Pipeline leakage detection method based on ARM processor, electronic equipment and medium

Abstract

The embodiment of the invention relates to the field of pipeline leakage detection, and discloses a pipeline leakage detection method based on an ARM processor, electronic equipment and a medium. An ARM processor of the detection equipment controls the detection equipment to enter a low-power-consumption mode; starting from the moment of entering the low power consumption mode, the ARM processor starts timing, and when a timing result reaches a preset detection time, the detection equipment starts to collect an original signal; carrying out median filtering on the collected original signal, and carrying out standard deviation calculation on a filtering result to obtain a standard deviation result; and determining a detection result according to a size relationship between the standard deviation result and a preset threshold value, and uploading information corresponding to the detection result to a cloud platform. According to the scheme, the low-power-consumption mode can be entered to save electricity when leakage does not need to be detected, the data are selectively uploaded to the cloud platform according to the detection result, and the calculation amount of the cloud platform is reduced.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of pipeline leakage detection, and in particular to a pipeline leakage detection method, electronic equipment and medium based on an ARM processor. Background Art

[0002] Urban underground pipe networks are an important part of the basic engineering facilities system that maintains the economic functions of cities and regions. With the increase in the number of pipelines and the increase in service life, as well as the inevitable corrosion, wear and tear and other natural or man-made damage, the pipelines leak, which also leads to frequent accidents. For urban water supply pipe networks, the average pipeline leakage rate in my country is above 20%, and the leakage rate is increasing year by year. The leakage rate in some small and medium-sized cities has reached nearly 70%.

[0003] However, due to the limited understanding of the mechanism of leakage vibration and sound generation, and the many interference factors in the on-site environment of the pipeline, such as background noise and incomplete pipeline layout information, in order to ensure the accuracy of detection, the detection equipment arranged around the pipeline is in real-time monitoring state, resulting in high power consumption and the need for frequent battery replacement; in addition, the real-time monitoring data needs to be uploaded to the platform for calculation synchronously, resulting in a huge amount of platform calculation, which brings challenges to pipeline leakage detection. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a pipeline leakage detection method, electronic device and medium based on an ARM processor, which can enter a low-power mode to save power when there is no need to detect leakage. After the detection time is reached, the detection equipment starts working to obtain the detection results, and selectively uploads data to the cloud platform based on the detection results, thereby reducing the computing power of the cloud platform.

[0005] To solve the above technical problems, an embodiment of the present invention provides a pipeline leakage detection method based on an ARM processor, comprising: when a detection device for detecting the pipeline leakage status does not receive a control instruction from a cloud platform within a preset time, the ARM processor of the detection device controls the detection device to enter a low power consumption mode; from the moment of entering the low power consumption mode, the ARM processor starts timing, and when the timing result reaches a preset detection time, the detection device starts to collect original signals; median filtering is performed on the collected original signals, and standard deviation is calculated on the filtering results to obtain standard deviation results; the detection result is determined according to the size relationship between the standard deviation result and a preset threshold, and the information corresponding to the detection result is uploaded to the cloud platform.

[0006] An embodiment of the present invention also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores control instructions executable by the at least one processor, and the control instructions are executed by the at least one processor so that the at least one processor can execute the pipeline leakage detection method based on the ARM processor as described above.

[0007] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the pipeline leakage detection method based on an ARM processor as described above.

[0008] In addition, as described above, before the detection device for detecting the leakage status of the pipeline receives no control instruction from the cloud platform within a preset time period, the method further includes: powering on the detection device, and after powering on, the detection device obtains and continuously synchronizes the network time; reporting basic information of the detection device to the cloud platform, and the basic information at least includes: any one of the ID of the detection device, the IMEI of the detection device, the GPS / Beidou positioning information of the detection device, the battery voltage of the detection device, and the posture information of the detection device.

[0009] In addition, the method as described above also includes: if the detection device receives the control instruction within the preset time after power-on, the detection device executes the control instruction; after the control instruction is executed, the timing is restarted for the preset time, and it is determined again whether the detection device has received the control instruction within the preset time.

[0010] In addition, the control instruction as described above is used to set the wake-up time after the detection device enters the low power consumption mode; the detection device executes the control instruction including: the detection device determines whether the wake-up time is the same as the preset detection time, and when the wake-up time is different from the preset detection time, updates the preset detection time according to the wake-up time.

[0011] In addition, the ARM processor starts timing as described above, including: the ARM processor uses the network time when entering the low power consumption mode as the first network time, and simultaneously uses the internal real-time clock to start timing from zero, and accumulates the first network time and the timing time to obtain the timing result.

