Busbar operation status monitoring system and method based on busbar merging unit device

Abstract

The present invention belongs to the technical field of electric power safety. A busbar operation status monitoring system and method based on a busbar merging unit device are provided. The method comprises: controlling a camera to capture a high-definition image of the busbar, extracting the operation status data of the busbar from the high-definition image based on an inspection plan; using an abnormal prediction composite model to perform abnormal analysis on the operation status data, and obtaining the abnormal type and abnormal probability of the busbar; when the abnormal probability is higher than the probability threshold, generating and outputting an abnormal type alarm signal. The present invention uses image recognition technology to extract the appearance features of three abnormal conditions, namely, busbar connection defects, busbar surface defects, and busbar overheating defects, to determine or infer whether there is an abnormal condition, especially a busbar overheating defect, without the need to configure an infrared sensor, thereby significantly reducing the hardware complexity and implementation cost of the monitoring equipment.

Description

Technical Field

[0001] The present invention relates to the field of electric power safety technology, and in particular to a busbar operation status monitoring system and method based on a busbar merging unit device. Background Art

[0002] In order to ensure that the bus is in good operating condition, monitoring equipment can be used to collect the operating status of the bus, and then the collected information is transmitted to the bus merging unit device, which will analyze and determine whether there are any defects in the bus.

[0003] Busbar faults mainly include defects in busbar connection parts, busbar surface defects, busbar overheating, etc. Defects in busbar connection parts: The busbar connection parts may become loose, broken or corroded, which will affect the conductivity and stability of the busbar. Busbar surface defects: Cracks, rust or wear and other defects may appear on the busbar surface, which will reduce the service life and safety of the busbar. Busbar overheating: Due to excessive current or poor heat dissipation, the busbar may overheat, resulting in abnormal increase in busbar temperature. Busbar connection defects and busbar surface defects can be directly detected by image recognition technology, but busbar overheating defects are difficult to detect directly by image recognition technology, and require auxiliary infrared sensing detection equipment, which makes the hardware structure of the monitoring equipment more complex and the implementation cost higher.

[0004] Therefore, how to monitor the above-mentioned defects during busbar operation, especially busbar overheating defects, based only on image recognition technology is a technical problem that needs to be solved at present. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a bus operation status monitoring method, system, electronic equipment, computer storage medium and computer program product based on a bus merging unit device.

[0006] The present invention provides a busbar operation status monitoring method based on a busbar merging unit device, the method comprising the following steps: controlling a camera to capture a high-definition image of a busbar, extracting operation status data of the busbar from the high-definition image based on an inspection plan, wherein the operation status data comprises busbar connection part features and busbar surface features; using an abnormality prediction composite model to perform abnormality analysis on the operation status data, and obtaining an abnormality type and abnormality probability of the busbar, wherein the abnormality type comprises a busbar connection part defect, a busbar surface defect, and a busbar overheating defect; wherein the abnormality prediction composite model comprises an abnormality detection sub-model and an abnormality prediction sub-model, wherein the abnormality detection sub-model is used to analyze the busbar connection part defect and the busbar surface defect, and the abnormality prediction sub-model is used to analyze the busbar overheating defect; when the abnormal probability is higher than a probability threshold, generating and outputting an alarm signal of the abnormality type.

[0007] Optionally, before the control camera takes a high-definition image of the busbar, the method also includes: obtaining the inspection cycle data of each of the two abnormal types, the busbar connection part defect and the busbar surface defect, and judging the matching relationship between the current time period and each of the inspection cycle data; if the current time period matches at least one of the above inspection cycle data, the busbar connection part defect and / or the busbar surface defect are added to the inspection plan; otherwise, the busbar connection part defect and / or the busbar surface defect are not added to the inspection plan; and, the busbar overheating defect is added to the inspection plan.

[0008] Optionally, extracting the operating status data of the bus from the high-definition image based on the inspection plan includes: receiving an inspection plan data packet, parsing the inspection plan data packet to obtain each inspection plan, extracting the bus surface features of the bus from the high-definition image; and determining whether each inspection plan includes defects in the bus connection parts, and if so, extracting the bus connection part features of the bus from the high-definition image.

