Fault detection method and device applied to power distribution network and electronic equipment

Abstract

The embodiment of the invention discloses a fault detection method and device applied to a power distribution network and electronic equipment. A specific embodiment of the method comprises the following steps: positioning at least one node to be detected in the power distribution network to obtain a node information set to be detected; for to-be-detected node information, the following processing steps are executed: collecting real-time node state information; according to the real-time node state information and a local network topological graph included in the to-be-detected node information, performing fault detection on a to-be-detected node corresponding to the to-be-detected node information; in response to the fact that the node fault probability is larger than a preset node fault probability and the node sweep degree is smaller than a preset node sweep degree, fault early warning matched with the node fault type is initiated; and in response to the fact that the node fault probability is greater than the preset node fault probability and the node sweep degree is greater than or equal to the preset node sweep degree, initiating risk early warning for a local power distribution network corresponding to a local network topological graph included in the to-be-detected node information. According to the embodiment, the stability of the power distribution network is ensured, and the equipment damage risk is reduced.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and more particularly to a fault detection method, device, and electronic device applied to a power distribution network. Background Art

[0002] With the increasing development of industrial modernization, large-scale distribution networks have emerged. At the same time, due to the increasing complexity of distribution networks, the probability of distribution network failure has also greatly increased. How to detect distribution network failures in a timely manner to ensure the stability of the distribution network, reduce the risk of equipment damage, and reduce the losses caused by power outages has become an urgent problem to be solved.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0004] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0005] Some embodiments of the present disclosure propose a fault detection method, device, and electronic device applied to a distribution network to solve one or more of the technical problems mentioned in the above background technology section.

[0006] In a first aspect, some embodiments of the present disclosure provide a fault detection method applied to a distribution network, the method comprising: locating at least one node to be inspected in the distribution network, and obtaining a set of information on the node to be inspected, wherein the distribution network is used to distribute the electric energy transmitted by the transmission network to the power consumption terminal, and the node to be inspected corresponds to the node to be inspected information one by one, and the node to be inspected information in the set of information on the node to be inspected comprises: the type of node to be inspected, the location of the node to be inspected, and a local network topology map, wherein the local network topology map represents the topological structure of the local distribution network where the node to be inspected is located, and the local distribution network is a subnet of the distribution network; for each node to be inspected information in the set of information on the node to be inspected, performing the following processing steps: collecting real-time node status information according to the type of node to be inspected and the location of the node to be inspected included in the information on the node to be inspected; collecting real-time node status information according to the real-time node status information and the above The information on the node to be inspected includes a local network topology map, and fault detection is performed on the node to be inspected corresponding to the above-mentioned node information to be inspected to generate node detection information, wherein the above-mentioned node detection information includes: node fault type, node fault probability and node spread, wherein the node fault probability represents the probability of occurrence of the fault corresponding to the node fault type, and the node spread represents the influence of the node to be inspected on the equipment in the local distribution network corresponding to the local network topology map; in response to the above-mentioned node failure probability being greater than the preset node failure probability and the above-mentioned node spread being less than the preset node spread, a fault warning matching the above-mentioned node failure type is initiated; in response to the above-mentioned node failure probability being greater than the above-mentioned preset node failure probability and the above-mentioned node spread being greater than or equal to the above-mentioned preset node spread, a risk warning is initiated for the local distribution network corresponding to the local network topology map included in the above-mentioned node information to be inspected.

[0007] In a second aspect, some embodiments of the present disclosure provide a fault detection device applied to a distribution network, the device comprising: a positioning unit, configured to locate at least one node to be inspected in the distribution network, and obtain a set of information about the node to be inspected, wherein the above-mentioned distribution network is used to distribute the electric energy transmitted by the transmission network to the power consumption terminal, and the node to be inspected corresponds to the node to be inspected information one by one, and the node to be inspected information in the above-mentioned set of information about the node to be inspected comprises: the type of node to be inspected, the location of the node to be inspected and a local network topology map, wherein the local network topology map represents the topological structure of the local distribution network where the node to be inspected is located, and the local distribution network is a subnet of the above-mentioned distribution network; an execution unit, configured to perform the following processing steps for each node to be inspected information in the above-mentioned set of information about the node to be inspected: according to the type of node to be inspected and the location of the node to be inspected included in the above-mentioned information about the node to be inspected, collecting real-time node status information; according to the above-mentioned real-time The node status information and the local network topology included in the above-mentioned node information to be inspected, perform fault detection on the node to be inspected corresponding to the above-mentioned node information to be inspected to generate node detection information, wherein the above-mentioned node detection information includes: node fault type, node fault probability and node spread, wherein the node fault probability represents the probability of occurrence of the fault corresponding to the node fault type, and the node spread represents the influence of the node to be inspected on the equipment in the local distribution network corresponding to the local network topology; in response to the above-mentioned node failure probability being greater than the preset node failure probability and the above-mentioned node spread being less than the preset node spread, initiate a fault warning matching the above-mentioned node failure type; in response to the above-mentioned node failure probability being greater than the above-mentioned preset node failure probability and the above-mentioned node spread being greater than or equal to the above-mentioned preset node spread, initiate a risk warning for the local distribution network corresponding to the local network topology included in the above-mentioned node information to be inspected.

[0008] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.

