Data acquisition device and sewer defect state diagnosis method

Abstract

Embodiments of the present disclosure provide a data acquisition device and a sewer defect state diagnosis method. The data acquisition device comprises: a remote control starting module, a micro-electromechanical six-axis inertial sensor, a data storage module, a power module, and a recovery module. The remote control starting module is used to start the micro-electromechanical six-axis inertial sensor when the data acquisition device is put into a target sewer by a user at a starting inspection well of the target sewer and moves with water flow. The sensor is used to collect a three-axis angular velocity data sequence and a three-axis acceleration data sequence of the data acquisition device after being started and store them in the data storage module. The recovery module is used to recover the data acquisition device when the data acquisition device reaches an ending inspection well of the target sewer or stops moving. After the data acquisition device is recovered, the data sequence in the data storage module is extracted for defect state diagnosis of the target sewer, thereby improving the defect state diagnosis effect of the sewer.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of sewer network, and particularly relates to a data acquisition device and a sewer pipeline defect state diagnosis method. BACKGROUND

[0002] At present, image acquisition is usually performed by using a pipeline robot, and the defect state of the sewer pipeline is identified through manual analysis, or the defect state of the sewer pipeline is automatically detected through a training algorithm by using machine learning. However, these schemes also have some deficiencies, resulting in poor diagnosis effect of the defect state of the sewer pipeline. For example, manual participation in image analysis is time-consuming and prone to errors; the accuracy of machine learning is limited by the quality and quantity of training data. Therefore, how to improve the diagnosis effect of the defect state of the sewer pipeline has become a technical problem to be solved at present. SUMMARY

[0003] Embodiments of the present disclosure provide a data acquisition device and a sewer pipeline defect state diagnosis method.

[0004] In a first aspect, embodiments of the present disclosure provide a data acquisition device, which comprises:

[0005] a remote control starting module, a micro-electromechanical six-axis inertial sensor, a data storage module, a power module, and a recovery module; the remote control starting module is connected with the micro-electromechanical six-axis inertial sensor, the micro-electromechanical six-axis inertial sensor is connected with the data storage module, and the power module supplies power to the remote control starting module, the micro-electromechanical six-axis inertial sensor, and the data storage module.

[0006] The remote control starting module is configured to start the micro-electromechanical six-axis inertial sensor when the data acquisition device is put into a target sewer pipeline by a user at a starting inspection well of the target sewer pipeline and moves with water flow in the target sewer pipeline.

[0007] The micro-electromechanical six-axis inertial sensor is configured to collect a three-axis angular velocity data sequence and a three-axis acceleration data sequence of the data acquisition device after being started and store the three-axis angular velocity data sequence and the three-axis acceleration data sequence in the data storage module.

[0008] The recovery module is configured to recover the data acquisition device when the data acquisition device reaches an ending inspection well of the target sewer pipeline or stops moving.

[0009] After the data acquisition device is recovered, the three-axis angular velocity data sequence and the three-axis acceleration data sequence in the data storage module are extracted for defect state diagnosis of the target sewer pipeline.

[0010] In some possible implementation manners of the first aspect, the micro-electro-mechanical six-axis inertial sensor comprises a gyroscope and an accelerometer.

[0011] The gyroscope is configured to collect a three-axis angular velocity data sequence of the data collection device.

[0012] The accelerometer is configured to collect a three-axis acceleration data sequence of the data collection device.

[0013] In some possible implementation manners of the first aspect, the positive output interface and the negative output interface of the remote control starting module are electrically connected to the positive input interface and the negative input interface of the micro-electro-mechanical six-axis inertial sensor respectively.

[0014] In some possible implementation manners of the first aspect, the recovery module comprises a recovery ring and a recovery rope with a scale, and one end of the recovery rope is fixed to the recovery ring.

[0015] In the second aspect, the embodiments of the present disclosure provide a method for diagnosing a defect state of a drainage pipeline based on the data collection device, and the method comprises the following steps.

[0016] After the data collection device is recovered, the three-axis angular velocity data sequence and the three-axis acceleration data sequence of the data collection device are extracted from the data storage module.

