Method and system for ensuring power supply of generator without power outage

Abstract

The embodiment of the invention provides a method and a system for power-off-free operation of power protection of a generator, and relates to the technical field of power protection. The method comprises the following steps: acquiring first operation data of a power distribution terminal, wherein the first operation data is used for representing data detected by a first sensor carried on the power distribution terminal; acquiring second operation data of the power distribution terminal, wherein the second operation data is used for representing data detected by a second sensor carried on the unmanned aerial vehicle; obtaining a first risk assessment value of the power distribution terminal according to the first operation data, the second operation data and historical operation data of the power distribution terminal; and when the first risk assessment value is smaller than a first risk threshold value, compensating the first sensor according to the first operation data and the second operation data, or compensating the first sensor according to the first operation data, the second operation data and environmental data at the power distribution terminal.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of power supply protection technology, and more particularly to a method and system for power supply protection without power outage of a generator. Background Art

[0002] There are many reasons for the failure of distribution terminals in actual operation, including bad operating environment (humid and rainy, etc.), human factors (parameter configuration errors, etc.), management factors (tower tilt), etc., which may cause partial or complete failure of distribution terminals. Generally, only when the distribution terminal is completely offline, the remote control refuses to operate, etc., can the operation and maintenance personnel know that the distribution terminal has failed, and they cannot diagnose the hidden dangers that may cause the failure at the early stage, thus reducing the reliability of the distribution terminal.

[0003] In the related art, the operating status of the distribution terminal is monitored in advance based on the status assessment system installed on the distribution master station. However, the amount of data generated by the distribution terminal is huge, including not only real-time information but also a large amount of historical data. The complexity of these data lies in the need to process and analyze them to accurately determine the type and location of the fault, which undoubtedly increases the computational burden and processing difficulty of the distribution master station control system. At the same time, the performance and reliability of the sensors and monitoring equipment in the distribution terminal will be affected by environmental factors and equipment aging, resulting in abnormal monitoring data, which will cause the status assessment system of the distribution master station to fail to obtain accurate results. This will greatly increase the risk of power outages for power supply without power outages. Summary of the invention

[0004] The embodiment of the present invention provides a method and system for operating a generator to ensure power supply without power outage, so as to improve the technical problem of poor status evaluation of a power distribution terminal in the related art.

[0005] In a first aspect, the present application provides a method for operating a generator without power outages, the method comprising: collecting first operating data of a distribution terminal, the first operating data being used to characterize data detected by a first sensor mounted on the distribution terminal; obtaining second operating data of the distribution terminal, the second operating data being used to characterize data detected by a second sensor mounted on a drone; obtaining a first risk assessment value of the distribution terminal based on the first operating data, the second operating data, and historical operating data of the distribution terminal; when the first risk assessment value is less than a first risk threshold, compensating the first sensor based on the first operating data, the second operating data, and environmental data at the distribution terminal.

[0006] In a possible implementation manner of the first aspect, the historical operation data includes historical operation time, historical number of failures, and historical average fault repair time.

[0007] In a possible implementation of the first aspect, obtaining the first risk assessment value of the distribution terminal according to the first operating data, the second operating data, and the historical operating data of the distribution terminal includes: obtaining the historical operating time, the historical number of faults, and the historical average fault repair time; normalizing the historical operating time, the historical number of faults, and the historical average fault repair time; and performing weighted calculation based on the normalized historical operating time, the historical number of faults, and the average fault repair time to obtain an intermediate risk assessment value;

[0008] A sensor influence coefficient is generated according to the first operation data and the second operation data; and a first risk assessment value is generated based on the sensor influence coefficient and the intermediate risk assessment value.

[0009] In a possible implementation manner of the first aspect, the first operating data includes a first operating temperature, the second operating data includes a second operating temperature, and the environmental data includes a current environmental temperature.

[0010] In a possible implementation of the first aspect, the compensating the sensor on the distribution terminal according to the first operating data, the second operating data and the environmental data at the distribution terminal includes: obtaining the difference between the first operating temperature and the second operating temperature; obtaining the historical temperature data of the first sensor during the initial use time and the historical environmental temperature data at the distribution terminal; obtaining the mean impact of the ambient temperature on the temperature of the first sensor based on the historical temperature data and the historical environmental data; generating a compensation value according to the difference, the mean impact and the current ambient temperature; and compensating the first sensor based on the compensation value.

[0011] In a possible implementation of the first aspect, when the first risk assessment value is greater than a first risk threshold, the first operating data, the second operating data, the historical operating data, and the environmental data at the distribution terminal are transmitted to a distribution automation master station.

[0012] In a possible implementation of the first aspect, the distribution automation master station obtains a second risk assessment value based on the first operating data, the second operating data, the historical operating data, and the environmental data at the distribution terminal, and determines a maintenance strategy on the distribution terminal based on the second risk assessment value.