[0009] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

[0010] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the fault detection method applied to the distribution network of some embodiments of the present disclosure, effective fault detection for the distribution network is realized, the stability of the distribution network is guaranteed, the risk of equipment damage is reduced, and the loss caused by power outage is reduced. Specifically, the reason for the failure of the distribution network is that the structure of the large-scale distribution network is complex. When the equipment in the distribution network fails, it is very easy to affect the stability of the distribution network as a whole, thereby affecting other equipment, causing equipment damage and power outage. Based on this, the fault detection method applied to the distribution network of some embodiments of the present disclosure, first, locate at least one node to be inspected in the distribution network, and obtain a set of information of the node to be inspected, wherein the above-mentioned distribution network is used to distribute the electric energy transmitted by the transmission network to the power consumption terminal, and the node to be inspected corresponds to the node to be inspected information one by one, and the node to be inspected in the above-mentioned node to be inspected information set includes: the type of the node to be inspected, the location of the node to be inspected and the local network topology map, wherein the local network topology map represents the topological structure of the local distribution network where the node to be inspected is located, and the local distribution network is a subnet of the above-mentioned distribution network. In practice, the network structure of large-scale distribution networks is complex. If fault detection is performed on the entire distribution network, the detection difficulty is large and the detection cycle is long. Therefore, by locating the key nodes (nodes to be inspected) in the distribution network, the detection complexity can be reduced. Secondly, for each node information to be inspected in the above-mentioned node information set to be inspected, the following processing steps are performed: the first step is to collect real-time node status information according to the node type to be inspected and the node position to be inspected included in the above-mentioned node information to be inspected. By collecting the real-time node status, the current operating status of the device corresponding to the node to be inspected is obtained. The second step is to perform fault detection on the node to be inspected corresponding to the above-mentioned node information to be inspected according to the local network topology map included in the above-mentioned real-time node status information and the above-mentioned node information to be inspected, so as to generate node detection information, wherein the above-mentioned node detection information includes: node fault type, node fault probability and node spread, wherein the node fault probability represents the probability of occurrence of the fault corresponding to the node fault type, and the node spread represents the influence of the node to be inspected on the equipment in the local distribution network corresponding to the local network topology map. In this way, from the perspective of the node to be inspected itself and the influence of the node to be inspected on the surrounding equipment, the influence of the fault type of the node to be inspected on the surrounding equipment is detected. The third step is to initiate a fault warning that matches the node fault type in response to the node fault probability being greater than the preset node fault probability and the node spread being less than the preset node spread. This achieves a single-point warning for the device corresponding to the node to be inspected. The fourth step is to initiate a risk warning for the local distribution network corresponding to the local network topology map included in the node information to be inspected in response to the node fault probability being greater than the preset node fault probability and the node spread being greater than or equal to the preset node spread. This achieves an overall warning for the node to be inspected and the local distribution network in which it is located.In this way, fault detection can be achieved efficiently, ensuring the stability of the distribution network, reducing the risk of equipment damage, and reducing losses caused by power outages. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0012] Figure 1 is a flow chart of some embodiments of a fault detection method applied to a power distribution network according to the present disclosure; Figure 2 is a structural schematic diagram of some embodiments of a fault detection device applied to a power distribution network according to the present disclosure; Figure 3 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0013] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0014] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0015] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0016] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0017] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0018] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0019] refer to Figure 1 , shows a process 100 of some embodiments of a fault detection method applied to a distribution network according to the present disclosure. The fault detection method applied to a distribution network comprises the following steps: Step 101: locate at least one node to be inspected in the distribution network and obtain a set of information about the node to be inspected.

[0020] In some embodiments, the execution subject (e.g., a computing device) of the fault detection method applied to the distribution network can locate at least one node to be inspected in the distribution network and obtain a set of information about the node to be inspected. Among them, the distribution network is used to distribute the electric energy transmitted by the transmission network to the power consumption terminal. Specifically, the distribution network may include but is not limited to: a low-voltage distribution network, a medium-voltage distribution network, and a high-voltage distribution network. The transmission network is a transmission network for transmitting the current generated by the power generation equipment. The power consumption terminal is a terminal that needs to consume current. For example, the power consumption terminal may be a factory or a power-consuming device. The node to be inspected represents the vulnerable and damaged equipment in the distribution network that requires high-frequency inspection, or the equipment that affects the normal operation of the distribution network after performing the corresponding operation. For example, the node to be inspected may be an insulator, or a transformer, a circuit breaker, etc. involved in the power grid closing operation. The node to be inspected corresponds to the node to be inspected information one by one. The node to be inspected information in the set of information about the node to be inspected includes: the type of the node to be inspected, the location of the node to be inspected, and the local network topology diagram. The type of the node to be inspected represents the device type of the device corresponding to the node to be inspected. For example, the type of the node to be inspected may be a "transformer". The local network topology diagram represents the topology of the local distribution network where the node to be inspected is located. In practice, the distribution network is often a mesh structure, and any device is connected to at least one other device through the distribution network. When a device fails, it may affect the stability of the device connected to it. Therefore, the local network topology diagram can represent the connection relationship between the node to be inspected and the devices that may be affected when a node to be inspected fails. The local distribution network is a subnet of the above distribution network.

[0021] As an example, the execution subject may traverse the distribution network to determine the nodes to be inspected in the distribution network, and pull the node information to be inspected corresponding to the nodes to be inspected to obtain the node information set to be inspected.

[0022] It should be noted that the above-mentioned computing device can be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the computing device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here. It should be understood that the number of computing devices can be any number according to the implementation needs.

[0023] In some optional implementations of some embodiments, the execution subject locates at least one node to be inspected in the distribution network and obtains a set of information of the node to be inspected, including: The first step is to monitor the control channel.

[0024] The control channel is a bidirectional communication channel. In practice, the control channel can be a communication channel for the execution subject to send control signals to the equipment in the distribution network, and for the equipment in the distribution network to send information and / or data to the execution subject.

[0025] In the second step, in response to monitoring the timing reminder request initiated by the first device, the device information of the first device is pulled as the node information to be inspected in the node information set to be inspected.

[0026] Among them, the first device is a device in the distribution network, and the corresponding inspection timer has expired. The inspection timer is a timer used to prompt the inspection of the device. In practice, each device in the distribution network will be set with a corresponding inspection timer. When the inspection timer returns to zero, the corresponding timing reminder request will be triggered. When the execution subject monitors the timing reminder request through the control channel, the device whose corresponding inspection timer has expired will be used as the first device. At the same time, the device information corresponding to the first device is pulled up as the node information to be inspected. For example, the pulled device information may include: device type, device location, and network topology diagram of the local network where the device is located. Among them, the inspection timer is controlled by the device type, historical inspection record, and number of faults of the device. Specifically, there are differences in the use cycles of different devices. For example, the device stability of device A is higher, so the corresponding inspection times and fault times are less. At this time, a longer inspection time can be set for the inspection timer of device A. For another example, the device stability of device B is poor, so the corresponding inspection times and fault times are more. Therefore, a shorter inspection time can be set for the inspection timer of device B. Specifically, it can be set to the basic duration corresponding to the equipment type, and on the basis of the basic duration, the basic duration is scaled by the number of inspections and the number of failures corresponding to the historical inspection records as a coefficient, as the timing duration of the inspection timer. Among them, the timing duration of the inspection timer is inversely proportional to the number of inspections and the number of failures corresponding to the historical inspection records, that is, the more inspections or the more failures corresponding to the historical inspection records, the shorter the timing duration of the inspection timer.

