Methods, devices, equipment, and storage media for diagnosing defects in drainage pipes.

Abstract

Embodiments of the present disclosure provide a sewer defect state diagnosis method, device, equipment and storage medium. The method comprises: acquiring a three-axis angular velocity data sequence of the data acquisition device, wherein the data acquisition device is put into the target sewer by a user at the starting point inspection well of the target sewer, and moves with the water flow in the target sewer; performing attitude angle calculation on the three-axis angular velocity data sequence to obtain attitude angle change data of the data acquisition device during the movement with the water flow, wherein the attitude angle change data comprises: heading angle change data, pitch angle change data and roll angle change data; and matching the attitude angle change data with typical attitude angle change characteristic values of various defect states in the established model library to determine the defect state of the target sewer. In this way, the sewer defect state diagnosis effect can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of drainage pipe network technology, and in particular to a method, apparatus, equipment, and storage medium for diagnosing the defect status of drainage pipes. Background Technology

[0002] Currently, drainage pipe defects are typically identified using pipeline robots for image acquisition and manual analysis, or by employing machine learning algorithms for automatic detection. However, these methods have limitations, resulting in poor diagnostic performance. For instance, manual 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, improving the diagnostic effectiveness of drainage pipe defects has become a pressing technical challenge. Summary of the Invention

[0003] Embodiments of this disclosure provide a method, apparatus, device, and storage medium for diagnosing defects in drainage pipes.

[0004] In a first aspect, embodiments of this disclosure provide a method for diagnosing defects in drainage pipes, the method comprising:

[0005] The data acquisition device acquires its own three-axis angular velocity data sequence, wherein 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;

[0006] The attitude angles of the three-axis angular velocity data sequence are calculated to obtain the attitude angle change data of the data acquisition device during its movement with the water flow. The attitude angle change data includes: heading angle change data, pitch angle change data, and roll angle change data.

[0007] 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 state of the target drainage pipe.

[0008] In some possible implementations of the first aspect, attitude angle calculation is performed on the triaxial angular velocity data sequence to obtain attitude angle change data of the data acquisition device during its movement with the water flow, including:

[0009] The quaternion algorithm is used to calculate the attitude angle of the three-axis angular velocity data sequence, so as to obtain the attitude angle change data of the data acquisition device during the movement with the water flow.

[0010] Among some possible implementations of the first aspect, the method also includes:

[0011] Acquire the triaxial acceleration data sequence collected by the data acquisition device itself;

[0012] The quaternion algorithm is used to calculate the attitude angles of the three-axis angular velocity data sequence, obtaining the attitude angle changes of the data acquisition device during its movement with the water flow, including:

[0013] The quaternion algorithm is used to calculate the attitude angle of the three-axis angular velocity data sequence, so as to obtain the initial attitude angle change data of the data acquisition device during the movement with the water flow.

[0014] The 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.

[0015] In some possible implementations of the first aspect, the model library is established through the following steps:

[0016] Various defect states of drainage pipes were set up using an indoor test device, and data acquisition equipment was put into operation under various defect states. The attitude angle was calculated from the three-axis angular velocity data sequence collected by the data acquisition equipment under various defect states to obtain the attitude angle change data under various defect states.

[0017] The hydraulic characteristics of drainage pipes under various defect conditions were simulated using Fluent software.

[0018] The attitude angle change data under various defect states are compared and analyzed with the hydraulic characteristics obtained from simulation under various defect states to determine the typical attitude angle change characteristic values ​​under various defect states, and a model library is established based on this.

[0019] In some possible implementations of the first aspect, the attitude angle change data is matched with typical attitude angle change characteristic values ​​of various defect states in an established model library to determine the defect state of the target drainage pipe, including:

[0020] The data on changes in heading angle, pitch angle, and roll angle are matched with the typical heading angle, pitch angle, and roll angle characteristics of various defect states in the established model library to determine the defect state of the target drainage pipeline.

[0021] In some possible implementations of the first aspect, the roll angle variation data is matched with typical roll angle variation characteristic values ​​from the typical attitude angle variation characteristic values ​​of various defect states in an established model library, including:

[0022] Identify anomalous roll angle changes from the roll angle change data;

[0023] 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.

[0024] In some possible implementations of the first aspect, the abnormal roll angle variation data is matched with typical roll angle variation characteristic values ​​from the typical attitude angle variation characteristic values ​​of various defect states in an established model library, including:

[0025] 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.

[0026] Secondly, embodiments of this disclosure provide a drainage pipe defect status diagnosis device, the device comprising:

[0027] The acquisition module is used to acquire the triaxial angular velocity data sequence collected by the data acquisition device itself. 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.

[0028] The calculation module is used to calculate the attitude angles of the three-axis angular velocity data sequence to obtain the attitude angle change data of the data acquisition device during the movement with the water flow. The attitude angle change data includes: heading angle change data, pitch angle change data, and roll angle change data.