[0012] In addition, the detection device as described above starts collecting signals including: the detection device uses internal sensors to collect signals within a preset collection time and temporarily stores them in a random access memory, and after the random access memory cache is full, writes the signal data cached in the random access memory to an external non-volatile memory.

[0013] In addition, the calculation formula for calculating the standard deviation of the filtering result as described above is: Among them, δ is the standard deviation result, N is the number of filtering results, y i is the i-th filtering result, and μ is the average value of the filtering results.

[0014] In addition, as described above, determining the detection result according to the size relationship between the standard deviation result and the preset threshold, and uploading the information corresponding to the detection result to the cloud platform includes: comparing the size of the standard deviation result and the preset threshold; if the standard deviation result is smaller than the preset threshold, the detection result is that the pipeline has not leaked, and the result that the pipeline has not leaked is uploaded to the cloud platform; if the standard deviation result is larger than the preset threshold, the detection result is that the pipeline has leaked, and the result that the pipeline has leaked is uploaded to the cloud platform together with the original signal collected by the detection device.

[0015] Compared with the prior art, the embodiment of the present invention determines whether the detection device has not received the control instruction of the cloud platform within the preset time length. When the detection device used to detect the pipeline leakage status has not received the control instruction of the cloud platform within the preset time length, the ARM processor of the detection device controls the detection device to enter the low power mode; from the moment of entering the low power mode, the ARM processor starts timing, and when the timing result reaches the preset detection time, the detection device starts to collect the original signal; the collected original signal is subjected to median filtering, and the standard deviation of the filtering result is calculated to obtain the standard deviation result; the detection result is determined according to the size relationship between the standard deviation result and the preset threshold value, and the information corresponding to the detection result is uploaded to the cloud platform. That is, this scheme uses the ARM processor to make the detection device enter the low power mode when no detection is needed, saving power. When the preset detection time is reached, the detection device turns off the low power mode and starts to work normally to collect signals, calculates the collected signals to obtain the detection result, and selectively uploads data to the cloud platform according to the detection result, reducing the calculation amount of the cloud platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0017] Figure 1 It is a specific flow chart of a pipeline leakage detection method based on an ARM processor according to an embodiment of the present invention;

[0018] Figure 2is a specific flow chart of a detection result judgment process according to an embodiment of the present invention;

[0019] Figure 3 is a schematic structural diagram of an electronic device according to another embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, it can be understood by those skilled in the art that in the embodiments of the present invention, many technical details are provided to enable readers to better understand the present invention. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present invention can be implemented.

[0021] The first embodiment of the present invention relates to a pipeline leakage detection method based on an ARM processor, such as Figure 1 As shown, the specific steps include the following steps.

[0022] Step 101: When a detection device for detecting pipeline leakage status does not receive a control instruction from a cloud platform within a preset time period, an ARM (Advanced RISC Machine) processor of the detection device controls the detection device to enter a low power consumption mode.

[0023] Specifically, the detection device refers to a hardware device arranged around the pipeline for detecting the leakage status of the pipeline. Each detection device is equipped with a vibration sensor or a hydroacoustic sensor. The state signal of the pipeline is collected by the vibration sensor or the hydroacoustic sensor, and the collected signal is analyzed and processed to obtain the detection result of the pipeline leakage status. The detection device in this embodiment adopts an ARM processor, such as an ARM Cortex-M series processor. The ARM processor used in this embodiment can provide a rich low-power mode for the detection device to meet the power consumption requirements in different scenarios. The detection device in this embodiment is connected to the cloud platform for communication, and is used to receive control instructions from the cloud platform and upload specified data to the cloud platform for analysis.

[0024] Specifically, when the detection device does not receive a control instruction from the cloud platform within a preset time, in order to reduce power consumption, the ARM processor of the detection device enters a low power mode, that is, the ARM processor controls the detection device where it is located to enter a low power mode. This embodiment does not specifically limit the preset time.

[0025] In one example, before step 101 is executed, the method of this embodiment further includes: powering on the detection device, after powering on, the detection device acquires and continuously synchronizes network time; reporting basic information of the detection device to the cloud platform;

[0026] Among them, the basic information includes at least: any one of the following: the ID (Identity Document, unique code) of the detection device, the IMEI (International Mobile Equipment Identity) of the detection device, the GPS (Global Positioning System) / Beidou positioning information of the detection device, the battery voltage of the detection device, and the posture information of the detection device.