[0027] In the third step, in response to monitoring the target device operation for the second device, the device information of the second device is pulled as the node information to be inspected in the node information set to be inspected.

[0028] The second device is a device in the distribution network used for closing the distribution network, and the target device operation is a distribution network closing operation. For example, the second device may be a transformer.

[0029] Step 102: for each piece of node information to be inspected in the set of node information to be inspected, perform the following processing steps: Step 1021 : collecting real-time node status information according to the node type and node position included in the node information.

[0030] In some embodiments, the above-mentioned execution subject can collect real-time node status information according to the type of node to be inspected and the position of the node to be inspected included in the node information to be inspected. Among them, the real-time node status information can characterize the real-time operating status corresponding to the node to be inspected, specifically, including but not limited to: the appearance image of the node (equipment) to be inspected, and the equipment operation parameters. The equipment operation parameters may include but are not limited to: the bus voltage of the closed-loop feeder corresponding to the node (equipment) to be inspected, the equivalent load of the upper power grid corresponding to the node (equipment) to be inspected, the reactive power of the node (equipment) to be inspected, the transmission power of the feeder corresponding to the node (equipment) to be inspected, the active power of the node (equipment) to be inspected, and the upper power grid flow directly connected to the feeder corresponding to the node (equipment) to be inspected. In practice, different types of equipment need to collect different real-time node status information. For example, when the node to be inspected is an insulator, since the insulator is an insulating control arranged between the conductor and the grounding member, when the insulator is damaged, it will affect the operation of the distribution network. Therefore, the real-time node status information can be the appearance image corresponding to the insulator. For example, when the node to be inspected is a transformer, it is necessary to consider the relevant parameters in the current transmission process to avoid the transformer being damaged by breakdown. At this time, the real-time node status information can be the equipment operating parameters corresponding to the transformer.

[0031] In some optional implementations of some embodiments, the execution subject collects real-time node status information according to the node type and node location included in the node information, including: In the first step, in response to the type of the node to be inspected included in the above-mentioned node to be inspected information being a device type corresponding to a device not involved in the distribution network loop closure, a collection area is determined according to the position of the node to be inspected included in the above-mentioned node to be inspected information.

[0032] Among them, the above-mentioned collection area is an electronic fence area centered on the position of the node to be inspected included in the above-mentioned node information to be inspected, and the area radius of the above-mentioned collection area is controlled by the device density of the monitoring equipment contained in the above-mentioned collection area. In practice, the device density is inversely proportional to the area radius. Specifically, when the number of monitoring devices in the collection area is large, that is, the device density is high, the area radius of the collection area can be smaller. When the number of monitoring devices in the collection area is small, that is, the device density is low, the area radius of the collection area can be larger. Therefore, the probability of collecting the appearance image (real-time device image) corresponding to the node to be inspected can be increased as much as possible.

[0033] The second step is to determine the device operating status of each monitoring device in at least one monitoring device within the above-mentioned collection area.

[0034] The device operation status represents the working status of the monitoring device. In practice, since the monitoring device needs to transmit the real-time collected monitoring video back when it is in working state, the monitoring device is often in a networked state. Therefore, the above-mentioned execution subject can determine the device operation status of each monitoring device in at least one monitoring device in the above-mentioned collection area through a wired connection or a wireless connection.

[0035] The third step is to determine whether there is a monitoring device that meets the screening condition among the at least one monitoring device.

[0036] The above screening conditions are: the corresponding device operation status indicates that the monitoring device is operating normally and the position of the node to be inspected included in the node to be inspected information is within the monitoring area of ​​the monitoring device. In practice, each monitoring device corresponds to a monitoring angle. When the position of the monitoring device, the monitoring angle of the monitoring device and the node to be inspected position of the node to be inspected are known, it can be calculated whether the node to be inspected is within the monitoring area of ​​the monitoring device.

[0037] The fourth step is to control the monitoring devices that meet the above screening conditions in response to the existence and collect real-time device images.

[0038] The real-time device image is an appearance image of the node to be inspected corresponding to the node information to be inspected. In practice, the execution subject can control the monitoring device that meets the screening conditions to change the focal length to collect an image of the device corresponding to the node information to be inspected as the real-time device image.

[0039] The fifth step is to plan the UAV path in response to the non-existence of the node, according to the location of the node to be inspected included in the above-mentioned node information to be inspected.

[0040] Among them, the drone path is a flight path with the position of the node to be inspected included in the above-mentioned node information as the end position and the drone take-off point as the starting position. In practice, the above-mentioned execution entity plans the above-mentioned drone path in combination with the high-precision map collected in advance for the distribution network. Specifically, since the distribution network contains overhead lines, the drone has a low accuracy problem in identifying the overhead lines, especially in low-light environments. Due to the installation phenomenon, it may cause damage to the drone at the least, and damage to the distribution network lines, short circuits, etc. at the worst. Therefore, when planning the drone path, the pre-collected high-precision map is combined to assist in the planning of the drone path to avoid collisions.

[0041] Step 6: Determine the target drone based on the above drone path.

[0042] The target drone is an idle drone that can travel back and forth along the drone path. In practice, in order to avoid the drone being unable to return due to insufficient power, the drone path is first planned, and then the target drone that can fly back and forth along the drone path is selected in combination with the drone path.

[0043] The seventh step is to control the target UAV to follow the UAV path and collect real-time equipment images.