[0017] Based on the three-axis acceleration data sequence, the three-axis angular velocity data sequence is calculated for a posture angle to obtain posture angle change data of the data collection device in the process of moving with the water flow, wherein the posture angle change data comprises heading angle change data, pitch angle change data and roll angle change data.

[0018] The posture angle change data is matched with typical posture angle change characteristic values of various defect states in the established model library to determine the defect state of the target drainage pipeline.

[0019] In some possible implementation manners of the second aspect, based on the three-axis acceleration data sequence, the three-axis angular velocity data sequence is calculated for a posture angle to obtain posture angle change data of the data collection device in the process of moving with the water flow, which comprises the following steps.

[0020] A quaternion algorithm is used to calculate the three-axis angular velocity data sequence for a posture angle to obtain initial posture angle change data of the data collection device in the process of moving with the water flow.

[0021] A Kalman filtering algorithm is used to take the three-axis angular velocity data sequence as a prediction value, take the three-axis acceleration data sequence as an observation value, correct the initial posture angle change data, and obtain the posture angle change data of the data collection device in the process of moving with the water flow.

[0022] In some implementations of the second aspect, the model library is established by:

[0023] Various defect states of the drainage pipeline are set by using an indoor test device, and the data acquisition equipment is put into the various defect states, and the attitude angle change data in the various defect states are obtained by solving the attitude angle of the three-axis angular velocity data sequence of the data acquisition equipment in the various defect states;

[0024] Hydraulic characteristics in the various defect states of the drainage pipeline are simulated by using Fluent software;

[0025] The attitude angle change data in the various defect states are compared and analyzed with the simulated hydraulic characteristics in the various defect states, and the typical attitude angle change characteristic values of the various defect states are determined, and the model library is established based on the same.

[0026] In some implementations of the second aspect, the matching of the attitude angle change data with the typical attitude angle change characteristic values of the various defect states in the established model library to determine the defect state of the target drainage pipeline comprises:

[0027] The heading angle change data, the pitch angle change data, and the roll angle change data are respectively matched with the typical heading angle change characteristic value, the typical pitch angle change characteristic value, and the typical roll angle change characteristic value in the typical attitude angle change characteristic values of the various defect states in the established model library to determine the defect state of the target drainage pipeline.

[0028] In some implementations of the second aspect, the matching of the roll angle change data with the typical roll angle change characteristic value in the typical attitude angle change characteristic values of the various defect states in the established model library comprises:

[0029] Abnormal roll angle change data are determined from the roll angle change data;

[0030] The abnormal roll angle change data are matched with the typical roll angle change characteristic value in the typical attitude angle change characteristic values of the various defect states in the established model library.

[0031] In some implementations of the second aspect, the matching of the abnormal roll angle change data with the typical roll angle change characteristic value in the typical attitude angle change characteristic values of the various defect states in the established model library comprises:

[0032] The multiple roll angle change data with regular alternation of positive and negative roll angle change difference values are determined from the abnormal roll angle change data, and the multiple roll angle change data are matched with the typical roll angle change characteristic value in the typical posture angle change characteristic values of the various defect states in the established model library.

[0033] In a third aspect, an electronic device is provided, which includes at least one processor, and a memory connected with the at least one processor in communication; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described above.

[0034] In a fourth aspect, a non-transitory computer-readable storage medium storing computer instructions is provided, and the computer instructions are used to enable a computer to perform the method described above.

[0035] In the embodiments of the present disclosure, the data acquisition device includes a remote control starting module, a micro-electromechanical six-axis inertial sensor, a data storage module, a power module, and a recovery module. When the data acquisition device moves with the water flow in the target drainage pipe, the micro-electromechanical six-axis inertial sensor can accurately acquire the three-axis angular velocity data sequence and the three-axis acceleration data sequence of the data acquisition device, and store them in the data storage module. After the data acquisition device is recovered, the three-axis angular velocity data sequence and the three-axis acceleration data sequence can be extracted from the data storage module of the data acquisition device for defect type diagnosis of the target drainage pipe, thereby improving the effect of drainage pipe defect type diagnosis.