[0009] Optionally, the use of the abnormal prediction composite model to perform abnormal analysis on the operating status data to obtain the abnormal type and abnormal probability of the bus includes: inputting the operating status data into the abnormal prediction composite model, the abnormal prediction composite model inputting the bus connection part characteristics and the bus surface characteristics into the abnormal detection sub-model, and inputting the bus surface characteristics into the abnormal prediction sub-model; the abnormal detection sub-model outputs a first abnormal probability of the bus connection part defect and / or a second abnormal probability of the bus surface defect; the abnormal prediction sub-model outputs a third abnormal probability of the bus overheating defect; wherein the bus surface characteristics input into the abnormal prediction sub-model include bus color characteristics and bus gloss characteristics.

[0010] Optionally, the abnormal prediction sub-model outputs a third abnormal probability of the busbar overheating defect, including: separating the busbar color feature and the busbar gloss feature from the busbar surface feature, inputting them into the abnormal prediction sub-model, and obtaining a fourth abnormal probability of the busbar overheating defect output by the abnormal prediction sub-model; if the fourth abnormal probability is lower than a probability threshold, using the fourth abnormal probability as the third abnormal probability; if the fourth abnormal probability is higher than a probability threshold, further extracting the thickness and uniformity of the oxide layer on the busbar from the high-definition image, obtaining an adjustment index based on the matching of the thickness and the uniformity, adjusting the fourth abnormal probability using the adjustment index, and using the adjusted fourth abnormal probability as the third abnormal probability.

[0011] Optionally, obtaining an adjustment index according to the matching of the thickness and the uniformity includes: ; In the formula, is the regulation index; is the thickness of the oxide layer on the busbar, is the average thickness of the oxide layer on all busbars in the associated area of ​​the busbar merging unit device; is the uniformity of the oxide layer on the busbar, is the average uniformity of the oxide layer on all busbars in the associated area of ​​the busbar merging unit device; is the adjustment coefficient and .

[0012] The present invention also provides a busbar operation status monitoring system based on a busbar merging unit device, the system comprising a processing module and a computer storage medium, the processing module calling and running a computer program in the computer storage medium to implement the following steps: controlling a camera to capture a high-definition image of the busbar, extracting the operation status data of the busbar from the high-definition image based on an inspection plan, the operation status data comprising busbar connection part features and busbar surface features; using an abnormality prediction composite model to perform abnormality analysis on the operation status data to obtain the abnormality type and abnormality probability of the busbar, the abnormality type comprising busbar connection part defects, busbar surface defects, and busbar overheating defects; wherein the abnormality prediction composite model comprises an abnormality detection sub-model and an abnormality prediction sub-model, the abnormality detection sub-model is used to analyze the busbar connection part defects and the busbar surface defects, and the abnormality prediction sub-model is used to analyze the busbar overheating defects; when the abnormal probability is higher than a probability threshold, generating and outputting an alarm signal of the abnormality type.

[0013] The present invention also provides an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the computer program implements the method described in any of the preceding items when executed by the processor.

[0014] The present invention also provides a computer storage medium storing a computer program executable by a processor to implement any of the methods described above.

[0015] The present invention also provides a computer program product, which includes a computer program that can be executed by a processor to implement any of the methods described above.

[0016] The beneficial effects of the present invention are as follows: the scheme of the present invention extracts the appearance characteristics of three defect conditions of the busbar, namely, busbar connection defects, busbar surface defects, and busbar overheating defects, through image recognition technology, and directly determines or infers whether the above three abnormal conditions exist. In particular, for the abnormal condition of busbar overheating, there is no need to configure an infrared sensor, thereby significantly reducing the hardware structure complexity and implementation cost of the monitoring equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of a bus operation status monitoring method based on a bus merging unit device disclosed in an embodiment of the present invention.

[0019] Figure 2 It is a schematic diagram of the structure of the abnormality prediction composite model disclosed in the embodiment of the present invention.

