Underground pipe gallery inspection method and device based on inertial navigation and electronic equipment

Abstract

The invention provides an underground pipe gallery inspection method and device based on inertial navigation and electronic equipment, and belongs to the technical field of urban safety inspection, and the method comprises the following steps: obtaining original inertial data when an inspection carrier moves in a pipe gallery, synchronously collecting micro-dynamic feature data of the inspection carrier, and generating a state feature group; judging whether the inspection carrier is in a high-confidence static state or not based on the original inertial data and the state feature group to obtain a judgment result; executing a corresponding inspection strategy according to the judgment result so as to suppress the accumulative error of inertial navigation, and continuously calculating the position, speed and attitude of the inspection carrier so as to output the current navigation positioning result of the inspection carrier; and evaluating the uncertainty of the current navigation positioning result in real time, and generating risk early warning information of the inspection operation based on the uncertainty. According to the invention, the positioning precision and the operation safety controllability of long-distance autonomous inspection of the underground pipe gallery are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban safety inspection, in particular to an underground pipe gallery inspection method and device based on inertial navigation and electronic equipment. BACKGROUND

[0002] Inertial navigation technology is an important means of realizing autonomous positioning and measurement based on inertial sensors. Through its built-in gyroscopes and accelerometers, the inertial measurement unit can sense the angular motion and linear motion of the carrier in real time, and through integration and other operations, it can continuously calculate the position, velocity and attitude of the carrier in an environment without external signal reference. This characteristic of not relying on external wireless signals and working autonomously makes it unique in application value in closed environments where global satellite navigation system signals cannot cover or are severely disturbed. Underground pipe gallery (full name: urban underground comprehensive pipe gallery) as a typical "GPS denial" environment, its internal structure inspection, maintenance and digital filing work have clear demands for positioning and mapping technology that can work continuously in it, and inertial navigation mapping system is therefore considered as a potential technical solution.

[0003] However, when the existing inertial navigation mapping method is directly applied to long-term and large-scale inspection tasks in urban underground comprehensive pipe gallery, the navigation error will continuously accumulate with time and increasing running distance, and there is an inherent divergence problem. In the complex environment of the pipe gallery, the divergence rate of the system positioning error far exceeds the theoretical expectation, so that the entire mapping system has lost practical precision before completing the long-distance inspection task, resulting in low precision of underground pipe gallery inspection task using this method.

[0004] Therefore, there is an urgent need for an inertial navigation mapping solution that can adapt to the complex dynamic environment of underground pipe gallery to meet the urgent needs of high-precision and long-duration underground pipe gallery inspection operations. SUMMARY

[0005] Therefore, it is necessary to provide an underground pipe gallery inspection method, device, electronic equipment and storage medium based on inertial navigation to solve the technical problem that the divergence error is large when the existing method uses inertial navigation in a complex environment and long cruise time, resulting in low precision of underground pipe gallery inspection operation.

[0006] In order to solve the above technical problems, in a first aspect, the present application provides an underground pipe gallery inspection method based on inertial navigation, comprising: acquiring original inertial data of an inspection carrier when moving in the pipe gallery, and synchronously collecting micro-dynamic characteristic data of the inspection carrier itself to generate a state feature group; determining whether the inspection carrier is in a high-confidence static state based on the original inertial data and the state feature group to obtain a determination result; perform a corresponding inspection strategy according to the discrimination result, so as to suppress the cumulative error of inertial navigation and continuously solve the position, velocity and attitude of the inspection carrier to output a current navigation positioning result of the inspection carrier; evaluate the uncertainty of the current navigation positioning result in real time, and generate risk warning information of an inspection operation based on the uncertainty.

[0007] In a possible implementation, the synchronously collecting the micro-dynamic characteristic data of the inspection carrier itself to generate a state characteristic group comprises: acquiring structural micro-vibration spectrum data and real-time rotation speed data of a driving wheel of the inspection carrier; collecting environmental and self-noise of the inspection carrier in operation as real-time acoustic signals, and extracting acoustic characteristics of the inspection carrier in a stationary state as reference acoustic characteristics; calculating a matching degree of the real-time acoustic signals and the reference acoustic characteristics to obtain an acoustic matching degree; generating the state characteristic group based on the structural micro-vibration spectrum data, the real-time rotation speed data of the driving wheel and the acoustic matching degree.

[0008] In a possible implementation, the discriminating whether the inspection carrier is in a high-confidence stationary state based on the original inertial data and the state characteristic group to obtain a discrimination result comprises: calculating a stationary confidence index based on a variance of the original inertial data, an energy of the structural micro-vibration spectrum data, a zero-value deviation degree of the real-time rotation speed data of the driving wheel and the acoustic matching degree; comparing the stationary confidence index with a preset stationary confidence threshold to determine the discrimination result.

[0009] In a possible implementation, the performing a corresponding inspection strategy according to the discrimination result to suppress the cumulative error of inertial navigation comprises: if the discrimination result indicates that the inspection carrier is in a high-confidence stationary state, triggering execution of the zero-speed update algorithm; if the discrimination result indicates that the inspection carrier is in a dynamic or micro-dynamic state, suppressing execution of the zero-speed update algorithm.

