Sensor data processing methods, devices, terminal equipment, and hill-climbing testing devices

Abstract

This application relates to the field of data processing technology, and discloses a sensor data processing method, apparatus, terminal device, and slope testing device. The method includes: calculating second elevation height data based on distance data and slope angle; obtaining a first weight corresponding to the first elevation height data and a second weight corresponding to the second elevation height data; and performing a weighted summation of the first and second elevation height data based on the first and second weights to obtain target elevation height data. This method can simulate various slope scenarios from gentle to steep, adapting to the testing needs of different models and specifications of robots, making the slope testing data more accurate.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a sensor data processing method, apparatus, terminal equipment, and hill-climbing test device. Background Technology

[0002] With the rapid development of robotics technology, mobile service robots are increasingly being used in fields such as exhibition hall guidance, logistics, and healthcare. Climbing ability, as one of the core performance indicators of mobile robots, directly relates to their reliability and safety in complex environments. However, current testing of robot climbing performance mainly uses fixed-angle ramp platforms or temporary, simple testing devices. These traditional methods lack convenient angle adjustment functions, making it difficult to guarantee the accuracy of test results. Summary of the Invention

[0003] In view of this, embodiments of this application provide a sensor data processing method, apparatus, terminal device, and ramp testing device, which can effectively solve the technical problem that traditional calibration methods are often difficult to perform frequently in field applications and have extremely low calibration efficiency.

[0004] In a first aspect, embodiments of this application provide a hill-climbing testing device, comprising: The bottom support assembly includes a slide rail and a roller assembly that moves horizontally along the slide rail from its starting position. A lifting assembly is installed above the bottom support assembly; A horizontal platform, located above the lifting assembly, is used to support the slope climbing test equipment; The ramp platform is hinged at one end to the horizontal platform and at the other end to the roller assembly. The roller assembly moves horizontally at different positions on the slide rail, causing the ramp platform to form different climbing angles, so that the climbing test equipment can perform climbing tests at different climbing angles and obtain climbing test data.

[0005] Secondly, embodiments of this application provide a sensor data processing method, including: The distance data between the roller assembly and the starting position of the slide rail measured by the distance sensor, the climbing angle between the ramp platform and the horizontal platform measured by the angle sensor, and the first lifting height data measured by the displacement sensor are obtained. Based on the distance data and the climbing angle, calculate the second elevation gain / loss data; Obtain the first weight corresponding to the first lifting height data and the second weight corresponding to the second lifting height data; Based on the first weight and the second weight, the first elevation height data and the second elevation height data are weighted and summed to obtain the target elevation height data.

[0006] Thirdly, embodiments of this application provide a sensor data processing apparatus, including: The first acquisition module is used to acquire the distance data between the roller assembly and the starting position of the slide rail measured by the distance sensor, the climbing angle between the ramp platform and the horizontal platform measured by the angle sensor, and the first lifting height data measured by the displacement sensor. The calculation module is used to calculate the second elevation gain / loss data based on the distance data and the climbing angle; The second acquisition module is used to acquire the first weight corresponding to the first lifting height data and the second weight corresponding to the second lifting height data; The processing module is used to perform a weighted summation of the first elevation height data and the second elevation height data based on the first weight and the second weight to obtain the target elevation height data.

[0007] Fourthly, this application also provides a terminal device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps: The distance data between the roller assembly and the starting position of the slide rail measured by the distance sensor, the climbing angle between the ramp platform and the horizontal platform measured by the angle sensor, and the first lifting height data measured by the displacement sensor are obtained. Based on the distance data and the climbing angle, calculate the second elevation gain / loss data; Obtain the first weight corresponding to the first lifting height data and the second weight corresponding to the second lifting height data; Based on the first weight and the second weight, the first elevation height data and the second elevation height data are weighted and summed to obtain the target elevation height data.

[0008] Fifthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps: The distance data between the roller assembly and the starting position of the slide rail measured by the distance sensor, the climbing angle between the ramp platform and the horizontal platform measured by the angle sensor, and the first lifting height data measured by the displacement sensor are obtained. Based on the distance data and the climbing angle, calculate the second elevation gain / loss data; Obtain the first weight corresponding to the first lifting height data and the second weight corresponding to the second lifting height data; Based on the first weight and the second weight, the first elevation height data and the second elevation height data are weighted and summed to obtain the target elevation height data.

