmission equipment, the key data of the target power transmission equipment can be determined, for example, the device type and / or application environment of the target power transmission equipment can be determined, and the key data of the target power transmission equipment can be determined according to the device type and / or application environment, and then the proportional coefficient corresponding to the key data of the target power transmission equipment can be determined from the corresponding relationship as the proportional coefficient of the target power transmission equipment.

[0042] As a non-limiting implementation, determining a standardized score of the operating data, and determining a comprehensive health score of the target power transmission device according to the standardized score of the operating data, the adaptive deviation correction term of the operating data, and a proportionality coefficient, includes:

[0043] The comprehensive health score of the target power transmission equipment is determined based on the following formula:

[0044] H score (t) = aK·ln(1+Z V (t) 2 +Z I (t) 2 +Z T (t) 2 +Z Vib (t) 2 )-A(t);

[0045] Among them, H score (t) is the comprehensive health score of the target power transmission equipment; Z V (t) is the standardized fraction of the voltage in the tth sampling period, Z I (t) is the normalized fraction of the current in the tth sampling period, Z T (t) is the normalized fraction of the temperature in the tth sampling period, Z Vib (t) is the standardized fraction of the vibration amount in the t-th sampling period, A(t) is the adaptive bias correction term in the t-th sampling period, K is the proportional coefficient, and a is the constant term.

[0046] In the above formula, a can be taken as 100, that is, the full score is set to 100, and the quantified value of the degree of deviation of the operating data in the current sampling cycle relative to the average value is subtracted from the full score, and then the quantified value of the degree of deviation of the operating data in the current sampling cycle relative to the operating data in the previous sampling cycles is subtracted, thereby reflecting the current comprehensive health score of the target transmission equipment.

[0047] The normalized fraction Z of the voltage at the tth sampling period V (t) is calculated according to the following formula:

[0048]

[0049] Where V smooth (t) is the voltage value after smoothing in the t-th sampling period, is the historical average voltage within the preset sampling period, σ V It is the historical standard deviation of the voltage within the preset sampling period.

[0050] Among them, the historical average value of the voltage within the preset sampling period Calculate according to the following formula:

[0051]

[0052] Where N is the number of sampling cycles used to calculate the average value. For example, if the average value is the average value of the past 50 sampling cycles, then N is 50. smooth (t) is the voltage value after smoothing in the t-th sampling period;

[0053] The historical standard deviation of the voltage within the preset sampling period σ V Calculated according to the following formula:

[0054]

[0055] Where N is the number of sampling cycles used to calculate the average value. For example, if the average value is the average value of the past 50 sampling cycles, then N is 50. smooth (t) is the voltage value after smoothing in the t-th sampling period, is the historical average value of voltage.

[0056] Normalized fraction Z of the current deviation in the tth sampling period I (t) is calculated according to the following formula:

[0057]

[0058] In the formula, I smooth (t) is the current value after smoothing in the t-th sampling period, is the historical average current value within the preset sampling period, σ I It is the historical standard deviation of the current within the preset sampling period.

[0059] Among them, the historical average value of the current within the preset sampling period Calculated according to the following formula:

[0060]

[0061] Where N is the number of sampling periods used to calculate the average value. For example, if the average value is the average value of the past 50 sampling periods, then N is 50. smooth (t) is the current value after smoothing in the t-th sampling period, It is the historical average value of the current within the preset sampling period.

[0062] The historical standard deviation of the current within the preset sampling period σ I Calculated according to the following formula:

[0063]

[0064] Where N is the number of sampling periods used to calculate the average value. For example, if the average value is the average value of the past 50 sampling periods, then N is 50. smooth (t) is the current value after smoothing in the t-th sampling period, is the historical average value of the current.

[0065] Normalized fraction Z of temperature deviation in the tth sampling period T (t) is calculated according to the following formula:

[0066]

[0067] Where, T smooth (t) is the temperature value after smoothing in the t-th sampling period, is the historical average temperature within the preset sampling period, σ T is the historical standard deviation of temperature within the preset sampling period.

[0068] The historical average temperature within the preset sampling period Calculated according to the following formula:

[0069]

[0070] Where N is the number of sampling periods used to calculate the average value. For example, if the average value is the average value of the past 50 sampling periods, then N is 50. smooth (t) is the temperature value after smoothing in the t-th sampling period, is the historical average temperature.

[0071] The historical standard deviation of temperature within the preset sampling period σ T Calculated according to the following formula:

[0072]

[0073] Where N is the number of sampling periods used to calculate the average value. For example, if the average value is the average value of the past 50 sampling periods, then N is 50. smooth (t) is the temperature value after smoothing in the t-th sampling period, is the historical average temperature.

[0074] The normalized fraction Z of the vibration deviation in the tth sampling period Vib (t) is calculated according to the following formula:

[0075]

[0076] Where, Vib smooth (t) is the vibration data after smoothing in the t-th sampling period, is the historical average value of the vibration value within the preset sampling period, σ Vib (t) is the historical standard deviation of the vibration quantity within the preset sampling period.

[0077] The historical average value of vibration within the preset sampling period Calculated according to the following formula:

[0078]

[0079] Where N is the number of sampling cycles used to calculate the average value. For example, if the average value is the average value of the past 50 sampling cycles, then N is 50. smooth (t) is the vibration value after smoothing in the t-th sampling period, is the historical average value of the vibration amount.

[0080] The historical standard deviation of the vibration quantity within the preset sampling period σ T Calculated according to the following formula:

[0081]

[0082] Where N is the number of sampling cycles used to calculate the average value. For example, if the average value is the average value of the past 50 sampling cycles, then N is 50. smooth (t) is the vibration value after smoothing in the t-th sampling period, is the historical average value of the vibration amount.

[0083] As a non-limiting implementation, the process of determining the smoothed operating data of the t-th sampling period for determining the normalized score of the operating data of the t-th sampling period includes:

[0084] Determine the operating data collected in the current sampling period and the operating data after smoothing in the previous sampling period;

[0085] The operation data collected in the current sampling period and the operation data after smoothing in the previous sampling period are weightedly summed to obtain the operation data after smoothing in the current sampling period; wherein the operation data includes at least one of voltage, current, temperature and vibration.

[0086] Exemplarily, the operation data is taken as vibration quantity as an example to illustrate the operation data after smoothing in the current sampling period.

[0087] Vib smooth (t) is the vibration amount after smoothing in the t-th sampling period and is calculated according to the following formula:

[0088] Vib smooth (t) = α·Vib(t) + (1-α)·Vib smooth (t-1);

[0089] Where α is the weight value corresponding to the vibration amount in the t-th sampling period (usually 0<α<1), Vib(t) is the vibration amount collected in the current sampling period, and its value is obtained by the vibration sensor. smooth (t-1) is the vibration amount after smoothing in the previous cycle.

[0090] As a non-limiting implementation method, the process of determining the weight value for weighted summation includes:

[0091] Determining monitoring timeliness data of the target power transmission equipment;

[0092] The weight value of the operation data collected in the current sampling period is made positively correlated with the monitoring timeliness data;

[0093] Taking the difference between the weight value of the operation data collected in the current sampling period and the weight value of the operation data collected in the previous sampling period as the operation data after smoothing;

[0094] The monitoring timeliness data is positively correlated with the monitoring timeliness of the target power transmission equipment.

[0095] For example, the monitoring timeliness of the target power transmission equipment can be quantified and represented by monitoring timeliness data. The weight value of the operating data collected in the current sampling period is positively correlated with the monitoring timeliness data. In the scenario with strong timeliness, the operating data of the current sampling period is mainly focused on, and the weight value of the operating data collected in the current sampling period minus the weight value is used as the operating data after smoothing in the previous sampling period. In the scenario with weak timeliness, the influence of the operating data in the previous sampling period is considered more.

[0096] Specifically, the specific value of the weight value α in the following scenarios can be:

[0097] 1) Rapid detection of mechanical failures in target power transmission equipment

[0098] In some scenarios with high sensitivity requirements, such as mechanical condition monitoring of high-voltage switches or transformers, rapid detection of changes in vibration data helps to identify equipment abnormalities in a timely manner. In this case, the smoothing of vibration data needs to respond more quickly to changes in the latest data, then: α = 0.3 to 0.5;

[0099] Among them, a higher α value can respond more quickly to changes in vibration quantity, which helps to detect mechanical failures in time and avoid potential risks.

[0100] 2) Target power transmission equipment for daily monitoring, such as medium frequency fluctuations:

[0101] For the target power transmission equipment monitored daily, such as transformers or circuit breakers, the vibration data has certain fluctuations, but the fluctuation frequency is low. At this time, the smoothing process needs to pay moderate attention to the latest data, while not being too sensitive to avoid short-term fluctuations affecting the judgment, then α = 0.15 to 0.25;

[0102] Among them, a moderate α value can balance the impact of the latest data and historical data, and is suitable for data with moderate fluctuations, so that the comprehensive health score does not change drastically due to short-term fluctuations.