[0010] In a possible implementation, the evaluating the uncertainty of the current navigation positioning result in real time comprises: calculating the uncertainty based on a position error covariance output in a navigation solving process and a time interval from a last successful execution of the zero-speed update.

[0011] In a possible implementation, the generating of the risk warning information of the inspection operation based on the uncertainty includes: comparing the uncertainty with a preset safety threshold and a warning threshold; if the uncertainty is less than the safety threshold, a first warning level is generated; if the uncertainty is greater than or equal to the safety threshold and less than the warning threshold, a second warning level is generated; if the uncertainty is greater than or equal to the warning threshold, a third warning level is generated.

[0012] In a possible implementation, the underground pipe gallery inspection method based on inertial navigation further includes: identifying a fixed marker pre-marked in the pipe gallery; obtaining absolute coordinates of the fixed marker, and performing global correction on a navigation position based on the absolute coordinates.

[0013] In another aspect, the present application also provides an underground pipe gallery inspection device based on inertial navigation, comprising: a data acquisition module configured to obtain original inertial data of an inspection carrier when moving in a pipe gallery, and synchronously acquire micro-dynamic characteristic data of the inspection carrier itself to generate a state feature group; a stationary discrimination module configured to discriminate whether the inspection carrier is in a high-confidence stationary state based on the original inertial data and the state feature group, to obtain a discrimination result; an inspection positioning module configured to execute a corresponding inspection strategy according to the discrimination result, to suppress the cumulative error of inertial navigation, and continuously solve the position, velocity and attitude of the inspection carrier to output a current navigation positioning result of the inspection carrier; a risk assessment module configured to assess the uncertainty of the current navigation positioning result in real time, and generate risk warning information of an inspection operation based on the uncertainty.

[0014] In a third aspect, the present application also provides an electronic device comprising a memory and a processor, wherein: the memory is configured to store a program; the processor is coupled to the memory, and is configured to execute the program stored in the memory to implement the steps in the underground pipe gallery inspection method based on inertial navigation in any of the above implementation modes.

[0015] In a fourth aspect, the present application also provides a computer-readable storage medium for storing computer-readable programs or instructions, which are executed by a processor to implement the steps in the underground pipe gallery inspection method based on inertial navigation in any of the above implementation modes.

[0016] The beneficial effects of the present application are: the underground pipe gallery inspection method based on inertial navigation provided by the present application realizes accurate discrimination of the real stationary state of the carrier in the complex micro-vibration environment of the pipe gallery by collecting and calculating the micro-dynamic characteristic data of the carrier to generate a state characteristic group, and fusing the original inertial data of the carrier when the carrier moves in the pipe gallery, so that the core error suppression means of zero speed update can be triggered in time and accurately, thereby effectively suppressing the inherent cumulative error divergence problem of the inertial navigation system. At the same time, the real-time quantitative evaluation of the uncertainty of the navigation result enables the inspection operation to change from passive acceptance of positioning data to active risk avoidance, thereby improving the positioning accuracy and operation safety controllability of long-distance autonomous inspection of underground pipe galleries. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 An embodiment flowchart of the underground pipe gallery inspection method based on inertial navigation provided by the present application is shown in the figure. Figure 2 An embodiment flowchart of the underground pipe gallery inspection method based on inertial navigation provided by the present application is shown in the figure. Figure 1 An embodiment flowchart of the underground pipe gallery inspection method based on inertial navigation provided by the present application is shown in the figure. Figure 3 An embodiment flowchart of the underground pipe gallery inspection method based on inertial navigation provided by the present application is shown in the figure. Figure 1 An embodiment flowchart of the underground pipe gallery inspection method based on inertial navigation provided by the present application is shown in the figure. Figure 4 An embodiment flowchart of the underground pipe gallery inspection method based on inertial navigation provided by the present application is shown in the figure. Figure 1 An embodiment flowchart of the underground pipe gallery inspection method based on inertial navigation provided by the present application is shown in the figure. Figure 5 An embodiment flowchart of the underground pipe gallery inspection method based on inertial navigation provided by the present application is shown in the figure. Figure 1 An embodiment flowchart of the underground pipe gallery inspection method based on inertial navigation provided by the present application is shown in the figure. Figure 6 An embodiment flowchart of the underground pipe gallery inspection method based on inertial navigation provided by the present application is shown in the figure. Figure 7 An embodiment structure diagram of the underground pipe gallery inspection device based on inertial navigation provided by the present application is shown in the figure. Figure 8 An embodiment structure diagram of the electronic device provided by the present application is shown in the figure. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of the present application.

[0020] In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more than two, unless otherwise specified. The association relationship of the associated objects is described by "and / or", which means that there can be three relationships, for example: A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone.

[0021] The "first", "second", and the like described in the embodiments of the present application are only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the technical features limited by "first" and "second" can explicitly or implicitly include at least one of the features.

[0022] In this document, the reference to "embodiments" means that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive or alternative to other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] Before the embodiments are described, the following terms are explained.

[0024] Inertial Navigation&Surveying System, a system that realizes autonomous positioning, pose determination and environment mapping based on inertial sensors (gyroscope, accelerometer). It does not rely on external signals, and calculates the motion trajectory of the carrier through integration operation.