[0044] In practice, the above-mentioned execution subject can send the above-mentioned drone path to the target drone so that the target drone flies along the above-mentioned drone path. At the same time, the drone can also combine its own flight control system and environmental detection system to adaptively fine-tune the drone path. When the target drone approaches the position of the node to be inspected, the target drone can control the camera to shoot the position of the node to be inspected to obtain the above-mentioned real-time device image.

[0045] In the eighth step, the real-time device image is determined as the real-time node status information.

[0046] In practice, real-time device images from multiple angles can be collected as the above-mentioned real-time node status information.

[0047] In the ninth step, in response to the node type to be inspected included in the node information to be inspected being a device type corresponding to a device involved in the closing of a distribution network, the closing current characteristics of the node to be inspected corresponding to the node information to be inspected are collected as the real-time node status information.

[0048] Among them, the closed-loop current characteristics include but are not limited to: the bus voltage of the closed-loop feeder corresponding to the node (equipment) to be tested, the equivalent load of the upper-level power grid corresponding to the node (equipment) to be tested, the reactive power of the node (equipment) to be tested, the transmission power of the feeder corresponding to the node (equipment) to be tested, the active power of the node (equipment) to be tested, and the upper-level power grid current directly connected to the feeder corresponding to the node (equipment) to be tested.

[0049] Step 1022: Perform fault detection on the node to be inspected corresponding to the node to be inspected information according to the local network topology diagram included in the real-time node status information and the node to be inspected information, so as to generate node detection information.

[0050] In some embodiments, the execution subject may perform fault detection on the node to be inspected corresponding to the node information to be inspected according to the local network topology diagram included in the real-time node status information and the node information to be inspected, so as to generate node detection information. The node detection information includes: node fault type, node fault probability and node spread, wherein the node fault probability represents the probability of occurrence of the fault corresponding to the node fault type. The node spread represents the influence of the node to be inspected on the equipment in the local distribution network corresponding to the local network topology diagram. The node fault type represents the type of fault that occurs in the equipment corresponding to the node to be inspected.

[0051] In some optional implementations of some embodiments, the execution subject performs fault detection on the node to be inspected corresponding to the node to be inspected information according to the real-time node status information and the local network topology map included in the node to be inspected information to generate node detection information, including: In the first step, in response to the real-time node status information being a real-time device image, adaptively scaling the real-time node status information to obtain a scaled device image.

[0052] In practice, the execution subject may perform operations such as scaling, cropping, splicing, and filling on the real-time node status information to obtain the scaled device image.

[0053] As an example, due to the different models of different monitoring equipment, the image sizes of the collected implementation device images may be different. Therefore, the real-time node information needs to be scaled to a unified image size as the scaled device image. Among them, the image size of the scaled device image is 640×640. Specifically, when it is not possible to scale proportionally, for example, the image size of the real-time device image is A×B. Among them, A>B. At this time, the above-mentioned execution subject can be scaled based on A. At this time, 0 is added to the two sides corresponding to B so that the image size of the scaled device image is 640×640.

[0054] In the second step, the scaled device image is enhanced to obtain an enhanced device image.

[0055] In practice, the above execution subject can perform Mosaic data enhancement on the scaled device image to obtain an enhanced device image.

[0056] In the third step, the local image is determined using the pre-trained fault location model and the enhanced device image.

[0057] The local image is a local image of the location of the region of interest contained in the enhanced device image. In practice, the fault location model includes: a first backbone network and a second backbone network.

[0058] The first backbone network includes: convolution layer A, convolution layer B, Bottleneck CSP layer A, convolution layer C, Bottleneck CSP layer B, convolution layer D, Bottleneck CSP layer C, convolution layer E and SPPF layer. The output of convolution layer A is the input of convolution layer B. The output of convolution layer B is the input of Bottleneck CSP layer A. The output of convolution layer A is the input of convolution layer C. The output of convolution layer C is the input of Bottleneck CSP layer B. The output of Bottleneck CSP layer B is the input of convolution layer D. The output of convolution layer D is the input of Bottleneck CSP layer C. The output of Bottleneck CSP layer C is the input of convolution layer E. The output of convolution layer E is the input of SPPF layer.

[0059] Specifically, the number of input channels of convolution layer A is 3, the number of output channels is 64, the convolution kernel size is 6×6, and the stride is 2. The number of input channels of convolution layer B is 64, the number of output channels is 128, the convolution kernel size is 3×3, and the stride is 2. The number of input channels and output channels of BottleneckCSP layer A are both 128. The number of input channels of convolution layer C is 128, the number of output channels is 256, the convolution kernel size is 3×3, and the stride is 2. The number of input channels and output channels of Bottleneck CSP layer B are both 256. The number of input channels of convolution layer D is 256, the number of output channels is 512, the convolution kernel size is 3×3, and the stride is 2. The number of input channels and output channels of Bottleneck CSP layer C are both 512. The number of input channels of convolution layer E is 512, the number of output channels is 1024, the convolution kernel size is 3×3, and the stride is 2. The number of output channels of the SPPF layer is 1024.

[0060] Among them, the second backbone network includes: Bottleneck CSP layer D, convolution layer F, upsampling layer A, splicing layer A, Bottleneck CSP layer E, convolution layer G, upsampling layer B, splicing layer B, Bottleneck CSP layer F, convolution layer H, splicing layer C, Bottleneck CSP layer G, convolution layer I, splicing layer D, Bottleneck CSP layer H. The input of Bottleneck CSP layer D is the SPPF layer. The output of Bottleneck CSP layer D is the input of convolution layer F. The output of convolution layer F is the input of upsampling layer A and the input of splicing layer D. The output of upsampling layer A is the input of splicing layer A, and the output of Bottleneck CSP layer C is also the input of splicing layer A. The output of splicing layer A is the input of Bottleneck CSP layer E. The output of Bottleneck CSP layer E is the input of convolution layer G. The output of convolution layer G is the input of upsampling layer B. At the same time, the output of convolution layer G is also the input of splicing layer C. The output of upsampling layer B is the input of splicing layer B. At the same time, the output of Bottleneck CSP layer B is also the input of splicing layer B. The output of splicing layer B is the output of Bottleneck CSP layer F. The output of Bottleneck CSP layer F is the input of convolution layer H. The output of convolution layer H is the input of splicing layer C. The output of splicing layer C is the input of Bottleneck CSP layer G. The output of Bottleneck CSP layer G is the input of convolution layer I. The output of convolution layer I is the input of splicing layer D. The output of splicing layer D is the input of Bottleneck CSP layer H. Among them, the outputs of Bottleneck CSP layer H, Bottleneck CSP layer G and Bottleneck CSP layer F are local images.