[0036] It should be understood that the content described in the summary section is not intended to limit the key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0037] The above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent by describing in detail the following embodiments with reference to the attached drawings. The attached drawings are intended to better understand the present disclosure and do not limit the present disclosure. In the drawings, the same or similar elements are denoted by the same or similar reference numerals, and:

[0038] Figure 1 A structural diagram of a data acquisition device provided by an embodiment of the present disclosure is shown;

[0039] Figure 2 An assembly schematic diagram of a data acquisition device provided by an embodiment of the present disclosure is shown;

[0040] Figure 3 A flowchart of a drainage pipe defect state diagnosis method provided by an embodiment of the present disclosure is shown;

[0041] Figure 4 A structural diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some but not all of the embodiments of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present disclosure.

[0043] In addition, the term "and / or" herein merely describes an association relationship of associated objects, and indicates that there can be three relationships, for example, A and / or B can represent the three cases of A existing alone, A and B existing simultaneously, and B existing alone. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects.

[0044] To solve the problems in the background art, the embodiments of the present disclosure provide a data acquisition device and a sewer pipeline defect state diagnosis method. Specifically, the data acquisition device comprises a remote control starting module, a micro-electromechanical six-axis inertial sensor, a data storage module, a power module, and a recovery module. When the data acquisition device moves with the water flow in the target sewer pipeline, it can accurately collect the three-axis angular velocity data sequence and the three-axis acceleration data sequence of itself through the micro-electromechanical six-axis inertial sensor, and store them in the data storage module. After the data acquisition device is recovered, the three-axis angular velocity data sequence and the three-axis acceleration data sequence can be extracted from the data storage module of the data acquisition device for defect type diagnosis of the target sewer pipeline, thereby improving the defect type diagnosis effect of the sewer pipeline.

[0045] The data acquisition device and the sewer pipeline defect state diagnosis method provided by the embodiments of the present disclosure will be described in detail below with reference to the drawings and specific embodiments.

[0046] Figure 1 A structural diagram of a data acquisition device provided by the embodiments of the present disclosure is shown, as shown in Figure 1 The data acquisition device can comprise a remote control starting module, a micro-electromechanical six-axis inertial sensor, a data storage module, a power module, and a recovery module.

[0047] The remote control starting module is connected with the micro-electromechanical six-axis inertial sensor, the micro-electromechanical six-axis inertial sensor is connected with the data storage module, and the power module supplies power to the remote control starting module, the micro-electromechanical six-axis inertial sensor, and the data storage module.

[0048] The remote start module is configured to start the micro-electromechanical six-axis inertial sensor when the data acquisition device is put into the target sewer by a user at a starting inspection well of the target sewer and moves (floats) with water flow in the target sewer.

[0049] The micro-electromechanical six-axis inertial sensor is configured to collect a three-axis angular velocity data sequence and a three-axis acceleration data sequence of the data acquisition device after being started and store the three-axis angular velocity data sequence and the three-axis acceleration data sequence in the data storage module.

[0050] The recovery module is configured to recover the data acquisition device when the data acquisition device reaches an ending inspection well of the target sewer or stops moving.

[0051] The three-axis angular velocity data sequence and the three-axis acceleration data sequence in the data storage module are extracted for defect state diagnosis of the target sewer after the data acquisition device is recovered.

[0052] In an embodiment of the present disclosure, the data acquisition device comprises a remote start module, a micro-electromechanical six-axis inertial sensor, a data storage module, a power supply module, and a recovery module. The data acquisition device can accurately collect a three-axis angular velocity data sequence and a three-axis acceleration data sequence of itself by the micro-electromechanical six-axis inertial sensor when the data acquisition device moves with water flow in the target sewer and store the three-axis angular velocity data sequence and the three-axis acceleration data sequence in the data storage module. The three-axis angular velocity data sequence and the three-axis acceleration data sequence can be extracted from the data storage module of the data acquisition device for defect type diagnosis of the target sewer after the data acquisition device is recovered, thereby improving the defect type diagnosis effect of the sewer.

[0053] In some embodiments, the positive output interface and the negative output interface of the remote start module are electrically connected to the positive input interface and the negative input interface of the micro-electromechanical six-axis inertial sensor, respectively.

[0054] In some embodiments, the micro-electromechanical six-axis inertial sensor comprises a gyroscope and an accelerometer.

[0055] The gyroscope is configured to collect a three-axis angular velocity data sequence of the data acquisition device.