[0103] 3) Stable equipment environment, such as low fluctuation monitoring:

[0104] In some scenarios where the equipment operating environment is very stable, such as indoor power transmission and transformer equipment, the vibration changes very little and the health status is relatively stable. At this time, the purpose of data smoothing is to keep the score stable and emphasize historical trends. Then α = 0.05 to 0.1;

[0105] Among them, a lower α value makes the smoothing result more dependent on historical data, which is suitable for smoothly running equipment and reduces the situation where the score is unstable due to short-term small fluctuations.

[0106] 4) Special monitoring tasks, such as seasonal load changes:

[0107] If the equipment environment or load conditions are subject to periodic fluctuations (e.g., seasonal load changes), the smoothing factor can be selected to be in a moderate range that can both smooth small fluctuations and capture larger changes, then α = 0.2 to 0.3;

[0108] Among them, choosing a medium to high smoothing coefficient can adapt to seasonal changes and avoid short-term fluctuation interference, making the equipment status more consistent with the actual situation of periodic loads.

[0109] As a non-limiting implementation, the process of determining the adaptive deviation correction item of the operating data includes:

[0110] For each sampling period, respectively determine the difference between the operating data in the sampling period and the average value of the operating data in each sampling period before the sampling period;

[0111] Determining a product of the difference and a time-weighted function corresponding to the sampling period, and determining an average value of the products corresponding to each sampling period as an adaptive deviation correction item of the operating data; wherein the operating data includes at least one of voltage, current, temperature and vibration;

[0112] The process of determining the adaptive deviation correction item of the target power transmission device includes:

[0113] The sum of the adaptive deviation correction items of each operation data is used as the adaptive deviation correction item of the target power transmission equipment.

[0114] In the embodiment of the present application, the operating data includes the current, voltage, temperature, vibration, comprehensive health score, and deviation information of each operating data (including current, voltage, temperature, and vibration) of the target power transmission equipment;

[0115] The adaptive bias correction term A(t) for the tth sampling period is calculated as follows:

[0116] A(t)=V adjust (t)+I adjust (t)+T adjust (t);

[0117] Where V adjust (t) is the voltage deviation correction term, I adjust (t) is the current deviation correction term, T adjust (t) is the temperature deviation correction term;

[0118] Among them, the voltage deviation correction term V adjust (t) is calculated according to the following formula:

[0119]

[0120] Where V(t) is the voltage value of the current sampling period t, μ V (t) is the average voltage value before the current sampling period t (i.e., the average value of the historical voltage), W(t-(tM))=W(M) is the time weighted function of the Mth sampling period before the current sampling period t, V(tM) is the voltage value of the Mth sampling period before the current sampling period t, μ V (tM) is the average voltage value before the Mth sampling period forward from the current sampling period t (i.e., the average voltage value before the Mth sampling period forward from the current sampling period t), V(t-(M-1)) is the voltage value before the M-1th sampling period forward from the current sampling period t, μ V (t-(M-1)) is the average voltage value before the M-1th sampling period before the current sampling period t (that is, the average voltage value before the M-1th sampling period before the current sampling period t), W(t-(t-(M-1))) is the time weighted function of the M-1th sampling period before the current sampling period t, W(tt) is the time weighted function of the current sampling period t, W(tt) = W(0) = e -λ*0 =1, M represents the number of past sampling cycles used for calculation, for example, the past 3 to 5 cycles.

[0121] In addition, the current deviation correction term I adjust (t) is calculated according to the following formula:

[0122]

[0123] Where I(t) is the current value of the current sampling period t, μ I (t) is the current average value before the current sampling period t (i.e., the average value of the historical current), W(t-(tM))=W(M) is the time weighted function of the Mth sampling period before the current sampling period t, I(tM) is the current value of the Mth sampling period before the current sampling period t, μ I (tM) is the average current value before the Mth sampling period forward from the current sampling period t, I(t-(M-1)) is the current value before the M-1th sampling period forward from the current sampling period t, μ I (t-(M-1)) is the current average value before the M-1th sampling period forward from the current sampling period t, W(t-(t-(M-1))) is the time weighted function of the M-1th sampling period forward from the current sampling period t, W(tt) is the time weighted function of the current sampling period t, W(tt) = W(0) = e -λ*0 =1, M represents the number of past sampling cycles used for calculation, for example, the past 3 to 5 cycles.

[0124] Similarly, the temperature deviation correction term T adjust (t) is calculated according to the following formula:

[0125]

[0126] Where T(t) is the temperature value of the current sampling period t, μ T (t) is the average temperature before the current sampling period t (i.e., the average historical temperature), W(t-(tM))=W(M) is the time weighted function of the Mth sampling period forward from the current sampling period t, T(tM) is the temperature of the Mth sampling period forward from the current sampling period t, μ T (tM) is the average temperature value before the Mth sampling period forward from the current sampling period t, T(t-(M-1)) is the temperature value of the M-1th sampling period forward from the current sampling period t, μ T (t-(M-1)) is the average temperature before the M-1th sampling period before the current sampling period t, W(t-(t-(M-1))) is the time weighting function of the M-1th sampling period before the current sampling period t, W(tt) is the time weighting function of the current sampling period t, W(tt) = W(0) = e -λ*0 =1, M represents the number of past sampling cycles used for calculation, for example, the past 3 to 5 cycles.

[0127] In calculating the voltage deviation correction term V adjust (t), current deviation correction term I adjust (t) and the temperature deviation correction term T adjust (t), if the data is less than M cycles, the default value is used to fill the gap, which is the initial value or the initial historical average value set by the system or the initial value measured under normal operating conditions.

[0128] As a non-limiting implementation method, the process of determining the time weighting function includes:

[0129] Determine the product of the time decay coefficient and the number of sampling periods before the current sampling period, and take the inverse to obtain the decay index;

[0130] The decay index is used as an independent variable of a natural exponential function to calculate a time weighted function corresponding to the sampling period.

[0131] The time weighting function W(t-(tM))=W(M) of the sampling period at the current time point t is calculated according to the following formula:

[0132] W(t-(tM))=W(M)=e -λM ;

[0133] Wherein, M represents the number of past sampling cycles used for calculation, for example, the past 3 to 5 cycles, and λ is the time attenuation coefficient, which is usually between 0.1 and 0.3; in this embodiment, a larger value of λ makes the weight of the recent cycle higher.

[0134] Specifically, when λ takes a larger value (close to 0.3), the time weighting function decays faster, which means that sampling periods closer to the current time t are given higher weights, while the weights of sampling periods farther away decrease rapidly. Therefore, a larger value of λ makes the formula more sensitive to recent data and has a smaller impact on historical data.

[0135] When λ takes a smaller value (close to 0.1), the decay speed is slower, and the data from the farther sampling period still has a relatively large influence when weighted. Therefore, a smaller value of λ makes the formula respond more smoothly and stably to the entire historical data. Assume that M = 3 (the past three sampling periods), taking voltage as an example, the voltage data of the past three sampling periods are 220V, 230V, and 225V, and the current sampling period is t.

[0136] Historical average μ V (t):

[0137] 1) Calculate μ V (t-3): Because t-3 is the earliest sampling period, and there is not enough sampling period data to refer to, the default value is filled with the initial value or the initial historical average value set by the system or the initial value measured under normal operating conditions as μ V (t-3)=220.

[0138] 2) Calculate μ V (t-2): The historical average value of t-2 can refer to the data of t-3.

[0139] Therefore, μ V (t-2)=220 / 1=220.

[0140] 3) Calculate μ V (t-1): The historical average value of t-1 can refer to the data of t-3 and t-2.

[0141] μ V (t-1)=(220+230) / 2=225.

[0142] 4) Calculate μ V (t): The historical average value of t can refer to the data of t-3, t-2, and t-1.

[0143] μ V (t)=(220+230+225) / 3=225.

[0144] Calculate the absolute value of the deviation for each sampling period:

[0145] Substituting these historical averages, calculate the absolute value of the deviation for each sampling period one by one:

[0146] The first sampling cycle (time point t-3) (voltage value: 220):

[0147] Absolute value of deviation | 220-μ V (t-3)∣=∣220-220∣=0;

[0148] The second sampling period (time point t-2) (voltage value: 230):

[0149] Absolute value of deviation | 230-μ V (t-2)∣=∣230-220∣=10;

[0150] The third sampling cycle (time point t-1) (voltage value: 225):

[0151] Absolute value of deviation | 225-μ V (t-1)∣=∣225-225∣=0;

[0152] Calculate the time weighting function: Assume λ = 0.1 (λ is the time attenuation coefficient, and the system sets the value to λ = 0.1 for a sampling interval of 3 periods), then:

[0153] For M=3: W(t-3)=e 0.1*3 =e -0.3 ;

[0154] For M = 2: W(t-2) = e 0.1*2 =e -0.2 ;

[0155] For M=1: W(t-1)=e 0.1*1 =e -0.1 ;

[0156] Calculate the adaptive bias correction term V adjust (t): Substitute the absolute value of the deviation and the time weighting function into the formula:

[0157] V adjust (t)=1 / 3(0*W(t-3)+10*W(t-2)+0*W(t-1))=1 / 3(0*e -0.3 +10*e -0.2 +0*

[0158] e -0.1 );

[0159] According to the above example steps, the voltage deviation correction term V is calculated respectively.adjust (t), current deviation correction term I adjust (t) and the temperature deviation correction term T adjust (t), which are summed to obtain A(t).

