Industrial equipment mobile ranging method and apparatus

Abstract

The application provides an industrial equipment mobile ranging method and device, and relates to the technical field of computers, and comprises the following steps: acquiring a wireless radio frequency signal channel state information sequence of a mobile device and an encoder pulse sequence of a device traveling wheel system; performing scattering path feature extraction according to the channel state information sequence to obtain a dynamic scattering spectrum; performing virtual mapping construction of a material pile surface according to the dynamic scattering spectrum to obtain an equivalent reflection point cloud of the material pile surface; performing motion trajectory joint optimization according to the equivalent reflection point cloud and the encoder pulse sequence to obtain a local displacement vector sequence of the device in a material pile coordinate system; performing displacement cumulative error calibration according to the local displacement vector sequence and the equivalent reflection point cloud to obtain a calibrated absolute displacement sequence of the device; and performing distance measurement according to the absolute displacement sequence of the device to obtain a ranging result. The application realizes high-precision, anti-cumulative error and robust ranging of a device moving trajectory in a non-line-of-sight, strong interference and complex industrial environment.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a method and apparatus for measuring the distance of moving industrial equipment. Background Technology

[0002] In the field of automated management of industrial bulk material yards, accurate measurement of the movement distance of mobile equipment such as stacker-reclaimers and loaders is a key foundation for achieving automated operations, precise inventory management, and intelligent scheduling. However, such scenarios are complex, presenting challenges such as signal propagation beyond line of sight due to large bulk material piles, variable equipment movement trajectories accompanied by slippage and turning, and severe dust obstruction during dynamic operations. These challenges pose serious challenges to traditional ranging technologies.

[0003] Currently, most existing ranging technologies employ single or simple fusion-based ranging schemes. For example, relying on a combination of global navigation satellite systems and inertial navigation for positioning is prone to a sharp decline in accuracy near material stockpiles due to signal blockage and multipath effects. Other methods use environmental perception tools such as lidar and visual sensors, which are significantly affected by severe weather and dust interference, have high computational demands, and struggle to guarantee real-time performance. Encoder-based dead reckoning algorithms, while unaffected by external environmental interference, suffer from unavoidable cumulative errors, failing to meet the requirements for long-term, high-precision ranging. These methods generally suffer from poor environmental adaptability, insufficient reliability in industrial settings, or the tendency to generate uncorrectable cumulative errors, making it difficult to provide stable and accurate motion ranging results in scenarios like bulk material stockpiles, which involve strong signal interference, complex motion patterns, and harsh observation conditions.

[0004] Based on the shortcomings of the existing technology, there is an urgent need for a method and device for measuring the distance of moving industrial equipment. Summary of the Invention

[0005] The purpose of this invention is to provide a method for measuring the distance of moving industrial equipment, thereby improving the aforementioned problems. To achieve this objective, the technical solution adopted by this invention is as follows:

[0006] In a first aspect, this application provides a method for measuring the distance of moving industrial equipment, including:

[0007] Acquire the sequence of channel status information of wireless radio frequency signals received by the mobile device when it is operating near the material pile, as well as the encoder pulse sequence of the device's traveling wheel system;

[0008] Scattering path features are extracted based on the channel state information sequence. By separating the Doppler frequency shift features caused by the relative displacement between the equipment and the surface of the large bulk material pile and the non-uniform scattering intensity distribution caused by the surface morphology of the material pile, a dynamic scattering spectrum characterizing the geometry of the material pile surface is obtained.

[0009] Based on the dynamic scattering spectrum, a virtual mapping of the material pile surface is constructed to obtain the equivalent reflection point cloud of the material pile surface;

[0010] Based on the equivalent reflection point cloud and the encoder pulse sequence, the motion trajectory is jointly optimized to obtain the local displacement vector sequence of the equipment in the material pile coordinate system;

[0011] Based on the local displacement vector sequence and the equivalent reflection point cloud, displacement cumulative error calibration is performed to obtain the calibrated absolute displacement sequence of the equipment.

[0012] Distance is measured based on the absolute displacement sequence of the device, and the overall motion path of the device is synthesized through integral calculation to obtain the distance measurement result.

[0013] Secondly, this application also provides an industrial equipment motion ranging device, comprising:

[0014] The acquisition module is used to acquire the wireless radio frequency signal channel status information sequence and the encoder pulse sequence of the equipment's traveling wheel system when the mobile device is operating near the material pile;

[0015] The extraction module is used to extract scattering path features based on the channel state information sequence. By separating the Doppler frequency shift features caused by the relative displacement between the equipment and the surface of the large bulk material pile in the signal and the non-uniform scattering intensity distribution caused by the surface morphology of the material pile, a dynamic scattering spectrum characterizing the geometry of the material pile surface is obtained.

[0016] A construction module is used to construct a virtual mapping of the material pile surface based on the dynamic scattering spectrum, thereby obtaining an equivalent reflection point cloud of the material pile surface;

[0017] The optimization module is used to perform joint optimization of motion trajectory based on the equivalent reflection point cloud and the encoder pulse sequence to obtain the local displacement vector sequence of the equipment in the material pile coordinate system;

[0018] The calibration module is used to perform displacement cumulative error calibration based on the local displacement vector sequence and the equivalent reflection point cloud to obtain the calibrated absolute displacement sequence of the device.

[0019] The output module is used to measure distance based on the absolute displacement sequence of the device, and to obtain the distance measurement result by synthesizing the overall motion path of the device through integral calculation.