[0025] Inertial Measurement Unit (IMU), the core sensor component of the system, usually containing three-axis gyroscope and three-axis accelerometer, used to directly measure the angular velocity and linear acceleration of the carrier.

[0026] Raw Inertial Data, the original measurement value output directly by the IMU, which is not error-compensated and navigation-solved, i.e. the time series signal of three-axis angular velocity and three-axis linear acceleration.

[0027] Cumulative Error, an inherent error characteristic of inertial navigation systems. Due to the continuous accumulation of small sensor errors (such as zero offset, noise) in the integration process, the output error of navigation parameters such as position and velocity will diverge and increase over time.

[0028] Zero-Velocity Update Algorithm (ZUPT), a commonly used inertial navigation error suppression technique. When the carrier is detected to be in a stationary state, the known condition that the true velocity is zero is used to estimate and correct the velocity error of the IMU and the sensor zero offset.

[0029] State Feature Set, in this invention, refers to a set of feature data collected synchronously by multiple source sensors, used to assist in identifying the stationary state of the carrier, usually including micro-vibration spectrum, wheel speed and acoustic matching degree, etc.

[0030] Micro-dynamic Feature Data, describes the physical characteristics of small vibrations, sliding and noise caused by internal components (such as gimbals, fans) running or environmental factors of the inspection carrier in the macro stationary or low speed motion state.

[0031] Structural Micro-vibration Spectrum Data, collected by high-frequency acceleration sensors, reflecting the frequency spectrum characteristics of high-frequency, low-amplitude vibrations generated by the carrier structure due to the operation of internal components.

[0032] Acoustic Fingerprint / Acoustic Feature, an identifiable acoustic signal feature pattern formed by the carrier's own device noise and environmental background noise under a specific operating state (especially the stationary state).

[0033] Static Confidence Index, a quantitative index calculated by fusing multiple source features (inertial data variance, vibration energy, wheel speed deviation, acoustic matching degree, etc.), used to represent the credibility of the inspection carrier in the true stationary state.

[0034] Navigation Solution, the process of using IMU data to integrate and filter (such as Kalman filtering) through inertial navigation mechanics equations to calculate and output the position, velocity and attitude information of the carrier in real time.

[0035] Navigation Uncertainty Index (NUI), a quantitative indicator that combines the position error covariance statistics and the time interval since the last valid correction, used to evaluate the reliability and risk level of the current navigation positioning result in real time.

[0036] Risk Warning Level (RWL), a classification signal (e.g., first, second, third, etc.) divided according to the comparison result of the Navigation Uncertainty Index and the preset threshold, used to intuitively reflect the positioning accuracy state and guide the operation decision.

[0037] Environmental Feature Matching (EFM), an auxiliary positioning means that identifies fixed markers (e.g., pipe joints, fireproof doors) with known coordinates in the environment through laser radar or visual camera, and uses their absolute coordinates to globally correct the inertial navigation position.

[0038] Kalman Filter (KF), a recursive algorithm for optimal estimation of dynamic system states (e.g., position, velocity, error), widely used in inertial navigation to fuse sensor data and suppress noise.

[0039] The present application provides an underground pipe gallery inspection method and device based on inertial navigation, electronic equipment and storage medium, the following will be combined with the drawings in the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely, obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0040] Figure 1 An embodiment flowchart of the underground pipe gallery inspection method based on inertial navigation provided by the present application is shown in FIG. Figure 1 As shown in FIG. S101, obtain the original inertial data of the inspection carrier when moving in the pipe gallery, and synchronously collect the micro-dynamic characteristic data of the inspection carrier itself to generate a state feature group; S102, based on the original inertial data and the state feature group, determine whether the inspection carrier is in a high-confidence stationary state, and obtain a determination result; S103, according to the determination result, execute the corresponding inspection strategy to suppress the cumulative error of inertial navigation, and continuously solve the position, velocity and attitude of the inspection carrier to output the current navigation positioning result of the inspection carrier; S104, real-time evaluate the uncertainty of the current navigation positioning result, and generate risk warning information of the inspection operation based on the uncertainty.

[0041] The technical steps described in this embodiment constitute a closed-loop inertial navigation mapping process for the urban underground comprehensive pipe gallery environment. Specifically, the execution process is as follows: First, two types of data are synchronously collected: one is the original inertial data obtained by the inertial measurement module during the movement of the inspection carrier, including but not limited to three-axis angular velocity and linear acceleration; the other is the micro-dynamic characteristic data of the carrier itself collected by the multi-source state perception module, such as structural micro-vibration, driving wheel speed and running noise, etc., and the state characteristic group is fused and generated.

[0042] Further, the two types of data are fused and analyzed, and a quantitative stationary confidence index is calculated to comprehensively evaluate the motion state of the carrier. The index integrates the inertial data fluctuation reflecting macro motion, the micro-vibration energy reflecting internal disturbance, the wheel speed deviation reflecting ground sliding, and the acoustic background matching degree reflecting the feature. If the index exceeds the preset stationary confidence threshold, it is determined that the carrier is in a high confidence stationary state that can be used for error correction; otherwise, it is determined as a dynamic or micro-dynamic state. The preset stationary confidence threshold can be set according to the actual situation.