[0020] Figure 3 It is a structural schematic diagram of a bus operation status monitoring system based on a bus merging unit device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following is a description of the implementation of the present application by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0022] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0023] like Figure 1As shown, an embodiment of the present invention discloses a busbar operation status monitoring method based on a busbar merging unit device, the method comprising the following steps: controlling a camera to capture a high-definition image of the busbar, extracting the operation status data of the busbar from the high-definition image based on an inspection plan, the operation status data comprising busbar connection part features and busbar surface features; using an abnormality prediction composite model to perform abnormality analysis on the operation status data to obtain the abnormality type and abnormality probability of the busbar, the abnormality type comprising busbar connection part defects, busbar surface defects, and busbar overheating defects; wherein the abnormality prediction composite model comprises an abnormality detection sub-model and an abnormality prediction sub-model, the abnormality detection sub-model is used to analyze the busbar connection part defects and the busbar surface defects, and the abnormality prediction sub-model is used to analyze the busbar overheating defects; when the abnormal probability is higher than the probability threshold, generating and outputting an alarm signal of the abnormality type.

[0024] The present invention controls the camera to capture a high-definition image of the bus, obtains the inspection plan for the bus section / group, and the bus merging unit device extracts the operating status data of the bus from the high-definition image transmitted by the camera based on the inspection plan, including the bus connection part characteristics and the bus surface characteristics. Among them, the bus connection part characteristics can be used to determine whether there are bus connection part defects, and the bus surface characteristics can be used to determine whether there are bus surface defects and bus overheating defects. Then, the extracted operating status data is transmitted to the abnormal prediction composite model, and the abnormal prediction composite model performs abnormal analysis to obtain the abnormal type and abnormal probability of the bus. Correspondingly, the abnormal type includes bus connection part defects, bus surface defects, and bus overheating defects. If the abnormal probability of any abnormal type is higher than the probability threshold, it means that the corresponding abnormal situation exists. At this time, an alarm signal of the abnormal type is generated and output to relevant personnel or terminal equipment to remind relevant personnel to handle it.

[0025] Among them, Figure 2 As shown, the above-mentioned abnormal prediction composite model in the present invention includes an abnormal detection sub-model and an abnormal prediction sub-model. The abnormal detection sub-model is specifically used to analyze whether there is a busbar connection part defect based on the characteristics of the busbar connection part, and to analyze whether there is a busbar surface defect based on the busbar surface characteristics, while the abnormal prediction sub-model is specifically used to analyze whether there is a busbar overheating defect based on the busbar surface characteristics.

[0026] It can be seen that the solution of the present invention extracts the appearance characteristics of the three defect conditions of the busbar, namely, busbar connection defects, busbar surface defects, and busbar overheating defects, through image recognition technology, and directly determines or infers whether the above three abnormal conditions exist. In particular, for the abnormal condition of busbar overheating, there is no need to configure an infrared sensor, thereby significantly reducing the hardware structure complexity and implementation cost of the monitoring equipment.

[0027] Optionally, before the control camera takes a high-definition image of the busbar, the method also includes: obtaining the inspection cycle data of each of the two abnormal types, the busbar connection part defect and the busbar surface defect, and judging the matching relationship between the current time period and each of the inspection cycle data; if the current time period matches at least one of the above inspection cycle data, the busbar connection part defect and / or the busbar surface defect are added to the inspection plan; otherwise, the busbar connection part defect and / or the busbar surface defect are not added to the inspection plan; and, the busbar overheating defect is added to the inspection plan.

[0028] In this embodiment, the busbar overheating defect is a defect that may occur at any time during the operation of the busbar, while the busbar connection part defect and busbar surface defect are different. Problems such as looseness, breakage or corrosion are busbar connection part defects that appear after long-term operation, and small cracks, rust or wear and other defects on the busbar surface are also busbar surface defects that will only appear after long-term operation. Therefore, the present invention is set to inspect the two defects of busbar connection part defects and busbar surface defects according to the preset inspection cycle data, while there is no need to preset the inspection cycle data for busbar overheating, that is, the busbar overheating detection must be performed every time the inspection is carried out.

[0029] Therefore, before starting the inspection, first obtain the preset inspection cycle data of the two abnormal types of bus connection defects and bus surface defects, and judge the matching relationship between the current time period and each inspection cycle data. If there is a match, the bus connection defects and / or bus surface defects corresponding to the matching inspection cycle data are added to the inspection plan, that is, the abnormal type is inspected this time, otherwise it is not inspected.