[0061] Specifically, the number of input channels and output channels of Bottleneck CSP layer D are both 1024. The number of input channels of convolution layer F is 1024, the number of output channels is 512, the convolution kernel size is 1×1, and the stride is 1. The number of input channels and output channels of upsampling layer A are both 512. The number of input channels and output channels of concatenation layer A are both 1024. The number of input channels and output channels of Bottleneck CSP layer E are 1024, and the number of output channels is 512. The number of input channels of convolution layer G is 512, the number of output channels is 256, the convolution kernel size is 1×1, and the stride is 1. The number of input channels of upsampling layer B is both 256. The number of input channels of concatenation layer B is both 512. The number of input channels of Bottleneck CSP layer F is 512, and the number of output channels is 256. The number of input channels of convolution layer H is 256, the number of output channels is 256, the convolution kernel size is 3×3, and the stride is 2. The number of input channels and output channels of splicing layer C is 512. The number of input channels and output channels of Bottleneck CSP layer G is 512. The number of input channels and output channels of convolution layer I is 512, the number of output channels is 512, the convolution kernel size is 3×3, and the stride is 2. The number of input channels and output channels of splicing layer D is 1024. The number of input channels and output channels of Bottleneck CSP layer H is 1024. By adopting the SPPF layer, that is, serializing the input through multiple maxpool layers contained in the SPPF layer, the calculation time can be greatly shortened, and the purpose of compressing the model can also be achieved. In addition, the second backbone network can effectively transmit semantic features through the feature pyramid structure, and strengthen the positioning information from top to bottom.

[0062] In addition, the above fault location model uses the CIOU_Loss loss function to calculate the intersection-over-union ratio loss and screens the region of interest by non-maximum suppression. In addition, considering the problem of device occlusion, weighted processing is performed during the non-maximum suppression process to filter out overlapping regions of interest. In addition, during the non-maximum suppression process, the intersection-over-union ratio uses the DIOU intersection-over-union ratio to better detect overlapping devices.

[0063] The fourth step is to generate the node detection information by using the pre-trained fault classification model, the local image and the local network topology map included in the node information to be inspected.

[0064] Among them, the above-mentioned fault location model and the above-mentioned fault classification model are included in the fault detection model, and the above-mentioned fault location model and the above-mentioned fault classification model are decoupled. In practice, considering that the monitoring equipment or drone also has a certain computing power, the fault location model and / or the fault classification model can be deployed in advance on the monitoring equipment or drone with sufficient computing power, so that the monitoring equipment or drone can directly generate node detection information locally after collecting real-time equipment images. In this way, only a certain amount of network traffic is consumed to transmit the node detection information calculated by the monitoring equipment or drone back to the execution subject. This method can greatly improve the computing power utilization efficiency of the execution subject and the edge end (drone or monitoring equipment), while reducing the computing power pressure of the execution subject.

[0065] Optionally, the fault classification model includes: a first convolution layer, a first feature multiplexing layer, a second convolution layer, a first pooling layer, a second feature multiplexing layer, a third convolution layer, a second pooling layer, a third feature multiplexing layer, a third pooling layer and a linear layer.

[0066] Optionally, the execution subject generates the node detection information by using a pre-trained fault classification model, the local image and the local network topology map included in the node information to be detected, including: Step 1: Perform convolution processing on the above local image through the above first convolution layer to obtain the first convolution feature.

[0067] Step 2: Input the first convolutional feature into the first feature multiplexing layer to obtain the second convolutional feature.

[0068] Step 3: Convolve the second convolutional feature through the second convolutional layer to obtain a third convolutional feature.

[0069] Step 4: Pool the third convolutional feature through the first pooling layer to obtain the pooled third convolutional feature.

[0070] Step 5: Input the third convolutional feature after pooling into the second feature multiplexing layer to obtain the fourth convolutional feature.

[0071] Step 6: Convolve the fourth convolution feature through the third convolution layer to obtain the fifth convolution feature.

[0072] Step 7: Pool the fifth convolutional feature through the second pooling layer to obtain the pooled fifth convolutional feature.

[0073] Step 8: Input the fifth convolutional feature after pooling into the third feature reuse layer to obtain the fifth convolutional feature after pooling.

[0074] Step 9: Input the fifth convolutional feature after pooling into the linear layer to obtain the node failure type and node failure probability included in the node detection information.

[0075] Step 10: Determine the node impact included in the node detection information according to the node failure type and node failure probability included in the node detection information and the local network topology included in the to-be-detected node information.

[0076] In practice, different node failure types correspond to different basic impacts. The node failure probability can be used as the weight A of the basic impact. In addition, the device density included in the local network topology corresponds to the weight B. Specifically, node impact = basic impact corresponding to the node failure type × (weight A + weight B). Among them, weight A and weight B are standardized before use so that weight A and weight B are in the same order of magnitude.

[0077] In a fifth step, in response to the real-time node status information being a closed-loop current feature, feature preprocessing is performed on the real-time node status information to generate a preprocessed closed-loop current feature.

[0078] Among them, the closed-loop current characteristics include but are not limited to: the bus voltage of the closed-loop feeder corresponding to the node (equipment) to be tested, the equivalent load of the upper power grid corresponding to the node (equipment) to be tested, the reactive power of the node (equipment) to be tested, the transmission power of the feeder corresponding to the node (equipment) to be tested, the active power of the node (equipment) to be tested, and the upper power grid current directly connected to the feeder corresponding to the node (equipment) to be tested. In practice, the above-mentioned execution subject can use methods such as outlier removal and data normalization to perform feature preprocessing on the above-mentioned real-time node status information to generate preprocessed closed-loop current characteristics.