[0043] Further, by means of navigation solution and error correction, differentiated strategies are executed for the discrimination results of the above different situations. When it is determined that the confidence is high, the zero-speed update algorithm is triggered, the physical constraint that the speed at this moment should be zero is used to estimate and compensate the cumulative error of the sensor, thereby effectively suppressing the divergence of navigation error. When it is determined that it is dynamic, the execution of zero-speed update is suppressed to avoid introducing error correction due to the carrier's slight movement or vibration. On this basis, continuous navigation solution is carried out to output the real-time position, speed and attitude information of the inspection carrier.

[0044] To further ensure reliability, the navigation positioning result obtained by the embodiment is evaluated in real time. By comprehensively considering the instantaneous positioning accuracy and the time since the last effective correction, a navigation uncertainty index is calculated. After comparing the index with the preset risk threshold, different levels of risk warning information are generated, thereby providing a quantitative basis for the decision of the inspection operation (such as continuing the operation, planning to stop for correction or executing the return trip).

[0045] The embodiment generates a state feature group by collecting the micro-dynamic characteristic data of itself, and fuses the original inertial data of the inspection carrier when moving in the pipe gallery, realizes the accurate discrimination of the real static state of the carrier in the complex micro-vibration environment of the pipe gallery, makes the core error suppression method of zero-speed update be triggered in time and accurately, and effectively suppresses the inherent cumulative error divergence problem of the inertial navigation system. At the same time, the real-time quantitative evaluation of the navigation result uncertainty makes the inspection operation change from passive acceptance of positioning data to active risk avoidance, and improves the positioning accuracy and operation safety controllability of long-distance autonomous inspection of underground pipe gallery.

[0046] In some embodiments of the present application, as shown in Figure 2 Step S101 synchronously collects the micro-dynamic characteristic data of the inspection carrier itself, generates a state feature group, including: S201, acquiring structural micro-vibration frequency spectrum data and real-time rotation speed data of the driving wheel of the inspection carrier; S202, collecting the environment and noise of the inspection carrier when running as real-time acoustic signals, and extracting the acoustic characteristics of the inspection carrier in the static state as reference acoustic characteristics; S203, calculating the matching degree of the real-time acoustic signals and the reference acoustic characteristics to obtain the acoustic matching degree; S204, generating a state feature group based on the structural micro-vibration frequency spectrum data, the real-time rotation speed data of the driving wheel and the acoustic matching degree.

[0047] Specifically, the present embodiment is realized by the cooperative work of a multi-source state perception module. The module includes but is not limited to: a vibration perception unit, a wheel speed perception unit, an acoustic perception unit, a data processing unit and the like.

[0048] The vibration perception unit is responsible for acquiring the structural micro-vibration frequency spectrum data. The vibration perception unit usually adopts a high-frequency acceleration sensor, which is directly installed on the main structure of the inspection carrier, and is used to monitor the structural micro-vibration with a frequency usually in the range of 10Hz to several hundred Hz caused by the work of internal components such as pan rotation, cooling fan running. Through spectrum analysis such as fast Fourier transform on the original vibration signal, the energy value of a specific frequency band is extracted as a feature. For example, when the pan servo motor starts to scan, the vibration sensor will capture the spectral energy related to the harmonic of the motor speed, which will be significantly prominent, so as to reflect the state of the internal action in the data layer.

[0049] The wheel speed sensing unit is responsible for obtaining real-time wheel speed data. The wheel speed sensing unit is usually based on a high-resolution wheel odometer or an optical encoder of the driving motor, and the output signal thereof directly reflects the actual rotation angle and speed of the wheel. Even when the carrier is macroscopically stopped (the speed of the carrier is zero), the unit can still detect extremely slow creep caused by a slight slope of the ground or a slight angular displacement caused by a gap in the braking system. For example, on a wet and slippery pipe gallery slope, the carrier may slide by a few millimeters after braking, and at this time the encoder will still output a series of small pulse signals, and the zero value deviation Wdev will be greater than zero, objectively indicating that the carrier is not in a mechanical state.

[0050] The acoustic sensing unit is responsible for processing acoustic information to obtain an acoustic matching degree. The acoustic sensing unit uses a microphone array to collect mixed signals of the ambient noise around the carrier during operation and the noise generated by the carrier itself, as real-time acoustic signals. On the other hand, a representative reference acoustic feature, which can also be referred to as an acoustic fingerprint, is generated by collecting and training when the carrier is confirmed to be absolutely stationary and the internal interference is the least. During the operation of the inspection carrier, the real-time acoustic signal is compared with the reference acoustic feature by calculating the spectral cross-correlation or the Mel frequency cepstral coefficient distance, and a matching degree value representing the similarity between the two is calculated. For example, when the carrier is stationary and the fan is in a low-speed standby mode, the real-time noise spectrum is highly similar to the reference fingerprint, and the matching degree is high. When the cooling fan speeds up due to high temperature, the high-frequency noise generated by the fan increases, causing the real-time spectrum to vary, and the matching degree with the stationary reference feature decreases.

[0051] The data processing unit encapsulates and formats the above-mentioned real-time obtained micro-vibration spectrum data, wheel speed data and calculated acoustic matching degree to form a state feature group for subsequent fusion decision. The state feature group digitally represents the real motion and disturbance state of the inspection carrier at the microscopic level from three independent and complementary physical dimensions of vibration, motion and acoustics.