[0027] Specifically, when the detection work starts, the detection device is first powered on to establish a communication connection with the cloud platform. The communication between the detection device and the cloud platform can be carried out through the NB-IoT (Narrow Band Internet of Things) module inside the detection device, such as the BC25 module, etc. This embodiment does not make specific restrictions on this. The NB-IoT module is a high-performance, low-power wireless communication module that can provide a rich external interface and protocol stack for the detection device, while supporting multiple Internet of Things platforms and Bluetooth functions. After the detection device is powered on, it communicates with the cloud platform through the NB-IoT module, and the detection device obtains the network time through the NB-IoT module; at the same time, the detection device reports the basic information of the detection device to the cloud platform based on the NB-IoT module. The basic information includes but is not limited to the ID of the detection device, the IMEI of the detection device, the GPS / Beidou positioning information of the detection device, the battery voltage of the detection device, and the posture information of the detection device.

[0028] In another example, after reporting the basic information of the detection device to the cloud platform, the method of this embodiment further includes the following steps:

[0029] If the detection device receives a control instruction within the preset time after power-on, the detection device executes the control instruction; after the control instruction is executed, the timing is restarted for the preset time, and it is determined again whether the detection device receives the control instruction within the preset time.

[0030] Specifically, the detection device can start timing after establishing a communication connection with the cloud platform, or it can choose any time node to start timing for a preset duration, and the present invention does not make specific restrictions on this. In this embodiment, the moment when the detection device establishes a communication connection with the cloud platform is the timing starting point, and within the time when the timing duration is less than the preset duration, it is determined whether the control instruction of the cloud platform is received. If the control instruction sent by the cloud platform is not received, then corresponding to the situation in step 101, the ARM processor of the detection device controls the detection device to enter the low power mode; if the control instruction sent by the cloud platform is received, then corresponding to the situation in step 201, the detection device will perform the action indicated by the control instruction after receiving the control instruction, and the control instruction is used to set the basic parameters of the detection device, such as the wake-up time after entering the low power mode, filter parameters, etc., and this embodiment does not make specific restrictions on this. After the control instruction is executed, the detection device will restart timing from zero for the preset duration, and repeat the above process of determining whether the control instruction of the cloud platform is received within the time when the timing duration is less than the preset duration, until the situation in step 101 is met, the detection device does not receive the control instruction within the preset duration and successfully enters the low power mode.

[0031] Accordingly, in one example, when the control instruction is used to set the wake-up time after the detection device enters the low power consumption mode, the step of the detection device executing the control instruction in step 201 may be specifically as follows:

[0032] The detection device determines whether the wake-up time is the same as the preset detection time, and if the wake-up time is different from the preset detection time, updates the preset detection time according to the wake-up time.

[0033] Specifically, after the detection device receives the control instruction for setting the wake-up time within the preset time, the wake-up time is compared with the preset detection time of the detection device to determine whether the two are the same time. If they are not different times, the preset detection time is updated according to the wake-up time, that is, the time point of the preset detection time is changed to the time point of the wake-up time. For example, if the preset detection time is 12 o'clock and the wake-up time is 12:30, the preset detection time is updated to 12:30, and the preset detection time when the step 102 is subsequently executed is based on the updated 12:30. This embodiment does not specifically limit the setting of the wake-up time and the preset detection time.

[0034] Step 102: From the moment of entering the low power consumption mode, the ARM processor starts timing, and when the timing result reaches the preset detection time, the detection device starts collecting the original signal.

[0035] Specifically, from the moment the ARM processor controls the detection device to enter the low power mode, the detection device can no longer obtain network time. In order to ensure that the detection device can exit the low power mode in time to start detection work at the preset detection time, the ARM processor starts timing from zero from the moment it enters the low power mode. When the timing result reaches the preset detection time, the detection device is controlled to exit the low power mode and start collecting signals.

[0036] In one example, the step of the ARM processor starting timing in step 102 can be specifically as follows: the ARM processor uses the network time when entering the low power mode as the first network time, and simultaneously uses the internal real-time clock to start timing from zero, and accumulates the first network time and the timing time to obtain the timing result.

[0037] Specifically, since the detection device can no longer obtain the real-time network time after the ARM processor controls the detection device to enter the low power mode, the ARM processor records the network time at the moment of entering the low power mode, and uses the time as the first network time. At the same time, the internal real-time clock (RTC) starts timing from zero from the moment of entering the low power mode, continuously accumulates the timing time and the first network time, and uses the sum of the first network time and the timing time as the timing result.