[0013] In a possible implementation of the first aspect, the maintenance strategy of the sensor on the distribution terminal is determined based on the second risk assessment value, including: when the second risk assessment value is greater than a second risk threshold, controlling the drone to be on duty at the distribution terminal, and replacing the data of the first sensor with the data of the second sensor; when the second risk assessment value is less than the second risk threshold, compensating the second sensor based on the first operating data, the second operating data and the environmental data at the distribution terminal.

[0014] In a possible implementation of the first aspect, the first operating data includes a first operating temperature, an operating status, and a first vibration intensity; the second operating data includes a second operating temperature, a second vibration intensity, and heat distribution of the distribution terminal; the environmental data includes ambient humidity and current ambient temperature; and the historical operating data includes historical operating time, historical number of failures, and historical average failure repair time.

[0015] In a second aspect, the present application further provides a generator power supply non-stop operation system, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the method as described in any implementation of the first aspect.

[0016] Through the present invention, the drone can be used as an edge computing node in the entire operating system, and the data collected by the drone and the data of the distribution terminal can be analyzed to preliminarily determine the operating status of the distribution terminal. The data evaluation is distributed to the edge computing node to reduce the data load and computing pressure of the distribution automation master station. The drone has sufficient mobility as a computing node in the operating system, which can avoid the computing power constraints brought by the deployment of computing power in fixed distribution terminals in traditional technologies. At the same time, the sensors of the distribution terminal can be monitored and calibrated by drones, avoiding the technical problem of abnormal operation data detection caused by aging and other reasons of the sensors at the distribution terminal in related technologies, and providing accurate data support for accurately evaluating the operating status of the distribution terminal. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic diagram of the structure of an operation system according to an embodiment of the present invention;

[0018] Figure 2 is a schematic flow chart of an operation method according to an embodiment of the present invention;

[0019] Figure 3 It is a flowchart of an operation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be 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, rather than all of the embodiments.

[0021] In the following, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0022] In addition, in the present application, directional terms such as "up", "down", "left" and "right" may be defined including but not limited to the orientation relative to the schematic placement of the components in the drawings. It should be understood that these directional terms may be relative concepts, which are used for relative description and clarification, and may change accordingly according to the change in the orientation of the components in the drawings.

[0023] In this application, unless otherwise specified or limited, the term "connection" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. In addition, the term "coupling" can be a way of achieving electrical connection for signal transmission.

[0024] As used herein, "about," "substantially," or "approximately" includes the stated value and an average value that is within an acceptable range of variation from the particular value as determined by one of ordinary skill in the art taking into account the measurements in question and the errors associated with the measurement of the particular quantity (i.e., the limitations of the measurement system).

[0025] There are many reasons for the failure of the power distribution terminal 200 during actual operation, including bad operating environment (humid and rainy, etc.), human factors (parameter configuration errors, etc.), management factors (tower tilt), etc., which may cause partial or complete failure of the power distribution terminal 200. Generally, only when the power distribution terminal 200 is completely offline, the remote control refuses to operate, etc., can the operation and maintenance personnel know that the power distribution terminal 200 has a failure, and they cannot diagnose the hidden dangers that may cause the failure at the early stage, thereby reducing the reliability of the power distribution terminal 200.

[0026] In the related art, the operating status of the distribution terminal 200 is monitored in advance based on the status assessment system installed on the distribution master station. However, the amount of data generated by the distribution terminal 200 is huge, including not only real-time information but also a large amount of historical data. The complexity of these data lies in the need to process and analyze them to accurately determine the type and location of the fault, which undoubtedly increases the calculation burden and processing difficulty of the distribution master station control system. At the same time, the performance and reliability of the sensors and monitoring equipment in the distribution terminal 200 will be affected by environmental factors and equipment aging, resulting in abnormal monitoring data, which will cause the status assessment system of the distribution master station to fail to obtain accurate results.

[0027] The embodiment of the present invention provides a method and system for operating a generator to ensure power supply without power outage, so as to improve the technical problem in the related art that the status evaluation effect of the power distribution terminal 200 is poor.

[0028] like Figure 1 As shown, the system includes a distribution automation master station 100, a drone 300 and a distribution terminal 200. A control module is provided in the distribution automation master station 100. The control module may include a processor and a memory. The control module may communicate remotely with the distribution terminal 200 to realize data transmission and command control, and the communication mode may include wireless communication and wired communication.

[0029] The drone 300 is connected to the control module in communication. The drone 300 is equipped with a processing unit, such as a microprocessor or other chip device with processing functions. It can be understood that the selection of the processing unit on the drone 300 can be selected according to the specific application scenario and computing power requirements of the drone 300. The drone 300 can also be equipped with sensors, such as temperature sensors, humidity sensors, vibration sensors, infrared sensors, etc. The type of sensor can also be set based on the data to be detected. The drone 300 can receive instructions from the control module in the distribution automation master station 100, and has a two-way data communication function with the distribution automation master station 100.