[0052] The embodiment breaks through the limitation of traditional inertial navigation relying only on IMU data for stationary discrimination, and introduces multi-dimensional and non-kinematic direct physical observation features to provide more accurate and reliable basis for the discrimination logic. The vibration data directly reveals the activity of the internal interference source, the wheel speed data directly judges whether the carrier mechanism is absolutely sliding, and the acoustic data provides state evidence from the overall background noise level. The three work together to effectively distinguish between "macroscopically stationary but microscopically disturbed" and "true static", thereby providing accurate judgment basis for subsequent high-confidence stationary discrimination and improving the reliability of the entire error suppression mechanism from the source.

[0053] In some embodiments of the present application, asFigure 3 As shown, step S102 determines whether the inspection carrier is in a high-confidence stationary state based on the original inertial data and the state feature set, to obtain a determination result, including: S301, based on the variance of the original inertial data, the energy of the structural micro-vibration spectrum data, the zero-value deviation of the real-time rotation speed data of the drive wheel, and the acoustic matching degree, a stationary confidence index is calculated; S302, compare the stationary confidence index with the preset stationary confidence threshold to determine the determination result.

[0054] Specifically, the embodiment performs information fusion and comprehensive evaluation on the original inertial data and the state feature set obtained in the foregoing steps to output an explicit and quantitative determination result. This method is not a simple threshold judgment on a single signal, but is realized by a fusion calculation model.

[0055] First, a stationary confidence index is calculated based on multi-source input data. The stationary confidence index is denoted as Jzx which depends on four input variables: one is the variance of the original inertial data, which is obtained by calculating the variance of the output acceleration or angular velocity data in a short time window, denoted as Ivar The value directly reflects whether the carrier has macroscopic acceleration change or turning motion; the second is the energy of the structural micro-vibration spectrum data, that is, the integral energy of the micro-vibration spectrum in the key frequency band extracted from the state feature set, denoted as Veng The value quantifies the disturbance intensity of the internal component operation to the carrier; the third is the zero-value deviation of the real-time rotation speed data of the drive wheel, that is, the mean square deviation or average absolute deviation of the wheel speed data relative to the zero-speed state extracted from the state feature set, denoted as Wdev The value directly represents whether the wheel is rolling or sliding; the fourth is the acoustic matching degree, that is, the similarity score of the real-time acoustic signal obtained from the state feature set and the reference acoustic feature. For example, when the inspection carrier completes movement and stabilizes, the gimbal and the fan are both closed, Ivar will be reduced to the sensor noise level, Veng close to the environmental vibration background value, Wdev tends to zero, and the acoustic matching degree will be at a high value due to stable environmental noise. At this time, the value of Jzx calculated by a specific fusion model will tend to the maximum value (for example, 1 in theory). On the contrary, if the carrier has braked but the fan is running at high speed, Veng the value will increase significantly, even if Ivar and Wdev are very small, it will also cause the calculated value of Jzx to decrease.

[0056] As a preferred way, the specific fusion process specifically adopts the following formula:

[0057] in, , , and These are respectively represented as the corresponding preset weight coefficients. The correction constant representing the acoustic matching degree.

[0058] Secondly, the calculated static confidence index is compared with a static confidence threshold pre-determined through experimental calibration and training. The specific comparison rule is as follows: if... Jzx > Jt If so, then the judgment result "the inspection vehicle is in a high-confidence static state" is generated; if Jzx < Jt If so, a judgment result is generated indicating that "the inspection vehicle is in a dynamic or micro-dynamic state".

[0059] For example, setting a threshold Jt =0.85. When the carrier is in the aforementioned ideal static state, the calculated value is... Jzx =0.95, since 0.95 > 0.85, it is judged as "high confidence static". When the carrier is affected by fan vibration... Jzx When the value is calculated to be 0.70, since 0.70 ≤ 0.85, it is determined to be a "micro-dynamic state", which logically prevents subsequent error correction from being rashly performed under non-static conditions.

[0060] This embodiment replaces the traditional single-threshold criterion with a quantitative, multi-factor fusion decision-making method, achieving precise quantitative assessment of static states under complex operating conditions. This method fully considers evidence from multiple dimensions, including macroscopic motion, internal vibration, ground slippage, and acoustic background, enabling the judgment results to effectively resist misjudgments caused by a single interference source. It solves the core deficiency of unreliably determining zero-speed update trigger conditions in micro-vibration environments, providing a prerequisite for accurate and reliable triggering of subsequent navigation error correction actions, thereby improving cruise accuracy.

[0061] In some embodiments of the present invention, such as Figure 4 As shown, step S103 executes the corresponding inspection strategy based on the discrimination result to suppress the accumulated error of inertial navigation, including: S401. If the judgment result indicates that the inspection vehicle is in a high-confidence static state, then the zero-speed update algorithm is triggered. S402. If the judgment result indicates that the inspection vehicle is in a dynamic or micro-dynamic state, then suppress the execution of the zero-speed update algorithm.