[0056] The accelerometer is configured to collect a three-axis acceleration data sequence of the data acquisition device.

[0057] In some embodiments, the data storage module uses an SD card (for example, an SD card with a capacity of 16 G or 32 G) as a storage medium, and the stored three-axis angular velocity data sequence and three-axis acceleration data sequence are in the form of a plurality of data frames as follows: 1) time; 2) 3D acceleration; and 3) 3D angular velocity.

[0058] In some embodiments, the power supply module is a rechargeable large-capacity polymer lithium battery.

[0059] In some embodiments, the recovery module includes a recovery ring and a graduated recovery rope, one end of which is fixed to the recovery ring to facilitate understanding the location and recovery progress of the data acquisition device in the target drainage pipe.

[0060] In some embodiments, the data acquisition device may further include: a formwork assembly module for assembling the data acquisition device.

[0061] For example, the external assembly module includes: a ship-shaped shell, a tank, a counterweight, a gasket, and a sealing cap.

[0062] The ship-shaped hull serves as the outer shell for the data acquisition equipment, offering the following two advantages:

[0063] It has good structural stability and is not prone to directional changes, avoiding the impact of directional deviation on data acquisition. At the same time, the hull shape, which is wider at the top and narrower at the bottom, helps to ensure that the attitude angle changes of the data acquisition equipment during the water drift process can accurately reflect the motion state of the data acquisition equipment itself.

[0064] It has good hydrodynamic characteristics, which can reduce the resistance of water flow to data acquisition equipment, help improve the accuracy and sensitivity of data acquisition equipment, and enable it to better respond to changes in water flow.

[0065] The tank is deployed inside the ship-shaped hull and serves as a carrier for the remote start module, the microelectromechanical six-axis inertial sensor, the data storage module, and the power supply module.

[0066] The counterweights are deployed inside the ship's hull and are used to adjust the center of gravity and weight of the data acquisition equipment. Specifically, the center of gravity and weight of the data acquisition equipment can be adjusted by adjusting the density of the counterweights.

[0067] Gaskets and sealing caps are used to seal the hull of a ship to prevent water intrusion.

[0068] As an example, the assembly of data acquisition equipment can be as follows: Figure 2 As shown, the entities corresponding to 1-7 are the ship-shaped hull, the tank, the counterweight, the gasket, the sealing cover, the recovery ring, and the recovery rope, respectively.

[0069] Figure 3 A flowchart illustrating a method for diagnosing the defect status of a drainage pipe according to an embodiment of this disclosure is shown, such as... Figure 3 As shown, the drainage pipe defect status diagnosis method 300 may include the following steps:

[0070] S310, after being retrieved from the data acquisition device, extracts the three-axis angular velocity data sequence and the three-axis acceleration data sequence from the data storage module.

[0071] It is worth noting that prior to the S310, the user assembles the data acquisition device and debugs the parameters of the MEMS six-axis inertial sensor. Then, the data acquisition device is placed into the target drainage pipe at the starting manhole, and the MEMS six-axis inertial sensor is remotely activated via the remote start module. After activation, the MEMS six-axis inertial sensor begins acquiring the three-axis angular velocity and three-axis acceleration data sequences from the data acquisition device, storing them in hexadecimal format in a TXT file in the data storage module. The user determines whether the data acquisition device has reached the ending manhole of the target drainage pipe or stops moving based on the scale on the retrieval rope, and then retrieves the data acquisition device.

[0072] For example, after the data acquisition device is retrieved, the user shuts down the MEMS six-axis inertial sensor using the remote start module, separates the ship-shaped outer shell from the sealing cover, and removes the data storage module (e.g., an SD card) from the tank. At this point, the user can extract the three-axis angular velocity data sequence and the three-axis acceleration data sequence of the data acquisition device from the data storage module using the accompanying host computer.

[0073] It is understandable that a triaxial angular velocity data sequence is a series of triaxial angular velocity data that varies over time, and a triaxial acceleration data sequence is a series of triaxial acceleration data that varies over time.

[0074] S320, based on a three-axis acceleration data sequence, performs attitude angle calculation on a three-axis angular velocity data sequence to obtain the attitude angle change data of the data acquisition device during its movement with the water flow.