[0030] Exemplarily, in order to realize the requirements of the method and the operating system of the present application for the operation of the drone 300, the drone 300 may be equipped with an operating platform. The operating unit may be hoisted on the drone 300. The operating unit may give the drone 300 the ability to detect and process data, so that the drone 300 can serve as an edge computing node in the system. The operating platform may be provided with components including but not limited to a processing unit, a storage unit, a sensor, a communication component, etc. For the convenience of subsequent description, the data processing related to the operating platform is described only with the drone 300 as the processing subject.

[0031] The power distribution terminal 200 can be connected to the power distribution automation master station 100 and the drone 300 for communication. The power distribution terminal 200 can include a generator, a switch station, a power distribution room, a ring network cabinet, a box-type substation, and a terminal device installed in the above equipment for collecting power distribution equipment operation fault information and controlling it.

[0032] Exemplarily, the power distribution terminal 200 is provided with sensors, such as temperature sensors, humidity sensors, vibration sensors, etc. The power distribution terminal 200 may have data collection, recording, and data processing capabilities that are weaker than those of the control module and the drone 300 operating platform. Exemplarily, the power distribution terminal 200 will collect information such as the temperature during its own operation, the humidity at the equipment, the vibration intensity of the equipment, and the operating status, and store it as historical operating data. Under normal conditions, only part of the historical operating data is reported to the distribution automation master station 100 to reduce the data load and computing burden of the control module.

[0033] For the convenience of description, in the description of subsequent implementation methods, the sensor mounted on the power distribution terminal 200 is referred to as the first sensor, and the data collected by the first sensor is referred to as the first operating data. The sensor mounted on the drone 300 is referred to as the second sensor, and the data on the power distribution terminal 200 collected by the second sensor is referred to as the second operating data. The environmental data at the power distribution terminal 200 collected by the second sensor is referred to as environmental data.

[0034] In some embodiments, the distribution automation master station 100 is communicatively connected with multiple distribution terminals 200 and multiple drone 300 operation nodes. When a certain distribution terminal 200 needs to be monitored, the control module issues a command to control the drone 300 to fly to the distribution terminal 200 to collect data and monitor the distribution terminal 200. After the drone 300 arrives at the designated distribution terminal 200, the second sensor mounted on the operation platform is used to collect data from the distribution terminal 200, such as its temperature, humidity, and vibration intensity of the distribution terminal 200. At the same time, environmental data at the distribution terminal 200 can also be collected.

[0035] The drone 300 analyzes the collected data in combination with the data at the distribution terminal 200, such as the first operating data, historical operating data, etc., to determine the operating status of the distribution terminal 200, such as obtaining a first risk assessment value based on the above data. When the first risk assessment value does not indicate an abnormal operating status, if it is less than the set first risk threshold, it indicates that the operating status of the distribution terminal 200 is good, and it is possible to continue to the next distribution terminal 200 for detection. When the first risk assessment value indicates an abnormality, if it is greater than the set first risk threshold, the drone 300 uploads the data to the distribution automation master station 100 for further analysis. It should be noted that the setting of the risk threshold can be calculated based on the calculation method of the first risk assessment value and past normal operating data, and can also be determined by a person skilled in the art based on other needs and their implementable means, and this application does not limit this.

[0036] In some exemplary embodiments, before performing the above data analysis, the drone 300 may also compare the collected second operating data with the first operating data collected by the power distribution terminal 200. When the gap is too large, it may indicate that the data is abnormal and needs to be analyzed in combination with the above data, otherwise the analysis will not continue. The above-mentioned large gap includes but is not limited to a large temperature gap, a large humidity gap, a large vibration intensity gap, and a large operating state gap. It can be understood that the operating data at the power distribution terminal 200 may include multiple types, and the specific data types and judgment criteria can be set by those skilled in the art, and this application does not limit this.

[0037] In some embodiments, the historical operation data may also include historical operation time, historical number of failures, and historical average fault repair time. The drone 300 communicates data with the power distribution terminal 200 and obtains the first operation data and historical operation data from the power distribution terminal 200. After obtaining the above data, the drone 300 normalizes the data to facilitate subsequent calculations, such as normalizing the historical operation time, the historical number of failures, and the historical average fault repair time.