[0062] Specifically, when the determination result indicates that the inspection carrier is in a high-confidence static state, a zero-speed update algorithm is triggered to be executed. At this time, according to the strong constraint condition that the true value of the carrier speed is zero, key error parameters such as the gyroscope zero bias and the accelerometer bias of an inertial measurement unit (IMU) are estimated and compensated online through Kalman filtering or other estimation algorithms, and the speed error state is reset.

[0063] For example, after a reliable stop, the determination is made that the carrier is in a high-confidence static state, and then ZUPT is started. Through analysis of the IMU output data during the static state, the drift of the gyroscope in a short time is accurately estimated, and the drift is deducted in subsequent calculation, thereby effectively correcting the position deviation caused by the accumulation of the drift.

[0064] Conversely, when the determination result indicates that the inspection carrier is in a dynamic or micro-dynamic state, a suppression strategy is adopted, that is, the execution process of the zero-speed update algorithm is prohibited or skipped. In this case, the navigation calculation only relies on the pure integration of inertial data and dynamic calculation, avoiding the situation that the motion signals such as vibration or slight sliding of the carrier are mistakenly corrected as sensor static errors.

[0065] For example, the carrier slowly crawls due to a wet ground, or high-frequency vibration caused by a fan is captured by the sensor. If ZUPT is executed by mistake at this moment, the algorithm will mistakenly judge the real motion acceleration or angular velocity as sensor zero bias and compensate for it, which will introduce a false force or rotation rate that exists continuously in the navigation calculation, causing systematic drift in the position calculation, which is much more harmful than not correcting. Therefore, the suppression mechanism is crucial in such scenarios.

[0066] The dual-mode control of “triggering” and “suppression” according to the high-confidence determination result described above realizes intelligent management of the key operation of zero-speed update, ensures that error correction is only performed under the condition that error estimation is reliable and safe, and eliminates the risk of incorrect correction caused by state misjudgment. The effectiveness and safety of error suppression are improved, which is conducive to maintaining high-precision navigation calculation of the entire system in a long-distance and complex disturbance environment.

[0067] In some embodiments of the present application, as shown in Figure 5 The step S104 generates risk warning information of the inspection operation based on the uncertainty, including: S501, comparing the uncertainty with a preset safety threshold and a warning threshold; S502, if the uncertainty is less than the safety threshold, a first warning level is generated; S503, if the uncertainty is greater than or equal to the safety threshold and less than the warning threshold, a second warning level is generated; S504, if the uncertainty is greater than or equal to the warning threshold, a third early warning level is generated.

[0068] The safety threshold and the warning threshold can be set according to actual application needs, and are not specifically limited here.

[0069] The uncertainty is measured by the navigation uncertainty index calculated in the previous step.

[0070] It should be noted that, by comparing the uncertainty with the safety threshold and the warning threshold, different levels of early warning are generated, and in actual implementation, different execution strategies are set according to the early warning level, to further realize accurate control of the inspection.

[0071] In this embodiment, different levels of early warning are generated to realize accurate control of the inspection, which is beneficial to timely intervention when misjudgment and accurate reduction occur, and ensures the smooth execution of long-time inspection operation.

[0072] In some embodiments of the present application, as shown in Figure 6 The underground pipe gallery inspection method based on inertial navigation further comprises: S601, identifying a pre-marked fixed marker in the pipe gallery; S602, obtaining the absolute coordinates of the fixed marker, and globally correcting the navigation position based on the absolute coordinates.

[0073] This embodiment serves as an auxiliary correction means in addition to zero-speed update, aiming to provide an absolute position reference for the inertial navigation system, thereby implementing global correction.

[0074] Specifically, an active detection sensor, such as a laser radar or a visual camera, is mounted on the inspection carrier, which continuously scans the surrounding environment during the movement of the carrier along the pipe gallery. And by storing or accessing the fixed marker database pre-mapped in the pipe gallery, the accurate absolute coordinates of the marker are obtained. The fixed marker database at least contains the accurate absolute coordinates (such as geodetic coordinates or coordinates in the global coordinate system of the pipe gallery) of the marker and its feature description. These markers are usually structures with long-term unchanged position and significant geometric or visual features, such as specific pipe welding joints, valves, fireproof door frames or specially designed positioning signs. For example, when the carrier travels to a section of the pipe gallery, the laser radar point cloud data identifies a fireproof door frame with unique size and shape, and through point cloud matching algorithm and comparison with the pre-recorded three-dimensional model of the marker, the identification is successfully completed.

[0075] Subsequently, through a sensor fusion algorithm such as an extended Kalman filter, the instantaneous position estimation of the inspection carrier relative to the marker obtained by inertial navigation is fused and compared with the real absolute coordinates of the marker. Specifically, the sensor fusion algorithm introduces the absolute coordinates of the marker as a strong observation, thereby resetting or constraining the position error, attitude error, and even sensor error parameters accumulated by the inertial navigation system. For example, the inertial navigation indicates that the carrier is currently located at coordinates (x, y, z) X1, Y1, Z1 ), but the identified fire door has a known coordinate of (x', y', z') X0, Y0, Z0 ), and according to the relative position relationship between the carrier and the door, it is deduced that the carrier should actually be located at (x'', y'', z'') X0+Δx, Y0+Δy, Z0+Δz The filter algorithm will use this observation to converge the navigation state vector to the true position, thereby achieving a significant global correction.