[0009] The embodiments of this application have the following beneficial effects: On the one hand, by controlling the platform height through lifting components and setting the slope using bottom rolling fixed wheels, it can simulate various slope scenarios from gentle to steep, adapting to the testing needs of different robot models and specifications, and overcoming the limitations of fixed slope testing platforms. Furthermore, it reduces structural complexity and manufacturing costs, thereby improving motion reliability and system robustness. On the other hand, by installing multiple sensors, the slope testing data can be made more accurate. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of this application, 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 this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic diagram of a slope testing device according to an embodiment of this application is shown; Figure 2 A schematic flowchart of a sensor data processing method according to an embodiment of this application is shown; Figure 3 This paper illustrates another flowchart of the sensor data processing method according to an embodiment of this application; Figure 4 A schematic diagram of a sensor data processing device according to an embodiment of this application is shown.

[0012] Explanation of key component symbols: Bottom support assembly 11, lifting assembly 12, horizontal platform 13, ramp platform 14, and roller assembly 15. Detailed Implementation

[0013] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0014] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0015] In the following text, the terms "comprising," "having," and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more combinations thereof. Furthermore, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0016] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in a generally used dictionary) shall be interpreted as having the same meaning as in the context of the relevant technical field and shall not be interpreted as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0017] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0018] With the rapid development of robotics technology, mobile service robots are increasingly being used in fields such as exhibition hall guidance, logistics, and healthcare. Climbing ability, as one of the core performance indicators of mobile robots, directly relates to their reliability and safety in complex environments. However, current testing of robot climbing performance mainly uses fixed-angle ramp platforms or temporary, simple testing devices. These traditional methods lack convenient angle adjustment functions, making it difficult to guarantee the accuracy of test results.

[0019] To address this, this application provides a sensor data processing method, apparatus, terminal device, and slope testing device. It can simulate various slope scenarios, from gentle to steep, adapting to the testing needs of different robot models and specifications, thus making slope testing data more accurate.

[0020] Figure 1A schematic diagram of a slope-climbing testing device according to an embodiment of this application is shown. Exemplarily, the slope-climbing testing device includes: The bottom support assembly 11 is provided with a slide rail and a roller assembly 15 that moves horizontally along the slide rail from the starting position of the slide rail; The lifting assembly 12 is installed above the bottom support assembly 11; The horizontal platform 13, located above the lifting assembly 12, is used to support the slope testing equipment; The ramp platform 14 is hinged at one end to the horizontal platform 13 and at the other end to the roller assembly 15. The roller assembly 15 moves horizontally at different positions on the slide rail, causing the ramp platform 14 to form different climbing angles, so that the climbing test equipment can perform climbing tests on the climbing test equipment at different climbing angles and obtain climbing test data.

[0021] Specifically, the slope testing device includes a bottom support assembly 11, a lifting assembly 12, a horizontal platform 13, a ramp platform 14, a roller assembly 15, and a PLC controller (programmable logic controller, specifically designed to perform the sensor data processing methods in the following embodiments).

[0022] The bottom support assembly 11 has an internal slide rail to support the weight of the entire climbing test device. The end of the slide rail is equipped with a distance sensor to measure the position of the bottom pulley and a slope sensor to measure the slope of the ramp platform 14. The horizontal platform 13 is set at the top of the lifting assembly 12 and is used to place the climbing test equipment, providing a stable, level and height-adjustable working reference surface for the climbing test equipment. The ramp platform 14 is hinged to the horizontal platform 13 to simulate the climbing scenario.

[0023] Optionally, the bottom support assembly 11 is equipped with a distance sensor for measuring the distance of the roller assembly 15 relative to the end of the slide rail; an angle sensor is provided at the hinge of the ramp platform 14 and the horizontal platform 13 for measuring the climbing angle between the ramp platform 14 and the horizontal platform 13 in real time; and the lifting assembly 12 is equipped with a displacement sensor for measuring the lifting height.

[0024] In one example, the roller assembly 15 includes rollers and axles. The axles cooperate with guide wheels to achieve stable support and slope adjustment of the platform. The roller assembly 15 can move along the slide rail. The pulley locking pin can be an electromagnetic locking pin, which is used to rigidly fix the platform when it is raised or lowered to the target height to prevent it from moving accidentally.