[0038] The drone 300 performs weighted calculation based on the normalized historical operating time, the number of historical failures, and the average failure repair time to obtain an intermediate risk assessment value. For example, the calculation formula of the intermediate risk assessment value is:

[0039] S=w1*R+w2*F+w3*T

[0040] Among them, S is the intermediate risk assessment value, R is the normalized historical operating time, F is the normalized historical number of failures, T is the normalized historical average fault repair time, w1, w2, w3 are the corresponding weight factors, which respectively represent the importance of historical operating time, historical number of failures and historical average fault repair time in status assessment. The values ​​of w1, w2, and w3 can be 0 to 1, and w1+w2+w3=1.

[0041] It is understandable that the present application does not limit the data used to calculate the intermediate risk assessment value. In light of the present application, technicians in this field can also set more evaluation dimensions and evaluation data.

[0042] In this way, the status evaluation result of the terminal device can be obtained based on the historical operation data. If the status evaluation result is lower than the set threshold, it is considered that the operating status of the power distribution terminal 200 is poor and requires special attention.

[0043] The drone 300 may also generate a sensor influence coefficient according to the first operation data and the second operation data, and generate a first risk assessment value based on the sensor influence coefficient and the intermediate risk assessment value. It should be noted that when the intermediate risk assessment value is not corrected according to the sensor influence coefficient, the intermediate risk assessment value obtained in the above steps can be regarded as the final first risk assessment value.

[0044] Exemplarily, the first operating data includes a first operating temperature, the second operating data includes a second operating temperature, and the environmental data includes a current environmental temperature. The drone 300 calculates a ratio of the first operating temperature to the second operating temperature, uses the ratio as an influence coefficient of the sensor, and uses the coefficient to correct the intermediate risk assessment value, such as multiplying the intermediate risk assessment value by the influence coefficient.

[0045] It is understandable that, under the inspiration of this application, the calculation method of the sensor influence coefficient and the correction method of the sensor influence coefficient can also be determined by those skilled in the art based on technical requirements. This application does not limit this. As long as the intermediate risk assessment value is corrected by obtaining the sensor influence coefficient based on the sensor data difference based on this application, it is within the protection scope of this application.

[0046] In this way, the impact of inaccurate data caused by sensors can be taken into account in the evaluation of the operating status of the distribution terminal 200, and / or the overall status of the distribution terminal 200 can be mapped and corrected based on the performance status of the sensor, thereby obtaining a more accurate evaluation result.

[0047] In this way, the operating platform carried by the drone 300 is used as an edge computing node in the entire operating system, and the data collected by the drone 300 and the data of the distribution terminal 200 are analyzed to preliminarily judge the operating status of the distribution terminal 200. The data evaluation is distributed to the edge computing node, thereby reducing the data load and computing pressure of the distribution automation master station 100. In addition, the drone 300 has sufficient mobility as a computing node in the operating system, which can avoid the computing power constraints brought about by deploying computing power on fixed distribution terminals 200 in traditional technologies.

[0048] At the same time, in the above-mentioned embodiment, the operating data at the distribution terminal 200 can be verified by the drone 300, which can avoid the technical problem of abnormal operating data detection caused by aging and other reasons of the sensor at the distribution terminal 200 in the related technology, and provide accurate data support for accurately evaluating the operating status of the distribution terminal 200.

[0049] In some embodiments, when the first risk assessment value is less than a first risk threshold, the drone 300 compensates the sensor on the power distribution terminal 200 according to the first operating data, the second operating data, and the environmental data at the power distribution terminal 200 .

[0050] Exemplarily, the drone 300 obtains the historical temperature data of the first sensor and the historical ambient temperature data at the power distribution terminal 200 during the initial use time. The initial use time may be a period of time when the power distribution terminal 200 is just put into operation. During this period of time, the accuracy and stability of the sensor are relatively high due to the short use time. The historical ambient temperature data may be obtained by the drone 300 through networking or based on the ambient temperature sensor on the power distribution terminal 200.

[0051] The drone 300 obtains the average value of the impact of the ambient temperature on the temperature of the first sensor based on the historical temperature data and the historical environmental data. For example, on the first day of the historical data, when the ambient temperature was 30°C, 31°C, and 32°C, the temperature data of the terminal device was 25°C, 25.8°C, and 26.5°C. On the second day, when the ambient temperature was 30°C, 31°C, and 32°C, the temperature data of the terminal device was 25.3°C, 26.0°C, and 26.4°C. Assuming that the distribution terminal 200 is in a laboratory at 28°C, the operating temperature is 24.5°C. When the temperature on the first day was 30°C, 31°C, and 32°C, the impact values ​​of the ambient temperature on the temperature of the distribution terminal 200 were 0.5°C, 1.3°C, and 2°C. When the temperature on the second day was 30°C, 31°C, and 32°C, the impact values ​​of the ambient temperature on the temperature of the distribution terminal 200 were 0.8°C, 1.5°C, and 1.9°C. By calculating the average of the impact values ​​of the corresponding ambient temperature on the temperature of the power distribution terminal 200 in each day, the average impact value of the ambient temperature on the temperature of the first sensor can be obtained.