[0076] The embodiment provides a discrete and global absolute position anchor for the inertial navigation system whose errors will diverge over time. This correction method is different from the internal suppression of velocity error and sensor zero offset by zero velocity update, but directly corrects the most influential position divergence error. This method effectively reduces the absolute dependence on zero velocity update correction, significantly slows down the decay rate of navigation accuracy, and improves the accuracy and reliability of the entire surveying and mapping task result.

[0077] In order to better implement the underground pipe gallery inspection method based on inertial navigation in the embodiment of the present application, on the basis of the underground pipe gallery inspection method based on inertial navigation, as shown in Figure 7 , the present embodiment also provides an underground pipe gallery inspection device based on inertial navigation. The underground pipe gallery inspection device based on inertial navigation 700 comprises: A data acquisition module 701 is configured to acquire original inertial data of an inspection carrier when moving in a pipe gallery, and synchronously acquire micro-dynamic characteristic data of the inspection carrier itself to generate a state characteristic group. A stationary state discrimination module 702 is configured to discriminate whether the inspection carrier is in a high-confidence stationary state based on the original inertial data and the state characteristic group, and obtain a discrimination result. An inspection positioning module 703 is configured to execute a corresponding inspection strategy according to the discrimination result to suppress the cumulative error of inertial navigation, and continuously solve the position, velocity, and attitude of the inspection carrier to output a current navigation positioning result of the inspection carrier. A risk assessment module 704 is configured to assess the uncertainty of the current navigation positioning result in real time, and generate risk warning information of an inspection operation based on the uncertainty.

[0078] The underground pipe gallery inspection device 700 based on inertial navigation provided by the above embodiments can implement the technical solutions described in the above underground pipe gallery inspection method embodiments based on inertial navigation, and the principles of implementation of the above various modules or units can be referred to the corresponding content in the above underground pipe gallery inspection method embodiments based on inertial navigation, which will not be described here again.

[0079] As shown in Figure 8 The present application also correspondingly provides an electronic device 800. The electronic device 800 includes a processor 801, a memory 802 and a display 803. Figure 8 Only part of the components of the electronic device 800 are shown, but it should be understood that all the shown components are not required to be implemented, and more or less components can be alternatively implemented.

[0080] The processor 801 can be a central processing unit (CPU), a microprocessor or other data processing chip in some embodiments, used to run the program code or process data stored in the memory 802, such as the underground pipe gallery inspection method based on inertial navigation in the present application.

[0081] In some embodiments, the processor 801 can be a single server or a server group. The server group can be centralized or distributed. In some embodiments, the processor 801 can be local or remote. In some embodiments, the processor 801 can be implemented in a cloud platform. In an embodiment, the cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-internal, a multiple cloud, etc., or any combination of the above.

[0082] The memory 802 can be an internal storage unit of the electronic device 800 in some embodiments, such as a hard disk or a memory of the electronic device 800. The memory 802 can also be an external storage device of the electronic device 800 in other embodiments, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 800.

[0083] Further, the memory 802 can include both the internal storage unit and the external storage device of the electronic device 800. The memory 802 is used to store the application software and various data installed on the electronic device 800.

[0084] The display 803 can be, in some embodiments, an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) toucher, and the like. The display 803 is used to display information at the electronic device 800 and to display visualized user interfaces. The components 801-803 of the electronic device 800 communicate with each other through a system bus.

[0085] In an embodiment, when the processor 801 executes the underground pipe gallery inspection program based on inertial navigation in the memory 802, the following steps can be implemented: Obtaining original inertial data of an inspection carrier when the inspection carrier moves in a pipe gallery, and synchronously collecting micro-dynamic characteristic data of the inspection carrier itself to generate a state characteristic group; Based on the original inertial data and the state characteristic group, determining whether the inspection carrier is in a high-confidence static state to obtain a determination result; According to the determination result, executing a corresponding inspection strategy to suppress the cumulative error of inertial navigation, and continuously solving the position, velocity, and attitude of the inspection carrier to output a current navigation positioning result of the inspection carrier; Real-time evaluating the uncertainty of the current navigation positioning result, and based on the uncertainty, generating risk warning information of an inspection operation.

[0086] It should be understood that, when the processor 801 executes the underground pipe gallery inspection program based on inertial navigation in the memory 802, in addition to the above functions, other functions can also be implemented. For details, refer to the description of the corresponding method embodiments.

[0087] Further, the type of the electronic device 800 referred to in the embodiments of the present application is not specifically limited, and the electronic device 800 can be a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop, and the like. Exemplary embodiments of the portable electronic device include, but are not limited to, a portable electronic device running an IOS, an android, a microsoft, or other operating system. The above-mentioned portable electronic device can also be other portable electronic devices, such as a laptop having a touch-sensitive surface (e.g., a touch panel). It should also be understood that, in some other embodiments of the present application, the electronic device 800 can also not be a portable electronic device, but a desktop computer having a touch-sensitive surface (e.g., a touch panel).

[0088] Correspondingly, the embodiment of the present application further provides a computer readable storage medium for storing computer readable programs or instructions, which can realize the steps or functions in the underground pipe gallery inspection method based on inertial navigation provided by the above-mentioned method embodiments when executed by a processor.