[0025] In another example, the surface of the ramp platform 14 is provided with an anti-slip texture to improve friction; an adjustable gap mechanism is provided between the horizontal platform 13 and the ramp platform 14 to simulate ramp scenarios at different heights.

[0026] The purely mechanical structure described in the above embodiments achieves reliable position locking without relying on hydraulic system pressure, providing independent safety assurance. Furthermore, by controlling the platform height through the lifting component 12 and setting the slope using the bottom rolling fixed wheels, it can simulate various slope scenarios from gentle to steep, adapting to the testing needs of different models and specifications of robots, and overcoming the limitations of fixed slope testing platforms.

[0027] Figure 2 A schematic flowchart of a sensor data processing method according to an embodiment of this application is shown. Exemplarily, the sensor data processing method includes the following steps: Step S202: Obtain the distance data between the roller assembly and the starting position of the slide rail measured by the distance sensor, the climbing angle between the ramp platform and the horizontal platform measured by the angle sensor, and the first lifting height data measured by the displacement sensor.

[0028] Specifically, the PLC controller first calculates and controls the lifting components based on the user-defined slope setting and real-time sensor data to complete the lifting and precise leveling of the platform.

[0029] Subsequently, all sensor signals are transmitted via shielded cables to the dedicated analog / digital input module of the PLC controller. The PLC controller first filters the original sensor signals using an amplitude-limiting averaging filter method to suppress interference, specifically including: Optionally, the continuous data stream acquired in real time by the sensor is subjected to amplitude limiting judgment: that is, based on the reasonable physical change range of the measured object, a maximum allowable deviation value (e.g., A) is set. If the difference between the current sampled value and the previous valid sampled value exceeds the threshold A, it is judged as impulse interference and discarded, and the previous valid value is used instead. Subsequently, the valid data that passes the amplitude limiting judgment is stored in a queue of length N, and the arithmetic mean of the N data in the queue is calculated. The arithmetic mean is used as the final output value of this filtering, including distance data, slope angle, and first rise / fall height data.

[0030] Through the above embodiments, pulse interference and random noise are effectively suppressed, sensor data stability and real-time control accuracy are improved, and millimeter-level reliability of platform slope adjustment and positioning is guaranteed.

[0031] Step S204: Calculate the second elevation and descent height data based on the distance data and the climbing angle.

[0032] The first lifting height data refers to the actual height value directly measured by the displacement sensor.

[0033] The second elevation gain data refers to the theoretical height value calculated through geometric relationships based on the distance data measured by the distance sensor and the climbing angle measured by the angle sensor.

[0034] Specifically, the distance difference between the distance data and the platform length of the horizontal platform is determined; the sine value of the climbing angle is determined, and the product of the distance difference and the sine value is used as the second elevation and descent height data.

[0035] The above embodiments provide a height verification path independent of the lifting component, enhance the geometric reliability of the slope setting, and effectively suppress the slope adjustment error caused by single-point sensor failure or drift.

[0036] Step S206: Obtain the first weight corresponding to the first lifting height data and the second weight corresponding to the second lifting height data.

[0037] The first weight refers to the fusion coefficient assigned to the first lift height data (displacement sensor value).

[0038] The second weight refers to the fusion coefficient assigned to the second elevation data (geometric calculation value).

[0039] Specifically, the methods for obtaining the first and second weights are not unique. They can be calculated based on factory parameters (e.g., factory standard deviation, variance). Alternatively, a fixed-length, first-in-first-out (FIFO) data queue can be maintained during actual use of the sensor, continuously storing the sensor's latest N sampled values. In each fusion cycle, the actual variance of the sensor within this window is calculated, and the first and second weights are further calculated based on this actual variance. Similarly, the weights calculated based on factory parameters and the weights calculated based on the actual variance can be fused to obtain the first and second weights.

[0040] Through the above embodiments, dynamic weight adaptation is achieved, taking into account both the inherent accuracy of the sensor and real-time operating condition drift, improving the robustness and long-term stability of the fused height value, and reducing reliance on manual calibration.

