Cable joint temperature prediction method and system based on machine learning

Abstract

The invention relates to a cable joint temperature prediction method and system based on machine learning, and the method comprises the steps: adjusting a signal collection end to a corresponding cable joint in response to an obtained instruction or a spontaneous patrol instruction; obtaining an infrared image of the cable joint and carrying out gray processing on the image to obtain a reference image; screening the reference image by using a plurality of gray domains to obtain a temperature distribution diagram; and counting the area of a color value region on each temperature distribution diagram and drawing a temperature change diagram of the corresponding cable joint according to the area of the color value region, wherein the temperature change diagram comprises the area value of each temperature interval and the area change trend of each temperature interval. According to the cable joint temperature prediction method and system based on machine learning disclosed by the invention, temperature detection and temperature prediction are carried out on a plurality of positions in a coverage range by means of a temperature detection mode of remote temperature area coverage, so that temperature anomaly and potential temperature anomaly are found.

Description

Technical Field

[0001] The present invention relates to the technical field of non-contact detection, and in particular to a cable joint temperature prediction method and system based on machine learning. Background Art

[0002] Cable joints are important components that connect two cables and are also the weakest link in power lines. During the manufacturing or installation process, cable joints may have contact resistance or defects, resulting in local temperature rise. In addition, excessive current carrying capacity in actual operation will also increase the joint temperature, accelerate insulation aging, and shorten the service life of the cable joint, which poses a threat to the stable transportation of electricity.

[0003] At present, there are many detection methods, such as thermocouple detection, infrared detection, distributed optical fiber temperature measurement, etc. These technologies are all for detection at fixed positions, which can reflect the temperature of cable joints to a certain extent, but the temperature obtained is a point temperature value. The so-called point temperature value means that it can only detect at a fixed position, and the fixed position cannot fully reflect the temperature distribution of the cable joint.

[0004] Because when the temperature of the cable joint is abnormal, it is uncertain whether it appears at the fixed position mentioned above, that is, the temperature value of the cable joint can only be discovered when the temperature abnormality occurs at the fixed position or is transmitted to the fixed position. Summary of the invention

[0005] The present invention provides a cable joint temperature prediction method and system based on machine learning, which uses a temperature detection method covered by a remote temperature area to perform temperature detection and temperature prediction at multiple locations within the coverage area, so as to discover temperature anomalies and potential temperature anomalies.

[0006] The above object of the present invention is achieved through the following technical solutions: In a first aspect, the present invention provides a cable joint temperature prediction method based on machine learning, comprising: In response to the acquired command or the spontaneous inspection command, the signal acquisition end is adjusted to the corresponding cable connector; Acquire an infrared image of the cable joint and perform grayscale processing on the image to obtain a reference image; The reference image is screened using multiple grayscale domains to obtain a temperature distribution map; Count the area of ​​the color value region on each temperature distribution map; and A temperature change graph of the corresponding cable joint is drawn according to the area of ​​the color value region. The temperature change graph includes the area value of each temperature interval and the area change trend of each temperature interval.

[0007] In a possible implementation manner of the first aspect, one signal acquisition end covers multiple cable connectors.

[0008] In a possible implementation manner of the first aspect, when the signal acquisition end points to a cable connector, temperature acquisition is performed on other cable connectors within a coverage range, and a deviation value is calculated according to the temperature acquisition result.

[0009] In a possible implementation manner of the first aspect, in time series, the signal acquisition end periodically corrects the deviation value.

[0010] In a possible implementation manner of the first aspect, the method further includes: Comparing the difference between the shapes of each temperature interval at two time points and obtaining a difference area, wherein the difference includes a local misalignment degree; Calculate the area and maximum height of the distinguished regions; The area change trend is given based on the area and maximum height of the distinguished areas.

[0011] In a possible implementation manner of the first aspect, the straight line where the maximum height is located passes through the center point of the temperature range.

[0012] In a possible implementation manner of the first aspect, when comparing the difference between the shapes of each temperature interval at two time points, the pointing positions of the signal collection ends at the two time points are the same.

[0013] In a second aspect, the present invention provides a device for predicting cable joint temperature based on machine learning, comprising: A position adjustment unit, used to adjust the signal acquisition end to the corresponding cable connector in response to an acquired instruction or a spontaneous inspection instruction; An image acquisition and processing unit, used to acquire an infrared image of the cable joint and perform grayscale processing on the image to obtain a reference image; A screening unit, used for screening the reference image using multiple grayscale domains to obtain a temperature distribution map; A statistical unit, used to count the area of ​​the color value region on each temperature distribution graph; and The drawing unit is used to draw a temperature change diagram of the corresponding cable joint according to the area of ​​the color value region, and the temperature change diagram includes the area value of each temperature interval and the area change trend of each temperature interval.