[0160] As a non-limiting implementation method, determining the comprehensive score index of the target power transmission equipment according to the comprehensive health score, the standardized score and the trend factor of the target power transmission equipment includes:

[0161] The comprehensive scoring index of the target power transmission equipment is determined according to the following formula:

[0162]

[0163] Among them, ER(t) is the comprehensive score index of the target power transmission equipment, H score (t) is the comprehensive health score of the target power transmission equipment, Z V (t) is the standardized fraction of the voltage in the tth sampling period, Z I (t) is the normalized fraction of the current in the tth sampling period, Z T (t) is the normalized fraction of the temperature in the tth sampling period, Z Vib (t) is the standardized fraction of the vibration quantity in the t-th sampling period, δ is the adjustment coefficient, and TF(t) is the trend factor in the t-th sampling period;

[0164]

[0165] Among them, σ TF is the historical standard deviation of the trend factor TF(t), μ TF is the historical average value of the trend factor TF(t).

[0166] Among them, the historical average value of trend factor TF(t) is μ TF Calculated according to the following formula:

[0167]

[0168] N is the number of sampling periods used to calculate the average value, and TF(t) is the trend factor of the t-th sampling period, and the formula is:

[0169]

[0170] In the formula, H score (t) is the comprehensive health score of the tth sampling period, H score (t-1) is the comprehensive health score of the previous sampling period.

[0171] In the embodiment of the present application, in this embodiment, it can be set to i=1 to start calculating the trend factor. The first data point is usually used to initialize the system state, so skipping its calculation will not affect the subsequent trend analysis and comprehensive health score. The calculation of the historical average value starts to accumulate from the second data point.

[0172] Among them, TF(t)>0: It means that the comprehensive health score has improved compared with the previous sampling period, and the device status is improving;

[0173] TF(t)<0: indicates that the comprehensive health score has decreased compared with the previous sampling period, and the device status may be deteriorating;

[0174] TF(t)=0: indicates that the comprehensive health score has not changed and the device status remains stable;

[0175] The historical standard deviation σ of the trend factor TF(t) TF Calculated according to the following formula:

[0176]

[0177] Where N is the number of sampling periods used to calculate the average value. For example, if the average value is the average value of the past 50 sampling periods, then N is 50. TF(t) is the trend factor of the t-th sampling period, μ TF is the historical average value of the trend factor TF(t).

[0178] As a non-limiting implementation, the method further includes:

[0179] In the case that the equipment operation state of the power transmission equipment does not reach the preset state, notifying the maintenance personnel through the terminal to perform maintenance on the target power transmission equipment, and determining the trend evaluation index of the maintenance personnel;

[0180] The trend assessment index of the maintenance personnel is determined based on the following formula:

[0181]

[0182] Among them, Completeness is the current completion degree of the delegated maintenance personnel, Completeness history is the historical average completion rate of the assigned maintenance personnel, and α is the trend influence coefficient;

[0183] Current completion

[0184] Time spent The time that maintenance personnel have wasted in executing maintenance work orders; Total time Theoretical time consumption for maintenance personnel to execute maintenance work orders;

[0185] Historical average completion rate

[0186] Completeness j is the completion degree of the assigned maintenance personnel in the jth cycle task, and M is the number of selected historical task cycles.

[0187] Figure 2 A schematic diagram of a power transmission equipment monitoring system based on the side provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, a power transmission equipment monitoring system provided in an embodiment of the present application includes:

[0188] A server 210, a data collection module 220, a side processing module 230, and an evaluation response module 240; the data collection module 220, the side processing module 230, and the evaluation response module 240 are respectively connected to the server 210;

[0189] The data acquisition module 220 acquires the operating data of the target power transmission equipment, the side processing module 230 performs local processing on the operating data transmitted by the data acquisition module 220, so as to perform real-time analysis on the operating status of the target power transmission equipment and generate a health status report of the target power transmission equipment, the evaluation response module 240 evaluates the health status report of the power transmission equipment to form an evaluation result, and generates the maintenance strategy of the target power transmission equipment according to the evaluation result:

[0190] The side processing module 230 includes a data receiving unit, a state analysis unit, and a local alarm unit. Figure 3 As shown, the data receiving unit receives the operating data from the data acquisition module, the status analysis unit processes and analyzes the received operating data to form an analysis result to obtain a health status report of the target power transmission equipment, and the local alarm unit triggers an early warning based on the health status report and prompts the maintenance personnel to handle it.

[0191] The target power transmission equipment real-time monitoring system also includes a central processing unit, which is respectively controlled and connected to the data acquisition module, the side processing module and the evaluation response module, and centrally controls the data acquisition module, the side processing module and the evaluation response module based on the central processing unit, and stores the control data of the central processing unit in a database, thereby improving the monitoring accuracy and reliability of the entire system on the target power transmission equipment.

[0192] Optionally, the data acquisition module includes a sensor unit, a data transmission unit and a memory, the sensor unit collects operating data of the target power transmission equipment, the data transmission unit transmits the operating data collected by the sensor unit to the memory, and the memory stores the operating data transmitted by the data transmission unit.

[0193] The sensor unit includes a current sensor, a voltage sensor, a temperature sensor, and a vibration sensor,

[0194] The current sensor is used to monitor the current size of the target power transmission equipment, the voltage sensor is used to monitor the voltage fluctuation of the target power transmission equipment, the temperature sensor detects the temperature data of the target power transmission equipment, and the vibration sensor collects the vibration data of the monitoring equipment;

[0195] The data transmission unit includes a data acquisition interface, a data transmission controller, and a communicator. The data acquisition interface is responsible for receiving data from the sensor unit and aggregating different types of sensor data. The data transmission controller manages and controls the transmission of each sensor data, formats, packages and transmits the data transmitted from the acquisition interface to the memory. The communicator transmits the packaged data in the data transmission controller to the memory.

[0196] The data transmission unit implements data transmission according to the following steps:

[0197] S1. Data acquisition and preprocessing: The data acquisition interface obtains current, voltage, temperature and vibration data from the sensor and performs preliminary signal conversion (such as ADC analog-to-digital conversion).

[0198] S2. Data formatting and packaging: The data transmission controller receives the data from the acquisition interface, formats the data (such as adding timestamp, device ID, etc.), and then packages it for subsequent processing and storage.

[0199] S3, Data transmission: The communicator selects the appropriate transmission method (such as RS-485, CAN, Ethernet, Wi-Fi) to send the formatted data to the memory. If it is wired transmission, the data will be sent directly to the memory address; if it is wireless transmission, the data transmission controller will perform short-term buffering in the wireless module to ensure data integrity.

[0200] S4, data storage: After receiving the data, the memory saves the data to the local storage area so that subsequent modules can read and process it from the memory.

[0201] Among them, the side processing module includes a data receiving unit, a status analysis unit and a local alarm unit. The data receiving unit receives the operating data from the data acquisition module, and the status analysis unit processes and analyzes the received operating data to form an analysis result to obtain the health status report of the target power transmission equipment. The local alarm unit triggers an early warning according to the health status report and prompts on-site personnel to handle it.

[0202] Optionally, the data receiving unit includes a scheduling requester, a receiver, a buffer and a data verifier. The scheduling requester sends a data request to the data acquisition module to coordinate the time and frequency of data acquisition. The receiver receives the operating data transmitted from the data acquisition module. The buffer temporarily stores the operating data during the data reception process. The data verifier verifies the received operating data.

[0203] The verification step of the data verifier comprises:

[0204] S11. Preset calibration standards:

[0205] Select a checksum algorithm: Based on system requirements and data characteristics, select an appropriate checksum algorithm, such as CRC-16 or CRC-32 (suitable for longer data and provide higher error detection capabilities), or a simple checksum (suitable for short data or resource-constrained applications).

[0206] Generator polynomial: For CRC check, select an appropriate polynomial generator. For example, the commonly used generator polynomial for CRC-16 is 0x8005, and the commonly used generator polynomial for CRC-32 is 0x04C11DB7.