[0075] The attitude angle change data includes: yaw angle change data, pitch angle change data, and roll angle change data. It can be understood that yaw angle change data is a series of yaw angle data that changes over time, pitch angle change data is a series of pitch angle data that changes over time, and roll angle change data is a series of roll angle data that changes over time.

[0076] It's important to understand that the heading angle refers to the rotational motion of the data acquisition device around its vertical axis; the direction and force of the water flow affect the heading angle. The pitch angle refers to the rotational motion of the data acquisition device around its horizontal axis; as the data acquisition device moves with the water flow, it may be subjected to longitudinal forces, causing it to pitch. The roll angle refers to the rotational motion of the data acquisition device around its longitudinal axis; as the data acquisition device moves with the water flow, it may be subjected to lateral forces, causing it to roll.

[0077] In some embodiments, a quaternion algorithm can be used to calculate the attitude angles of the three-axis angular velocity data sequence to obtain the initial attitude angle change data of the data acquisition device during its movement with the water flow. Then, a Kalman filter algorithm is used to correct the initial attitude angle change data by using the three-axis angular velocity data sequence as the predicted value and the three-axis acceleration data sequence as the observed value, thereby accurately obtaining the attitude angle change data of the data acquisition device during its movement with the water flow.

[0078] For example, the principle of the quaternion algorithm can be described as follows:

[0079] When using quaternions to represent attitude rotation, it is represented as:

[0080]

[0081] in, Represented as unit vectors of mutually orthogonal rotation axes δ is the rotation angle.

[0082] The quaternion attitude update equation is:

[0083]

[0084] As can be seen from the quaternion attitude update equation, the quaternion can be solved at any time based on the initial attitude quaternion information of the object and the derivative of the quaternion.

[0085] Quaternion derivative:

[0086]

[0087] because E represents the geographic coordinate system, and b represents the sensor coordinate system.

[0088]

[0089] Because the angular velocity measured by the gyroscope is Therefore, Convert to It is quite convenient.

[0090]

[0091]

[0092]

[0093] Expanding, we get:

[0094]

[0095] Sorted as:

[0096]

[0097] in:

[0098]

[0099] The data measured by the gyroscope is the angular velocity of rotation, and the angle value of the object's attitude can be obtained by integration.

[0100] θ k =ω k dt+θ k―1 (11)

[0101] Where, θ k Let θ be the angle value at time k. k―1 Let ω be the angle value at time k-1. k dt is the angular velocity value of the gyroscope at time k, and dt is the integration time.

[0102] As can be seen from the above, the quaternion attitude update equation in the quaternion algorithm can be represented by triaxial angular velocity data. By converting the quaternion into Euler angles, the attitude angle data can be represented. This allows for attitude angle calculation without blind spots. Furthermore, by using the quaternion algorithm to calculate the attitude angles of the triaxial angular velocity data sequence, the initial attitude angle change data of the data acquisition device during its movement with the water flow can be obtained quickly.

[0103] S330 matches the attitude angle change data with the typical attitude angle change characteristic values ​​of various defect states in the established model library to determine the defect state of the target drainage pipe.

[0104] For example, a model library can be quickly built using the following steps:

[0105] Various defect states of drainage pipes (such as pipe siltation, pipe damage and leakage, pipe breakage, external water infiltration, etc.) are set up using an indoor test device. Data acquisition equipment is deployed under various defect states, and attitude angle calculation is performed on the three-axis angular velocity data sequence of the data acquisition equipment under various defect states to obtain attitude angle change data under various defect states.

[0106] The hydraulic characteristics of drainage pipes under various defect conditions were simulated using Fluent software.

[0107] Attitude angle change data under various defect states were compared and analyzed with the simulated hydraulic characteristics under various defect states to determine the typical attitude angle change characteristic values ​​for each defect state, and a model library was established based on this. The typical attitude angle change characteristic values ​​include: typical heading angle change characteristic values, typical pitch angle change characteristic values, and typical roll angle change characteristic values.

[0108] In some embodiments, the heading angle change data, pitch angle change data, and roll angle change data can be matched with the typical heading angle change characteristic values, typical pitch angle change characteristic values, and typical roll angle change characteristic values ​​of the typical attitude angle change characteristic values ​​of various defect states in the established model library, and the defect state of the target drainage pipeline can be determined based on the matching results.