[0014] In a third aspect, the present invention provides a cable joint temperature prediction system based on machine learning, the system comprising: one or more memories for storing instructions; and One or more processors, used to call and run the instructions from the memory to execute the method as described in the first aspect and any possible implementation of the first aspect.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium, the computer-readable storage medium comprising: Program, when the program is executed by a processor, the method described in the first aspect and any possible implementation of the first aspect is executed.

[0016] In a fifth aspect, the present invention provides a computer program product, comprising program instructions, and when the program instructions are executed by a computing device, the method described in the first aspect and any possible implementation of the first aspect is executed.

[0017] In a sixth aspect, the present invention provides a chip system comprising a processor for implementing the functions involved in the above aspects, for example, generating, receiving, sending, or processing the data and / or information involved in the above methods.

[0018] The chip system may be composed of chips, or may include chips and other discrete devices.

[0019] In a possible design, the chip system also includes a memory, which is used to store necessary program instructions and data. The processor and the memory can be decoupled and respectively set on different devices, connected by wired or wireless means, or the processor and the memory can also be coupled on the same device. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention is a schematic flow chart of the steps of a cable joint temperature prediction method.

[0021] Figure 2 It is a schematic diagram of adjusting the position of a signal acquisition terminal provided by the present invention.

[0022] Figure 3 This is a temperature distribution diagram of a cable joint provided by the present invention.

[0023] Figure 4 It is a schematic flow chart of the steps for obtaining the area change trend provided by the present invention. DETAILED DESCRIPTION

[0024] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings.

[0025] The present invention discloses a cable joint temperature prediction method based on machine learning. Figure 1The cable joint temperature prediction method based on machine learning disclosed in the present invention comprises the following steps: S101, in response to an acquired instruction or a spontaneous inspection instruction, adjusting a signal acquisition end to a corresponding cable connector; S102, acquiring an infrared image of the cable joint and performing grayscale processing on the image to obtain a reference image; S103, using multiple grayscale domains to screen the reference image to obtain a temperature distribution map; S104, counting the area of ​​the color value region on each temperature distribution graph; and S105, drawing a temperature change diagram of the corresponding cable joint according to the area of ​​the color value region, wherein the temperature change diagram includes an area value of each temperature interval and an area change trend of each temperature interval.

[0026] In step S101, the remote infrared terminal receives an instruction to start or starts according to the patrol instruction stored in itself. After starting, the signal collection end will be adjusted to the corresponding cable joint first, such as Figure 2 As shown, then carry out temperature collection.

[0027] After the position adjustment is completed, step S102 is executed. In this step, the infrared image of the cable joint is obtained and the image is gray-scaled to obtain a reference image. The reference image is processed in step S102. In step S103, the reference image is screened using multiple grayscale domains to obtain a temperature distribution map, such as Figure 3 shown.

[0028] Each temperature distribution graph represents the distribution area of ​​a temperature interval on the reference image, and multiple temperature distribution graphs can form the reference image obtained in step S102. Step S103 can also be regarded as performing segmentation processing on the reference image obtained in step S102.

[0029] Then, in step S104, the area of ​​the color value region on each temperature distribution graph is counted, and the area represents the projection of the part of the cable joint in the same temperature range in a two-dimensional space. Finally, in step S105, the temperature change graph of the corresponding cable joint is drawn according to the area of ​​the color value region. The temperature change graph has two dimensions, namely: 1. Area value of each temperature interval; 2. The area change trend in each temperature range. The area change trends include increasing, unchanged and decreasing.

[0030] Through the temperature change graph, you can see the temperature changes on the cable connector. According to the temperature changes, you can determine whether the cable connector is in a normal working state or an abnormal working state.

[0031] In some possible implementations, a signal acquisition terminal covers multiple cable connectors, with the goal of increasing the utilization rate of the signal acquisition terminal. For example, the temperature acquisition time can be allocated, and each cable connector can be allocated a certain amount of time, thereby achieving the goal of reducing the equipment deployment price.