[0052] The drone 300 generates a compensation value for the first sensor based on the difference between the first operating temperature and the second operating temperature, the average value of the influence of the ambient temperature on the temperature of the first sensor, and the current temperature value.

[0053] For example, the current ambient temperature is 30°C, the influence mean at 30°C is 0.6, the first operating temperature is 26.5°C, and the second operating temperature is 28.3°C, then the compensation value is 28.3-26.5-0.6=1.2. After obtaining the compensation value, the drone 300 compensates the first sensor based on the compensation value, that is, the drone 300 interacts with the power distribution terminal 200 for data, such as program modification, and setting the data recorded by the power distribution terminal 200 to add the compensation value to the data measured by the first sensor.

[0054] In the above examples, the effect of ambient temperature on the sensor is taken into account when calculating the compensation value, while in other examples, the compensation value may be generated based only on the difference between the first operating temperature and the second operating temperature, that is, the effect of ambient temperature is not taken into account in the calculation of the compensation value.

[0055] In this way, the sensors of the distribution terminal 200 can be monitored and calibrated by the drone 300, avoiding the technical problem of abnormal operation data detection caused by aging and other reasons at the sensors at the distribution terminal 200 in the related technology, and providing accurate data support for accurately evaluating the operation status of the distribution terminal 200.

[0056] When the first risk assessment value is greater than the first risk threshold, the first operating data, the second operating data, the historical operating data, and the environmental data at the distribution terminal 200 are transmitted to the distribution automation master station 100. The distribution automation master station 100 obtains a second risk assessment value based on the first operating data, the second operating data, the historical operating data, and the environmental data at the distribution terminal 200, and determines a maintenance strategy on the distribution terminal 200 based on the second risk assessment value.

[0057] Exemplarily, the first operating data may include a first operating temperature, an operating state, and a first vibration intensity, the second operating data may include a second operating temperature, a second vibration intensity, and the heat distribution of the power distribution terminal 200, the environmental data may include the environmental humidity and the current environmental temperature, and the historical operating data may include historical operating time, historical number of faults, historical average fault repair time, etc. It can be understood that the data uploaded to the distribution automation master station 100 may also include audio, video, and images, etc., and the above information may be collected by the drone 300 or by the power distribution terminal 200.

[0058] The distribution automation master station 100 can analyze and evaluate one or more of the above data through an evaluation model to obtain a second risk assessment value. The evaluation model may include a substation equipment fault diagnosis model based on deep learning and knowledge graph, a distribution network fault diagnosis model based on multi-source data fusion, and a power transformer state evaluation and fault diagnosis technology based on big data analysis. It can be understood that the selection of the model can be selected or independently constructed according to the needs of technicians in this field, and this application does not limit this.

[0059] When the second risk assessment value is greater than the second risk threshold, the drone 300 is controlled to be on duty at the power distribution terminal 200, and the data of the second sensor is replaced by the data of the first sensor. In this step, the influence of the ambient temperature on the sensor of the drone 300 can also be considered, that is, the second sensor is compensated by the mean value of the influence of the ambient temperature. When the second risk assessment value is less than the second risk threshold, the drone 300 can use the method in the above embodiment to compensate the first sensor. For the supplementary method, please refer to the above embodiment and will not be repeated here.

[0060] In this way, the sensors on the drone 300 can be used to replace the sensors on the power distribution terminal 200 to achieve sensor maintenance at the power distribution terminal 200, which is conducive to monitoring the operating status of the power distribution terminal 200, and the drone 300 can also monitor the abnormalities of the power distribution terminal 200 in a timely manner. Therefore, in this application, while using the drone 300 as an edge computing node to evaluate the status of the power distribution terminal 200, it also uses its mobile operation capabilities to dynamically maintain the power distribution terminal 200 and perform on-duty monitoring when the risk of the power distribution terminal 200 is too high.

[0061] like Figure 2 , Figure 3 As shown, based on the above operation system, the present application also provides a generator power protection and non-stop operation method, which may include:

[0062] S100 , collecting first operating data of the power distribution terminal 200 , where the first operating data is used to represent data detected by a first sensor mounted on the power distribution terminal 200 .

[0063] The first operating data may be collected by a first sensor on the power distribution terminal 200, and the first operating data may include one or more of a first operating temperature, an operating state, and a first vibration intensity. The historical operating data includes historical operating time, historical fault times, historical average fault repair time, and the like.

[0064] S200 , obtaining second operating data of the power distribution terminal 200 , where the second operating data is used to represent data detected by a second sensor mounted on the drone 300 .