[0109] Taking the matching of roll angle change data as an example, abnormal roll angle change data can be identified from the roll angle change data, and the abnormal roll angle change data can be matched with the typical roll angle change feature values ​​in the typical attitude angle change feature values ​​of various defect states in the established model library.

[0110] Furthermore, in order to reduce the amount of data processing and improve matching efficiency, multiple roll angle change data with alternating positive and negative roll angle change differences can be identified from the abnormal roll angle change data, and these can be matched with the typical roll angle change feature values ​​in the typical attitude angle change feature values ​​of various defect states in the established model library.

[0111] As a specific example, the matching of roll angle variation data can be as follows:

[0112] The difference in roll angle change between adjacent time points in the roll angle change data can be specifically expressed as:

[0113] Δθ=θ(t)―θ(t―1) (12)

[0114] Where Δθ represents the difference in roll angle between adjacent time points, in rad; θ(t) represents the roll angle data at time t, in rad; and θ(t―1) represents the roll angle data at time t-1, in rad.

[0115] Roll angle data with Δθ outside the range of roll angle variation for the target drainage pipe are identified as abnormal roll angle variation data, or roll angle data with an absolute value of Δθ greater than or equal to a preset threshold (e.g., 15) are identified as abnormal roll angle variation data.

[0116] The range of roll angle variation corresponding to the target drainage pipe can be determined through the following steps:

[0117] The probability of Δθ can be calculated using the roll angle variation data of normal drainage pipes:

[0118] p = n(a,b) / N (13)

[0119] Where n(a,b) represents the number of roll angle change data within a certain interval (a,b) of Δθ;

[0120] N represents the total number of roll angle variation values;

[0121] When p≥95%, the interval (a,b) is taken as the interval of the roll angle change difference corresponding to the target drainage pipe. When Δθ is outside the interval (a,b), it can be considered that the data acquisition equipment at the corresponding time is moving in the defective pipe section of the target drainage pipe.

[0122] The portion of abnormal roll angle variation data where Δθ appears in a regular alternation pattern is matched with the typical roll angle variation characteristic values ​​of typical attitude angle variation characteristics of various defect states in the established model library.

[0123] Understandably, the matching of heading angle change data and pitch angle change data is similar to the matching of roll angle change data, so it will not be elaborated here.

[0124] In the embodiments of this disclosure, the attitude angle change data of the data acquisition device moving with the water flow in the target drainage pipe can be calculated based on the three-axis angular velocity data sequence of the data acquisition device. Then, it can be matched with the typical attitude angle change feature values ​​of various defect states in the established model library to quickly and accurately determine the defect state of the target drainage pipe. This enables continuous water flow diagnosis of the drainage pipe, which has the advantages of low diagnosis cost, short time consumption, easy operation, and high reliability. It can improve the diagnosis effect of drainage pipe defect state and provide technical support for the operation and maintenance of drainage pipe network.

[0125] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0126] Figure 4 A structural diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Electronic device 400 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 400 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0127] like Figure 4As shown, the electronic device 400 may include a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. The RAM 403 may also store various programs and data required for the operation of the electronic device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0128] Multiple components in electronic device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of displays, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows electronic device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0129] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as method 300. For example, in some embodiments, method 300 may be implemented as a computer program product, including a computer program tangibly contained in a computer-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of method 300 described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform method 300 by any other suitable means (e.g., by means of firmware).

[0130] The various embodiments described above 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), system-on-a-chip (SoCs), payload programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0132] In the context of this disclosure, a computer-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0133] It should be noted that this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute method 300 and achieve the corresponding technical effects achieved by the embodiments of this disclosure in executing the method. For the sake of brevity, these will not be elaborated here.

[0134] In addition, this disclosure also provides a computer program product including a computer program that implements method 300 when executed by a processor.

[0135] To provide interaction with a user, the embodiments described above 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 provides 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 sound input, voice input, or tactile input).