[0207] S12, data check code generation

[0208] Calculate the check code of the sending end: When sending the operating data, the data acquisition module calculates the check code of the operating data according to the set check algorithm, and attaches it to the end of the operating data frame and sends it to the data receiving unit.

[0209] Verification code transmission: After the data acquisition module generates and packages the verification code, it sends the running data and the verification code as a complete transmission frame to the receiving unit.

[0210] S13, receiving data and extracting verification code:

[0211] Receiving data frame: After receiving the running data frame, the receiver in the data receiving unit passes the running data frame to the data verifier.

[0212] Extracting the check code: The data checker separates the original running data and the additional check code from the received running data frame.

[0213] Assuming that the data frame structure is [running data|check code], the checker will separate the "running data part" and the "check code part".

[0214] S14, recalculate the receiving end check code:

[0215] Calculate the checksum at the receiving end: The data verifier recalculates the checksum based on the received running data content using the same checksum algorithm.

[0216] For CRC checking, the checker operates on the data bit by bit and recalculates the check value using the generator polynomial.

[0217] For example, for CRC-16, the checksum can be calculated as follows:

[0218] Initialize the CRC register to 0xFFFF.

[0219] The running data bits of each byte are subjected to an XOR operation bit by bit, and a shift operation is performed according to the generating polynomial until all data bits are processed.

[0220] The value obtained after the calculation is the check code at the receiving end.

[0221] S15. Compare the check code

[0222] Checksum comparison: The data checker compares the checksum calculated by the receiving end with the checksum in the data frame:

[0223] If the two are consistent, the data verification is passed and the data is complete and correct;

[0224] If the two are inconsistent, it indicates that an error may have occurred during the transmission of the operating data.

[0225] S16. Error handling:

[0226] Error flag or retransmission request:

[0227] If the verification passes, the running data is passed to the buffer and stored, and the subsequent modules can use the data normally.

[0228] If the check fails, the data checker may mark the running data as erroneous and send a retransmission request to the scheduling requester or the data acquisition module.

[0229] Error information is recorded during the verification process to facilitate the system to monitor the quality of data reception or trigger alarms according to system requirements.

[0230] Optionally, the state analysis unit obtains the operating data collected by the data receiving unit, and processes the operating data, wherein the processing includes removing abnormal values ​​and smoothing, and calculating the comprehensive health score Hscore(t) of the t-th sampling period according to the following formula:

[0231] H score (t) = 100-K·ln(1+Z V (t) 2 +Z I (t) 2 +Z T (t) 2 +Z Vib (t) 2 )-A(t).

[0232] Comprehensive health score H score (t) A higher score indicates that the device is in good working condition, while a lower score usually means that there may be problems or performance degradation. score When (t) is lower than the preset threshold, the local alarm unit triggers an early warning. V (t) is the standardized fraction of the pressure deviation in the tth sampling period, Z I (t) is the normalized fraction of the current deviation in the tth sampling period, Z T (t) is the normalized fraction of the temperature deviation in the tth sampling period, Z Vib (t) is the standardized score of the vibration deviation in the t-th sampling period, A(t) is the adaptive deviation correction term in the t-th sampling period, K is the proportional coefficient, and its value range is: [10, 20]. The value of the proportional coefficient K should be adjusted according to the stability of the equipment operating environment and the requirements for the sensitivity of the comprehensive health score. Specifically:

[0233] High-sensitivity scenarios: Choose a higher K value (such as 18-20) to make the comprehensive health score more sensitive and able to respond quickly to smaller deviations.

[0234] Medium sensitivity scenario: Choose a medium K value (such as 14-17), which ensures a certain sensitivity while not fluctuating too frequently, and is suitable for most scenarios.

[0235] Low sensitivity scenario: Choose a lower K value (such as 10-13), which is suitable for environments with high stability and avoid frequent changes in the comprehensive health score to small fluctuations.

[0236] At the same time, in this embodiment, an example of the value of the proportionality coefficient K is given:

[0237] 1) Key target transmission equipment or scenarios with high failure risk (such as substation main transformer):

[0238] In this scenario, the stability of the equipment is crucial to the operation of the entire system. It is necessary to have a high sensitivity to the comprehensive health score, detect deviations in time and respond to maintenance. The K value range is: 18-20;

[0239] Among them, the higher K value makes the comprehensive health score more sensitive to the fluctuation of each deviation, and can quickly reflect the change of the equipment status. For example, if there is a slight fluctuation in vibration or current, the comprehensive health score will drop significantly, reminding the monitoring personnel to take prompt inspection measures to prevent the risk of failure.

[0240] 2) Target transmission equipment for daily monitoring (such as regional transmission lines):

[0241] For daily monitored transmission lines, the operating environment of the equipment is relatively stable, but it is still necessary to maintain moderate sensitivity in order to detect abnormalities early and not be overly sensitive to short-term fluctuations. The value range of K is: 14-17;

[0242] Among them, the medium K value can ensure a certain sensitivity while not fluctuating too frequently. The comprehensive health score of the target power transmission equipment will not change frequently due to short-term small fluctuations, but when there are significant deviations in temperature, current, etc., the comprehensive health score will be significantly reduced, reminding maintenance personnel to pay attention.

[0243] 3) Target power transmission equipment with stable environment and stable status (such as indoor substation equipment or backup equipment):

[0244] In this case, the equipment operating environment is basically not affected by external factors, with small fluctuations, and the sensitivity requirements for the comprehensive health score are low. More attention is paid to the long-term changes in the overall status of the equipment. The K value range is: 10-13;

[0245] Among them, the lower K value makes the comprehensive health score less responsive to small deviations, which helps to maintain the stability of the score. When the long-term data changes little, the score will not change frequently due to short-term small fluctuations, but when the state of the target transmission equipment deviates greatly, the comprehensive health score can still clearly reflect the change.

[0246] 4) Scenarios with significant seasonal load fluctuations (such as target transmission equipment with large seasonal load fluctuations):

[0247] In this scenario, the load of the equipment changes with the seasons. The comprehensive health score needs to adapt to the periodic fluctuations of the load, while avoiding frequent fluctuations caused by small seasonal fluctuations. The K value range is: 15-18;

[0248] Among them, a medium to high K value can take into account both load fluctuations and equipment stability requirements. The score of the target transmission equipment will not drop significantly during peak loads, but it can still maintain a high sensitivity to severe fluctuations.

[0249] In short, the specific value of the proportional coefficient K value needs to be determined in combination with the specific scene and actual situation, and input from the human-computer interaction interface. This is a technical means well known to those skilled in the art, so it will not be described in detail in this embodiment;

[0250] When the comprehensive health score H of the tth sampling period score After (t) is obtained, the current status of the comprehensive operation data generates a health status analysis report for the tth sampling period.

[0251] The normalized fraction Z of the voltage at the tth sampling period V (t) is calculated according to the following formula:

[0252]

[0253] Where V smooth (t) is the voltage value after smoothing in the t-th sampling period, is the historical average voltage within the preset sampling period, σ V It is the historical standard deviation of the voltage within the preset sampling period.

[0254] Among them, the historical average value of the voltage within the preset sampling period Calculate according to the following formula:

[0255]

[0256] Where N is the number of sampling cycles used to calculate the average value. For example, if the average value is the average value of the past 50 sampling cycles, then N is 50. smooth (t) is the voltage value after smoothing in the t-th sampling period;

[0257] The historical standard deviation of the voltage within the preset sampling period σ V Calculated according to the following formula:

[0258]

[0259] Where N is the number of sampling cycles used to calculate the average value. For example, if the average value is the average value of the past 50 sampling cycles, then N is 50. smooth (t) is the voltage value after smoothing in the t-th sampling period, is the historical average value of voltage.

[0260] Normalized fraction Z of the current deviation in the tth sampling period I (t) is calculated according to the following formula:

[0261]

[0262] In the formula, Ismooth (t) is the current value after smoothing in the t-th sampling period, is the historical average current value within the preset sampling period, σ I It is the historical standard deviation of the current within the preset sampling period.

[0263] Among them, the historical average value of the current within the preset sampling period Calculated according to the following formula:

[0264]

[0265] Where N is the number of sampling periods used to calculate the average value. For example, if the average value is the average value of the past 50 sampling periods, then N is 50. smooth (t) is the current value after smoothing in the t-th sampling period, It is the historical average value of the current within the preset sampling period.

[0266] The historical standard deviation of the current within the preset sampling period σ I Calculated according to the following formula:

[0267]

[0268] Where N is the number of sampling periods used to calculate the average value. For example, if the average value is the average value of the past 50 sampling periods, then N is 50. smooth (t) is the current value after smoothing in the t-th sampling period, is the historical average value of the current.

[0269] Normalized fraction Z of temperature deviation in the tth sampling period T (t) is calculated according to the following formula:

[0270]

[0271] Where, T smooth (t) is the temperature value after smoothing in the t-th sampling period, is the historical average temperature within the preset sampling period, σ T is the historical standard deviation of temperature within the preset sampling period.