[0041] Step S208: Based on the first weight and the second weight, the first elevation height data and the second elevation height data are weighted and summed to obtain the target elevation height data.

[0042] Among them, the target elevation data refers to the optimal elevation estimate obtained after weighted fusion.

[0043] Specifically, ; in, This refers to the target's elevation and descent data. First weight, Hs refers to the first lifting height, and Hs refers to the second lifting height data. This refers to the second weight.

[0044] Through the above embodiments, a high-confidence fused height value is generated, which balances real-time performance and robustness, provides accurate feedback for closed-loop control, and significantly improves the accuracy of slope setting and long-term operational stability.

[0045] Optionally, a self-correcting algorithm can be used to correct errors in the height sensor, ensuring long-term measurement accuracy without manual intervention. The steps of the self-correcting algorithm include: First, the current actual theoretical height Hs is calculated by multi-sensor data fusion, and the actual reading H of the height sensor is compared with the actual height sensor reading. height The difference is ΔH = Hs - H. height This is the overall error of the system. This deviation value ΔH will be stored by the PLC controller, which will continuously track and record the overall error ΔH calculated in the most recent N calibration cycles, forming a dynamically updated error sequence.

[0046] Subsequently, the dominant type of current error is identified by calculating the arithmetic mean (μ) and standard deviation (σ) of the sequence in real time. A threshold determination is then performed, where the first and second thresholds can be determined based on actual field conditions (for example, the thresholds can be set by using σ of a data segment collected during stable no-load operation of the system as a benchmark).

[0047] 1. When the average deviation (μ) is significantly non-zero, its absolute value exceeds a preset first threshold, and the fluctuation (σ) is less than a second preset threshold, the system determines that a stable system deviation exists (e.g., always around +0.5mm). This indicates that the height sensor has a fixed zero-point drift or scaling error. The arithmetic mean of the most recent ΔH is used to directly compensate for the calibration, effectively eliminating the zero-point drift.

[0048] 2. When the average deviation (μ) is close to zero and its absolute value is less than the first preset threshold, but the fluctuation (σ) is large and exceeds the second preset threshold, the system determines that the dominant error is random noise. At this point, the focus is not on using ΔH to "correct" the reading, but on smoothing and filtering to suppress the noise. Here, we use a moving average filter to smooth the height sensor reading H. height Real-time smoothing is performed. The algorithm maintains a fixed-length FIFO (First-In, First-Out) data queue in the PLC. New data from each sample is stored at the tail of the queue, and the oldest data at the head is removed. The arithmetic mean of all data in the queue is then calculated as the effective output of this filtering operation. By taking the average, Hb can be eliminated. height Random noise.

[0049] 3. When the average deviation (μ) is close to zero, the absolute value is less than the first preset threshold, and the fluctuation (σ) is less than the second preset threshold, the system determines that the current state is good and maintains the existing parameters unchanged.

[0050] Based on the characteristics of ΔH, the PLC sends a signal to drive the electric hydraulic cylinder in the lifting assembly to correct and compensate for the measured height value.

[0051] In one embodiment, step S204 further includes: the formula for calculating the second elevation gain / loss data based on the distance data and the climbing angle is as follows: H s = ; Among them, H s This refers to the second lifting height data. Distance data, This refers to the length of the horizontal platform. Angle of ascent.

[0052] In one embodiment, step S206 may further include: if the first lifting height data and the second lifting height data are both lifting height data at the current moment, then obtain the factory standard deviation of each of the distance sensor, angle sensor and displacement sensor; determine the first factory variance based on the factory standard deviation of each of the distance sensor and angle sensor, and determine the second factory variance based on the factory standard deviation of the displacement sensor; determine the first weight corresponding to the first lifting height data and the second weight corresponding to the second lifting height data based on the first factory variance and the second factory variance.

[0053] Specifically, the first and second weights are calculated using factory parameters (factory variance / factory standard deviation). This calculation method is, understandably, a static weight calculation method.

[0054] First, based on the sensor's factory parameters, the factory standard deviations σh and σs of the displacement sensor and slope / distance sensor combination are obtained. Then, the variance σh² of the displacement sensor and the factory variance σs² of the slope / distance sensor combination are calculated.

