Low-voltage user power failure diagnosis method and system based on Internet of Things

Abstract

The invention relates to the field of fault diagnosis, in particular to a low-voltage user power failure diagnosis method and system based on the Internet of Things, and the method comprises the steps: obtaining the historical power failure data of a target region, calculating the power failure coefficient of a sub-target region, dividing a power failure risk region, determining a power failure monitoring state, and obtaining the power utilization data in the sub-target region. And analyzing the identity of the load curve and the peak-valley difference of the load to perform power failure diagnosis, judging whether the power failure state of the sub-target area is conventional power failure so as to determine whether to send a power failure warning, controlling a power supply device super capacitor to be turned on so as to continue to supply power to the sub-target area, or sending the power failure warning to a user corresponding to the sub-target area so as to send the power failure warning to the user corresponding to the sub-target area. Returning the power failure state of the sub-target area to historical power failure data, and updating a power failure monitoring state; and in response to the power failure warning, recording the sub-target area corresponding to the qualified power failure corresponding quantity value, judging whether the power failure response quantity value is qualified, recording the corresponding sub-target area, and updating the power failure diagnosis method.

Description

Technical Field

[0001] The present invention relates to the field of fault diagnosis, and in particular to a low-voltage user power outage diagnosis method and system based on the Internet of Things. Background Art

[0002] With the rapid development of technologies such as big data, artificial intelligence, and the Internet of Things, the power industry has also made great progress. However, there are still many problems that affect the normal power consumption of users. At the same time, the current power grid enterprises' perception of power outage and restoration can basically only reach the substation level, resulting in lagging and passive acquisition of power outage and restoration information of low-voltage customers under the substation, seriously affecting the power consumption experience of low-voltage customers, causing great pressure on customer service of basic power supply units, and affecting customer satisfaction. Therefore, the perception of power outage and restoration of low-voltage customers is one of the main research and development directions for subsequent construction of power grid enterprises.

[0003] Chinese Patent Publication No.: CN106646104A, discloses a distribution network fault diagnosis method, the method includes: constructing a full model of the power grid; obtaining fault information of a feeder, and locating the feeder where the fault is located according to the fault information and the full model of the power grid; obtaining fault indication information of a fault indicator, and locating the first fault interval where the fault is located according to the fault indication information and the full model of the power grid; obtaining a power outage event of a distribution transformer, and locating the first tripping device where the fault is located according to the power outage event of the distribution transformer and the full model of the power grid; obtaining measured sudden drop information of an outgoing line switch, and locating the second fault interval and the second tripping device where the fault is located according to the measured sudden drop information and the full model of the power grid. The above distribution network fault diagnosis method can accurately obtain the line segment where the fault occurs through fault indication information, power outage events of distribution transformers, and measured sudden drop information, in combination with the full model of the power grid, effectively improving the fault diagnosis efficiency.

[0004] Chinese Patent Publication No.: CN112345972A, discloses a method, device and system for diagnosing abnormal line-transformer relationship in a distribution network based on power outage events. The method includes generating a historical power outage distribution transformer set based on the power data of the distribution transformer obtained; obtaining the busbars and distribution transformers nearby based on the longitude and latitude information of the power outage distribution transformers in the power outage distribution transformer set; calculating the correlation coefficient index between the power outage distribution transformer and its nearby busbars to find out the suspected busbars that should belong; screening out all the lines under the suspected busbars that should belong to the power outage distribution transformer; calculating the proportion index of the number of power outage distribution transformers corresponding to all the lines under the suspected busbars that should belong, and seeking the threshold of the proportion index of the number of power outage distribution transformers; screening out the suspected lines that should belong to the power outage distribution transformer based on the threshold, and finding the power outage distribution transformers with incorrect line-transformer relationship and their suspected lines that should belong. The invention has simple calculation and can help operators timely discover abnormal line-transformer relationship distribution transformers and recommend their lines that should belong.

[0005] However, the following problems still exist in the prior art.