[0032] Furthermore, when the signal acquisition end points to a cable joint, the temperature of other cable joints within the coverage area is collected at the same time, and the deviation value is calculated according to the temperature collection result. That is, the temperature collection work is also performed synchronously for the cable joints that the signal acquisition end does not point to.

[0033] It should be noted that the temperature data obtained by temperature collection at this time is only used as a reference to determine whether the cable connector is in a normal working state or an abnormal working state. When it is found that the cable connector is in an abnormal working state, it is necessary to immediately adjust the direction of the signal collection end and re-perform temperature collection.

[0034] In some possible implementations, in the time series, the signal acquisition end periodically corrects the deviation value, and the purpose of the correction is to ensure the accuracy of the temperature acquisition data acquisition result.

[0035] For further information, see Figure 4 , also includes: S201, comparing the difference between the shapes of each temperature interval at two time points and obtaining a difference area, wherein the difference includes a local mismatch; S202, calculating the area and maximum height of the distinguished regions; S203: providing an area change trend according to the area and maximum height of the distinguished regions.

[0036] In step S201 to step S203, a difference area will be obtained. The difference area refers to the difference in the shape of the temperature range at two time points. The difference here is evaluated from two dimensions of area and maximum height. Finally, the area change trend is given according to the area and maximum height of the difference area.

[0037] The changing trend of area refers to the amount of change in area, including both increase and decrease.

[0038] Maximum height refers to the maximum change in height, which also includes increases and decreases.

[0039] In some possible implementations, the straight line where the maximum height is located passes through the center point of the temperature interval.

[0040] In some possible implementations, when comparing the difference between the shapes of each temperature interval at two time points, the pointing positions of the signal acquisition terminals at the two time points are the same.

[0041] The present invention also provides a device for predicting cable joint temperature based on machine learning, comprising: A position adjustment unit, used to adjust the signal acquisition end to the corresponding cable connector in response to an acquired instruction or a spontaneous inspection instruction; An image acquisition and processing unit, used to acquire an infrared image of the cable joint and perform grayscale processing on the image to obtain a reference image; A screening unit, used for screening the reference image using multiple grayscale domains to obtain a temperature distribution map; A statistical unit, used to count the area of ​​the color value region on each temperature distribution graph; and The drawing unit is used to draw a temperature change diagram of the corresponding cable joint according to the area of ​​the color value region, and the temperature change diagram includes the area value of each temperature interval and the area change trend of each temperature interval.

[0042] Furthermore, one signal acquisition end covers multiple cable connectors.

[0043] Furthermore, when the signal acquisition end points to a cable joint, the temperature of other cable joints within the coverage area is collected at the same time, and the deviation value is calculated according to the temperature collection result.

[0044] Furthermore, in terms of time series, the signal acquisition end regularly corrects the deviation value.

[0045] Furthermore, it also includes: A comparison unit, used to compare the difference between the shapes of each temperature interval at two time points and obtain a difference area, wherein the difference includes a local mismatch; A first calculation unit, used for calculating the area and maximum height of the distinguished area; The second calculation unit is used to provide the area change trend according to the area and the maximum height of the distinguished area.

[0046] Furthermore, the straight line where the maximum height is located passes through the center point of the temperature range.

[0047] Furthermore, when comparing the difference between the shapes of each temperature interval at two time points, the pointing positions of the signal acquisition terminals at the two time points are the same.

[0048] In one example, the unit in any of the above devices can be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASICs), or, one or more digital signal processors (DSPs), or, one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0049] For another example, when the units in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call a program. For another example, these units can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0050] Various objects such as various messages / information / equipment / network elements / systems / devices / actions / operations / processes / concepts that may appear in the present invention are named. It can be understood that these specific names do not constitute a limitation on the relevant objects. The names assigned may change with factors such as scenarios, contexts or usage habits. The understanding of the technical meaning of the technical terms in the present invention should be mainly determined from the functions and technical effects embodied / executed in the technical scheme.

[0051] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0052] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0053] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0054] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0055] It should also be understood that in various embodiments of the present invention, the first, second, etc. are only used to indicate that multiple objects are different. For example, the first time window and the second time window are only used to indicate different time windows. They should not have any impact on the time window itself, and the first, second, etc. mentioned above should not impose any limitations on the embodiments of the present invention.