[0038] For example, assuming that the preset detection time is eleven o'clock, and the ARM processor records the network time when entering the low power consumption mode as ten forty, then the first network time is ten forty. At this time, the RTC inside the ARM processor starts timing and continuously accumulates it with the first network time. When the RTC timing time reaches twenty minutes, the cumulative sum of the timing time and the first network time, that is, the timing result reaches eleven o'clock, which is the preset detection time. The ARM processor controls the detection device to exit the low power consumption mode and start collecting signals.

[0039] In another example, the process of the detection device collecting signals can be specifically as follows: the detection device uses internal sensors to collect signals within a preset collection time and temporarily stores them in a random access memory (RAM). After the RAM cache is full, the signal data cached in the RAM is written to an external non-volatile memory (flash).

[0040] Specifically, the detection device uses an internal vibration sensor or hydroacoustic sensor to collect corresponding signals, and uses the collected vibration signal or hydroacoustic signal as the original signal for subsequent analysis and processing. The preset collection time can be 20 seconds, and this embodiment does not make specific restrictions on this.

[0041] Step 103: Perform median filtering on the collected original signal, and calculate the standard deviation of the filtering result to obtain the standard deviation result.

[0042] Specifically, after the detection equipment completes the signal acquisition work, it performs median filtering on the collected original signal. Median filtering is a nonlinear signal processing technology based on sorting statistics theory that can effectively suppress noise. The specific process of median filtering can be: define a long window with an odd length L, L = 2k + 1, k is a positive integer. Suppose at a certain moment, the original signal samples in window L are x[ik], ..., x[i], ..., x[i+k], where x[i] is the signal sample value located at the center of the window. At this time, the median filtering expression is: y i =median{x[ik],…,x[i],…,x[i+k]},y i Then it is the filtering result of the original signal x[i]. After obtaining the filtering result of the original signal through median filtering, the standard deviation of the filtering result is calculated to obtain the standard deviation result.

[0043] In another example, the formula for calculating the standard deviation of the filtering result is:

[0044]

[0045] Among them, δ is the standard deviation result, N is the number of filtering results, y i is the i-th filtering result, and μ is the average value of the filtering results.

[0046] Step 104: Determine the test result based on the size relationship between the standard deviation result and the preset threshold, and upload the information corresponding to the test result to the cloud platform.

[0047] Specifically, this embodiment sets a preset threshold for pipeline leakage detection based on relevant experience. After the standard deviation result is calculated according to the filtering result, the standard deviation result is compared with the preset threshold to determine the size relationship between the standard deviation result and the preset threshold. The detection result is determined based on the size relationship between the standard deviation result and the preset threshold. Since different detection results upload different data to the cloud platform, the information corresponding to the detection result is uploaded to the cloud platform based on the determined detection result.

[0048] In an example, Figure 2 As shown, the content of step 104 may specifically include the following steps 1041-1043:

[0049] Step 1041: Compare the standard deviation result with a preset threshold.

[0050] Step 1042: If the standard deviation result is less than the preset threshold, the detection result is that the pipeline has no leakage, and the result that the pipeline has no leakage is uploaded to the cloud platform.

[0051] Step 1043: If the standard deviation result is greater than a preset threshold, the detection result is that the pipeline is leaking, and the result of the pipeline leakage and the original signal collected by the detection equipment are uploaded to the cloud platform.

[0052] Specifically, if the standard deviation result is less than the preset threshold, the detection device preliminarily determines that no leakage has occurred, and there is no need to upload the original signal to the cloud platform for analysis. It is only necessary to inform the cloud platform of the result that no leakage has occurred. If the standard deviation result is greater than the preset threshold, the detection result preliminarily determined by the detection device is that the pipeline has leaked. At this time, the cloud platform needs to perform further data analysis based on the original signal in order to locate the leak and take corresponding measures. Therefore, when the standard deviation result is greater than the preset threshold, the result of the pipeline leak and the original signal collected by the detection device are uploaded to the cloud platform together.

[0053] Compared with the related art, in the embodiment of the present invention, when the detection device used to detect the leakage status of the pipeline does not receive the control instruction of the cloud platform within the preset time, the ARM processor of the detection device controls the detection device to enter the low power mode; from the moment of entering the low power mode, the ARM processor starts timing, and when the timing result reaches the preset detection time, the detection device starts to collect the original signal; the collected original signal is subjected to median filtering, and the standard deviation of the filtering result is calculated to obtain the standard deviation result; the detection result is determined according to the size relationship between the standard deviation result and the preset threshold value, and the information corresponding to the detection result is uploaded to the cloud platform. That is, this scheme enables the detection device to enter the low power mode when no detection is needed, saving power. When the preset detection time is reached, the detection device turns off the low power mode and starts to work normally to collect signals, calculates the collected signals to obtain the detection result, and selectively uploads data to the cloud platform according to the detection result, reducing the calculation amount of the cloud platform.