[0006] When conducting power outage diagnosis for low-voltage customers, for power outages of low-voltage customers, currently, it can only be judged by sensing the power outage of the substation area to determine the power outage of low-voltage customers under the substation area or by means of low-voltage customers actively reporting repairs, etc. This will lead to low efficiency of power outage diagnosis, slow response speed, and at the same time, maintaining the same high frequency of monitoring for different regions will cause the collected data to be severely overloaded, affecting the accuracy of power outage diagnosis. Summary of the Invention

[0007] Therefore, the present invention provides a method for diagnosing power outages of low-voltage users based on the Internet of Things to solve the problem that when conducting power outage diagnosis for low-voltage customers, currently, it can only be judged by sensing the power outage of the substation area to determine the power outage of low-voltage customers under the substation area or by means of low-voltage customers actively reporting repairs, etc. This will lead to low efficiency of power outage diagnosis, slow response speed, and at the same time, maintaining the same high frequency of monitoring for different regions will cause the collected data to be severely overloaded, affecting the accuracy of power outage diagnosis.

[0008] To achieve the above object, the present invention provides a method for diagnosing power outages of low-voltage users based on the Internet of Things, which includes:

[0009] Obtain the historical power outage data of the target area, determine the power outage frequency and the change rate of power consumption load of the sub-target area, calculate the power outage coefficient of the sub-target area to divide the power outage risk area, and determine the power outage monitoring status;

[0010] Based on the power outage monitoring status, obtain the power consumption data within the sub-target area, analyze the identity of the load curve and the peak-valley difference of the load to conduct power outage diagnosis, and determine whether the power outage status of the sub-target area is a regular power outage to determine whether to send a power outage warning. Among them,

[0011] Control the super capacitor of the power supply to open to continue powering the sub-target area;

[0012] Or, send a power outage warning to the users corresponding to the sub-target area, return the power outage status of the sub-target area to the historical power outage data, and update the power outage monitoring status;

[0013] In response to the power outage warning, judge whether the power outage response quantity value is qualified, record the sub-target area corresponding to the qualified power outage response quantity value, and update the diagnosis method.

[0014] Further, the process of calculating the power outage coefficient of the sub-target area includes,

[0015] Determine the power outage frequency of the target area as the frequency influence factor;

[0016] Determine the change rate of power consumption load as the load influence factor;

[0017] Determine the weighted sum value of the frequency influence factor and the load influence factor as the power outage coefficient of the sub-target area.

[0018] Furthermore, divide the power outage risk area and determine the power outage monitoring status, where

[0019] If the power outage coefficient of the sub-target area is greater than the reference power outage coefficient, divide the area into a high power outage risk area and perform a high-frequency monitoring status on the area;

[0020] If the power outage coefficient of the sub-target area is less than or equal to the reference power outage coefficient, divide the area into a low power outage risk area and maintain a preset frequency monitoring status on the area.

[0021] Furthermore, the process of analyzing the identity of the load curve and the load peak-valley difference includes

[0022] Determine the similarity between the historical non-power outage load curve and the load curve as the identity of the load curve;

[0023] Determine the average value of the historical non-power outage load peak-valley difference as the load peak-valley difference.

[0024] Furthermore, the process of performing a power outage diagnosis includes

[0025] Determine the identity probability of the load curve as the identity influence factor;

[0026] Determine the ratio of the load peak-valley difference to the reference load peak-valley difference as the peak-valley influence factor;

[0027] Determine the weighted sum value of the identity influence factor and the peak-valley influence factor as the power outage diagnosis characterization value;

[0028] Perform a power outage diagnosis on the sub-target area based on the power outage diagnosis characterization value.

[0029] Furthermore, determine whether the power outage status of the sub-target area is a regular power outage, where

[0030] If the power outage diagnosis characterization value is greater than the preset threshold, determine that the power outage status of the sub-target area is an unconventional power outage;

[0031] If the power outage diagnosis characterization value is less than or equal to the preset threshold, determine that the power outage status of the sub-target area is a regular power outage.