[0030] It should be noted that the monitoring equipment in the present invention can be a mobile inspection equipment, such as an inspection vehicle, a drone, etc., on which a camera is deployed; or it can be a fixed camera, that is, a high-definition image of the corresponding bus is captured by controlling a camera that can capture each section / group of busbars. The monitoring equipment transmits the captured high-definition image of the busbar to the busbar merging unit device, which detects whether it has the above-mentioned defects and completes the inspection.

[0031] Optionally, extracting the operating status data of the bus from the high-definition image based on the inspection plan includes: receiving an inspection plan data packet, parsing the inspection plan data packet to obtain each inspection plan, extracting the bus surface features of the bus from the high-definition image; and determining whether each inspection plan includes defects in the bus connection parts, and if so, extracting the bus connection part features of the bus from the high-definition image.

[0032] In this embodiment, after the inspection system receives the inspection plan data packet, it parses the inspection plan data packet to obtain various inspection plans. These inspection plans are the inspection plans determined in the previous embodiment, namely, busbar overheating defects, or busbar overheating defects and busbar connection part defects and / or busbar surface defects. Since the busbar overheating defect is a must-check item, the busbar surface features of the busbar are directly extracted from the high-definition image. And, it is determined whether the busbar connection part defect is parsed from the inspection plan data packet. If so, the busbar connection part features of the busbar are extracted from the high-definition image. It should be noted that the aforementioned extracted busbar surface features can be used to analyze busbar surface defects in addition to busbar overheating defects, so there is no need to analyze whether the busbar surface features should be extracted corresponding to the busbar surface defects.

[0033] It should be noted that the above inspection plan is formulated based on the preset inspection cycle data, but it can also be formulated not based on the inspection cycle data, that is, busbar connection defects, busbar surface defects, and busbar overheating defects are all added to the inspection plan, or only part of the three defects, that is, one or more, are added to the inspection plan.

[0034] Optionally, the use of the abnormal prediction composite model to perform abnormal analysis on the operating status data to obtain the abnormal type and abnormal probability of the bus includes: inputting the operating status data into the abnormal prediction composite model, the abnormal prediction composite model inputting the bus connection part characteristics and the bus surface characteristics into the abnormal detection sub-model, and inputting the bus surface characteristics into the abnormal prediction sub-model; the abnormal detection sub-model outputs a first abnormal probability of the bus connection part defect and / or a second abnormal probability of the bus surface defect; the abnormal prediction sub-model outputs a third abnormal probability of the bus overheating defect; wherein the bus surface characteristics input into the abnormal prediction sub-model include bus color characteristics and bus gloss characteristics.

[0035] In this embodiment, after the above-mentioned operating status data is input into the abnormal prediction composite model, the abnormal prediction composite model can input the bus surface features into the abnormal detection sub-model and the abnormal prediction sub-model respectively, and if there are bus connection part features in the operating status data, the bus connection part features are also input into the abnormal detection sub-model. Correspondingly, the abnormal detection sub-model can output the first abnormal probability of bus connection part defects and the second abnormal probability of bus surface defects, and the abnormal prediction sub-model outputs the third abnormal probability of bus overheating defects. Among them, the abnormal prediction sub-model predicts whether the bus has an overheating defect based on the input bus color features and bus gloss features.

[0036] Among them, the anomaly detection sub-model and the anomaly prediction sub-model can be built and trained separately. The algorithm based on which they are built can be any one of the graph convolutional neural network, generative adversarial network, Transformer, etc., or they can be obtained by fine-tuning the large model, such as the Bert large model. Then, the anomaly detection sub-model and the anomaly prediction sub-model are integrated and packaged into an anomaly prediction composite model. The specific integration and packaging process will not be repeated here.