[0065] The second operation data may be collected by a drone 300 equipped with an operation platform, and a plurality of second sensors are provided on the drone 300. The second operation data may include one or more of a second operation temperature, a second vibration intensity, and heat distribution information of the power distribution terminal 200. The drone 300 operation platform may also collect environmental data, and the environmental data may include the environmental humidity and the current environmental temperature.

[0066] S300 . Obtain a first risk assessment value of the power distribution terminal 200 according to the first operating data, the second operating data, and historical operating data of the power distribution terminal 200 .

[0067] Exemplarily, S300, obtaining a first risk assessment value of the power distribution terminal 200 according to the first operating data, the second operating data, and historical operating data of the power distribution terminal 200, includes:

[0068] S310: Obtain the historical running time, the historical number of failures, and the historical average fault repair time.

[0069] S320: Normalize the historical operating time, the historical number of failures, and the historical average fault repair time.

[0070] S330, performing weighted calculation based on the normalized historical operating time, the number of historical failures, and the average failure repair time to obtain an intermediate risk assessment value.

[0071] S340: Generate a sensor influence coefficient according to the first operating data and the second operating data.

[0072] S350: Generate a first risk assessment value based on the sensor influence coefficient and the intermediate risk assessment value.

[0073] Exemplarily, the historical operation data may also include historical operation time, historical number of failures, and historical average fault repair time. The drone 300 communicates data with the power distribution terminal 200 and obtains the first operation data and historical operation data from the power distribution terminal 200. After obtaining the above data, the drone 300 normalizes the data to facilitate subsequent calculations, such as normalizing the historical operation time, the historical number of failures, and the historical average fault repair time.

[0074] The drone 300 performs weighted calculation based on the normalized historical operating time, the number of historical failures, and the average failure repair time to obtain an intermediate risk assessment value. For example, the calculation formula of the intermediate risk assessment value is:

[0075] S=w1*R+w2*F+w3*T

[0076] Among them, S is the intermediate risk assessment value, R is the normalized historical operating time, F is the normalized historical failure count index, TT is the normalized historical mean failure repair time index, w1, w2, w3 are the corresponding weight factors, which respectively represent the importance of historical operating time, historical failure count and historical mean failure repair time in status assessment. The values ​​of w1, w2 and w3 can be 0 to 1, and w1+w2+w3=1.

[0077] It is understandable that the present application does not limit the data used to calculate the intermediate risk assessment value. In light of the present application, technicians in this field can also set more evaluation dimensions and evaluation data.

[0078] In this way, the status evaluation result of the terminal device can be obtained based on the historical operation data. If the status evaluation result is lower than the set threshold, it is considered that the operating status of the power distribution terminal 200 is poor and requires special attention.

[0079] The drone 300 may also generate a sensor influence coefficient according to the first operation data and the second operation data, and generate a first risk assessment value based on the sensor influence coefficient and the intermediate risk assessment value. It should be noted that when the intermediate risk assessment value is not corrected according to the sensor influence coefficient, the intermediate risk assessment value obtained in the above steps can be regarded as the final first risk assessment value.

[0080] Exemplarily, the first operating data includes a first operating temperature, the second operating data includes a second operating temperature, and the environmental data includes a current environmental temperature. The drone 300 calculates a ratio of the first operating temperature to the second operating temperature, uses the ratio as an influence coefficient of the sensor, and uses the coefficient to correct the intermediate risk assessment value, such as multiplying the intermediate risk assessment value by the influence coefficient.

[0081] It is understandable that, under the inspiration of this application, the calculation method of the sensor influence coefficient and the correction method of the sensor influence coefficient can also be determined by those skilled in the art based on technical requirements. This application does not limit this. As long as the intermediate risk assessment value is corrected by obtaining the sensor influence coefficient based on the sensor data difference based on this application, it is within the protection scope of this application.

[0082] S400. When the first risk assessment value is less than a first risk threshold, the first sensor is compensated according to the first operating data and the second operating data, or the first sensor is compensated according to the first operating data, the second operating data and the environmental data at the distribution terminal 200.

[0083] Exemplarily, S400, compensating a sensor on the power distribution terminal 200 according to the first operating data, the second operating data, and environmental data at the power distribution terminal 200, includes:

[0084] S410: Obtain a difference between the first operating temperature and the second operating temperature.

[0085] S420 , obtaining historical temperature data of the first sensor and historical ambient temperature data of the power distribution terminal 200 during the initial use time.

[0086] S430: Obtain an average value of the effect of the ambient temperature on the temperature of the first sensor based on the historical temperature data and the historical environmental data.

[0087] S440: Generate a compensation value according to the difference, the impact mean, and the current ambient temperature.

[0088] S450: Compensate the first sensor based on the compensation value.