[0079] The sixth step is to generate the node detection information according to the pre-processed closed-loop current characteristics, the pre-trained closed-loop current prediction model and the local network topology map included in the node information to be detected.

[0080] In practice, the above-mentioned closed-loop current prediction model adopts the model structure of CNN (Convolutional Neural Networks) model + LSTM (Long Short-Term Memory) model. Specifically, the CNN model is first used to perform linear convolution processing on the closed-loop current features after preprocessing, and then the processed convolution features are input into the LSTM model. In addition, the LSTM model is connected with a fully connected layer to output the node fault type and node fault probability. Based on the node fault type and node fault probability, the node impact can be determined by calculating the node impact in step 10 of the fourth step above, which will not be repeated here.

[0081] In addition, based on the output of the LTSM model, the above-mentioned execution entity can further use the quantile regression method to calculate the carryable combined current under safe conditions.

[0082] Step 1023: In response to the node failure probability being greater than a preset node failure probability and the node spread being less than a preset node spread, a fault warning matching the node failure type is initiated.

[0083] In some embodiments, the execution subject may initiate a fault warning that matches the node fault type in response to the node fault probability being greater than a preset node fault probability and the node spread being less than a preset node spread. In practice, the execution subject may initiate a fault warning that matches the node fault type for the device corresponding to the node to be inspected on the warning platform. In this case, only the fault warning corresponding to the node device to be inspected is initiated, which can effectively avoid the problem of inaccurate subsequent fault location caused by large-scale alarms.

[0084] Step 1024, in response to the node failure probability being greater than the preset node failure probability and the node reach being greater than or equal to the preset node reach, initiate a risk warning for the local distribution network corresponding to the local network topology diagram included in the node information to be inspected.

[0085] In some embodiments, in response to the node failure probability being greater than the preset node failure probability and the node spread being greater than or equal to the preset node spread, a risk warning for the local distribution network corresponding to the local network topology map included in the node information to be inspected is initiated. In practice, the above-mentioned execution entity can initiate a risk warning for the local distribution network corresponding to the local network topology map included in the node information to be inspected on the warning platform. In this case, effective fault warnings can be made for possible cascading failures to avoid the impact on the overall operation of the distribution network caused by large-scale cascading failures.

[0086] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the fault detection method applied to the distribution network of some embodiments of the present disclosure, effective fault detection for the distribution network is realized, the stability of the distribution network is guaranteed, the risk of equipment damage is reduced, and the loss caused by power outage is reduced. Specifically, the reason for the failure of the distribution network is that the structure of the large-scale distribution network is complex. When the equipment in the distribution network fails, it is very easy to affect the stability of the distribution network as a whole, thereby affecting other equipment, causing equipment damage and power outage. Based on this, the fault detection method applied to the distribution network of some embodiments of the present disclosure, first, locate at least one node to be inspected in the distribution network, and obtain a set of information of the node to be inspected, wherein the above-mentioned distribution network is used to distribute the electric energy transmitted by the transmission network to the power consumption terminal, and the node to be inspected corresponds to the node to be inspected information one by one, and the node to be inspected in the above-mentioned node to be inspected information set includes: the type of the node to be inspected, the location of the node to be inspected and the local network topology map, wherein the local network topology map represents the topological structure of the local distribution network where the node to be inspected is located, and the local distribution network is a subnet of the above-mentioned distribution network. In practice, the network structure of large-scale distribution networks is complex. If fault detection is performed on the entire distribution network, the detection difficulty is large and the detection cycle is long. Therefore, by locating the key nodes (nodes to be inspected) in the distribution network, the detection complexity can be reduced. Secondly, for each node information to be inspected in the above-mentioned node information set to be inspected, the following processing steps are performed: the first step is to collect real-time node status information according to the node type to be inspected and the node position to be inspected included in the above-mentioned node information to be inspected. By collecting the real-time node status, the current operating status of the device corresponding to the node to be inspected is obtained. The second step is to perform fault detection on the node to be inspected corresponding to the above-mentioned node information to be inspected according to the local network topology map included in the above-mentioned real-time node status information and the above-mentioned node information to be inspected, so as to generate node detection information, wherein the above-mentioned node detection information includes: node fault type, node fault probability and node spread, wherein the node fault probability represents the probability of occurrence of the fault corresponding to the node fault type, and the node spread represents the influence of the node to be inspected on the equipment in the local distribution network corresponding to the local network topology map. In this way, from the perspective of the node to be inspected itself and the influence of the node to be inspected on the surrounding equipment, the influence of the fault type of the node to be inspected on the surrounding equipment is detected. The third step is to initiate a fault warning that matches the node fault type in response to the node fault probability being greater than the preset node fault probability and the node spread being less than the preset node spread. This achieves a single-point warning for the device corresponding to the node to be inspected. The fourth step is to initiate a risk warning for the local distribution network corresponding to the local network topology map included in the node information to be inspected in response to the node fault probability being greater than the preset node fault probability and the node spread being greater than or equal to the preset node spread. This achieves an overall warning for the node to be inspected and the local distribution network in which it is located.In this way, fault detection can be achieved efficiently, ensuring the stability of the distribution network, reducing the risk of equipment damage, and reducing losses caused by power outages.

[0087] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a fault detection device applied to a distribution network. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the fault detection device applied to the power distribution network can be specifically applied to various electronic devices.