[0037] Optionally, the abnormal prediction sub-model outputs a third abnormal probability of the busbar overheating defect, including: separating the busbar color feature and the busbar gloss feature from the busbar surface feature, inputting them into the abnormal prediction sub-model, and obtaining a fourth abnormal probability of the busbar overheating defect output by the abnormal prediction sub-model; if the fourth abnormal probability is lower than a probability threshold, using the fourth abnormal probability as the third abnormal probability; if the fourth abnormal probability is higher than a probability threshold, further extracting the thickness and uniformity of the oxide layer on the busbar from the high-definition image, obtaining an adjustment index based on the matching of the thickness and the uniformity, adjusting the fourth abnormal probability using the adjustment index, and using the adjusted fourth abnormal probability as the third abnormal probability.

[0038] In this embodiment, the abnormal prediction sub-model in the present invention predicts whether the busbar has an overheating defect by analyzing the busbar color and busbar gloss. The specific principle is as follows: when the busbar is operating normally, its color is usually close to the original color of the material, such as purple-red for copper busbars and silver-white for aluminum busbars. When the busbar is overheated, due to the thermal effect of the current, an oxidation reaction will occur on the surface of the busbar, causing the color to change. For example, a copper busbar will oxidize at high temperature to generate black or dark red copper oxide, while an aluminum busbar may become grayish white due to oxidation. Therefore, by observing the color change on the surface of the busbar, it is possible to preliminarily determine whether the busbar is overheated (i.e., analyzing whether the busbar color is different from the original color of the corresponding type of busbar).

[0039] When the busbar is operating normally, its surface usually has a certain gloss. When the busbar is overheated, due to the high temperature, the gloss of the busbar surface will gradually weaken, or even become dull. This is because high temperature will cause physical and chemical changes in the surface material of the busbar, such as oxidation, melting, etc., which will affect its gloss. Therefore, by observing the gloss changes on the busbar surface, it is also possible to make a preliminary judgment on whether the busbar is overheated.

[0040] Therefore, the trained abnormal prediction sub-model can predict the preliminary abnormal probability of busbar overheating defect, i.e., the fourth abnormal probability, based on the busbar color characteristics and busbar gloss characteristics. If the fourth abnormal probability is lower than the probability threshold, it means that the busbar basically does not have a busbar overheating defect, and the fourth abnormal probability can be directly used as the third abnormal probability. If the fourth abnormal probability is higher than the probability threshold, it means that the busbar has a high probability of busbar overheating defect, and other factors need to be assisted for further verification and confirmation.

[0041] The thickness and uniformity of the oxide layer will have a promoting effect on busbar overheating, specifically: 1. The promoting effect of the oxide layer thickness on busbar overheating: Thermal resistance effect: As a layer of material attached to the surface of the busbar, the oxide layer has a certain thermal resistance. When the thickness of the oxide layer increases, the thermal resistance also increases, which makes it more difficult for the heat generated by the busbar when transmitting current to dissipate. When the heat cannot be dissipated in time, the busbar temperature will rise, thereby increasing the risk of overheating.

[0042] Thermal expansion and stress: The busbar will expand thermally at high temperatures, and the thermal expansion coefficient of the oxide layer is different from that of the busbar body. This difference is particularly obvious when the oxide layer is thick, which will cause stress between the busbar and the oxide layer. Over a long period of time, this stress may cause fatigue and damage to the busbar material, leading to overheating or other failures.

[0043] 2. The effect of the uniformity of the oxide layer on the overheating of the busbar: The unevenness of the oxide layer will affect the distribution of current on the busbar surface. In areas with thicker oxide layers, due to the larger resistance, the current may bypass these areas and flow to other areas with smaller resistance. This uneven current distribution will cause local overheating and stress concentration of the busbar, increasing the risk of overheating failure.

[0044] In this regard, the present invention obtains a corresponding adjustment index based on the thickness and uniformity of the oxide layer on the busbar, and then uses the adjustment index (greater than 1, such as 1.1, 1.2) to adjust the fourth abnormal probability, and uses the adjusted fourth abnormal probability as the third abnormal probability.

[0045] Optionally, obtaining an adjustment index according to the matching of the thickness and the uniformity includes: ; In the formula, is the regulation index; is the thickness of the oxide layer on the busbar, is the average thickness of the oxide layer on all busbars in the associated area of ​​the busbar merging unit device; is the uniformity of the oxide layer on the busbar, is the average uniformity of the oxide layer on all busbars in the associated area of ​​the busbar merging unit device; is the adjustment coefficient and .