[0055] The first and second weights are then calculated using the following formulas: The first weight of the displacement sensor, Wh = σs² / (σh² + σs²), and similarly, Ws = σh² / (σh² + σs²). Optionally, the factory variance σ² (including the first and second factory variances) is obtained as follows: Use the standard deviation (σ) or noise variance parameter explicitly given in the sensor datasheet. If the datasheet does not provide it directly, convert it based on the nominal accuracy specification. For example, a common accuracy specification is the error boundary ±a at a specific confidence level. According to industry practice, a is usually taken as 2, from which the standard deviation σ = a / 2 is calculated, and then the variance σ² is obtained.

[0056] Through the above embodiments, the weights are initialized using factory calibration parameters to ensure high-precision fusion capability from the very beginning, avoiding slope deviation caused by weight mismatch in the initial stage and improving deployment reliability.

[0057] In one embodiment, step S206 may further include: if the first elevation data and the second elevation data are both elevation data of the current time series, then calculate the first actual variance of the first elevation data and the second actual variance of the second elevation data in the current time series respectively; based on the first actual variance and the second actual variance, determine the first weight corresponding to the first elevation data and the second weight corresponding to the second elevation data.

[0058] Specifically, the first and second weights are calculated using the current time series' rise and fall data (actual variance). This calculation method is, understandably, a dynamic weighting method.

[0059] However, in practical engineering applications, sensor performance is not static. External environmental interference, sensor temperature drift, aging, and even transient failures can all cause dynamic changes in the actual measurement noise variance. Therefore, it is necessary to incorporate dynamically adjusted weight values.

[0060] First, a sliding time window method is used to maintain a fixed-length first-in-first-out data queue for each sensor (a combination of height sensor and slope / distance sensor), which is the current time series of rise and fall height data. This data queue continuously stores the latest N sampled values ​​of each sensor.

[0061] Subsequently, in each fusion cycle, using the data within that window, the measurement variance σ² of the sensor in the current short period is calculated (including the first and second actual variances). The magnitude of the variance directly reflects the degree of fluctuation in the sensor output: a small variance indicates stable data, low noise, and high reliability; a large variance indicates drastic data fluctuations, potential interference or performance degradation, and low reliability. Similarly, according to the static weight calculation formula Wh=σs² / (σh²+σs²), we can obtain the dynamically adjusted weight values ​​Whi and Wsi, where Whi+Wsi=1.

[0062] Through the above embodiments, online adaptive updates of weights are achieved, real-time responses are made to performance degradation caused by sensor aging, temperature drift, or interference, the robustness of fusion accuracy is improved, and the reliability of slope setting under long-term operation is ensured.

[0063] In one embodiment, reference Figure 3 , Figure 3 Another schematic flowchart of the sensor data processing method according to an embodiment of this application is shown, including: Step S302: Obtain the manufacturing year information of the distance sensor, angle sensor, and displacement sensor respectively.

[0064] Step S304: Based on the manufacturing year information, determine the manufacturing ratio coefficient and the actual ratio coefficient.

[0065] Step S306: Based on the factory ratio coefficient and the actual ratio coefficient, perform a weighted summation of the first actual variance and the first factory variance to obtain the first fused variance.

[0066] Step S308: Based on the factory ratio coefficient and the actual ratio coefficient, the second actual variance and the second factory variance are weighted and summed to obtain the second fused variance.

[0067] Step S310: Based on the first fusion variance and the second fusion variance, determine the first weight corresponding to the first rise and fall height data and the second weight corresponding to the second rise and fall height data.

[0068] Among them, the manufacturing life information refers to the cumulative usage time of each sensor (distance, angle, displacement sensor) from the date of factory calibration to the current operating time, which is used to quantify its aging degree and performance degradation trend.

[0069] The factory ratio coefficient refers to the weighting coefficient (denoted as α) assigned to the factory variance in the initial stage of weight fusion. It decays linearly with the system running time (α=1-t / T), reflecting the degree of confidence in the initial calibration parameters; α=1 at startup and α=0 after the sliding window is filled.

[0070] The actual proportional coefficient refers to the dynamic coefficient (i.e., 1-α) that complements the factory proportional coefficient. It is used for weighted real-time calculation of the actual variance and reflects the system's response strength to the sensor's true performance under the current operating conditions.