[0272] The historical average temperature within the preset sampling period Calculated according to the following formula:

[0273]

[0274] Where N is the number of sampling periods used to calculate the average value. For example, if the average value is the average value of the past 50 sampling periods, then N is 50. smooth (t) is the temperature value after smoothing in the t-th sampling period, is the historical average temperature.

[0275] The historical standard deviation of temperature within the preset sampling period σ T Calculated according to the following formula:

[0276]

[0277] Where N is the number of sampling periods used to calculate the average value. For example, if the average value is the average value of the past 50 sampling periods, then N is 50. smooth (t) is the temperature value after smoothing in the t-th sampling period, is the historical average temperature.

[0278] The normalized fraction Z of the vibration deviation in the tth sampling period Vib (t) is calculated according to the following formula:

[0279]

[0280] Where, Vib smooth (t) is the vibration data after smoothing in the t-th sampling period, is the historical average value of the vibration value within the preset sampling period, σ Vib (t) is the historical standard deviation of the vibration quantity within the preset sampling period.

[0281] The historical average value of vibration within the preset sampling period Calculated according to the following formula:

[0282]

[0283] Where N is the number of sampling cycles used to calculate the average value. For example, if the average value is the average value of the past 50 sampling cycles, then N is 50. smooth (t) is the vibration value after smoothing in the t-th sampling period, is the historical average value of the vibration amount.

[0284] The historical standard deviation of the vibration quantity within the preset sampling period σ T Calculated according to the following formula:

[0285]

[0286] Where N is the number of sampling cycles used to calculate the average value. For example, if the average value is the average value of the past 50 sampling cycles, then N is 50. smooth (t) is the vibration value after smoothing in the t-th sampling period, is the historical average value of the vibration amount.

[0287] Exemplarily, the operation data is taken as vibration quantity as an example to illustrate the operation data after smoothing in the current sampling period.

[0288] Vib smooth (t) is the vibration amount after smoothing in the t-th sampling period and is calculated according to the following formula:

[0289] Vib smooth (t) = α·Vib(t) + (1-α)·Vib smooth (t-1);

[0290] Where α is the weight value corresponding to the vibration amount in the t-th sampling period (usually 0<α<1), Vib(t) is the vibration amount collected in the current sampling period, and its value is obtained by the vibration sensor. smooth (t-1) is the vibration amount after smoothing in the previous cycle.

[0291] Specifically, the specific value of the weight value α in the following scenarios can be:

[0292] 1) Rapid detection of mechanical failures in target power transmission equipment

[0293] In some scenarios with high sensitivity requirements, such as mechanical condition monitoring of high-voltage switches or transformers, rapid detection of changes in vibration data helps to identify equipment abnormalities in a timely manner. In this case, the smoothing of vibration data needs to respond more quickly to changes in the latest data, then: α = 0.3 to 0.5;

[0294] Among them, a higher α value can respond more quickly to changes in vibration quantity, which helps to detect mechanical failures in time and avoid potential risks.

[0295] 2) Target power transmission equipment for daily monitoring, such as medium frequency fluctuations:

[0296] For the target power transmission equipment monitored daily, such as transformers or circuit breakers, the vibration data has certain fluctuations, but the fluctuation frequency is low. At this time, the smoothing process needs to pay moderate attention to the latest data, while not being too sensitive to avoid short-term fluctuations affecting the judgment, then α = 0.15 to 0.25;

[0297] Among them, a moderate α value can balance the impact of the latest data and historical data, and is suitable for data with moderate fluctuations, so that the comprehensive health score does not change drastically due to short-term fluctuations.

[0298] 3) Stable equipment environment, such as low fluctuation monitoring:

[0299] In some scenarios where the equipment operating environment is very stable, such as indoor power transmission and transformer equipment, the vibration changes very little and the health status is relatively stable. At this time, the purpose of data smoothing is to keep the score stable and emphasize historical trends. Then α = 0.05 to 0.1;

[0300] Among them, a lower α value makes the smoothing result more dependent on historical data, which is suitable for smoothly running equipment and reduces the situation where the score is unstable due to short-term small fluctuations.

[0301] 4) Special monitoring tasks, such as seasonal load changes:

[0302] If the equipment environment or load conditions are subject to periodic fluctuations (e.g., seasonal load changes), the smoothing factor can be selected to be in a moderate range that can both smooth small fluctuations and capture larger changes, then α = 0.2 to 0.3;

[0303] Among them, choosing a medium to high smoothing coefficient can adapt to seasonal changes and avoid short-term fluctuation interference, making the equipment status more consistent with the actual situation of periodic loads.

[0304] In the embodiment of the present application, the operating data includes the current, voltage, temperature, vibration, comprehensive health score, and deviation information of each operating data (including current, voltage, temperature, and vibration) of the target power transmission equipment;

[0305] The adaptive bias correction term A(t) for the tth sampling period is calculated as follows:

[0306] A(t)=V adjust (t)+I adjust (t)+T adjust (t);

[0307] Where V adjust (t) is the voltage deviation correction term, I adjust (t) is the current deviation correction term, T adjust (t) is the temperature deviation correction term;

[0308] Among them, the voltage deviation correction term V adjust (t) is calculated according to the following formula:

[0309]

[0310] Where V(t) is the voltage value of the current sampling period t, μ V (t) is the average voltage value before the current sampling period t (i.e., the average value of the historical voltage), W(t-(tM))=W(M) is the time weighted function of the Mth sampling period before the current sampling period t, V(tM) is the voltage value of the Mth sampling period before the current sampling period t, μV (tM) is the average voltage value before the Mth sampling period forward from the current sampling period t (i.e., the average voltage value before the Mth sampling period forward from the current sampling period t), V(t-(M-1)) is the voltage value before the M-1th sampling period forward from the current sampling period t, μ V (t-(M-1)) is the average voltage value before the M-1th sampling period before the current sampling period t (that is, the average voltage value before the M-1th sampling period before the current sampling period t), W(t-(t-(M-1))) is the time weighted function of the M-1th sampling period before the current sampling period t, W(tt) is the time weighted function of the current sampling period t, W(tt) = W(0) = e -λ*0 =1, M represents the number of past sampling cycles used for calculation, for example, the past 3 to 5 cycles.

[0311] In addition, the current deviation correction term I adjust (t) is calculated according to the following formula:

[0312]

[0313] Where I(t) is the current value of the current sampling period t, μ I (t) is the current average value before the current sampling period t (i.e., the average value of the historical current), W(t-(tM))=W(M) is the time weighted function of the Mth sampling period before the current sampling period t, I(tM) is the current value of the Mth sampling period before the current sampling period t, μ I (tM) is the average current value before the Mth sampling period forward from the current sampling period t, I(t-(M-1)) is the current value before the M-1th sampling period forward from the current sampling period t, μ I (t-(M-1)) is the current average value before the M-1th sampling period forward from the current sampling period t, W(t-(t-(M-1))) is the time weighted function of the M-1th sampling period forward from the current sampling period t, W(tt) is the time weighted function of the current sampling period t, W(tt) = W(0) = e -λ*0 =1, M represents the number of past sampling cycles used for calculation, for example, the past 3 to 5 cycles.

[0314] Similarly, the temperature deviation correction term T adjust (t) is calculated according to the following formula:

[0315]

[0316] Where T(t) is the temperature value of the current sampling period t, μ T(t) is the average temperature before the current sampling period t (i.e., the average historical temperature), W(t-(tM))=W(M) is the time weighted function of the Mth sampling period forward from the current sampling period t, T(tM) is the temperature of the Mth sampling period forward from the current sampling period t, μ T (tM) is the average temperature value before the Mth sampling period forward from the current sampling period t, T(t-(M-1)) is the temperature value of the M-1th sampling period forward from the current sampling period t, μ T (t-(M-1)) is the average temperature before the M-1th sampling period before the current sampling period t, W(t-(t-(M-1))) is the time weighting function of the M-1th sampling period before the current sampling period t, W(tt) is the time weighting function of the current sampling period t, W(tt) = W(0) = e -λ*0 =1, M represents the number of past sampling cycles used for calculation, for example, the past 3 to 5 cycles.

[0317] In calculating the voltage deviation correction term V adjust (t), current deviation correction term I adjust (t) and the temperature deviation correction term T adjust (t), if the data is less than M cycles, the default value is used to fill the gap, which is the initial value or the initial historical average value set by the system or the initial value measured under normal operating conditions.

[0318] The time weighting function W(t-(tM))=W(M) of the sampling period at the current time point t is calculated according to the following formula:

[0319] W(t-(tM))=W(M)=e -λM ;

[0320] Wherein, M represents the number of past sampling cycles used for calculation, for example, the past 3 to 5 cycles, and λ is the time attenuation coefficient, which is usually between 0.1 and 0.3; in this embodiment, a larger value of λ makes the weight of the recent cycle higher.