[0046] The above calculation formula can be used to calculate the adjustment index. Based on the expression of the above calculation formula, it can be seen that the adjustment index is positively correlated with the thickness of the oxide layer on the busbar, and negatively correlated with the uniformity of the oxide layer on the busbar. In addition, since the generation of the oxide layer is slow, within a certain statistical period, and It can basically be considered a constant value.

[0047] The above-mentioned associated area is the area assigned to the corresponding bus merging unit device, and the defect inspection task of the bus in the area is the responsibility of the bus merging unit device.

[0048] like Figure 3 As shown, the present invention also discloses a busbar operation status monitoring system based on a busbar merging unit device, the system comprising a processing module and a computer storage medium, the processing module calls and runs a computer program in the computer storage medium to implement the following steps: controlling a camera to capture a high-definition image of the busbar, extracting operation status data of the busbar from the high-definition image based on an inspection plan, wherein the operation status data comprises busbar connection part features and busbar surface features; using an abnormality prediction composite model to perform abnormality analysis on the operation status data, and deriving an abnormality type and abnormality probability of the busbar, wherein the abnormality type comprises a busbar connection part defect, a busbar surface defect, and a busbar overheating defect; wherein the abnormality prediction composite model comprises an abnormality detection sub-model and an abnormality prediction sub-model, wherein the abnormality detection sub-model is used to analyze the busbar connection part defect and the busbar surface defect, and the abnormality prediction sub-model is used to analyze the busbar overheating defect; when the abnormal probability is higher than a probability threshold, generating and outputting an alarm signal of the abnormality type.

[0049] The present invention also discloses an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the computer program implements the method described in any of the preceding items when executed by the processor.

[0050] The present invention also discloses a computer storage medium, which stores a computer program that can be executed by a processor to implement any of the methods described in the preceding items.

[0051] The present invention also discloses a computer program product, which includes a computer program that can be executed by a processor to implement any of the methods described above.

[0052] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0053] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0054] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.

[0055] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, audio input, or tactile input).

[0056] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communications network). Examples of communications networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0057] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document is not limited here.

[0058] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A busbar operation status monitoring method based on a busbar merging unit device, characterized in that: The method comprises the following steps: Controlling the camera to capture a high-definition image of the busbar, and extracting the operating status data of the busbar from the high-definition image based on the inspection plan, wherein the operating status data includes the characteristics of the busbar connection parts and the surface characteristics of the busbar; Use the abnormal prediction composite model to perform abnormal analysis on the operating status data to obtain the abnormal type and abnormal probability of the busbar, wherein the abnormal type includes a busbar connection part defect, a busbar surface defect, and a busbar overheating defect; wherein the abnormal prediction composite model includes an abnormal detection sub-model and an abnormal prediction sub-model, wherein the abnormal detection sub-model is used to analyze the busbar connection part defect and the busbar surface defect, and the abnormal prediction sub-model is used to analyze the busbar overheating defect; When the abnormal probability is higher than the probability threshold, generating and outputting an alarm signal of the abnormal type; The abnormality prediction composite model is used to perform abnormal analysis on the operating status data to obtain the abnormality type and abnormality probability of the bus, including: The operation status data is input into the abnormality prediction composite model, the abnormality prediction composite model inputs the bus connection part characteristics and the bus surface characteristics into the abnormality detection sub-model, and the bus surface characteristics into the abnormality prediction sub-model; The abnormality detection sub-model outputs a first abnormality probability of a defect at the busbar connection portion and / or a second abnormality probability of a defect on the busbar surface; The abnormal prediction sub-model outputs a third abnormal probability of the busbar overheating defect; wherein the busbar surface features input into the abnormal prediction sub-model include busbar color features and busbar gloss features.

2. A busbar operation status monitoring method based on a busbar merging unit device according to claim 1, characterized in that: Before the control camera captures the high-definition image of the busbar, the method further includes: Obtaining the inspection cycle data of the two abnormal types, namely, the busbar connection part defect and the busbar surface defect, and determining the matching relationship between the current time period and each of the inspection cycle data; If the current time period matches at least one of the inspection cycle data, the busbar connection part defect and / or the busbar surface defect are added to the inspection plan; otherwise, the busbar connection part defect and / or the busbar surface defect are not added to the inspection plan; And, the busbar overheating defect is added to the inspection plan.