[0020] The beneficial effects of this invention are as follows:

[0021] This invention utilizes the multipath scattering characteristics formed by the communication signals of mobile devices on the surface of bulk material piles, transforming them from interference factors into effective ranging information sources. It also constructs dynamic scattering spectra and equivalent reflection point clouds, achieving high-precision and robust ranging against cumulative errors for measuring the movement trajectory of equipment in complex industrial environments with non-line-of-sight and strong interference. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of a method for measuring the distance of moving industrial equipment as described in an embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of the structure of an industrial equipment moving distance measuring device according to an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of the structure of an industrial equipment mobile ranging device as described in an embodiment of the present invention.

[0026] The markings in the figure are as follows: 800, a mobile ranging device for industrial equipment; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, acquisition module; 902, extraction module; 903, construction module; 904, optimization module; 905, calibration module; 906, output module. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0028] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] Example 1:

[0030] This embodiment provides a method for measuring the distance of moving industrial equipment.

[0031] See Figure 1 The figure shows that the method includes steps S100 to S600.

[0032] Step S100: Obtain the wireless radio frequency signal channel status information sequence and the encoder pulse sequence of the device's traveling wheel system when the mobile device is operating near the material pile;

[0033] Understandably, the acquired radio frequency signal channel state information sequence and encoder pulse sequence capture the electromagnetic interaction between the equipment and the external environment (especially the surface of the stockpile) and the equipment's own motion information, respectively. In the typical unstructured scenario of a large bulk material stockpile, the presence of the stockpile itself alters the propagation environment of the radio signal, generating rich multipath effects, which traditional ranging methods typically consider harmful interference. Simultaneously, while the pulse signals from the equipment's wheel system can reflect relative motion, they are prone to errors due to slippage and sinking on the bulk material surface. This step aims to simultaneously acquire these two complementary types of raw observation data, providing an information source for subsequent processing.

[0034] Step S200: Extract scattering path features based on the channel state information sequence. By separating the Doppler frequency shift features caused by the relative displacement between the equipment and the surface of the large bulk material pile in the signal and the non-uniform scattering intensity distribution caused by the surface morphology of the material pile, a dynamic scattering spectrum characterizing the geometric morphology of the material pile surface is obtained.

[0035] Step S200 essentially involves mining and refining the physical information contained in the signal. The processing approach is not to suppress multipath scattering caused by the material pile, but rather to actively extract two key features: the Doppler frequency shift reflects the relative velocity between the equipment and the material pile surface, while the non-uniform scattering intensity distribution implies local geometric features such as the surface roughness and slope of the material pile. By separating these features, the scattering signal, originally considered noise, is transformed into a dynamic scattering spectrum that can be used to invert environmental geometric characteristics.

[0036] Step S300: Construct a virtual mapping of the material pile surface based on the dynamic scattering spectrum to obtain the equivalent reflection point cloud of the material pile surface;

[0037] The goal of step S300 is to construct a virtual, computable environmental model. Dynamic scattering spectra are signal-level features, and this step aims to elevate them to the geometric level. Through scattering center localization and spatial geometry reconstruction, the signal features are mapped to an equivalent reflection point cloud describing the three-dimensional spatial locations of the main scattering points on the material pile surface. This construction process utilizes the radio frequency signals carried by the mobile device itself as a "probe" to perform a dynamic, hardware-free three-dimensional sampling of the surrounding complex environment, laying the foundation for subsequent precise positioning using the environment as a natural reference frame.

[0038] Step S400: Perform joint optimization of motion trajectory based on equivalent reflection point cloud and encoder pulse sequence to obtain local displacement vector sequence of equipment in material pile coordinate system;

[0039] It should be noted that while the equivalent reflection point cloud provides spatial constraints for the environment and the encoder pulses provide preliminary motion estimations, both contain errors. This step employs joint optimization to tightly couple the absolute spatial reference provided by the point cloud with the relative motion increments provided by the encoder. The core principle is to utilize the physical constraint that the same batch of scattering points observed at consecutive times should remain spatially fixed to correct trajectory drift generated solely by encoder calculations. This results in a more reliable sequence of local displacement vectors, effectively suppressing the cumulative errors of simple inertial navigation or dead reckoning in such scenarios.

[0040] Step S500: Perform displacement cumulative error calibration based on the local displacement vector sequence and the equivalent reflection point cloud to obtain the calibrated absolute displacement sequence of the equipment;

[0041] Step S500 focuses on addressing the error accumulation problem in long-term distance measurement. Even after joint optimization, small errors still exist in the local displacement sequence after integration. This step treats reflection points on the surface of the material pile that remain stable during the observation period (such as fixed points deep within the material pile or large, stable structures) as natural reference points. By comparing the deviation between the reference point positions predicted based on displacement integrals and their actual observed positions, the accumulated error of the displacement integral can be estimated, and the absolute displacement sequence can be closed-loop calibrated accordingly. This is a strategy that utilizes the invariance of the environment to achieve system self-correction.

[0042] Step S600: Measure the distance based on the absolute displacement sequence of the equipment, and obtain the distance measurement result by synthesizing the overall motion path of the equipment through integral calculation.

[0043] Step S600 is the final stage of information processing, realizing the conversion from displacement to distance. The calibrated absolute displacement sequence describes the motion increment of the equipment in each discrete time period. This step connects these discrete displacement increments into a continuous motion trajectory through vector synthesis and path integration, and calculates its total length to obtain the required travel distance. This process ensures that the final distance measurement result is consistent with the physical length of the actual travel path of the equipment, providing a direct basis for applications such as work measurement and efficiency analysis.