[0054] Another embodiment of the present invention relates to an electronic device, such as Figure 3 As shown, it includes at least one processor 202; and a memory connected to the at least one processor 202 in communication; wherein the memory 201 stores control instructions that can be executed by the at least one processor 202, and the control instructions are executed by the at least one processor 202, so that the at least one processor 202 can execute any of the above-mentioned pipeline leakage detection method embodiments based on the ARM processor.

[0055] Among them, the memory 201 and the processor 202 are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects one or more processors 202 and various circuits of the memory 201 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art, and therefore, are not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor 202 is transmitted on a wireless medium through an antenna, and further, the antenna also receives data and transmits the data to the processor 202.

[0056] The processor 202 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management and other control functions. The memory 201 can be used to store data used by the processor 202 when performing operations.

[0057] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned pipeline leakage detection method embodiments based on an ARM processor.

[0058] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by controlling the hardware related to the instructions through a program, and the program is stored in a storage medium, including a number of control instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0059] Those skilled in the art will appreciate that the above-mentioned embodiments are specific examples for implementing the present invention, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A pipeline leakage detection method based on ARM processor, characterized in that: include: When the detection device for detecting the pipeline leakage status does not receive a control instruction from the cloud platform within a preset time, the ARM processor of the detection device controls the detection device to enter a low power consumption mode; From the moment of entering the low power consumption mode, the ARM processor starts timing, and when the timing result reaches the preset detection time, the detection device starts collecting the original signal; Performing median filtering on the collected original signal, and calculating the standard deviation of the filtering result to obtain a standard deviation result; The detection result is determined according to the size relationship between the standard deviation result and the preset threshold, and the information corresponding to the detection result is uploaded to the cloud platform.

2. The method according to claim 1, characterized in that Before the detection device for detecting the pipeline leakage status does not receive the control instruction of the cloud platform within a preset time, the method further includes: Powering on the detection device, and after powering on, the detection device acquires and continuously synchronizes network time; Report the basic information of the detection device to the cloud platform, wherein the basic information includes at least: any one of the ID of the detection device, the IMEI of the detection device, the GPS / Beidou positioning information of the detection device, the battery voltage of the detection device, and the posture information of the detection device.

3. The method according to claim 2, characterized in that The method further comprises: If the detection device receives the control instruction within the preset time period after power-on, the detection device executes the control instruction; After the control instruction is executed, the timing for the preset time period is restarted to determine again whether the detection device has received the control instruction within the preset time period.

4. The method according to claim 3, characterized in that The control instruction is used to set the wake-up time after the detection device enters the low power consumption mode; The detection device executing the control instruction includes: The detection device determines whether the wake-up time is the same as the preset detection time, and updates the preset detection time according to the wake-up time when the wake-up time is different from the preset detection time.

5. The method according to claim 1, characterized in that The ARM processor starts timing, including: The ARM processor uses the network time at the time of entering the low power consumption mode as the first network time, and simultaneously uses the internal real-time clock to start timing from zero, and accumulates the first network time and the timing time to obtain the timing result.

6. The method according to claim 1, characterized in that The detection device starts collecting signals including: The detection device uses an internal sensor to collect the original signal within a preset collection time and temporarily stores it in a random access memory. After the random access memory cache is full, the original signal data cached in the random access memory is written to an external non-volatile memory.

7. The method according to claim 1, characterized in that The calculation formula for calculating the standard deviation of the filtering result is: Among them, δ is the standard deviation result, N is the number of filtering results, y i is the i-th filtering result, and μ is the average value of the filtering results.

8. The method according to claim 1, characterized in that Determining a detection result according to a size relationship between the standard deviation result and a preset threshold, and uploading information corresponding to the detection result to the cloud platform, including: Comparing the standard deviation result with the preset threshold; If the standard deviation result is less than the preset threshold, the detection result is that the pipeline does not leak, and the result that the pipeline does not leak is uploaded to the cloud platform; If the standard deviation result is greater than the preset threshold, the detection result is that the pipeline is leaking, and the result of the pipeline leakage is uploaded to the cloud platform together with the original signal collected by the detection equipment.

9. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores control instructions executable by the at least one processor, and the control instructions are executed by the at least one processor so that the at least one processor can execute the pipeline leakage detection method based on the ARM processor as described in any one of claims 1-8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the pipeline leakage detection method based on an ARM processor according to any one of claims 1 to 8 is implemented.

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