[0032] Furthermore, determine whether to send a power outage warning, where

[0033] If the sub-target area is a regular power outage, do not send a power outage warning and control the super capacitor of the power supply to turn on to continue powering the power outage area;

[0034] If the regular power outage is an irregular power outage, a power outage warning is sent to the users corresponding to the sub-target area, the power outage status of the sub-target area is returned to the historical power outage data, and the power outage monitoring status is updated.

[0035] Further, the process of determining whether the power outage response value is qualified includes

[0036] Determine the power outage response value;

[0037] If the power outage response value is greater than or equal to the preset power outage response value, it is determined that the power outage response value is unqualified;

[0038] If the power outage response value is less than the preset power outage response value, it is determined that the power outage response value is qualified.

[0039] Further, the preset power outage response value is six minutes.

[0040] Further, the present invention also provides an Internet of Things-based low-voltage user power outage diagnosis system, which is characterized by including

[0041] A data analysis module, which is used to obtain the historical power outage data of the target area, determine the power outage frequency and the change rate of the power consumption load in the target area, calculate the power outage coefficient of the target area, divide the power outage risk area, and determine the power outage monitoring status;

[0042] A power outage monitoring module, which is connected to the data analysis module. Based on the power outage monitoring status, it obtains the power consumption data in the sub-target area, analyzes the identity of the load curve and the peak-valley difference of the load, determines the power outage probability for power outage diagnosis, and determines whether the power outage status of the sub-target area is a regular power outage to determine whether to send a power outage warning;

[0043] A warning control module, which is connected to the power outage monitoring module, controls the super capacitor to turn on to continue to supply power to the power outage area

[0044] Or, send a power outage warning to the users corresponding to the sub-target area, return the power outage status of the sub-target area to the historical power outage data, and update the power outage monitoring status;

[0045] A power outage analysis module, which is connected to the power outage warning module. In response to the power outage warning, it determines whether the power outage response value is qualified, records the sub-target area corresponding to the qualified power outage response value, and updates the diagnosis method.

[0046] Compared with the prior art, the present invention obtains historical power outage data of a target area, determines the power outage frequency and the change rate of power consumption load in the target area, calculates the power outage coefficient of the target area, divides the power outage risk area, determines the power outage monitoring status, obtains the power consumption data in the sub-target area, analyzes the identity of the load curve and the peak-valley difference of the load for power outage diagnosis, determines whether the power outage status of the sub-target area is a regular power outage, and determines whether to send a power outage warning. Among them, the super capacitor of the power supply is controlled to be turned on to continue to supply power to the sub-target area, or a power outage warning is sent to the users corresponding to the sub-target area, and the power outage status of the sub-target area is returned to the historical power outage data to update the power outage monitoring status; in response to the power outage warning, the sub-target area corresponding to the qualified power outage response quantity value is recorded, it is judged whether the power outage response quantity value is qualified, the corresponding sub-target area is recorded, the power outage diagnosis method is updated, the false alarm rate and the delayed report rate are reduced, and the efficiency of power restoration is improved.

[0047] In particular, the present invention calculates the power outage coefficient of the sub-target area, divides the power outage risk area, and then determines the power outage monitoring status. In actual situations, when conducting power outage risk monitoring, most directly conduct high-frequency monitoring on the entire target area. However, not all areas are high-incidence areas of power outages. Such a monitoring method will lead to data accumulation, overload the collected data, and reduce the efficiency and accuracy of power outage diagnosis. Based on this, the present invention considers dividing the entire target area, determining different power outage monitoring statuses for different power outage risk areas, improving the pertinence and accuracy of power outage diagnosis, reducing the false alarm rate and the delayed report rate, and improving the efficiency of power restoration.