[0044] Further, step S200 includes steps S210 to S230.

[0045] Step S210: Analyze the time-varying characteristics of the signal based on the channel state information sequence. By extracting the phase change rate and amplitude attenuation factor at each signal reception moment, obtain the parameter sequence describing the instantaneous changes of the signal.

[0046] Step S220: Perform scattering mode separation processing based on parameter sequence, and distinguish the steady-state scattering component from the fixed surface of the bulk material pile from the transient scattering component generated by equipment movement through spectrum analysis to obtain scattering mode characteristics representing different physical sources.

[0047] Step S230: Construct a dynamic scattering spectrum based on the scattering mode characteristics. By mapping the time-frequency distribution of transient scattering components with the direction of equipment movement, a dynamic scattering spectrum characterizing the geometry of the material pile surface is obtained.

[0048] Specifically, step S210 first performs a refined analysis of the original channel state information. By calculating the phase change rate at each signal reception moment, it captures the minute relative velocity changes between the device and the scatterer. Simultaneously, it extracts the amplitude attenuation factor to reflect the loss characteristics along the signal propagation path. This transforms the complex radio frequency signal into a set of dynamic parameter sequences that can describe the instantaneous frequency changes and energy attenuation of the signal, providing a more fundamental physical quantity representation for subsequent mode separation. Step S220, based on this, considers the complex signal components in the bulk material yard scenario and uses the parameter sequence obtained in step S210 for time-frequency analysis. The core of this analysis lies in distinguishing signal components based on the differences in the geometric stability of the scattering paths: the signal characteristics of reflection paths from fixed surfaces such as the general outline of the stockpile remain stable for a short time, exhibiting steady-state components in the time-frequency domain; while the scattering paths newly generated by the movement of the mobile device itself exhibit transient components with specific patterns. This separation process allows the originally aliased signals to be decoupled according to their physical causes. Step S230 utilizes the isolated transient scattering component, which best reflects the instantaneous interaction between the equipment and the environment, to analyze the energy distribution pattern on the time-frequency plane and correlate it with the equipment's motion direction vector, ultimately constructing a dynamic scattering spectrum. This spectrum essentially establishes a model that links signal characteristics with the local geometry of the equipment relative to the material pile surface, laying the foundation for subsequent virtual mapping.

[0049] Further, step S220 includes steps S221 to S223.

[0050] Step S221: Perform time-frequency joint distribution calculation based on the parameter sequence. By applying sliding time window Fourier analysis to the phase change rate sequence, the time-domain signal is converted into a frequency-domain energy distribution with time stamps, and a high-resolution time-frequency characterization of the signal is obtained.

[0051] Step S222: Distinguish scattering components based on high-resolution time-frequency characterization. By establishing the correspondence between the continuity of frequency domain energy distribution and the geometric stability of signal propagation path, identify the quasi-static scattering component that maintains stable energy concentration in the time-frequency plane and the dynamic scattering component that exhibits linear frequency modulation characteristics.

[0052] Step S223: Reconstruct the physical characteristics based on the quasi-static scattering component and the dynamic scattering component. By establishing an energy transfer model of the two types of components on the time-frequency plane, the scattering mode characteristics corresponding to the reflection from the fixed surface and the movement of the equipment are analyzed.

[0053] Preferably, step S221 first processes the phase change rate sequence through sliding time window Fourier analysis. The core of this method is to use a specific time window as the observation unit and convert the continuous phase change signal in the time domain into a series of time-stamped spectral slices, thereby obtaining a high-resolution time-frequency characterization that can simultaneously reflect the frequency components of the signal and their evolution over time, generating a detailed spectrum of the signal energy distribution in a two-dimensional time and frequency plane. Step S222 then performs reverse reasoning of the physical mechanism based on this time-frequency characterization. The key is that it demonstrates a direct correlation between the continuity of the frequency domain energy distribution and the geometric stability of the scattering path that generates the signal: in a bulk material stockpile environment, reflections from the fixed foundation structure and other immutable parts of the stockpile have a basically stable propagation path length, and the corresponding scattered signal in the Doppler frequency domain is a quasi-static component with concentrated energy and stable position; while scattering generated by the recent interaction between the mobile equipment and the surface of the stockpile has a continuously changing path length, exhibiting a dynamic component with linear frequency sweep characteristics in the time-frequency plane. By identifying these two typical modes, the physical separation of the scattering components can be achieved. Step S223 further utilizes this separation result to analyze the energy interaction and transfer relationship between the quasi-static component and the dynamic component in the time-frequency plane, such as the frequency offset and energy change of the dynamic component relative to the quasi-static component, to construct a model that can infer the physical process of the scattering event. This allows for the analysis of more representative scattering mode characteristics corresponding to two different physical sources: fixed environmental reflectors and moving equipment, laying the foundation for the final construction of a high-quality dynamic scattering spectrum.