[0089] Exemplarily, the drone 300 obtains the historical temperature data of the first sensor and the historical ambient temperature data at the power distribution terminal 200 during the initial use time. The initial use time may be a period of time when the power distribution terminal 200 is just put into operation. During this period of time, the accuracy and stability of the sensor are relatively high due to the short use time. The historical ambient temperature data can be obtained by the drone 300 through networking.

[0090] The drone 300 obtains the average value of the impact of the ambient temperature on the temperature of the first sensor based on the historical temperature data and the historical environmental data. For example, on the first day of the historical data, when the ambient temperature was 30°C, 31°C, and 32°C, the temperature data of the terminal device was 25°C, 25.8°C, and 26.5°C. On the second day, when the ambient temperature was 30°C, 31°C, and 32°C, the temperature data of the terminal device was 25.3°C, 26.0°C, and 26.4°C. Assuming that the distribution terminal 200 is in a laboratory at 28°C, the operating temperature is 24.5°C. When the temperature on the first day was 30°C, 31°C, and 32°C, the impact values ​​of the ambient temperature on the temperature of the distribution terminal 200 were 0.5°C, 1.3°C, and 2°C. When the temperature on the second day was 30°C, 31°C, and 32°C, the impact values ​​of the ambient temperature on the temperature of the distribution terminal 200 were 0.8°C, 1.5°C, and 1.9°C. By calculating the average of the impact values ​​of the corresponding ambient temperature on the temperature of the power distribution terminal 200 in each day, the average impact value of the ambient temperature on the temperature of the first sensor can be obtained.

[0091] The drone 300 generates a compensation value for the first sensor based on the difference between the first operating temperature and the second operating temperature, the average value of the influence of the ambient temperature on the temperature of the first sensor, and the current temperature value.

[0092] For example, the current ambient temperature is 30°C, the influence mean at 30°C is 0.6, the first operating temperature is 26.5°C, and the second operating temperature is 28.3°C, then the compensation value is 28.3-26.5-0.6=1.2. After obtaining the compensation value, the drone 300 compensates the first sensor based on the compensation value, that is, the drone 300 interacts with the power distribution terminal 200 for data, such as program modification, and setting the data recorded by the power distribution terminal 200 to add the compensation value to the data measured by the first sensor.

[0093] In the above examples, the effect of ambient temperature on the sensor is taken into account when calculating the compensation value, while in other examples, the compensation value may be generated based only on the difference between the first operating temperature and the second operating temperature, that is, the effect of ambient temperature is not taken into account in the calculation of the compensation value.

[0094] S500 . When the first risk assessment value is greater than a first risk threshold, the first operating data, the second operating data, the historical operating data, and the environmental data at the distribution terminal 200 are transmitted to the distribution automation master station 100 .

[0095] S510, the distribution automation master station 100 obtains a second risk assessment value according to the first operating data, the second operating data, the historical operating data and the environmental data at the distribution terminal 200, and determines a maintenance strategy on the distribution terminal 200 according to the second risk assessment value.

[0096] S511, when the second risk assessment value is greater than a second risk threshold, controlling the drone 300 to be on duty at the power distribution terminal 200, and replacing the data of the first sensor with the data of the second sensor;

[0097] S512 . When the second risk assessment value is less than a second risk threshold, the first sensor is compensated according to the first operating data, the second operating data, and the environmental data at the power distribution terminal 200 .

[0098] When the first risk assessment value is greater than the first risk threshold, the first operating data, the second operating data, the historical operating data, and the environmental data at the distribution terminal 200 are transmitted to the distribution automation master station 100. The distribution automation master station 100 obtains a second risk assessment value based on the first operating data, the second operating data, the historical operating data, and the environmental data at the distribution terminal 200, and determines a maintenance strategy on the distribution terminal 200 based on the second risk assessment value.

[0099] Exemplarily, the first operating data may include a first operating temperature, an operating state, and a first vibration intensity, the second operating data may include a second operating temperature, a second vibration intensity, and the heat distribution of the power distribution terminal 200, the environmental data may include the environmental humidity and the current environmental temperature, and the historical operating data may include historical operating time, historical number of faults, historical average fault repair time, etc. It can be understood that the data uploaded to the distribution automation master station 100 may also include audio, video, and images, etc., and the above information may be collected by the drone 300 or by the power distribution terminal 200.

[0100] The distribution automation master station 100 can analyze and evaluate one or more of the above data through an evaluation model to obtain a second risk assessment value. The evaluation model may include a substation equipment fault diagnosis model based on deep learning and knowledge graph, a distribution network fault diagnosis model based on multi-source data fusion, and a power transformer state evaluation and fault diagnosis model based on big data analysis. It can be understood that the selection of the model can be based on the needs of technicians in this field to select an existing model or build it independently, and this application does not limit this.