3. A busbar operation status monitoring method based on a busbar merging unit device according to claim 2, characterized in that: The extracting the busbar operation status data from the high-definition image based on the inspection plan includes: Receiving an inspection plan data packet, parsing the inspection plan data packet to obtain each inspection plan, and extracting the busbar surface features of the busbar from the high-definition image; And, determine whether each inspection plan includes the bus connection part defect, and if so, extract the bus connection part characteristics of the bus from the high-definition image.

4. A busbar operation status monitoring method based on a busbar merging unit device according to claim 3, characterized in that: The abnormality prediction sub-model outputs a third abnormality probability of the busbar overheating defect, including: Separating the busbar color feature and the busbar gloss feature from the busbar surface feature, inputting the busbar color feature and the busbar gloss feature into the abnormality prediction sub-model, and obtaining a fourth abnormality probability of the busbar overheating defect output by the abnormality prediction sub-model; If the fourth abnormal probability is lower than the probability threshold, the fourth abnormal probability is used as the third abnormal probability; If the fourth abnormal probability is higher than the probability threshold, the thickness and uniformity of the oxide layer on the busbar are further extracted from the high-definition image, and an adjustment index is obtained by matching the thickness and the uniformity. The fourth abnormal probability is adjusted using the adjustment index, and the adjusted fourth abnormal probability is used as the third abnormal probability.

5. A busbar operation status monitoring method based on a busbar merging unit device according to claim 4, characterized in that: The obtaining of the adjustment index according to the matching of the thickness and the uniformity includes: ; In the formula, is the regulation index; is the thickness of the oxide layer on the busbar, is the average thickness of the oxide layer on all busbars in the associated area of ​​the busbar merging unit device; is the uniformity of the oxide layer on the busbar, is the average uniformity of the oxide layer on all busbars in the associated area of ​​the busbar merging unit device; is the adjustment coefficient and .

6. A busbar operation status monitoring system based on a busbar merging unit device, the system comprising a processing module and a computer storage medium, characterized in that: The processing module calls and runs the computer program in the computer storage medium to implement the following steps: Controlling the camera to capture a high-definition image of the busbar, and extracting the operating status data of the busbar from the high-definition image based on the inspection plan, wherein the operating status data includes the characteristics of the busbar connection parts and the surface characteristics of the busbar; Use the abnormal prediction composite model to perform abnormal analysis on the operating status data to obtain the abnormal type and abnormal probability of the busbar, wherein the abnormal type includes a busbar connection part defect, a busbar surface defect, and a busbar overheating defect; wherein the abnormal prediction composite model includes an abnormal detection sub-model and an abnormal prediction sub-model, wherein the abnormal detection sub-model is used to analyze the busbar connection part defect and the busbar surface defect, and the abnormal prediction sub-model is used to analyze the busbar overheating defect; When the abnormal probability is higher than the probability threshold, generating and outputting an alarm signal of the abnormal type; The abnormality prediction composite model is used to perform abnormal analysis on the operating status data to obtain the abnormality type and abnormality probability of the bus, including: The operation status data is input into the abnormality prediction composite model, the abnormality prediction composite model inputs the bus connection part characteristics and the bus surface characteristics into the abnormality detection sub-model, and the bus surface characteristics into the abnormality prediction sub-model; The abnormality detection sub-model outputs a first abnormality probability of a defect at the busbar connection portion and / or a second abnormality probability of a defect on the busbar surface; The abnormal prediction sub-model outputs a third abnormal probability of the busbar overheating defect; wherein the busbar surface features input into the abnormal prediction sub-model include busbar color features and busbar gloss features.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the computer program implements the method according to any one of claims 1 to 5 when executed by the processor.

8. A computer storage medium, characterized in that: The computer storage medium stores a computer program that can be executed by a processor to implement the method according to any one of claims 1 to 5.

9. A computer program product, characterized in that: The computer program product comprises a computer program executable by a processor to implement the method according to any one of claims 1 to 5.

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