[0029] The matching module is used to match the attitude angle change data with the 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.

[0030] Thirdly, embodiments of this disclosure provide an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described above.

[0031] Fourthly, embodiments of this disclosure provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the methods described above.

[0032] In the embodiments of this disclosure, the attitude angle change data of the data acquisition device during its movement with the water flow can be calculated based on the triaxial angular velocity data sequence collected by the data acquisition device moving with the water flow in the target drainage pipe. 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, realize continuous water flow diagnosis of the drainage pipe, and have 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.

[0033] It should be understood that the description in the Summary of the Invention section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0034] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0035] Figure 1 A flowchart of a method for diagnosing the defect status of a drainage pipe provided by an embodiment of this disclosure is shown;

[0036] Figure 2 A structural diagram of a drainage pipe defect status diagnostic device provided by an embodiment of the present disclosure is shown;

[0037] Figure 3 A structural diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0039] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0040] To address the problems in the background art, embodiments of this disclosure provide a method, apparatus, device, and storage medium for diagnosing the defect status of drainage pipelines. Specifically, the method involves acquiring a three-axis angular velocity data sequence collected by a data acquisition device. The data acquisition device is inserted into the target drainage pipeline by a user at the starting manhole, moving with the water flow within the pipeline. Attitude angle calculations are performed on the three-axis angular velocity data sequence to obtain attitude angle change data of the data acquisition device during its movement with the water flow. This attitude angle change data includes heading angle change data, pitch angle change data, and roll angle change data. The attitude angle change data is then matched with typical attitude angle change characteristic values ​​of various defect states in an established model library to determine the defect status of the target drainage pipeline.

[0041] In this way, the triaxial angular velocity data sequence collected by the data acquisition device moving with the water flow in the target drainage pipe can be used to calculate the attitude angle change data of the data acquisition device during the movement with the water flow. Then, it can be matched with the typical attitude angle change characteristic 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.

[0042] The following detailed description, with reference to the accompanying drawings and specific embodiments, illustrates the drainage pipeline defect status diagnosis method, apparatus, equipment, and storage medium provided in the embodiments of this disclosure.

[0043] Figure 1 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 1 As shown, the drainage pipe defect status diagnosis method 100 may include the following steps:

[0044] S110: Acquire the triaxial angular velocity data sequence collected by the data acquisition device.

[0045] The data acquisition device is inserted into the target drainage pipe by the user through the starting manhole. It moves (drifts) with the water flow within the pipe and is retrieved when it reaches the ending manhole or stops moving. During this process, the data acquisition device collects its own triaxial angular velocity data sequence in real time. This triaxial angular velocity data sequence is a series of triaxial angular velocity data that varies over time.

[0046] S120 performs attitude angle calculation on the 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.

[0047] 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.

[0048] 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.

[0049] In some embodiments, a quaternion algorithm can be used to calculate the attitude angles of the three-axis angular velocity data sequence, so as to quickly obtain the attitude angle change data of the data acquisition device during the movement with the water flow.

[0050] For example, the principle of the quaternion algorithm can be described as follows:

[0051] When using quaternions to represent attitude rotation, it is represented as:

[0052]

[0053] in, Represented as unit vectors of mutually orthogonal rotation axes δ is the rotation angle.

[0054] The quaternion attitude update equation is:

[0055]

[0056] 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.

[0057] Quaternion derivative:

[0058]

[0059] because E represents the geographic coordinate system, and b represents the sensor coordinate system.

[0060]

[0061] Because the angular velocity measured by the gyroscope is Therefore, Convert to It is quite convenient.

[0062]

[0063]

[0064]

[0065] Expanding, we get:

[0066]

[0067]

[0068] Sorted as:

[0069]

[0070] in:

[0071]

[0072] 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.

[0073] θ k =ω k dt+θ k―1 (11)

[0074] 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.

[0075] 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 attitude angle change data of the data acquisition device during its movement with the water flow can be obtained quickly.

[0076] In other embodiments, the data acquisition device can collect its own triaxial acceleration data sequence in real time while moving with the water flow. To improve the accuracy of the attitude angle change data, the triaxial acceleration data sequence collected by the data acquisition device can be obtained. A quaternion algorithm is then used to calculate the attitude angles of the triaxial angular velocity data sequence, yielding the initial attitude angle change data of the data acquisition device during its movement with the water flow. Finally, a Kalman filter algorithm is used to correct the initial attitude angle change data, using the triaxial angular velocity data sequence as the predicted value and the triaxial acceleration data sequence as the observed value, thus obtaining the attitude angle change data of the data acquisition device during its movement with the water flow.

[0077] S130, Match 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.