[0048] In particular, the present invention sends a power outage warning to the users corresponding to the sub-target area with an unconventional power outage, and returns the power outage data to the historical power outage data to keep the power outage monitoring status updated in real time. In actual situations, the monitoring data will be affected by various internal and external factors. If only historical data is used for power outage diagnosis, it will lead to diagnostic errors and cause accidents. Based on this, the present invention dynamically determines the power outage monitoring status, returns the data of each unconventional power outage to the historical power outage data, and re-determines the monitoring status, ensuring the accuracy of power outage diagnosis, reducing the false alarm rate and the delayed report rate, and improving the efficiency of power restoration. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a step schematic diagram of the low-voltage user power outage diagnosis method based on the Internet of Things according to the embodiment of the invention;

[0050] Figure 2 It is a logic block diagram for dividing the power outage risk area and determining the power outage monitoring status according to the embodiment of the invention;

[0051] Figure 3Logic block diagram for determining whether the power outage status of the sub-goal area in the invention embodiment is a regular power outage;

[0052] Figure 4 Logic block diagram for determining whether to send a power outage warning in the invention embodiment;

[0053] Figure 5 Structural schematic diagram of the low-voltage user power outage diagnosis system based on the Internet of Things in the invention embodiment. Detailed implementation manners

[0054] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0056] It should be noted that in the description of the present invention, unless otherwise clearly defined and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0057] Please refer to Figure 1 as shown Figure 1 Steps schematic diagram of the low-voltage user power outage diagnosis method based on the Internet of Things in the invention embodiment. A low-voltage user power outage diagnosis method based on the Internet of Things of the present invention includes:

[0058] Obtain the historical power outage data of the target area, determine the power outage frequency and the change rate of the electricity load of the sub-goal area, calculate the power outage coefficient of the sub-goal area, use it to divide the power outage risk area, and determine the power outage monitoring status;

[0059] Based on the power outage monitoring status, obtain the electricity consumption data in the sub-goal area, analyze the identity of the load curve and the peak-valley difference of the load to perform a power outage diagnosis, determine whether the power outage status of the sub-goal area is a regular power outage, and determine whether to send a power outage warning. Among them,

[0060] Control the super capacitor of the power supply to open to continue to supply power to the sub-goal area;

[0061] Alternatively, send a power outage warning to the users corresponding to the sub-target area, return the power outage status of the sub-target area to the historical power outage data, and update the power outage monitoring status;

[0062] In response to the power outage warning, determine whether the power outage response quantity value is qualified, record the sub-target area corresponding to the qualified power outage response quantity value, and update the diagnostic method.

[0063] Specifically, the supercapacitor can charge and discharge quickly and can provide high-power output in a short time. Therefore, the supercapacitor can supply power to the power outage area in a timely manner during a regular power outage, so that the area remains powered on, enabling the power outage area to be powered on within the power supply time.

[0064] Specifically, the process of calculating the power outage coefficient of the sub-target area includes

[0065] Determine the power outage frequency of the target area as the frequency impact factor;

[0066] Determine the change rate of the power consumption load as the load impact factor;

[0067] Determine the weighted sum value of the frequency impact factor and the load impact factor as the power outage coefficient of the sub-target area.

[0068] Specifically, the power outage frequency represents the average value of the power outage frequency within a specific period. In possible implementations, the average value of the power outage frequency within the entire period can also be determined as the power outage frequency. Those skilled in the art can make judgments according to the actual situation, which will not be elaborated here.

[0069] Specifically, the determination method of the load change rate needs to be consistent with the determination method of the power outage frequency, which will not be elaborated here.

[0070] Specifically, the sum of the weight coefficients of the frequency impact factor and the load impact factor is 1. The weight coefficient of the frequency impact factor is 0.43, and the weight coefficient of the load impact factor is 0.57.

[0071] Please refer to Figure 2 , Figure 2 For the logical block diagram of dividing the power outage risk area and determining the power outage monitoring status in the embodiment of the invention. Specifically, divide the power outage risk area and determine the power outage monitoring status, where

[0072] If the power outage coefficient of the sub-target area is greater than the reference power outage coefficient, divide the area into a high power outage risk area and perform a high-frequency monitoring status on the area;

[0073] If the power outage coefficient of the sub-target area is less than or equal to the reference power outage coefficient, divide the area into a low power outage risk area and maintain a preset frequency monitoring status on the area.