[0136] The embodiments described above can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with the implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication (e.g., a communication network) of any form or medium. Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0137] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0138] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0139] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A data acquisition device, characterized in that, The device includes: The system includes a remote start module, a microelectromechanical six-axis inertial sensor, a data storage module, a power supply module, and a recovery module. The remote start module is connected to the microelectromechanical six-axis inertial sensor, the microelectromechanical six-axis inertial sensor is connected to the data storage module, and the power supply module provides power to the remote start module, the microelectromechanical six-axis inertial sensor, and the data storage module. The remote start module is used to activate the microelectromechanical six-axis inertial sensor when the data acquisition device is inserted into the target drainage pipe by the user at the starting manhole of the target drainage pipe and moves with the water flow in the target drainage pipe. After startup, the microelectromechanical six-axis inertial sensor is used to collect the three-axis angular velocity data sequence and the three-axis acceleration data sequence of the data acquisition device, and store them in the data storage module; The recovery module is used to recover the data acquisition device when it reaches the end manhole of the target drainage pipeline or stops moving. After the data acquisition device is retrieved, the triaxial angular velocity data sequence and triaxial acceleration data sequence in the data storage module are extracted for defect status diagnosis of the target drainage pipe. Specifically: a quaternion algorithm is used to calculate the attitude angles of the triaxial angular velocity data sequence to obtain the initial attitude angle change data of the data acquisition device during its movement with the water flow; a Kalman filter algorithm is used to correct the initial attitude angle change data by using the triaxial angular velocity data sequence as the predicted value and the triaxial acceleration data sequence as the observed value, thereby obtaining the attitude angle change data of the data acquisition device during its movement with the water flow; wherein, the attitude angle change data includes: heading angle change data, pitch angle change data, and roll angle change data; the attitude angle change data is matched with the typical attitude angle change feature values ​​of various defect states in the established model library to determine the defect status of the target drainage pipe; The model library was established through the following steps: Various defect states of the drainage pipeline were set up using an indoor testing device, and the data acquisition equipment was put into operation under various defect states. The attitude angle was calculated from the three-axis angular velocity data sequence of the data acquisition equipment under various defect states to obtain the attitude angle change data under various defect states. The hydraulic characteristics of the drainage pipeline under various defect states were simulated using Fluent software. The attitude angle change data under various defect states were compared and analyzed with the simulated hydraulic characteristics under various defect states to determine the typical attitude angle change characteristic values ​​of various defect states, and a model library was established based on this.

2. The device according to claim 1, characterized in that, The microelectromechanical six-axis inertial sensor includes: a gyroscope and an accelerometer; The gyroscope is used to acquire the three-axis angular velocity data sequence of the data acquisition device; The accelerometer is used to collect the triaxial acceleration data sequence of the data acquisition device.

3. The device according to claim 1, characterized in that, The positive and negative output interfaces of the remote start module are electrically connected to the positive and negative input interfaces of the microelectromechanical six-axis inertial sensor, respectively.

4. The device according to claim 1, characterized in that, The recycling module includes a recycling ring and a graduated recycling rope, one end of which is fixed to the recycling ring.

5. The device according to claim 1, characterized in that, The step of matching the attitude angle change data with typical attitude angle change feature values ​​of various defect states in the established model library to determine the defect state of the target drainage pipe includes: The heading angle change data, pitch angle change data, and roll angle change data are matched with the typical heading angle change feature values, typical pitch angle change feature values, and typical roll angle change feature values ​​of typical attitude angle change feature values ​​of various defect states in the established model library to determine the defect state of the target drainage pipeline.

6. The device according to claim 5, characterized in that, The roll angle variation data is matched with the typical roll angle variation feature values ​​in the typical attitude angle variation feature values ​​of various defect states in the established model library, including: Identify abnormal roll angle changes from the roll angle change data; The abnormal roll angle change data is matched with the typical roll angle change feature values ​​in the typical attitude angle change feature values ​​of various defect states in the established model library.

7. The device according to claim 6, characterized in that, The process of matching abnormal roll angle change data with typical roll angle change feature values ​​from typical attitude angle change feature values ​​of various defect states in the established model library includes: From the abnormal roll angle change data, identify multiple roll angle change data with alternating positive and negative roll angle change differences, and match them with the typical roll angle change feature values ​​in the typical attitude angle change feature values ​​of various defect states in the established model library.

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