[0071] The first fusion variance refers to the comprehensive variance obtained by weighting the actual variance of the distance / angle sensor combination with the factory variance by α and (1-α).

[0072] The second fusion variance refers to the comprehensive variance obtained by weighting the actual variance of the displacement sensor with the factory variance using the same fusion coefficient α.

[0073] Specifically, the system first reads the manufacturing dates recorded in the configuration files of the distance sensor, angle sensor, and displacement sensor, and then automatically calculates the number of years each sensor has been in operation, based on the current system time. For example, if a displacement sensor was manufactured two years ago and the current system has been running for a full year, its manufacturing age is recorded as one year; if an angle sensor was manufactured only six months ago, it is recorded as 0.5 years.

[0074] Subsequently, the factory proportional coefficient and the actual proportional coefficient are set according to the time limit: when the sensor has been running for less than three months, the factory proportional coefficient is one and the actual proportional coefficient is zero; as the usage time increases, the factory proportional coefficient decreases daily according to a linear law, and the actual proportional coefficient increases synchronously and equally; when the sensor has been running for a total of two years, the factory proportional coefficient drops to zero and the actual proportional coefficient rises to one.

[0075] Next, the factory variance values ​​of the distance and angle sensor combination and the displacement sensor are retrieved from the pre-stored data. At the same time, the actual variance values ​​of the distance and angle combination and the displacement sensor are calculated in real time from the sliding window of sensor data collected in the last thirty seconds.

[0076] Then, the factory variance and actual variance of the distance and angle combination are multiplied by the corresponding factory proportional coefficient and actual proportional coefficient, respectively, and then summed to obtain the first fusion variance; similarly, the factory variance and actual variance of the displacement sensor are weighted and summed with the same coefficient to obtain the second fusion variance.

[0077] Finally, based on the principle of the inverse variance method, the first weight corresponding to the first rise and fall height data is obtained by dividing the second fusion variance by the sum of the two fusion variances; the second weight corresponding to the second rise and fall height data is obtained by dividing the first fusion variance by the sum of the two fusion variances.

[0078] Through the above embodiments, the system integrates factory-provided data with real-time operating conditions, taking into account both sensor aging trends and instantaneous states, achieving smooth weight transition and long-term accuracy maintenance, and significantly improving the measurement reliability throughout the system's entire lifecycle.

[0079] Figure 4 This illustration shows a schematic diagram of a sensor data processing apparatus according to an embodiment of the present application. The sensor data processing apparatus 400 includes: The first acquisition module 402 is used to acquire the distance data between the roller assembly and the starting position of the slide rail measured by the distance sensor, the climbing angle between the ramp platform and the horizontal platform measured by the angle sensor, and the first lifting height data measured by the displacement sensor. Calculation module 404 is used to calculate the second elevation and descent height data based on the distance data and the climbing angle; The second acquisition module 406 is used to acquire the first weight corresponding to the first lifting height data and the second weight corresponding to the second lowering height data; The processing module 408 is used to perform a weighted summation of the first elevation height data and the second elevation height data based on the first weight and the second weight to obtain the target elevation height data.

[0080] It is understood that the device in this embodiment corresponds to the sensor data processing method in the above embodiments, and the options in the above embodiments are also applicable to this embodiment, so they will not be described again here.

[0081] This application also provides a terminal device, exemplary of which includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to enable the terminal device to perform the functions of the various modules in the above-described sensor data processing method or sensor data processing apparatus.

[0082] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0083] Memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), and Electrically Erasable Programmable Read-Only Memory (EEPROM). Memory is used to store computer programs, and the processor can execute these programs upon receiving execution instructions.

[0084] This application also provides a computer-readable storage medium for storing computer programs used in the aforementioned terminal devices. For example, the computer-readable storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0085] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that, as an alternative implementation, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0086] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0087] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application.

[0088] The above are merely specific embodiments of this application, but the scope of protection of this application 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 this application should be included within the scope of protection of this application.