[0101] When the second risk assessment value is greater than the second risk threshold, the drone 300 is controlled to be on duty at the power distribution terminal 200, and the data of the second sensor is replaced by the data of the first sensor. In this step, the influence of the ambient temperature on the sensor of the drone 300 can also be considered, that is, the second sensor is compensated by the mean value of the influence of the ambient temperature. When the second risk assessment value is less than the second risk threshold, the drone 300 can use the method in the above embodiment to compensate the first sensor. For the supplementary method, please refer to the above embodiment and will not be repeated here.

[0102] In this way, the sensors on the drone 300 can be used to replace the sensors on the power distribution terminal 200 to achieve sensor maintenance at the power distribution terminal 200, which is conducive to monitoring the operating status of the power distribution terminal 200, and the drone 300 can also monitor the abnormalities of the power distribution terminal 200 in a timely manner. Therefore, in this application, while using the drone 300 as an edge computing node to evaluate the status of the power distribution terminal 200, it also uses its mobile operation capabilities to dynamically maintain the power distribution terminal 200 and perform on-duty monitoring when the risk of the power distribution terminal 200 is too high.

[0103] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0104] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0105] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0106] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0107] An embodiment of the present invention further provides an electronic system, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0108] In an exemplary embodiment, the electronic system may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0109] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above.

[0110] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0111] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0112] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0113] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0114] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application shall be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for maintaining power supply of a generator without power outage, characterized in that: The method comprises: Collecting first operation data of the power distribution terminal, where the first operation data is used to represent data detected by a first sensor mounted on the power distribution terminal; Acquire second operating data of the power distribution terminal, where the second operating data is used to represent data detected by a second sensor carried on the drone; Obtaining a first risk assessment value of the power distribution terminal according to the first operating data, the second operating data, and historical operating data of the power distribution terminal; When the first risk assessment value is less than a first risk threshold, the first sensor is compensated according to the first operating data and the second operating data, or the first sensor is compensated according to the first operating data, the second operating data and environmental data at the power distribution terminal.

2. The method according to claim 1, characterized in that The historical operation data includes historical operation time, historical failure times, and historical average failure repair time.

3. The method according to claim 2, characterized in that The obtaining, according to the first operating data, the second operating data and the historical operating data of the power distribution terminal, a first risk assessment value of the power distribution terminal comprises: Obtain the historical running time, the historical number of failures, and the historical average failure repair time; Normalizing the historical operating time, the historical number of failures, and the historical average failure repair time; The intermediate risk assessment value is obtained by weighted calculation based on the normalized historical operating time, historical failure times, and average failure repair time; generating a sensor influence coefficient according to the first operation data and the second operation data; A first risk assessment value is generated based on the sensor influence coefficient and the intermediate risk assessment value.

4. The method according to claim 3, characterized in that The first operating data includes a first operating temperature, the second operating data includes a second operating temperature, and the environmental data includes a current environmental temperature.

5. The method according to claim 4, characterized in that The compensating the sensor on the power distribution terminal according to the first operating data, the second operating data and the environmental data at the power distribution terminal comprises: Obtaining a difference between the first operating temperature and the second operating temperature; Acquire historical temperature data of the first sensor and historical ambient temperature data at the power distribution terminal during the initial use time; Obtaining a mean value of the influence of the ambient temperature on the temperature of the first sensor based on the historical temperature data and the historical ambient temperature data; generating a compensation value according to the difference, the impact mean and the current ambient temperature; The first sensor is compensated based on the compensation value.

6. The method according to claim 1, characterized in that When the first risk assessment value is greater than a first risk threshold, the first operating data, the second operating data, the historical operating data, and the environmental data at the distribution terminal are transmitted to a distribution automation master station.

7. The method according to claim 6, characterized in that The distribution automation master station obtains a second risk assessment value based on the first operating data, the second operating data, the historical operating data and the environmental data at the distribution terminal, and determines a maintenance strategy on the distribution terminal based on the second risk assessment value.

8. The method according to claim 7, characterized in that Determining a maintenance strategy for the sensor on the power distribution terminal according to the second risk assessment value includes: When the second risk assessment value is greater than a second risk threshold, controlling the drone to be on duty at the power distribution terminal, and replacing the data of the first sensor with the data of the second sensor; When the second risk assessment value is less than a second risk threshold, the first sensor is compensated according to the first operating data, the second operating data, and environmental data at the power distribution terminal.

9. The method according to claim 7, characterized in that: The first operating data includes a first operating temperature, an operating status, and a first vibration intensity; the second operating data includes a second operating temperature, a second vibration intensity, and heat distribution of the distribution terminal; the environmental data includes ambient humidity and current ambient temperature; and the historical operating data includes historical operating time, historical number of failures, and historical average failure repair time.

10. A generator power supply non-stop operation system, comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method as claimed in any one of claims 1 to 9.

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