[0054] The formula for calculating the time-frequency energy distribution based on sliding time window Fourier analysis is as follows:

[0055] ;

[0056] The formula for the dynamic scattering component frequency modulation model is:

[0057] ;

[0058] In the formula, For time-frequency representation, in time and frequency The complex value at the location, the square of its magnitude This represents the signal energy density of that frequency component at that moment; The input phase change rate sequence directly reflects the Doppler frequency shift caused by the relative radial motion between the device and the scattering point; For time The window function centered on the center (such as Hamming window, Gaussian window); To analyze the time center, that is, the center time corresponding to the current sliding window; ω is the angular frequency, corresponding to the Doppler frequency shift; The imaginary unit; It is a natural constant; The differential symbol; For the dynamic scattering component at time... The instantaneous angular frequency; For reference time The initial angular frequency of the dynamic scattering component; A linear frequency modulation (RFM) represents the rate of change of instantaneous angular frequency with time. Physically, It is proportional to the radial acceleration between the device and the scattering point; It is a time variable; For reference start time.

[0059] Further, step S300 includes steps S310 to S330.

[0060] Step S310: Locate the scattering center based on the dynamic scattering spectrum. By identifying the time-frequency distribution characteristics of the energy peaks in the spectrum, obtain the parameter combination. The parameter combination includes the signal propagation delay and Doppler frequency shift parameters corresponding to each major scattering point on the surface of the material pile.

[0061] Step S320: Reconstruct the spatial geometry of the scattering points based on the parameter combination. By establishing the vector projection relationship between the Doppler frequency shift and the relative motion velocity of the device, and combining the signal delay constraint, solve for the initial spatial coordinates of each scattering center in the device coordinate system.

[0062] Step S330: Optimize the spatial consistency of the point cloud based on the initial spatial coordinates. Utilize the physical constraints of the continuity of the material pile surface and eliminate the spatial contradictions between the coordinates of the scattering points through graph optimization methods to form an equivalent reflection point cloud that describes the surface morphology of the material pile.

[0063] Specifically, step S310 first extracts features from the dynamic scattering spectrum. By identifying the time-frequency distribution characteristics of energy peaks in the spectrum, each significant peak is associated with a physical scattering center, and the key parameter combination characterizing the scattering center is analyzed. These parameters are the propagation delay of the signal from the device to the scattering point and back, and the Doppler frequency shift caused by the relative radial motion between the device and the scattering point. This step realizes the conversion from signal energy distribution to a discrete scattering point parameter set. Step S320 then uses the above parameter combination to solve for geometric positioning, establishing radial velocity constraints based on Doppler frequency shift and distance constraints based on time delay. By projecting the device's motion velocity vector towards the scattering point, the projection amount should be consistent with the radial velocity calculated by the Doppler frequency shift. At the same time, the signal time delay determines that the scattering point is located on a sphere centered on the device. Combining these two geometric constraints, the initial three-dimensional spatial coordinates of each scattering center in the current device coordinate system can be solved. Step S330 aims to improve the rationality and consistency of the initial coordinates. It uses the prior knowledge that the surface of the bulk material stockpile should be physically continuous and smooth as a constraint. Through graph optimization, each scattering point is regarded as a node in the graph, and the spatial continuity relationship that should be satisfied between the points is regarded as an edge. An optimization problem is constructed. By minimizing the deviation between the initial coordinates and these physical constraints, the spatial position contradictions caused by measurement noise or model imperfections are effectively eliminated. Finally, an equivalent reflection point cloud with a more realistic macroscopic morphology and reasonable spatial structure is generated.

[0064] Further, step S400 includes steps S410 to S450.

[0065] Step S410: Estimate the initial motion value based on the equivalent reflection point cloud and the encoder pulse sequence, establish the initial trajectory of the device motion by pulse counting, and align the initial trajectory with the point cloud data in time and space as the observation geometry to obtain the initial device pose sequence with timestamps.

[0066] Step S420: Perform dynamic calibration of the point cloud based on the initial device pose sequence. Using the re-observation data of the same batch of scattering points under continuous poses, construct an optimization problem constrained by the consistency of the spatial position of the scattering points, and solve the calibrated scattering point coordinates by inversion.

[0067] Step S430: Perform point cloud association processing based on the initial device pose sequence and the calibrated scattering point coordinates. By calculating the spatial similarity of point cloud sets at consecutive time moments, establish the correspondence between scattering points at adjacent time moments to obtain a point cloud association relationship with temporal continuity.

[0068] Step S440: Solve the pose optimization problem based on the point cloud association relationship, construct an optimization function with the objective of minimizing the spatial distance of the point cloud, and calculate the optimized device pose by using the temporal consistency constraint of multi-frame observation data.

[0069] Step S450: Calculate the local displacement vector based on the optimized equipment pose, calculate the transformation matrix between adjacent timestamps of the equipment pose by differential calculation, and extract the translation components to form the local displacement vector sequence of the equipment in the material pile coordinate system.

[0070] It should be noted that step S410 first uses the relative displacement estimation of the device provided by the encoder pulse sequence to calculate the preliminary trajectory of the device's movement. This trajectory is then used as a reference frame and time-stamped and spatially registered with the equivalent reflection point cloud observed at the same time, thereby assigning a preliminary device position and attitude estimate for each observation moment, forming an initial device pose sequence. Step S420, based on this, uses this initial pose sequence to transform the point cloud data observed at different times into a unified reference coordinate system. By analyzing the observation coordinates of the same scattering point at consecutive times and constructing an optimization objective based on the physical constraint that the position of the scattering point in space should remain fixed, a point cloud coordinate that better conforms to the actual spatial distribution is obtained through inversion. Step S430 then establishes the correlation of the point cloud data in the time dimension. By calculating the geometric feature similarity between point cloud sets at adjacent times, a correct correspondence is established for the scattering points observed at different times, thereby forming a coherent point cloud trajectory across time points. Step S440 utilizes the established spatiotemporal correlation of the point cloud to perform precise pose calculation. An optimization problem is constructed with the goal of minimizing the overall spatial distance between the transformed point cloud based on pose estimation and the reference point cloud, incorporating constraints from multi-time observation data to solve for the optimal equipment pose. Step S450 is the final output stage. Differential calculations are performed on the optimized continuous equipment poses to calculate the pose transformation between adjacent time points, extracting pure translational motion vectors to ultimately form a sequence of local displacement vectors describing the precise motion state of the equipment in the material pile coordinate system.