Claims

1. A slope climbing test device, characterized in that, include: The bottom support assembly includes a slide rail and a roller assembly that moves horizontally along the slide rail from its starting position. A lifting assembly is installed above the bottom support assembly; A horizontal platform, located above the lifting assembly, is used to support the slope climbing test equipment; The ramp platform is hinged at one end to the horizontal platform and at the other end to the roller assembly. The roller assembly moves horizontally at different positions on the slide rail, causing the ramp platform to form different climbing angles, so that the climbing test equipment can perform climbing tests at different climbing angles and obtain climbing test data.

2. The apparatus according to claim 1, characterized in that, The bottom support assembly is equipped with a distance sensor for measuring the distance of the roller assembly relative to the end of the slide rail; An angle sensor is provided at the hinge point between the ramp platform and the horizontal platform to measure the climbing angle between the ramp platform and the horizontal platform in real time. The lifting assembly is equipped with a displacement sensor for measuring the lifting height.

3. A sensor data processing method, characterized in that, Applied to the apparatus of claim 2, the climbing test data includes the distance of the roller assembly relative to the end of the slide rail, the climbing angle, and the lifting height, and the method includes: The distance data between the roller assembly and the starting position of the slide rail measured by the distance sensor, the climbing angle between the ramp platform and the horizontal platform measured by the angle sensor, and the first lifting height data measured by the displacement sensor are obtained. Based on the distance data and the climbing angle, calculate the second elevation gain / loss data; Obtain the first weight corresponding to the first lifting height data and the second weight corresponding to the second lifting height data; Based on the first weight and the second weight, the first elevation height data and the second elevation height data are weighted and summed to obtain the target elevation height data.

4. The method according to claim 3, characterized in that, The calculation of the second elevation gain / loss data based on the distance data and the climbing angle includes: Determine the distance difference between the distance data and the platform length of the horizontal platform; Determine the sine value of the climbing angle, and use the product of the distance difference and the sine value as the second elevation and descent height data.

5. The method according to claim 3, characterized in that, Obtaining the first weight corresponding to the first elevation height data and the second weight corresponding to the second elevation height data includes: If both the first lifting height data and the second lifting height data are lifting height data at the current moment, then obtain the factory standard deviation of each of the distance sensor, the angle sensor, and the displacement sensor; A first factory variance is determined based on the factory standard deviation of the distance sensor and the angle sensor, and a second factory variance is determined based on the factory standard deviation of the displacement sensor. Based on the first factory variance and the second factory variance, a first weight corresponding to the first lifting height data and a second weight corresponding to the second lifting height data are determined.

6. The method according to claim 5, characterized in that, The method further includes: If both the first elevation and rise data and the second elevation and rise data are elevation and rise data of the current time series, then calculate the first actual variance of the first elevation and rise data and the second actual variance of the second elevation and rise data within the current time series, respectively. Based on the first actual variance and the second actual variance, a first weight corresponding to the first lifting height data and a second weight corresponding to the second lifting height data are determined.

7. The method according to claim 6, characterized in that, The method further includes: Obtain the manufacturing year information of each of the distance sensor, the angle sensor, and the displacement sensor; Based on the aforementioned manufacturing year information, determine the manufacturing ratio coefficient and the actual ratio coefficient; Based on the factory ratio coefficient and the actual ratio coefficient, the first actual variance and the first factory variance are weighted and summed to obtain the first fusion variance. Based on the factory ratio coefficient and the actual ratio coefficient, the second actual variance and the second factory variance are weighted and summed to obtain the second fusion variance. Based on the first fusion variance and the second fusion variance, a first weight corresponding to the first elevation and descent data and a second weight corresponding to the second elevation and descent data are determined.

8. A sensor data processing device, characterized in that, include: The first acquisition module is used to acquire the distance data between the roller assembly and the starting position of the slide rail measured by the distance sensor, the climbing angle between the ramp platform and the horizontal platform measured by the angle sensor, and the first lifting height data measured by the displacement sensor. The calculation module is used to calculate the second elevation gain / loss data based on the distance data and the climbing angle; The second acquisition module is used to acquire the first weight corresponding to the first lifting height data and the second weight corresponding to the second lifting height data; The processing module is used to perform a weighted summation of the first elevation height data and the second elevation height data based on the first weight and the second weight to obtain the target elevation height data.

9. A terminal device, characterized in that, The terminal device includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the sensor data processing method according to any one of claims 3-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed on a processor, implements the sensor data processing method according to any one of claims 3-7.

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