[0088] like Figure 2 As shown, some embodiments of the fault detection device 200 applied to the distribution network include: a positioning unit 201 and an execution unit 202. The positioning unit 201 is configured to locate at least one node to be inspected in the distribution network and obtain a set of information about the node to be inspected, wherein the distribution network is used to distribute the electric energy transmitted by the transmission network to the power consumption terminal, and the node to be inspected corresponds to the node to be inspected information one by one, and the node to be inspected information in the set of information about the node to be inspected includes: the type of node to be inspected, the location of the node to be inspected and the local network topology map, wherein the local network topology map represents the topological structure of the local distribution network where the node to be inspected is located, and the local distribution network is a subnet of the distribution network; the execution unit 202 is configured to perform the following processing steps for each node to be inspected information in the set of information about the node to be inspected: according to the type of node to be inspected and the location of the node to be inspected included in the information about the node to be inspected, real-time node status information is collected; according to the real-time node status information and the node to be inspected information The local network topology diagram included is used to perform fault detection on the nodes to be inspected corresponding to the above-mentioned node information to be inspected, so as to generate node detection information, wherein the above-mentioned node detection information includes: node fault type, node fault probability and node spread, wherein the node fault probability represents the probability of occurrence of the fault corresponding to the node fault type, and the node spread represents the influence of the node to be inspected on the equipment in the local distribution network corresponding to the local network topology diagram; in response to the above-mentioned node fault probability being greater than the preset node fault probability and the above-mentioned node spread being less than the preset node spread being, a fault warning matching the above-mentioned node fault type is initiated; in response to the above-mentioned node fault probability being greater than the above-mentioned preset node fault probability and the above-mentioned node spread being greater than or equal to the above-mentioned preset node spread being, a risk warning is initiated for the local distribution network corresponding to the local network topology diagram included in the above-mentioned node information to be inspected.

[0089] It can be understood that the units described in the fault detection device 200 for distribution network are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the fault detection device 200 applied to the power distribution network and the units contained therein, and will not be described in detail here.

[0090] Reference below Figure 3 , which shows a structural schematic diagram of an electronic device (eg, a computing device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0091] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory 302 or a program loaded from a storage device 308 into a random access memory 303. Various programs and data required for the operation of the electronic device 300 are also stored in the random access memory 303. The processing device 301, the read-only memory 302, and the random access memory 303 are connected to each other via a bus 304. An input / output interface 305 is also connected to the bus 304.

[0092] Typically, the following devices may be connected to the input / output interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0093] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the read-only memory 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0094] It should be noted that the computer-readable medium recorded in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0095] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0096] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: locates at least one node to be inspected in the distribution network and obtains a set of information about the node to be inspected, wherein the above-mentioned distribution network is used to distribute the electric energy transmitted by the transmission network to the power consumption terminal, and the node to be inspected and the node to be inspected information correspond one to one. The node to be inspected information in the above-mentioned node to be inspected information set includes: the type of node to be inspected, the location of the node to be inspected and the local network topology map, wherein the local network topology map represents the topological structure of the local distribution network where the node to be inspected is located, and the local distribution network is a subnet of the above-mentioned distribution network; for each node to be inspected information in the above-mentioned node to be inspected information set, the following processing steps are performed: according to the type of node to be inspected and the location of the node to be inspected included in the above-mentioned node to be inspected information, real-time node status information is collected; according to the above-mentioned real-time node status The node status information and the local network topology included in the above-mentioned node information to be inspected are used to perform fault detection on the node to be inspected corresponding to the above-mentioned node information to be inspected, so as to generate node detection information, wherein the above-mentioned node detection information includes: node fault type, node fault probability and node spread, wherein the node fault probability represents the probability of occurrence of the fault corresponding to the node fault type, and the node spread represents the influence of the node to be inspected on the equipment in the local distribution network corresponding to the local network topology; in response to the above-mentioned node fault probability being greater than the preset node fault probability and the above-mentioned node spread being less than the preset node spread being, a fault warning matching the above-mentioned node fault type is initiated; in response to the above-mentioned node fault probability being greater than the above-mentioned preset node fault probability and the above-mentioned node spread being greater than or equal to the above-mentioned preset node spread being, a risk warning for the local distribution network corresponding to the local network topology included in the above-mentioned node information to be inspected is initiated.

[0097] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0098] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0099] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be set in a processor, for example, may be described as: a processor includes a positioning unit and an execution unit. Wherein, the names of these units do not constitute a limitation on the unit itself in some cases, for example, the positioning unit may also be described as "locating at least one node to be inspected in the distribution network, and obtaining a set of information about the node to be inspected, wherein the above distribution network is used to distribute the electric energy transmitted by the transmission network to the power user terminal, and the node to be inspected and the node to be inspected information correspond one to one, and the node to be inspected information in the above set of information about the node to be inspected includes: the type of node to be inspected, the location of the node to be inspected and the local network topology map, wherein the local network topology map represents the topological structure of the local distribution network where the node to be inspected is located, and the local distribution network is a unit of the subnet of the above distribution network".

[0100] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0101] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A fault detection method applied to a distribution network, comprising: Locate at least one node to be inspected in the distribution network, and obtain a set of information about the node to be inspected, wherein the distribution network is used to distribute the electric energy transmitted by the transmission network to the power consumption terminal, and the node to be inspected corresponds to the node to be inspected information one by one, and the node to be inspected information in the set of information about the node to be inspected includes: the type of the node to be inspected, the location of the node to be inspected, and a local network topology map, wherein the local network topology map represents the topological structure of the local distribution network where the node to be inspected is located, and the local distribution network is a subnet of the distribution network; For each piece of node information to be inspected in the set of node information to be inspected, the following processing steps are performed: Collecting real-time node status information according to the node type and node location included in the node information; According to the real-time node status information and the local network topology included in the node information to be inspected, fault detection is performed on the node to be inspected corresponding to the node information to be inspected to generate node detection information, wherein the node detection information includes: node fault type, node fault probability and node spread, wherein the node fault probability represents the probability of occurrence of the fault corresponding to the node fault type, and the node spread represents the influence of the node to be inspected on the equipment in the local distribution network corresponding to the local network topology; In response to the node failure probability being greater than a preset node failure probability and the node spread being less than a preset node spread, initiating a fault warning matching the node failure type; In response to the node failure probability being greater than the preset node failure probability and the node reach being greater than or equal to the preset node reach, a risk warning for a local distribution network corresponding to a local network topology diagram included in the node information to be inspected is initiated.