[0071] Further, step S500 includes steps S510 to S530.

[0072] Step S510: Screen environmental reference points based on the local displacement vector sequence and the equivalent reflection point cloud. By analyzing the spatial position stability of each reflection point in the point cloud during continuous observation, select the reflection points whose positions remain stable as the set of reference points for displacement calibration.

[0073] Step S520: Calculate the cumulative displacement error based on the set of benchmark points. By comparing the difference between the predicted values ​​of the benchmark point positions based on the local displacement vector and the actual observed values, construct an error vector sequence describing the cumulative displacement error.

[0074] Step S530: Perform absolute displacement sequence calibration based on the error vector sequence. Feed the error vector back to the displacement integration process in a sliding window manner. Optimize the displacement sequence globally using the least squares adjustment method to obtain the calibrated absolute displacement sequence of the equipment.

[0075] Specifically, step S510 first identifies a stable spatial reference benchmark in the dynamic environment, based on the physical fact that despite the movement of the equipment, there are numerous essentially fixed scattering points in the material pile scenario (such as fixed structures at the bottom of the material pile or deep deposits). This step analyzes the coordinate values ​​of each reflection point in the equivalent reflection point cloud at multiple consecutive observation times, calculates its positional variance or stability index, and thus selects those reflection points that exhibit minimal spatial coordinate fluctuations and high stability during the observation period, forming a reliable set of benchmark points, providing a static reference framework for subsequent error calibration. Step S520 uses the stable benchmark points selected in step S510 as a scale: on the one hand, the coordinates of the benchmark point at a certain initial moment are used as the true value reference; on the other hand, the local displacement vector sequence is integrated (i.e., accumulated) from that initial moment to deduce the predicted values ​​of the benchmark point coordinates at subsequent moments. Due to the cumulative error in the displacement vector sequence, these predicted values ​​will gradually deviate from the actual observed coordinates of the benchmark points. By calculating the average deviation between the predicted coordinates and the actual observed coordinates of all reference points at each moment, an error vector sequence describing the magnitude and direction of the cumulative displacement error can be constructed. This sequence quantitatively characterizes the drift in pose estimation. Step S530 constructs a global optimization problem, the goal of which is to find a set of corrected absolute displacement sequences such that the overall difference between the predicted reference point position and the actual observation value of the device motion trajectory obtained by integrating this sequence and after coordinate transformation is minimized over the entire time range. This is usually solved using the least squares adjustment method, ultimately outputting a highly reliable absolute displacement sequence of the device that eliminates cumulative drift and is consistent with the static reference point information in the environment. The entire process essentially utilizes the inherent static characteristics of the environment itself to provide a continuous external absolute reference for the relative displacement measurement of the device, thereby achieving a self-correction function.

[0076] Further, step S600 includes steps S610 to S630.

[0077] Step S610: Perform path vector synthesis based on the absolute displacement sequence of the equipment. By performing vector addition on the displacement vectors at each moment in the sequence in the material pile coordinate system, the preliminary motion path trajectory of the equipment is obtained.

[0078] Step S620: Perform kinematic smoothing processing on the preliminary motion path trajectory, and optimize and correct the path trajectory by introducing the maximum acceleration and turning curvature constraints of the equipment to obtain a motion path that conforms to the laws of physical motion.

[0079] Step S630: Calculate the distance based on the movement path. By performing arc length integration on the path and accumulating the total length of the device's movement trajectory, the distance measurement result is obtained.

[0080] Specifically, step S610 first transforms the discrete absolute displacement sequence into a continuous motion path representation. The displacement vectors at each moment are superimposed in chronological order in the stockpile coordinate system, meaning the starting point of each displacement vector connects to the ending point of the previous vector, thus forming a preliminary motion trajectory broken line composed of line segments. This process essentially integrates the segmented motion states of the equipment in space into a complete path description. Step S620 then performs physical rationality correction on the preliminary trajectory based on the actual motion characteristics of the industrial equipment. The key point is to introduce inherent kinematic constraints of the equipment—including the maximum acceleration limit provided by the equipment's power system and the minimum turning radius of curvature allowed by the mechanical structure. By checking for abrupt changes in the preliminary trajectory that violate these physical limits (such as sharp turns or instantaneous high-speed changes) and smoothing them, the final trajectory conforms to the trend of the original displacement data and satisfies the actual achievable motion state of the equipment in an unstructured environment such as a bulk material stockpile, thus obtaining a physically reliable motion path. Step S630 ultimately achieves precise quantification from path to distance by performing arc length integration on the smooth continuous motion path obtained in step S620. That is, the length is calculated and accumulated along the trajectory curve segment by segment, rather than simply accumulating the magnitude of discrete displacement vectors. This method more accurately reflects the total length of the curved path actually traveled by the equipment, and is especially suitable for complex motion modes such as curved travel and obstacle avoidance that equipment often encounters in bulk material yards. Finally, it outputs a highly accurate distance measurement result.