[0078] For example, a model library can be quickly built using the following steps:

[0079] Various defect states of drainage pipes are set up using an indoor test device (such as pipe siltation, pipe damage and leakage, pipe breakage, and external water infiltration). Data acquisition equipment is deployed under various defect states, and attitude angle calculation is performed on the three-axis angular velocity data sequence collected by the data acquisition equipment under various defect states to obtain attitude angle change data under various defect states.

[0080] The hydraulic characteristics of drainage pipes under various defect conditions were simulated using Fluent software.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] As a specific example, the matching of roll angle variation data can be as follows:

[0086] The difference in roll angle change between adjacent time points in the roll angle change data can be specifically expressed as:

[0087] Δθ=θ(t)―θ(t―1)(12)

[0088] 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.

[0089] 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.

[0090] The range of roll angle variation corresponding to the target drainage pipe can be determined through the following steps:

[0091] The probability of Δθ can be calculated using the roll angle variation data of normal drainage pipes:

[0092] p = b(a, b) / N (13)

[0093] Where n(a,b) represents the number of roll angle change data within a certain interval (a,b) of Δθ;

[0094] N represents the total number of roll angle variation values;

[0095] 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.

[0096] 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.

[0097] 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.

[0098] In the embodiments of this disclosure, the attitude angle change data of the data acquisition device during its movement with the water flow can be calculated based on the triaxial angular velocity data sequence collected by the data acquisition device moving with the water flow in the target drainage pipe. 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, realize continuous water flow diagnosis of the drainage pipe, and have 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.

[0099] 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.

[0100] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.

[0101] Figure 2 A structural diagram of a drainage pipe defect status diagnosis device provided by an embodiment of this disclosure is shown, as follows: Figure 2 As shown, the drainage pipe defect status diagnosis device 200 may include:

[0102] The acquisition module 210 is used to acquire the three-axis angular velocity data sequence of the data acquisition device itself. The data acquisition device is put 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.

[0103] The calculation module 220 is used to perform attitude angle calculation on the three-axis angular velocity data sequence to obtain the attitude angle change data of the data acquisition device during the movement with the water flow. The attitude angle change data includes: heading angle change data, pitch angle change data and roll angle change data.

[0104] The matching module 230 is used to match the attitude angle change data with the 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.

[0105] In some embodiments, the solving module 220 is specifically used for:

[0106] The quaternion algorithm is used to calculate the attitude angle of the three-axis angular velocity data sequence, so as to obtain the attitude angle change data of the data acquisition device during the movement with the water flow.

[0107] In some embodiments, the acquisition module 210 is further configured to:

[0108] Acquire the triaxial acceleration data sequence collected by the data acquisition device itself.

[0109] The solution module 220 is specifically used for:

[0110] The quaternion algorithm is used to calculate the attitude angle of the three-axis angular velocity data sequence, so as to obtain the initial attitude angle change data of the data acquisition device during the movement with the water flow.

[0111] The 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.

[0112] In some embodiments, the model library is established through the following steps:

[0113] Various defect states of drainage pipes were set up using an indoor test device, and data acquisition equipment was put into operation under various defect states. The attitude angle was calculated from the three-axis angular velocity data sequence collected by the data acquisition equipment under various defect states to obtain the attitude angle change data under various defect states.

[0114] The hydraulic characteristics of drainage pipes under various defect conditions were simulated using Fluent software.

[0115] The attitude angle change data under various defect states are compared and analyzed with the hydraulic characteristics obtained from simulation under various defect states to determine the typical attitude angle change characteristic values ​​under various defect states, and a model library is established based on this.

[0116] In some embodiments, the matching module 230 is specifically used for:

[0117] The data on changes in heading angle, pitch angle, and roll angle are matched with the typical heading angle, pitch angle, and roll angle characteristics of various defect states in the established model library to determine the defect state of the target drainage pipeline.

[0118] In some embodiments, the matching module 230 is specifically used for:

[0119] Identify anomalous roll angle changes from the roll angle change data;

[0120] 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.

[0121] In some embodiments, the matching module 230 is specifically used for:

[0122] 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.

[0123] Understandable, Figure 2 Each module / unit in the drainage pipe defect status diagnosis device 200 shown has the ability to realize Figure 1 The functions of each step in the drainage pipe defect status diagnosis method 100 shown, and the corresponding technical effects they achieve, will not be elaborated here for the sake of brevity.

[0124] Figure 3 A structural diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Electronic device 300 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 300 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.

[0125] like Figure 3 As shown, the electronic device 300 may include a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0126] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0127] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 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 301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer program product, including a computer program tangibly contained in a computer-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 100 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.