[0321] Specifically, when λ takes a larger value (close to 0.3), the time weighting function decays faster, which means that sampling periods closer to the current time t are given higher weights, while the weights of sampling periods farther away decrease rapidly. Therefore, a larger value of λ makes the formula more sensitive to recent data and has a smaller impact on historical data.

[0322] When λ takes a smaller value (close to 0.1), the decay speed is slower, and the data from the farther sampling period still has a relatively large influence when weighted. Therefore, a smaller value of λ makes the formula respond more smoothly and stably to the entire historical data. Assume that M = 3 (the past three sampling periods), taking voltage as an example, the voltage data of the past three sampling periods are 220V, 230V, and 225V, and the current sampling period is t.

[0323] Historical average μ V (t):

[0324] 1) Calculate μ V (t-3): Because t-3 is the earliest sampling period, and there is not enough sampling period data to refer to, the default value is filled with the initial value or the initial historical average value set by the system or the initial value measured under normal operating conditions as μ V (t-3)=220.

[0325] 2) Calculate μ V (t-2): The historical average value of t-2 can refer to the data of t-3.

[0326] Therefore, μ V (t-2)=220 / 1=220.

[0327] 3) Calculate μ V (t-1): The historical average value of t-1 can refer to the data of t-3 and t-2.

[0328] μ V (t-1)=(220+230) / 2=225.

[0329] 4) Calculate μ V (t): The historical average value of t can refer to the data of t-3, t-2, and t-1.

[0330] μ V (t)=(220+230+225) / 3=225.

[0331] Calculate the absolute value of the deviation for each sampling period:

[0332] Substituting these historical averages, calculate the absolute value of the deviation for each sampling period one by one:

[0333] The first sampling cycle (time point t-3) (voltage value: 220):

[0334] Absolute value of deviation | 220-μ V (t-3)∣=∣220-220∣=0;

[0335] The second sampling period (time point t-2) (voltage value: 230):

[0336] Absolute value of deviation | 230-μ V (t-2)∣=∣230-220∣=10;

[0337] The third sampling cycle (time point t-1) (voltage value: 225):

[0338] Absolute value of deviation | 225-μ V (t-1)∣=∣225-225∣=0;

[0339] Calculate the time weighting function: Assume λ = 0.1 (λ is the time attenuation coefficient, and the system sets the value to λ = 0.1 for a sampling interval of 3 periods), then:

[0340] For M=3: W(t-3)=e 0.1*3 =e -0.3 ;

[0341] For M = 2: W(t-2) = e 0.1*2 =e -0.2 ;

[0342] For M=1: W(t-1)=e 0.1*1 =e -0.1 ;

[0343] Calculate the adaptive bias correction term V adjust (t): Substitute the absolute value of the deviation and the time weighting function into the formula:

[0344] V adjust (t)=1 / 3(0*W(t-3)+10*W(t-2)+0*W(t-1))=1 / 3(0*e -0.3 +10*e -0.2 +0*

[0345] e -0.1 );

[0346] According to the above example steps, the voltage deviation correction term V is calculated respectively. adjust (t), current deviation correction term I adjust (t) and the temperature deviation correction term T adjust (t), which are summed to obtain A(t).

[0347] The local alarm unit includes an alarm trigger, a signal controller and an alarm output device. The alarm trigger receives and parses key status information in the health status report to determine whether an alarm needs to be issued. The signal controller converts the signal of the alarm trigger into a visual or audible alarm signal to ensure that on-site personnel can notice it in time. The alarm output device is used to provide alarm prompts to the on-site personnel, and the alarm prompts include visual, auditory and text reminders.

[0348] The alarm output device includes an alarm, a buzzer and a display screen, wherein the alarm light flashes red or yellow light to visually indicate the fault level, the buzzer emits a beep or other audio alarm to alert on-site personnel, and the display screen (LCD and / or OLED) displays a detailed health status report, including the current value and deviation of the operating data, to help on-site personnel understand the cause of the fault.

[0349] The specific process is:

[0350] The local alarm unit receives the health status report from the status analysis unit, and the alarm trigger reads the comprehensive health score, each standardized score (such as Z V , Z I , Z T , Z Vib ) and trend information.

[0351] Compare the operating data with the preset alarm threshold to determine whether the current status of the equipment reaches the alarm condition. For example:

[0352] Mild alarm: The overall health score is lower than 80, or any parameter deviation exceeds a certain set range;

[0353] Moderate alarm: The overall health score is lower than 60, or any parameter deviation exceeds the set range significantly;

[0354] Severe Alert: The overall health score is below 40, indicating that the device is in a high-risk state.

[0355] If the alarm trigger determines that the current state exceeds the threshold, an alarm signal is issued, and the signal controller controls the alarm light, buzzer and display screen to start accordingly.

[0356] Different alarms can have different prompts. For example, a mild alarm only flashes the alarm light, a moderate alarm adds a buzzer prompt, and a severe alarm displays a detailed report on the display.

[0357] When an alarm is triggered, the display screen can show the current comprehensive health score, the parameters that exceed the threshold and their specific values, so that on-site personnel can understand the detailed equipment status.

[0358] The display can also provide maintenance recommendations, such as "check device temperature and current sensors," to help field personnel respond promptly.

[0359] Through the cooperation between the side processing module and the data acquisition module, the status of the target power transmission equipment is accurately collected, ensuring that the entire system has the advantages of strong processing capability, high data processing efficiency, good real-time performance, strong monitoring capability, reliable evaluation means and high intelligence;

[0360] In addition, through the cooperation between the side processing module and the data acquisition module, the status data of the target power transmission equipment can be accurately collected and locally processed in real time, ensuring that the system has strong data processing capabilities, efficient data processing efficiency and excellent real-time monitoring capabilities, further improving the accuracy of perception of the operating status of the target power transmission equipment.

[0361] Optionally, the assessment response module includes a health status analysis unit, a status assessment unit, and a maintenance strategy generation unit, the health status analysis unit extracts and analyzes the comprehensive health score of the tth sampling period, the voltage deviation value of the tth sampling period, the current deviation value of the tth sampling period, the temperature deviation value of the tth sampling period, and the vibration deviation value of the tth sampling period from the health status report, the status assessment unit comprehensively assesses the current status of the equipment based on the comprehensive health score, various standardized scores, and trend analysis results, and generates an assessment result, and the maintenance strategy generation unit formulates a corresponding maintenance strategy based on the assessment result.

[0362] Optionally, the health status parsing unit includes a data receiver, a parsing processor and a data buffer. The data receiver is responsible for receiving the health status report data. The parsing processor parses the received original health status report data, extracts the numerical value of each field, formats the parsing result into structured data, and stores the parsed data for calling by the status evaluation unit.

[0363] The state evaluation unit calculates the comprehensive score index ER based on the comprehensive health score, each standardized score and trend analysis result according to the following formula:

[0364]

[0365] Among them, ER(t) is the comprehensive score index of the target power transmission equipment, H score (t) is the comprehensive health score of the target power transmission equipment, Z V (t) is the standardized fraction of the voltage in the tth sampling period, Z I (t) is the normalized fraction of the current in the tth sampling period, Z T (t) is the normalized fraction of the temperature in the tth sampling period, Z Vib(t) is the standardized fraction of the vibration quantity in the t-th sampling period, δ is the adjustment coefficient, and TF(t) is the trend factor in the t-th sampling period;

[0366]

[0367] Among them, σ TF is the historical standard deviation of the trend factor TF(t), μ TF is the historical average value of the trend factor TF(t).

[0368] Among them, the historical average value of trend factor TF(t) is μ TF Calculated according to the following formula:

[0369]

[0370] N is the number of sampling periods used to calculate the average value, and TF(t) is the trend factor of the t-th sampling period, and the formula is:

[0371]

[0372] In the formula, H score (t) is the comprehensive health score of the tth sampling period, H score (t-1) is the comprehensive health score of the previous sampling period.

[0373] In the embodiment of the present application, in this embodiment, it can be set to i=1 to start calculating the trend factor. The first data point is usually used to initialize the system state, so skipping its calculation will not affect the subsequent trend analysis and comprehensive health score. The calculation of the historical average value starts to accumulate from the second data point.

[0374] Among them, TF(t)>0: It means that the comprehensive health score has improved compared with the previous sampling period, and the device status is improving;

[0375] TF(t)<0: indicates that the comprehensive health score has decreased compared with the previous sampling period, and the device status may be deteriorating;

[0376] TF(t)=0: indicates that the comprehensive health score has not changed and the device status remains stable;

[0377] The historical standard deviation σ of the trend factor TF(t) TF Calculated according to the following formula:

[0378]

[0379] Where N is the number of sampling periods used to calculate the average value. For example, if the average value is the average value of the past 50 sampling periods, then N is 50. TF(t) is the trend factor of the t-th sampling period, μ TFis the historical average value of the trend factor TF(t).