[0081] Example 2:

[0082] like Figure 2 As shown, this embodiment provides an industrial equipment movement ranging device, the device comprising:

[0083] The acquisition module 901 is used to acquire the wireless radio frequency signal channel status information sequence and the encoder pulse sequence of the equipment's traveling wheel system received by the mobile device when it is operating near the material pile.

[0084] Extraction module 902 is used to extract scattering path features based on channel state information sequence. By separating the Doppler frequency shift features caused by the relative displacement between the equipment and the surface of the large bulk material pile in the signal and the non-uniform scattering intensity distribution caused by the surface morphology of the material pile, a dynamic scattering spectrum characterizing the geometry of the material pile surface is obtained.

[0085] Module 903 is used to construct a virtual mapping of the material pile surface based on the dynamic scattering spectrum, so as to obtain the equivalent reflection point cloud of the material pile surface;

[0086] Optimization module 904 is used to jointly optimize the motion trajectory based on the equivalent reflection point cloud and the encoder pulse sequence to obtain the local displacement vector sequence of the equipment in the material pile coordinate system;

[0087] The calibration module 905 is used to perform displacement cumulative error calibration based on the local displacement vector sequence and the equivalent reflection point cloud to obtain the calibrated absolute displacement sequence of the equipment.

[0088] The output module 906 is used to measure distance based on the absolute displacement sequence of the equipment and obtain the distance measurement result by synthesizing the overall motion path of the equipment through integral calculation.

[0089] In one specific embodiment of this application, the extraction module 902 includes:

[0090] The first extraction unit is used to analyze the time-varying characteristics of the signal based on the channel state information sequence. By extracting the phase change rate and amplitude attenuation factor at each signal reception moment, a parameter sequence describing the instantaneous changes of the signal is obtained.

[0091] The second extraction unit is used to perform scattering mode separation processing based on the parameter sequence. Through spectrum analysis, it distinguishes the steady-state scattering component from the fixed surface of the bulk material pile from the transient scattering component generated by the movement of the equipment, and obtains the scattering mode characteristics that characterize different physical sources.

[0092] The third extraction unit is used to construct a dynamic scattering spectrum based on the characteristics of the scattering mode. By associating and mapping the time-frequency distribution of the transient scattering components with the direction of equipment movement, a dynamic scattering spectrum characterizing the geometry of the material pile surface is obtained.

[0093] In one specific embodiment of this application, the second extraction unit includes:

[0094] The fourth extraction unit is used to perform time-frequency joint distribution calculation based on the parameter sequence. By applying sliding time window Fourier analysis to the phase change rate sequence, the time-domain signal is converted into a time-stamped frequency-domain energy distribution, thus obtaining a high-resolution time-frequency characterization of the signal.

[0095] The fifth extraction unit is used to distinguish scattering components based on high-resolution time-frequency characterization. By establishing the correspondence between the continuity of frequency domain energy distribution and the geometric stability of signal propagation path, it identifies quasi-static scattering components that maintain stable energy concentration in the time-frequency plane and dynamic scattering components that exhibit linear frequency modulation characteristics.

[0096] The sixth extraction unit is used to reconstruct physical features based on the quasi-static scattering component and the dynamic scattering component. By establishing an energy transfer model of the two types of components on the time-frequency plane, the scattering mode features corresponding to the reflection from the fixed surface and the movement of the equipment are analyzed.

[0097] Example 3:

[0098] Corresponding to the above method embodiments, this embodiment also provides an industrial equipment moving distance measuring device. The industrial equipment moving distance measuring device described below and the industrial equipment moving distance measuring method described above can be referred to in correspondence.

[0099] Figure 3 This is a block diagram illustrating an industrial equipment mobile ranging device 800 according to an exemplary embodiment. Figure 3 As shown, the industrial equipment mobile ranging device 800 may include a processor 801 and a memory 802. The industrial equipment mobile ranging device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0100] The processor 801 controls the overall operation of the industrial equipment motion ranging device 800 to complete all or part of the steps in the aforementioned industrial equipment motion ranging method. The memory 802 stores various types of data to support the operation of the industrial equipment motion ranging device 800. This data may include, for example, instructions for any application or method operating on the industrial equipment motion ranging device 800, and application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the industrial equipment mobile ranging device 800 and other devices. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0101] In an exemplary embodiment, an industrial equipment motion ranging device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the aforementioned industrial equipment motion ranging method.

[0102] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described industrial equipment movement ranging method. For example, the computer-readable storage medium may be the memory 802 including the program instructions, which may be executed by a processor 801 of an industrial equipment movement ranging device 800 to complete the above-described industrial equipment movement ranging method.