[0074] Specifically, the reference power outage coefficient is selected within the range of [0.47, 0.59].

[0075] Specifically, the preset frequency monitoring status is a predetermined value. The historical monitoring frequencies under several cycles are obtained in advance, and 0.75 times the average value of the historical monitoring frequencies is determined as the preset frequency monitoring. At the same time, the high-frequency monitoring status is set to 1.5 times the preset frequency monitoring, which will not be elaborated here.

[0076] Specifically, the present invention calculates the power outage coefficient of the sub-target area, divides the power outage risk area, and then determines the power outage monitoring status. In actual situations, when conducting power outage risk monitoring, high-frequency monitoring is mostly directly carried out on the entire target area. However, not all areas are high-incidence areas of power outages. Such a monitoring method will lead to data accumulation, causing the collected data to be severely overloaded, reducing the efficiency and accuracy of power outage diagnosis. Based on this, the present invention considers dividing the entire target area, determining different power outage monitoring statuses for different power outage risk areas, improving the pertinence and accuracy of power outage diagnosis, reducing the false alarm rate and delay rate, and enhancing the efficiency of power restoration.

[0077] Specifically, the process of analyzing the identity of the load curve and the peak-valley difference of the load includes

[0078] Determining the similarity between the historical non-power-outage load curve and the load curve as the identity of the load curve;

[0079] Determining the average value of the historical non-power-outage load peak-valley difference as the load peak-valley difference.

[0080] Specifically, the method for determining the curve similarity is not limited. For example, it can be cosine similarity, Euclidean distance, feature point matching, etc. In implementation, the curve similarity is determined by using the pre-similarity method, which is prior art and will not be elaborated here.

[0081] Specifically, the method for obtaining the historical non-power-outage load peak-valley difference is not limited. In implementation, the data can be directly exported through equipment to determine the historical non-power-outage load peak-valley difference, which is prior art and will not be elaborated here.

[0082] Specifically, the process of conducting power outage diagnosis includes

[0083] Determining the identity probability of the load curve as the identity influence factor;

[0084] Determining the ratio of the load peak-valley difference to the reference load peak-valley difference as the peak-valley influence factor;

[0085] Determining the weighted sum value of the identity influence factor and the peak-valley influence factor as the power outage diagnosis characterization value;

[0086] Perform a power outage diagnosis on the sub-target area based on the power outage diagnosis characterization value.

[0087] Specifically, the weight coefficient of the identity influence factor and the peak-valley influence factor is 1, the weight coefficient of the identity influence factor is 0.47, and the weight coefficient of the peak-valley influence factor is 0.53.

[0088] Please refer to Figure 3 , Figure 3 , which is a logic block diagram for determining whether the power outage state of the sub-target area in the invention embodiment is a regular power outage. Specifically, determine whether the power outage state of the sub-target area is a regular power outage, where

[0089] If the power outage diagnosis characterization value is greater than the preset threshold, it is determined that the power outage state of the sub-target area is an irregular power outage;

[0090] If the power outage diagnosis characterization value is less than or equal to the preset threshold, it is determined that the power outage state of the sub-target area is a regular power outage.

[0091] Specifically, the preset threshold is selected within the interval [0.98, 1.18].

[0092] Please refer to Figure 4 , Figure 4 , which is a logic block diagram for determining whether to send a power outage warning in the invention embodiment. Specifically, determine whether to send a power outage warning, where

[0093] If the sub-target area is a regular power outage, do not send a power outage warning, and control the super capacitor of the power supply to turn on to continue powering the power outage area;

[0094] If the regular power outage is an irregular power outage, send a power outage warning to the users corresponding to the sub-target area, return the power outage state of the sub-target area to the historical power outage data, and update the power outage monitoring status.