[0089] Those skilled in the art can understand that all or part of the processes of the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware (such as a processor, a controller, etc.) to complete, and the computer program can be stored in a computer readable storage medium. The computer readable storage medium is a disk, an optical disk, a read-only memory or a random access memory, etc.

[0090] The above describes in detail the underground pipe gallery inspection method based on inertial navigation, the device, the electronic equipment and the storage medium provided by the present application. The principle and implementation mode of the present application are described by applying specific examples in this paper. The above embodiment is only used to help understand the method and the core idea of the present application. Meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation mode and the application range will be changed. In conclusion, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for inspecting underground utility tunnels based on inertial navigation, characterized in that, include: The original inertial data of the inspection vehicle moving in the pipe gallery is obtained, and the micro-dynamic characteristic data of the inspection vehicle itself is collected simultaneously to generate a state characteristic group. Based on the original inertial data and the state feature group, it is determined whether the inspection vehicle is in a high-confidence stationary state, and the determination result is obtained. The corresponding inspection strategy is executed according to the discrimination result to suppress the cumulative error of inertial navigation, and the position, velocity and attitude of the inspection vehicle are continuously calculated to output the current navigation and positioning result of the inspection vehicle. The uncertainty of the current navigation and positioning result is evaluated in real time, and risk warning information for the inspection operation is generated based on the uncertainty.

2. The underground utility tunnel inspection method based on inertial navigation according to claim 1, characterized in that, The synchronous acquisition of the micro-dynamic feature data of the inspection vehicle itself, generating a state feature group, includes: Acquire the structural micro-vibration spectrum data and real-time rotational speed data of the drive wheel of the inspection carrier; The environmental and self-noise of the inspection vehicle during operation are collected as real-time acoustic signals, and the acoustic features of the inspection vehicle in a static state are extracted as reference acoustic features. The matching degree between the real-time acoustic signal and the reference acoustic feature is calculated to obtain the acoustic matching degree; The state feature group is generated based on the structural micro-vibration spectrum data, the real-time rotational speed data of the drive wheel, and the acoustic matching degree.

3. The underground utility tunnel inspection method based on inertial navigation according to claim 2, characterized in that, The step of determining whether the inspection vehicle is in a high-confidence stationary state based on the original inertial data and the state feature group, and obtaining the determination result, includes: Based on the variance of the original inertial data, the energy of the structural micro-vibration spectrum data, the zero-value deviation of the real-time rotational speed data of the drive wheel, and the acoustic matching degree, the static confidence index is calculated. The static confidence index is compared with a preset static confidence threshold to determine the generated discrimination result.

4. The underground utility tunnel inspection method based on inertial navigation according to claim 1, characterized in that, The step of executing the corresponding inspection strategy based on the discrimination result to suppress the cumulative error of inertial navigation includes: If the discrimination result indicates that the inspection vehicle is in a high-confidence static state, then the zero-speed update algorithm is triggered. If the discrimination result indicates that the inspection vehicle is in a dynamic or micro-dynamic state, then the execution of the zero-speed update algorithm is suppressed.

5. The underground utility tunnel inspection method based on inertial navigation according to claim 1, characterized in that, The real-time assessment of the uncertainty of the current navigation and positioning result includes: The uncertainty is calculated based on the position error covariance output during the navigation solution process and the time interval since the last successful execution of the zero-rate update algorithm.

6. The underground utility tunnel inspection method based on inertial navigation according to claim 1, characterized in that, The process of generating risk warning information for inspection operations based on the uncertainty includes: The uncertainty is compared with preset safety thresholds and warning thresholds; If the uncertainty is less than the safety threshold, a first warning level is generated; If the uncertainty is greater than or equal to the safety threshold and less than the warning threshold, a second warning level is generated; If the uncertainty is greater than or equal to the warning threshold, a third warning level is generated.

7. The underground utility tunnel inspection method based on inertial navigation according to any one of claims 1 to 6, characterized in that, The method further includes: Identify pre-marked fixed markers within the utility tunnel; Obtain the absolute coordinates of the fixed landmark, and make a global correction to the navigation position based on the absolute coordinates.

8. An underground utility tunnel inspection device based on inertial navigation, characterized in that, include: The data acquisition module is used to acquire the original inertial data of the inspection carrier when it moves in the pipe gallery, and simultaneously acquire the micro-dynamic feature data of the inspection carrier itself to generate a state feature group. The stationary discrimination module is used to determine whether the inspection vehicle is in a high-confidence stationary state based on the original inertial data and the state feature group, and to obtain the discrimination result. The inspection and positioning module is used to execute the corresponding inspection strategy according to the discrimination result, so as to suppress the cumulative error of inertial navigation, and continuously calculate the position, velocity and attitude of the inspection vehicle, so as to output the current navigation and positioning result of the inspection vehicle. The risk assessment module is used to assess the uncertainty of the current navigation and positioning results in real time, and generate risk warning information for the inspection operation based on the uncertainty.

9. An electronic device, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the underground utility tunnel inspection method based on inertial navigation as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the underground utility tunnel inspection method based on inertial navigation as described in any one of claims 1 to 7.

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