[0103] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. An industrial equipment mobile ranging method, characterized by, The method comprises the following steps: acquiring a sequence of wireless radio frequency signal channel state information received by a mobile device when the device is working near a stockpile and a sequence of encoder pulses of a wheel system of the device; extracting scattering path features from the sequence of channel state information, separating the Doppler shift features generated by the relative displacement between the device and the surface of the large stockpile body and the non-uniform scattering intensity distribution caused by the surface morphology of the stockpile in the signals to obtain a dynamic scattering spectrum representing the surface geometry of the stockpile; constructing a virtual mapping of the surface of the stockpile according to the dynamic scattering spectrum to obtain an equivalent reflection point cloud of the surface of the stockpile; jointly optimizing a motion trajectory according to the equivalent reflection point cloud and the sequence of encoder pulses to obtain a sequence of local displacement vectors of the device in the coordinate system of the stockpile; calibrating displacement cumulative error according to the sequence of local displacement vectors and the equivalent reflection point cloud to obtain a calibrated sequence of absolute displacement of the device; measuring distance according to the sequence of absolute displacement of the device to obtain a ranging result by integrating the overall motion path of the device; wherein the jointly optimizing a motion trajectory according to the equivalent reflection point cloud and the sequence of encoder pulses comprises: estimating motion initial values according to the equivalent reflection point cloud and the sequence of encoder pulses to establish a preliminary trajectory of the device motion by pulse counting, and performing space-time alignment of the preliminary trajectory as an observation geometry basis and the point cloud data to obtain an initial sequence of device poses with time stamps; performing dynamic calibration of the point cloud according to the initial sequence of device poses, using the re-observation data of the same batch of scattering points under consecutive poses to construct an optimization problem with the spatial position consistency of the scattering points as a constraint, and inversely solving calibrated coordinates of the scattering points; performing point cloud association processing according to the initial sequence of device poses and the calibrated coordinates of the scattering points, establishing a corresponding relationship between the scattering points at adjacent time instants by calculating the spatial similarity of the point cloud sets at consecutive time instants to obtain a time-continuous point cloud association relationship; solving the pose optimization according to the point cloud association relationship, constructing an optimization function with the minimization of the spatial distance of the point cloud as the target, and calculating the optimized device pose by using the time consistency constraint of the multi-frame observation data; calculating the local displacement vector according to the optimized device pose, calculating the transformation matrix between the device poses at adjacent time stamps by difference calculation, extracting the translation component to form the sequence of local displacement vectors of the device in the coordinate system of the stockpile.

2. The industrial equipment mobile ranging method of claim 1, wherein The method of extracting scattering path features according to the sequence of channel state information comprises: analyzing time-varying features of the signals according to the sequence of channel state information, extracting the phase change rate and amplitude attenuation factor at each signal receiving time to obtain a sequence of parameters describing the instantaneous change of the signals; performing scattering mode separation processing according to the sequence of parameters, distinguishing the steady-state scattering components from the fixed surface of the stockpile body and the transient scattering components generated by the movement of the device by spectral analysis to obtain scattering mode features representing different physical sources; According to the dynamic scattering spectrum feature, a dynamic scattering spectrum is constructed, a time-frequency distribution of the transient scattering component is associated and mapped with a device motion direction, and a dynamic scattering spectrum representing a surface geometry of the material pile is obtained.

3. The industrial equipment mobile ranging method of claim 2, wherein, According to the parameter sequence, a scattering mode separation process is performed, including: According to the parameter sequence, a time-frequency joint distribution is calculated, a time-domain signal is converted into a frequency-domain energy distribution with a time marker by applying a sliding time window Fourier analysis to a phase change rate sequence, and a high-resolution time-frequency representation of the signal is obtained; According to the high-resolution time-frequency representation, a scattering component is distinguished, a corresponding relationship between a frequency-domain energy distribution continuity and a signal propagation path geometry stability is established, a quasi-static scattering component maintaining stable energy concentration and a dynamic scattering component presenting a linear frequency modulation feature are identified on a time-frequency plane; According to the quasi-static scattering component and the dynamic scattering component, physical features are reconstructed, an energy transfer model of the two types of components on the time-frequency plane is established, and scattering mode features corresponding to fixed surface reflection and device movement are analyzed.

4. The industrial equipment mobile ranging method of claim 1, wherein, According to the dynamic scattering spectrum, a virtual mapping construction of the material pile surface is performed, and an equivalent reflection point cloud of the material pile surface is obtained, including: According to the dynamic scattering spectrum, a scattering center is located, a parameter combination is analyzed by identifying a time-frequency distribution feature of an energy peak value in the spectrum, and the parameter combination includes signal propagation time delay and Doppler shift parameters corresponding to each main scattering point on the material pile surface; According to the parameter combination, a scattering point space geometry is reconstructed, an initial space coordinate of each scattering center in a device coordinate system is solved by establishing a vector projection relationship between the Doppler shift and the relative motion speed of the device, and combining a signal time delay constraint; According to the initial space coordinate, a point cloud space consistency is optimized, a space contradiction between scattering point coordinates is eliminated by a graph optimization method through a physical constraint of material pile surface continuity, and an equivalent reflection point cloud describing a material pile surface morphology is formed.

5. The industrial equipment mobile ranging method of claim 1, wherein, According to the local displacement vector sequence and the equivalent reflection point cloud, a displacement cumulative error is calibrated, a calibrated absolute displacement sequence of the device is obtained, including: According to the local displacement vector sequence and the equivalent reflection point cloud, an environment reference point is selected, a reference point set maintaining stable position is selected as a displacement calibration reference point set by analyzing a space position stability of each reflection point in the point cloud in continuous observation; According to the reference point set, a displacement cumulative error is calculated, an error vector sequence describing the displacement cumulative error is constructed by comparing a difference between a reference point position predicted value based on the local displacement vector and an actual observation value; According to the error vector sequence, an absolute displacement sequence is calibrated, the error vector is fed back to a displacement integration process in a sliding window manner, a global optimization of the displacement sequence is performed by a least square adjustment method, and a calibrated absolute displacement sequence of the device is obtained.