[0132] In addition, this disclosure also provides a computer program product including a computer program that implements method 100 when executed by a processor.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 sewer pipe defect state diagnosis method characterized by, The method comprises: acquiring a three-axis angular velocity data sequence of the data acquisition device itself, wherein the data acquisition device is put into a target sewer at a starting inspection well of the target sewer by a user, and moves with water flow in the target sewer; performing attitude angle calculation on the three-axis angular velocity data sequence to obtain attitude angle change data of the data acquisition device during movement with the water flow, wherein the attitude angle change data comprises heading angle change data, pitch angle change data, and roll angle change data; matching the attitude angle change data with typical attitude angle change characteristic values of various defect states in an established model library to determine a defect state of the target sewer; The method further comprises: acquiring a three-axis acceleration data sequence of the data acquisition device itself; The attitude angle calculation on the three-axis angular velocity data sequence to obtain the attitude angle change data of the data acquisition device during movement with the water flow comprises: performing attitude angle calculation on the three-axis angular velocity data sequence by using a quaternion algorithm to obtain initial attitude angle change data of the data acquisition device during movement with the water flow; correcting the initial attitude angle change data by using a Kalman filter algorithm, taking the three-axis angular velocity data sequence as a prediction value and the three-axis acceleration data sequence as an observation value, to obtain the attitude angle change data of the data acquisition device during movement with the water flow; The model library is established by the following steps: setting various defect states of the sewer by using an indoor test device, putting the data acquisition device into the sewer in the various defect states, and performing attitude angle calculation on three-axis angular velocity data sequences of the data acquisition device itself collected in the various defect states to obtain attitude angle change data in the various defect states; simulating hydraulic characteristics of the sewer in the various defect states by using Fluent software; comparing and analyzing the attitude angle change data in the various defect states with the simulated hydraulic characteristics in the various defect states respectively to determine typical attitude angle change characteristic values of the various defect states, and establishing a model library based on the typical attitude angle change characteristic values, wherein the typical attitude angle change characteristic values comprise typical heading angle change characteristic values, typical pitch angle change characteristic values, and typical roll angle change characteristic values.

2. The method of claim 1, wherein, 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 sewer comprises: matching the heading angle change data, the pitch angle change data, and the roll angle change data respectively with the typical heading angle change characteristic values, the typical pitch angle change characteristic values, and the typical roll angle change characteristic values 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 sewer.

3. The method of claim 2, wherein, The matching of the roll angle change data with the typical roll angle change characteristic values in the typical attitude angle change characteristic values of the various defect states in the established model library comprises: determining abnormal roll angle change data from the roll angle change data; The abnormal roll angle change data is matched with a typical roll angle change characteristic value in the typical attitude angle change characteristic values of the various defect states in the established model library.

4. The method of claim 3, wherein, 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: The roll angle change data with regular alternation of positive and negative roll angle change difference values is determined from the abnormal roll angle change data, and is 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.

5. A sewer defect state diagnosing apparatus characterized by comprising: The device comprises: The acquisition module is configured to acquire a three-axis angular velocity data sequence of the data acquisition device, wherein the data acquisition device is put into a target sewer at a starting well of the target sewer by a user, and moves with water flow in the target sewer; The solving module is configured to perform attitude angle solving on the three-axis angular velocity data sequence to obtain attitude angle change data of the data acquisition device during movement with the water flow, wherein the attitude angle change data comprises heading angle change data, pitch angle change data, and roll angle change data; The matching module is configured to match the attitude angle change data with typical attitude angle change characteristic values of various defect states in an established model library to determine a defect state of the target sewer. The acquisition module is further configured to acquire a three-axis acceleration data sequence of the data acquisition device. The solving module is specifically configured to perform attitude angle solving on the three-axis angular velocity data sequence by using a quaternion algorithm to obtain initial attitude angle change data of the data acquisition device during movement with the water flow, and correct the initial attitude angle change data by using a Kalman filtering algorithm to obtain the attitude angle change data of the data acquisition device during movement with the water flow, wherein the Kalman filtering algorithm takes the three-axis angular velocity data sequence as a prediction value and takes the three-axis acceleration data sequence as an observation value. The model library is established by the following steps: setting various defect states of the sewer by using an indoor test device, putting the data acquisition device into the sewer in the various defect states, performing attitude angle solving on a three-axis angular velocity data sequence of the data acquisition device in the various defect states to obtain attitude angle change data in the various defect states, simulating hydraulic characteristics of the sewer in the various defect states by using Fluent software, comparing and analyzing the attitude angle change data in the various defect states with the simulated hydraulic characteristics in the various defect states, determining typical attitude angle change characteristic values of the various defect states, and establishing the model library based on the typical attitude angle change characteristic values, wherein the typical attitude angle change characteristic values comprise typical heading angle change characteristic values, typical pitch angle change characteristic values, and typical roll angle change characteristic values.

6. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.

7. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer instructions are for causing a computer to perform the method of any one of claims 1-4.

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