[0095] Specifically, the present invention sends a power outage warning to the users corresponding to the sub-target area of the irregular power outage, returns the power outage data to the historical power outage data, and keeps the power outage monitoring status updated in real time. In actual situations, the monitoring data will be affected by various internal and external factors. If only the historical data is used for power outage diagnosis, it will lead to diagnostic errors and cause accidents. Based on this, the present invention dynamically determines the power outage monitoring status, returns the data of each irregular power outage to the historical power outage data, and re-determines the monitoring status, ensuring the accuracy of the power outage diagnosis, reducing the false alarm rate and delay rate, and improving the efficiency of power restoration.

[0096] Specifically, the process of judging whether the power outage response value is qualified includes

[0097] Determine the power outage response value;

[0098] If the power outage response value is greater than or equal to the preset power outage response value, it is determined that the power outage response value is unqualified;

[0099] If the power outage response value is less than the preset power outage response value, it is determined that the power outage response value is qualified.

[0100] Specifically, the preset power outage response value is six minutes.

[0101] It can be understood that for the sub-target area corresponding to the unqualified power outage response value, the above method can be looped until the power outage response value is qualified. Of course, other methods can also be used, which will not be elaborated here.

[0102] Please refer to Figure 5 , Figure 5 , a schematic structural diagram of the low-voltage user power outage diagnosis system based on the Internet of Things according to the invention embodiment. Specifically, a system for low-voltage user power outage diagnosis based on the Internet of Things, characterized by including,

[0103] A data analysis module, which is used to obtain the historical power outage data of the target area, determine the power outage frequency and the change rate of the electricity load in the target area, calculate the power outage coefficient of the target area, divide the power outage risk area, and determine the power outage monitoring status;

[0104] A power outage monitoring module, which is connected to the data analysis module. Based on the power outage monitoring status, it obtains the electricity consumption data in the sub-target area, analyzes the identity of the load curve and the peak-valley difference of the load, determines the power outage probability for power outage diagnosis, determines whether the power outage status of the sub-target area is a regular power outage, and determines whether to send a power outage warning;

[0105] A warning control module, which is connected to the power outage monitoring module, controls the super capacitor to open to continue power supply to the power outage area,

[0106] or, sends a power outage warning to the users corresponding to the sub-target area, returns the power outage status of the sub-target area to the historical power outage data, and updates the power outage monitoring status;

[0107] A power outage analysis module, which is connected to the power outage warning module. In response to the power outage warning, it judges whether the power outage response value is qualified, records the sub-target area corresponding to the qualified power outage response value, and updates the diagnosis method.

[0108] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0109] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A low-voltage user power outage diagnosis method based on the Internet of Things, characterized in that: include: Obtain historical power outage data of the target area, determine the power outage frequency and power load change rate of the sub-target area, calculate the power outage coefficient of the sub-target area, divide the power outage risk area, and determine the power outage monitoring status; Based on the power outage monitoring status, the power consumption data in the sub-target area is obtained, the identity of the load curve and the load peak-to-valley difference are analyzed to perform power outage diagnosis, and whether the power outage status in the sub-target area is a regular power outage is determined to determine whether to send a power outage warning, wherein: Controlling the power supply supercapacitor to turn on to continue supplying power to the sub-target area; Or, sending a power outage warning to users corresponding to the sub-target area, returning the power outage status of the sub-target area to the historical power outage data, and updating the power outage monitoring status; In response to the power outage warning, determine whether the power outage response value is qualified, record the sub-target area corresponding to the qualified power outage response value, and update the diagnosis method.

2. The low-voltage user power outage diagnosis method based on the Internet of Things according to claim 1 is characterized in that: The process of calculating the power outage coefficient of the sub-target area includes: Determine the power outage frequency in the target area as the frequency impact factor; Determine the power load change rate as the load impact factor; The weighted sum of the frequency impact factor and the load impact factor is determined as the power outage coefficient of the sub-target area.