6. The industrial equipment mobile ranging method of claim 1, wherein, According to the absolute displacement sequence of the device, a distance measurement is performed, a ranging result is obtained by integrating and operating a whole motion path of the device, including: The path vector synthesis is performed according to the absolute displacement sequence of the device, the displacement vectors at each time in the sequence are connected in sequence in the vector addition operation in the stockpile coordinate system, and a preliminary motion path trajectory of the device is obtained; Kinematic smoothing processing is performed according to the preliminary motion path trajectory, the path trajectory is optimized and corrected by introducing the maximum acceleration and turning curvature constraints of the device, and a motion path conforming to the physical motion law is obtained; The ranging calculation is performed according to the motion path, the total length of the device moving trajectory is accumulated by performing arc length integral operation on the path, and the ranging result is obtained.

7. An industrial equipment mobile ranging device, characterized by, Comprise: The acquisition module is used for acquiring the wireless radio frequency signal channel state information sequence received by the mobile device when working near the stockpile and the encoder pulse sequence of the device traveling wheel system; The extraction module is used for extracting the scattering path features according to the channel state information sequence, separating the Doppler frequency shift features generated by the relative displacement between the device and the surface of the large bulk material pile and the non-uniform scattering intensity distribution caused by the surface morphology of the stockpile in the signal, and obtaining a dynamic scattering spectrum representing the surface geometry of the stockpile; The construction module is used for constructing a virtual mapping of the stockpile surface according to the dynamic scattering spectrum, and obtaining an equivalent reflection point cloud of the stockpile surface; The optimization module is used for jointly optimizing the motion trajectory according to the equivalent reflection point cloud and the encoder pulse sequence, and obtaining a local displacement vector sequence of the device in the stockpile coordinate system; The calibration module is used for calibrating the displacement accumulation error according to the local displacement vector sequence and the equivalent reflection point cloud, and obtaining a calibrated absolute displacement sequence of the device; The output module is used for distance measurement according to the absolute displacement sequence of the device, and the ranging result is obtained by integrating the operation to synthesize the overall motion path of the device; Wherein, the motion trajectory joint optimization according to the equivalent reflection point cloud and the encoder pulse sequence comprises: Motion initial value estimation is performed according to the equivalent reflection point cloud and the encoder pulse sequence, a preliminary trajectory of the device motion is established by pulse counting, and the preliminary trajectory is used as an observation geometry basis to perform space-time alignment with the point cloud data, to obtain an initial device pose sequence with a time stamp; Point cloud dynamic calibration is performed according to the initial device pose sequence, the re-observation data of the same batch of scattering points under continuous poses is used to construct an optimization problem with the consistency of the spatial positions of the scattering points as a constraint, and the calibrated scattering point coordinates are solved by inversion; Point cloud correlation processing is performed according to the initial device pose sequence and the calibrated scattering point coordinates, the spatial similarity of the point cloud sets at continuous time instants is calculated to establish the corresponding relationship between the scattering points at adjacent time instants, and a point cloud correlation relationship with time continuity is obtained; The pose optimization solution is obtained according to the point cloud correlation relationship, an optimization function with the minimum distance of the point cloud space as the target is constructed, and the optimized device pose is calculated by using the time consistency constraint of the multi-frame observation data; The local displacement vector is calculated according to the optimized device pose, the transformation matrix between the device poses at adjacent time stamps is calculated by difference, and the translation component is extracted to form the local displacement vector sequence of the device in the stockpile coordinate system.

8. The industrial plant mobile ranging device of claim 7, wherein, The extraction module comprises: The first extraction unit is configured to perform signal time-varying feature analysis according to the channel state information sequence, and obtain a parameter sequence describing signal instantaneous variation by extracting a phase change rate and an amplitude attenuation factor of each signal receiving moment; The second extraction unit is configured to perform scattering mode separation processing according to the parameter sequence, and obtain scattering mode features of different physical sources by distinguishing a steady-state scattering component from a fixed surface of the bulk material pile and a transient scattering component generated by movement of the device through spectrum analysis; The third extraction unit is configured to perform dynamic scattering spectrum construction according to the scattering mode features, and obtain a dynamic scattering spectrum representing a geometric morphology of the surface of the bulk material pile by associating and mapping a time-frequency distribution of the transient scattering component with a movement direction of the device.

9. The industrial plant mobile ranging device of claim 8, wherein, The second extraction unit comprises: The fourth extraction unit is configured to perform time-frequency joint distribution calculation according to the parameter sequence, and obtain a high-resolution time-frequency representation of the signal by applying sliding time window Fourier analysis to the phase change rate sequence to convert a time-domain signal into a frequency-domain energy distribution with a time marker; The fifth extraction unit is configured to perform scattering component distinction according to the high-resolution time-frequency representation, and identify quasi-static scattering components maintaining stable energy concentration and dynamic scattering components presenting a linear frequency modulation characteristic on a time-frequency plane by establishing a corresponding relationship between continuity of the frequency-domain energy distribution and geometric stability of a signal propagation path; The sixth extraction unit is configured to perform physical feature reconstruction according to the quasi-static scattering components and the dynamic scattering components, and analyze scattering mode features corresponding to a fixed surface reflection and movement of the device by establishing an energy transfer model of the two types of components on the time-frequency plane.

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