[0380] Optionally, the maintenance types generated by the maintenance strategy generation unit include: regular monitoring, preventive maintenance, corrective maintenance, and emergency maintenance.

[0381] The maintenance strategy generation unit obtains the evaluation result of the status evaluation unit and formulates the following maintenance strategies:

[0382] Regular monitoring (ER(t)>80): The equipment is in good operating condition.

[0383] Preventive maintenance (60<ER(t)≤80): The equipment status is slightly abnormal and there are potential risks.

[0384] Corrective maintenance (40<ER(t)≤60): The equipment status shows significant deviation and needs to be inspected and maintained as soon as possible.

[0385] Emergency maintenance (ER(t)≤40): The equipment status has deteriorated severely or failed, and it needs to be shut down immediately for repair.

[0386] According to the above maintenance strategy classification, the maintenance strategy generation unit can formulate the following specific maintenance strategies:

[0387] 1) Regular monitoring (ER(t)>80) The equipment is in good condition and does not require additional maintenance, only regular monitoring is needed:

[0388] Maintenance measures:

[0389] Routine monitoring: Maintain the original monitoring frequency (such as monitoring once a month or once a quarter), and record the changes in the health status.

[0390] Data recording: Regularly record the health status report and the changes in various parameters for subsequent trend analysis.

[0391] Analysis report: Generate a status analysis report after the monitoring cycle ends, as a reference for equipment management and maintenance.

[0392] Execution frequency: Maintain the regular monitoring frequency without increasing the inspection and maintenance burden.

[0393] 2) Preventive maintenance (60<ER(t)≤80)

[0394] Strategy description: The equipment status is slightly abnormal and preventive maintenance is required to prevent the further development of potential risks.

[0395] Maintenance measures:

[0396] Operating data inspection: Increase the inspection and recording of operating data with large deviations, such as temperature and current, to ensure that the operating data is within the normal range.

[0397] Periodic review: Shorten the monitoring period (e.g., monitor once a week) to promptly track the equipment status.

[0398] Environmental adjustment: If the deviation is related to environmental factors, adjust the equipment operating environment or parameters as needed (e.g., increase cooling or reduce load).

[0399] Execution frequency: Appropriately shorten the monitoring period and focus on relevant parameters in the next routine inspection.

[0400] 3) Corrective maintenance (40 < ER(t) ≤ 60) When the equipment status shows significant deviation and poor operating stability, corrective maintenance measures need to be taken as soon as possible:

[0401] Maintenance measures:

[0402] Fault troubleshooting: Conduct a detailed inspection of the operating data outside the normal range (such as vibration, voltage) to detect possible abnormalities inside the equipment.

[0403] Key component maintenance: Inspect high-risk components (such as the temperature regulation module or current regulation module), and clean or replace components at risk of wear or failure.

[0404] Health status recheck: Re-evaluate the equipment health status after repair to ensure the repair effect.

[0405] Execution frequency: Arrange an on-site inspection within 1 week, and then recheck the equipment status weekly until it returns to the normal state.

[0406] 4) Emergency maintenance (ER(t) ≤ 40) When the equipment status deteriorates severely and there are serious faults or fault risks, immediately stop the machine and take emergency maintenance measures:

[0407] Maintenance measures:

[0408] Emergency shutdown: Immediately stop the equipment operation to prevent the fault from expanding or causing safety accidents.

[0409] Comprehensive overhaul: Arrange a professional maintenance team to conduct a comprehensive inspection of the equipment, focusing on all parameters with deviations exceeding the threshold.

[0410] Replace or repair key components: If serious damage to key components is found, immediately replace the damaged components or perform repairs to ensure equipment safety.

[0411] Fault analysis and improvement: After the emergency maintenance is completed, conduct a fault analysis and generate a detailed fault report to identify and analyze the fault causes and provide improvement measures for subsequent maintenance.

[0412] Execution frequency: Immediately execute the maintenance, and the equipment cannot be put back into use until it is fully restored after shutdown.

[0413] In this embodiment, the maintenance strategy formulation process of the maintenance strategy generation unit includes:

[0414] Obtaining evaluation results: Obtaining comprehensive evaluation results ER(t) from the state evaluation unit;

[0415] Determine the maintenance type: Select the corresponding maintenance type according to the ER(t) value;

[0416] Formulate maintenance measures: formulate specific maintenance tasks according to the requirements of the measures in the maintenance type, including inspection cycle, key components, execution frequency, etc.;

[0417] Generate maintenance plan: organize maintenance measures into a maintenance plan, assign tasks and set priorities;

[0418] Perform maintenance and provide feedback: After maintenance is completed, feedback the results to the system, track the equipment recovery, and adjust the monitoring frequency or maintenance measures based on the recovery situation;

[0419] Through the cooperation between the evaluation response module and the side processing module, the generation and evaluation of the health status report of the target transmission equipment are more efficient and accurate. The evaluation response module can respond quickly based on the real-time analysis of the health status data, effectively improving the monitoring accuracy of the system and the reliability of maintenance decisions, ensuring that the system can generate reasonable maintenance strategies in time when anomalies occur.

[0420] Through the cooperation between the evaluation response module and the data acquisition module, it is ensured that the collected target transmission equipment status data can be continuously used for health status monitoring and maintenance strategy generation. The real-time data provided by the data acquisition module provides an accurate basis for the evaluation response, effectively supports the scientific nature of the maintenance strategy, and enables the system to have reliable status tracking capabilities and dynamic evaluation advantages.

[0421] In this embodiment, through the comprehensive cooperation of the data acquisition module, the side processing module and the evaluation response module, a complete closed loop is formed from data acquisition, state analysis to evaluation response. The system can realize intelligent monitoring and adaptive maintenance of the target power transmission equipment, and has the advantages of strong processing capability, timely data feedback, accurate real-time monitoring, and flexible maintenance response, which greatly improves the reliability and intelligence level of the system.

[0422] On the basis of the above embodiment, the evaluation response module further includes a maintenance dispatch unit, a maintenance status analysis unit and a feedback unit. The maintenance dispatch unit dispatches a maintenance work order according to the maintenance type of the maintenance strategy generation unit. The maintenance status analysis unit collects the maintenance status of the maintenance work order, and analyzes the real-time status of the maintenance work order according to the collected maintenance status data to form a maintenance result, and triggers the feedback unit to provide feedback to the control center. The feedback unit feeds back the maintenance result to the control center.

[0423] The maintenance dispatch unit includes a work order generator, a personnel matcher, a task scheduler, and a data dispatcher. The work order generator generates a detailed maintenance work order based on the maintenance type and equipment status information provided by the maintenance strategy generation unit, and the task scheduler selects the most suitable maintenance personnel according to the work order requirements (taking into account technical level, geographical location, current working status, etc.). The data dispatcher is responsible for assigning the work order to the selected maintenance personnel and sending the work order information to the maintenance personnel's terminal device (such as a mobile phone, tablet or smart watch, etc.).

[0424] The maintenance status analysis unit includes a task collection subunit and a task analysis subunit, wherein the task collection subunit collects the real-time status data of the maintenance work order of the maintenance personnel, and the task analysis subunit analyzes the real-time status of the maintenance work order according to the collected real-time status data to form a maintenance result;

[0425] The task collection subunit includes a status collector and a status data buffer. The status collector collects maintenance status data uploaded by maintenance personnel through terminal devices, such as "task start", "pause", "resume", "task completion" and other status, as well as progress information of maintenance operations (such as the percentage of completed tasks and details of current operations). The status data buffer caches the collected status data to ensure that the data will not be lost in the case of network instability, and provides data support for the task analysis subunit.

[0426] The real-time status data includes time consumption data and the current completion degree of the assigned maintenance workers; the task analysis subunit obtains the real-time status data and calculates the trend evaluation index TEI according to the following formula:

[0427]

[0428] In the formula, Completeness historyis the historical average completion rate of the assigned maintenance personnel, α is the trend influence coefficient, and its value is determined according to the type of maintenance work order. Specifically, α=0.4 is used for urgent tasks, α=0.2 is used for general tasks, and α=0.1 is used for slack tasks. △t is the time interval, that is, the time difference between the current sampling time and the time point used to calculate the historical average progress;

[0429] Among them, the current completion degree of the assigned maintenance worker is calculated according to the following formula:

[0430]

[0431] In the formula, Times spent The time wasted executing the maintenance work order. Its value is directly collected by the status collector. time The theoretical time required to execute a maintenance work order. Its value is set by the system according to the type of maintenance strategy.

[0432] Average historical completion rate of maintenance workers assigned history Calculate according to the following formula:

[0433]

[0434] In the formula, Completeness j is the completion degree of the assigned maintenance worker in the jth cycle task, and M is the selected number of historical task cycles, such as the past 3 or 5 cycle tasks.