3. The low-voltage user power outage diagnosis method based on the Internet of Things according to claim 1 is characterized in that: The power outage risk area is divided and the power outage monitoring status is determined, wherein If the power outage coefficient of the sub-target area is greater than the reference power outage coefficient, the area is classified as a high power outage risk area, and a high-frequency monitoring state is performed on the area; If the power outage coefficient of the sub-target area is less than or equal to the benchmark power outage coefficient, the area is classified as a low power outage risk area, and the preset frequency monitoring state is maintained for the area.

4. The low-voltage user power outage diagnosis method based on the Internet of Things according to claim 1 is characterized in that: The process of analyzing the identity of the load curve and the load peak-to-valley difference includes: Determine the similarity between the historical load curve without power outage and the load curve as the identity of the load curve; The average value of the historical load peak-to-valley difference before power outages is determined as the load peak-to-valley difference.

5. The low-voltage user power outage diagnosis method based on the Internet of Things according to claim 1 is characterized in that: The process of performing power outage diagnosis includes: Determine the identity probability of the load curve as the identity influencing factor; Determine the ratio of the load peak-to-valley difference to the reference load peak-to-valley difference as the peak-to-valley impact factor; Determine a weighted sum of the identity impact factor and the peak-valley impact factor as a power outage diagnosis characterization value; A power outage diagnosis is performed on the sub-target area based on the power outage diagnosis characterization value.

6. The method for diagnosing power outages for low-voltage users based on the Internet of Things according to claim 5 is characterized in that: The step of determining whether the power outage state of the sub-target area is a regular power outage includes: If the power outage diagnosis characteristic value is greater than a preset threshold, determining that the power outage state of the sub-target area is an irregular power outage; If the power outage diagnosis characterization value is less than or equal to a preset threshold, it is determined that the power outage state of the sub-target area is a regular power outage.

7. The low-voltage user power outage diagnosis method based on the Internet of Things according to claim 1 is characterized in that: The step of determining whether to send a power outage warning comprises: If the sub-target area is experiencing a regular power outage, no power outage warning is sent, and the power supply supercapacitor is controlled to open to continue supplying power to the power outage area; If the conventional power outage is an unconventional power outage, a power outage warning is sent to users corresponding to the sub-target area, the power outage status of the sub-target area is returned to the historical power outage data, and the power outage monitoring status is updated.

8. The low-voltage user power outage diagnosis method based on the Internet of Things according to claim 1 is characterized in that: The process of judging whether the power outage response value is qualified includes: Determine the power outage response value; If the power outage response value is greater than or equal to a preset power outage response value, determining that the power outage response value is unqualified; If the power outage response value is less than a preset power outage response value, it is determined that the power outage response value is qualified.

9. The method for diagnosing power outages for low-voltage users based on the Internet of Things according to claim 8, characterized in that: The preset power outage response value is six minutes.

10. A system using the low-voltage user power outage diagnosis method based on the Internet of Things according to any one of claims 1 to 9, characterized in that: include, A data analysis module is used to obtain historical power outage data of the target area, determine the power outage frequency and power load change rate of the target area, calculate the power outage coefficient of the target area, divide the power outage risk area, and determine the power outage monitoring status; A power outage monitoring module is connected to the data analysis module, and based on the power outage monitoring status, obtains the power consumption data in the sub-target area, analyzes the identity of the load curve and the load peak-to-valley difference, so as to determine the power outage probability for power outage diagnosis, and determines whether the power outage status of the sub-target area is a regular power outage, so as to determine whether to send a power outage warning; The warning control module is connected to the power outage monitoring module to control the supercapacitor to open so as to continue to supply power to the power outage area. Or, sending a power outage warning to users corresponding to the sub-target area, returning the power outage status of the sub-target area to the historical power outage data, and updating the power outage monitoring status; A method update module is connected to the warning control module, responds to the power outage warning, determines whether the power outage response value is qualified, records the sub-target area corresponding to the qualified power outage response value, and updates the diagnosis method.

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