[0056] It should also be understood that in the various embodiments of the present invention, unless otherwise specified or there is any logical conflict, the terms and / or descriptions between the different embodiments are consistent and can be referenced to each other, and the technical features in the different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0057] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a computer-readable storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned computer-readable storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0058] The present invention also provides a cable joint temperature prediction system based on machine learning, the system comprising: one or more memories for storing instructions; and One or more processors are used to call and run the instructions from the memory to execute the method as described above.

[0059] The present invention also provides a computer program product, which includes instructions. When the instructions are executed, the temperature prediction system performs operations of the temperature prediction system corresponding to the above method.

[0060] The present invention also provides a chip system, which includes a processor for implementing the functions involved in the above content, such as generating, receiving, sending, or processing the data and / or information involved in the above method.

[0061] The chip system may be composed of chips, or may include chips and other discrete devices.

[0062] The processor mentioned in any of the above places can be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for executing programs for controlling the above-mentioned feedback information transmission method.

[0063] In a possible design, the chip system also includes a memory, which is used to store necessary program instructions and data. The processor and the memory can be decoupled and respectively set on different devices, connected by wire or wireless means to support the chip system to implement various functions in the above embodiments. Alternatively, the processor and the memory can also be coupled on the same device.

[0064] Optionally, the computer instructions are stored in a memory.

[0065] Optionally, the memory is a storage unit within the chip, such as a register, a cache, etc. The memory can also be a storage unit within the terminal located outside the chip, such as a ROM or other types of static storage devices that can store static information and instructions, RAM, etc.

[0066] It can be understood that the memory in the present invention can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0067] The nonvolatile memory may be a ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory.

[0068] The volatile memory may be a RAM, which is used as an external cache. There are many different types of RAM, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct memory bus random access memory.

[0069] The embodiments of this specific implementation method are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A cable joint temperature prediction method based on machine learning, characterized in that: include: In response to the acquired command or the spontaneous inspection command, the signal acquisition end is adjusted to the corresponding cable connector; Acquire an infrared image of the cable joint and perform grayscale processing on the image to obtain a reference image; The reference image is screened using multiple grayscale domains to obtain a temperature distribution map; Count the color value area on each temperature distribution map; as well as A temperature change graph of the corresponding cable joint is drawn according to the area of ​​the color value region. The temperature change graph includes the area value of each temperature interval and the area change trend of each temperature interval.

2. The cable joint temperature prediction method based on machine learning according to claim 1 is characterized in that: One signal acquisition end covers multiple cable connectors.

3. The cable joint temperature prediction method based on machine learning according to claim 2 is characterized in that: When the signal acquisition end points to a cable connector, the temperature of other cable connectors within the coverage area is collected at the same time, and the deviation value is calculated based on the temperature collection results.

4. The cable joint temperature prediction method based on machine learning according to claim 3 is characterized in that: In the time series, the signal acquisition end regularly corrects the deviation value.

5. The cable joint temperature prediction method based on machine learning according to any one of claims 1 to 4, characterized in that: Also includes: Comparing the difference between the shapes of each temperature interval at two time points and obtaining a difference area, wherein the difference includes a local misalignment degree; Calculate the area and maximum height of the distinguished regions; The area change trend is given based on the area and maximum height of the distinguished areas.

6. The cable joint temperature prediction method based on machine learning according to claim 5 is characterized in that: The straight line of maximum height passes through the center point of the temperature range.

7. The cable joint temperature prediction method based on machine learning according to claim 5 is characterized in that: When comparing the difference in the shape of each temperature interval at two time points, the pointing positions of the signal acquisition terminals at the two time points are the same.

8. A cable joint temperature prediction device based on machine learning, characterized in that: include: A position adjustment unit, used to adjust the signal acquisition end to the corresponding cable connector in response to an acquired instruction or a spontaneous inspection instruction; An image acquisition and processing unit, used to acquire an infrared image of the cable joint and perform grayscale processing on the image to obtain a reference image; A screening unit, used for screening the reference image using multiple grayscale domains to obtain a temperature distribution map; A statistical unit is used to count the area of ​​the color value region on each temperature distribution map; as well as The drawing unit is used to draw a temperature change diagram of the corresponding cable joint according to the area of ​​the color value region, and the temperature change diagram includes the area value of each temperature interval and the area change trend of each temperature interval.

9. A cable joint temperature prediction system based on machine learning, characterized in that: The system comprises: one or more memories for storing instructions; and One or more processors, configured to call and execute the instructions from the memory to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer readable storage medium comprises: The program, when the program is executed by a processor, the method according to any one of claims 1 to 7 is executed.

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