2. The method according to claim 1, wherein: The step of locating at least one node to be inspected in the power distribution network and obtaining a set of information of the node to be inspected includes: Performing channel monitoring on a control channel, wherein the control channel is a bidirectional communication channel; In response to monitoring a timing reminder request initiated by a first device, pulling device information of the first device as the node information to be inspected in the node information set to be inspected, wherein the first device is a device in the distribution network and the timing of the corresponding inspection timer has expired, and the inspection timer is controlled by the device type, historical inspection records and number of faults of the device; In response to monitoring a target device operation for a second device, the device information of the second device is pulled as the node information to be inspected in the node information set to be inspected, wherein the second device is a device within the distribution network used for distribution network closing, and the target device operation is a distribution network closing operation.

3. The method according to claim 2, wherein: The collecting of real-time node status information according to the node type and node position included in the node information includes: In response to the type of the node to be inspected included in the node to be inspected information being a device type corresponding to a device not involved in the power distribution network loop, a collection area is determined according to the position of the node to be inspected included in the node to be inspected information, wherein the collection area is an electronic fence area centered on the position of the node to be inspected included in the node to be inspected information, and the area radius of the collection area is controlled by the device density of the monitoring devices contained in the collection area; Determining a device operating status of each monitoring device in at least one monitoring device within the collection area; Determine whether there is a monitoring device that meets the screening condition in the at least one monitoring device, wherein the screening condition is: the corresponding device operation status indicates that the monitoring device is operating normally and the position of the node to be inspected included in the node to be inspected information is located within the monitoring area of ​​the monitoring device; In response to existence, controlling the monitoring device that meets the screening condition to collect a real-time device image, wherein the real-time device image is an appearance image of the node to be inspected corresponding to the information of the node to be inspected; In response to the absence of the node, planning a path of the drone according to the location of the node to be inspected included in the information of the node to be inspected; Determine a target drone according to the drone path, wherein the target drone is a drone that is in an idle state and can travel back and forth along the drone path; Controlling the target UAV along the UAV path to collect real-time device images; Determine the real-time device image as the real-time node status information; In response to the node type to be inspected included in the node information to be inspected being a device type corresponding to a device involved in the closing loop of a distribution network, closing loop current characteristics of the node to be inspected corresponding to the node information to be inspected are collected as the real-time node status information.

4. The method according to claim 3, wherein: The performing fault detection on the node to be inspected corresponding to the node to be inspected information according to the real-time node status information and the local network topology diagram included in the node to be inspected information to generate node detection information includes: In response to the real-time node status information being a real-time device image, adaptively scaling the real-time node status information to obtain a scaled device image; Performing image enhancement on the scaled device image to obtain an enhanced device image; Determine a local image by using a pre-trained fault location model and the enhanced device image, wherein the local image is a local image of a location where an area of ​​interest is located and is contained in the enhanced device image; Generate the node detection information through a pre-trained fault classification model, the local image and a local network topology map included in the information of the node to be detected, wherein the fault location model and the fault classification model are included in the fault detection model, and the fault location model and the fault classification model are decoupled; In response to the real-time node status information being a closed-loop current feature, performing feature preprocessing on the real-time node status information to generate a preprocessed closed-loop current feature; The node detection information is generated according to the pre-processed closed-loop current characteristics, the pre-trained closed-loop current prediction model and the local network topology diagram included in the node information to be detected.

5. The method according to claim 4, wherein: The fault classification model includes: a first convolution layer, a first feature multiplexing layer, a second convolution layer, a first pooling layer, a second feature multiplexing layer, a third convolution layer, a second pooling layer, a third feature multiplexing layer, a third pooling layer and a linear layer; and The node detection information is generated by using the pre-trained fault classification model, the local image and the local network topology map included in the node information to be detected, including: Performing convolution processing on the local image through the first convolution layer to obtain a first convolution feature; Inputting the first convolutional feature into the first feature multiplexing layer to obtain a second convolutional feature; Performing convolution processing on the second convolution feature through the second convolution layer to obtain a third convolution feature; Performing pooling processing on the third convolutional feature through the first pooling layer to obtain a pooled third convolutional feature; Inputting the pooled third convolutional feature into the second feature multiplexing layer to obtain a fourth convolutional feature; Performing convolution processing on the fourth convolution feature through the third convolution layer to obtain a fifth convolution feature; Performing pooling processing on the fifth convolutional feature through the second pooling layer to obtain a pooled fifth convolutional feature; Inputting the pooled fifth convolutional feature into the third feature multiplexing layer to obtain the pooled fifth convolutional feature; Inputting the pooled fifth convolutional feature into the linear layer to obtain the node fault type and node fault probability included in the node detection information; The node impact degree included in the node detection information is determined according to the node failure type and node failure probability included in the node detection information and the local network topology map included in the to-be-detected node information.

6. A fault detection device for a power distribution network, comprising: A positioning unit is configured to locate at least one node to be inspected in the distribution network, and obtain a set of information about the node to be inspected, wherein the distribution network is used to distribute the electric energy transmitted by the transmission network to the power consumption terminal, and the node to be inspected corresponds to the node to be inspected information one by one, and the node to be inspected information in the set of information about the node to be inspected includes: the type of the node to be inspected, the location of the node to be inspected, and a local network topology map, wherein the local network topology map represents the topology structure of the local distribution network where the node to be inspected is located, and the local distribution network is a subnet of the distribution network; The execution unit is configured to perform the following processing steps for each node information to be inspected in the node information set to be inspected: collecting real-time node status information according to the node type and node position to be inspected included in the node information to be inspected; performing fault detection on the node to be inspected corresponding to the node information to be inspected according to the real-time node status information and the local network topology included in the node information to be inspected, so as to generate node detection information, wherein the node detection information includes: node fault type, node fault probability and node spread, wherein the node fault probability represents the probability of occurrence of the fault corresponding to the node fault type, and the node spread represents the influence of the node to be inspected on the equipment in the local distribution network corresponding to the local network topology; in response to the node fault probability being greater than the preset node fault probability and the node spread being less than the preset node spread, initiating a fault warning matching the node fault type; in response to the node fault probability being greater than the preset node fault probability and the node spread being greater than or equal to the preset node spread, initiating a risk warning for the local distribution network corresponding to the local network topology included in the node information to be inspected.

7. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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