[0435] If the trend evaluation index TEI is greater than the warning threshold Worn set by the system, the feedback unit is triggered to provide feedback to the control center;

[0436] If the value evaluation index TEI is less than or equal to the warning threshold Worn set by the system, there is no need to immediately provide feedback to the control center;

[0437] Among them, the system-set warning threshold Worn is set by the system or the administrator according to the type of maintenance work order or maintenance type to be executed, and is input from the human-computer interaction interface. This is a technical means well known to technicians in this field. Technicians in this field can refer to relevant technical manuals to learn about the technology, so it will not be described one by one in this embodiment.

[0438] The feedback unit includes a data collector and an information transmitter. The data collector collects and summarizes the data of the maintenance status analysis unit (including task status, task completion, abnormal events, etc.), and the information transmitter transmits the feedback information to the control center to ensure the real-time and reliability of information transmission;

[0439] The information transmitter transmits data via a wireless network (such as Wi-Fi, 4G, 5G) or a wired network (such as Ethernet), and a regular or event-driven feedback frequency can be set.

[0440] Through the cooperation between the maintenance dispatch unit, maintenance status analysis unit and feedback unit, the entire maintenance process becomes clearer and more transparent, ensuring the real-time monitoring capability of the entire system on the target power transmission equipment, more accurate monitoring efficiency, and more automated and intelligent overall operation and maintenance efficiency.

[0441] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the information expected by the technical solution of this application can be achieved, and this document is not limited here.

[0442] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A side-based power transmission equipment monitoring method, characterized in that: The method comprises: Monitoring the indicators of the target power transmission equipment through sensors to obtain the operating data of the target power transmission equipment; wherein the operating data includes at least one of the current, voltage, temperature and vibration of the target power transmission equipment; Determine a standardized score of the operating data, and determine a comprehensive health score of the target power transmission equipment according to the standardized score of the operating data, the adaptive deviation correction item of the operating data, and the proportionality coefficient; wherein the standardized score reflects the degree of deviation of the operating data of each sampling period from the average value; and the adaptive deviation correction item reflects the degree of deviation of the operating data of the current sampling period from the operating data of each previous sampling period; Determining a comprehensive score index of the target power transmission equipment according to the comprehensive health score, the standardized score, and the trend factor of the target power transmission equipment; The device operating state of the target power transmission device is determined according to the comprehensive scoring index of the target power transmission device.

2. The method according to claim 1, characterized in that The method further comprises: For each existing power transmission device, determine key data of each existing power transmission device according to the device type and / or usage scenario of the existing power transmission device; Dividing the key data into different data intervals, and determining the corresponding relationship between the data intervals and the proportional coefficients; wherein the key data in different data intervals are positively correlated with the proportional coefficients; The process of determining the proportionality coefficient includes: The key data of the target power transmission device is determined, and the proportional coefficient corresponding to the data interval where the key data of the target power transmission device is located is determined from the corresponding relationship as the proportional coefficient of the target power transmission device.

3. The method according to claim 1, characterized in that Determining a standardized score of the operating data, and determining a comprehensive health score of the target power transmission device according to the standardized score of the operating data, an adaptive deviation correction term of the operating data, and a proportionality coefficient, including: The comprehensive health score of the target power transmission equipment is determined based on the following formula: H score (t)=a-K·ln(1+Z V (t) 2 +Z I (t) 2 +Z T (t) 2 +Z Vib (t) 2 )-A(t); Among them, H score (t) is the comprehensive health score of the target power transmission equipment; Z V (t) is the standardized fraction of the voltage in the tth sampling period, Z I (t) is the normalized fraction of the current in the tth sampling period, Z T (t) is the normalized fraction of the temperature in the tth sampling period, Z Vib (t) is the standardized fraction of the vibration amount in the t-th sampling period, A(t) is the adaptive bias correction term in the t-th sampling period, K is the proportional coefficient, and a is the constant term.

4. The method according to claim 3, characterized in that The process of determining the smoothed operating data of the t-th sampling period for determining the normalized score of the operating data of the t-th sampling period includes: Determine the operating data collected in the current sampling period and the operating data after smoothing in the previous sampling period; The operation data collected in the current sampling period and the operation data after smoothing in the previous sampling period are weightedly summed to obtain the operation data after smoothing in the current sampling period; wherein the operation data includes at least one of voltage, current, temperature and vibration.

5. The method according to claim 4, characterized in that The process of determining the weight values ​​for weighted summation includes: Determining monitoring timeliness data of the target power transmission equipment; The weight value of the operation data collected in the current sampling period is made positively correlated with the monitoring timeliness data; Taking the difference between the weight value of the operation data collected in the current sampling period and the weight value of the operation data collected in the previous sampling period as the operation data after smoothing; The monitoring timeliness data is positively correlated with the monitoring timeliness of the target power transmission equipment.

6. The method according to claim 1, characterized in that The process of determining the adaptive deviation correction item of the operating data includes: For each sampling period, respectively determine the difference between the operating data in the sampling period and the average value of the operating data in each sampling period before the sampling period; Determining a product of the difference and a time-weighted function corresponding to the sampling period, and determining an average value of the products corresponding to each sampling period as an adaptive deviation correction item of the operating data; wherein the operating data includes at least one of voltage, current, temperature and vibration; The process of determining the adaptive deviation correction item of the target power transmission device includes: The sum of the adaptive deviation correction items of each operation data is used as the adaptive deviation correction item of the target power transmission equipment.

7. The method according to claim 6, characterized in that The process of determining the time weighting function includes: Determine the product of the time decay coefficient and the number of sampling periods before the current sampling period, and take the inverse to obtain the decay index; The decay index is used as an independent variable of a natural exponential function to calculate a time weighted function corresponding to the sampling period.

8. The method according to claim 1, characterized in that Determining a comprehensive score index of the target power transmission equipment according to the comprehensive health score, the standardized score, and the trend factor of the target power transmission equipment includes: The comprehensive scoring index of the target power transmission equipment is determined according to the following formula: Among them, ER(t) is the comprehensive score index of the target power transmission equipment, H score (t) is the comprehensive health score of the target power transmission equipment, Z V (t) is the standardized fraction of the voltage in the tth sampling period, Z I (t) is the normalized fraction of the current in the tth sampling period, Z T (t) is the normalized fraction of the temperature in the tth sampling period, Z Vib (t) is the standardized fraction of the vibration quantity in the t-th sampling period, δ is the adjustment coefficient, and TF(t) is the trend factor in the t-th sampling period; Among them, σ TF is the historical standard deviation of the trend factor TF(t), μ TF is the historical average value of the trend factor TF(t). Among them, the historical average value of trend factor TF(t) is μ TF Calculated according to the following formula: N is the number of sampling periods used to calculate the average value, and TF(t) is the trend factor of the t-th sampling period, and the formula is: In the formula, H score (t) is the comprehensive health score of the tth sampling period, H score (t-1) is the comprehensive health score of the previous sampling period.

9. The method according to claim 1, characterized in that: The method further comprises: In the case that the equipment operation state of the power transmission equipment does not reach the preset state, notifying the maintenance personnel through the terminal to perform maintenance on the target power transmission equipment, and determining the trend evaluation index of the maintenance personnel; The trend assessment index of the maintenance personnel is determined based on the following formula: Among them, Completeness is the current completion degree of the delegated maintenance personnel, Completeness history is the historical average completion rate of the assigned maintenance personnel, and α is the trend influence coefficient; Current completion Time spent The time that maintenance personnel have wasted in executing maintenance work orders; Total time Theoretical time consumption for maintenance personnel to execute maintenance work orders; Historical average completion rate Completeness j is the completion degree of the assigned maintenance personnel in the jth cycle task, and M is the number of selected historical task cycles.

10. A power transmission equipment monitoring system based on the edge, characterized in that: The system comprises: a server, a data acquisition module, a side processing module, and an evaluation response module; the data acquisition module, the side processing module, and the evaluation response module are respectively connected to the server; The data acquisition module acquires the operating data of the target power transmission equipment, the side processing module performs local processing on the operating data transmitted by the data acquisition module, so as to perform real-time analysis on the operating status of the target power transmission equipment and generate a health status report of the target power transmission equipment, the evaluation response module evaluates the health status report of the power transmission equipment to form an evaluation result, and generates a maintenance strategy for the target power transmission equipment according to the evaluation result: Among them, the side processing module includes a data receiving unit, a status analysis unit, and a local alarm unit. The data receiving unit receives the operation data from the data acquisition module, and the status analysis unit processes and analyzes the received operation data to form an analysis result to obtain the health status report of the target power transmission equipment. The local alarm unit triggers an early warning according to the health status report and prompts the maintenance personnel to handle it.

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