Self-adaptive constant deceleration control method and system for mining lithium battery monorail crane

Abstract

The invention provides a self-adaptive constant deceleration control method and system for a mining lithium battery monorail crane, and relates to the technical field of mining transportation equipment control, and the method comprises the following steps: continuously obtaining multi-source speed information used for representing the speed of the monorail crane, the multi-source speed information comprises a first speed estimated by an inertial measurement unit, a second speed estimated by a driving motor controller, a third speed estimated by an orbit mark point and a fourth speed measured and calculated by a motor speed encoder; obtaining a purity score, an operation state score and a consistency score; according to the purity score, the operation state score and the consistency score, adjusting the contribution weight of each speed in the multi-source speed information; according to each contribution weight, performing weighted fusion on each speed in the multi-source speed information to obtain a fusion speed value; and according to the fusion speed value, the monorail crane is controlled to conduct self-adaptive constant deceleration. The deceleration control precision, the energy recovery efficiency and the riding comfort can be improved.

Description

Technical Field

[0001] This application relates to the field of control technology for mining transportation equipment, and in particular to an adaptive constant deceleration control method and system for a mining lithium battery monorail crane. Background Technology

[0002] In deep mining operations, lithium battery-powered monorail cranes are crucial equipment for transporting heavy objects and personnel. Their operation requires smooth and precise deceleration control based on factors such as load, gradient, and battery power. This system heavily relies on real-time speed signals from the speed encoder on the drive motor to adjust braking force, recover energy, and ensure accurate braking distance. Ideally, the encoder signal should remain clear and stable to guarantee a smooth and safe deceleration process.

[0003] However, the high concentration of coal dust and water vapor in mine tunnels, combined over a long period, easily adheres to the encoder housing, terminals, and signal cable surfaces, gradually eroding the integrity of their insulation and electromagnetic shielding layers. Once the shielding layer suffers microscopic damage, its electromagnetic interference resistance decreases significantly. At this point, the high-frequency switching noise generated by the main drive motor inverter will intrude into the signal cable through electromagnetic coupling, superimposing periodic low-amplitude jitter related to the motor's operating frequency onto the speed pulse. This jitter manifests as minute voltage fluctuations or pulse width changes at the pulse edges, with low amplitude, insufficient to trigger conventional hardware alarms based on signal interruptions or significant anomalies, and difficult to completely filter out by general-purpose digital filters—because its frequency components often overlap with the effective signal or fall outside the filter's attenuation band.

[0004] Therefore, the speed data received by the control system continuously carries periodic errors. In situations requiring precise braking, such as heavy-load downhill driving, these errors can lead to deviations in gradient compensation and load feedforward calculations, thus interfering with the switching logic between regenerative braking and mechanical braking, causing temporary fluctuations in braking force distribution. This results in a "pulsating" effect on the actual deceleration curve, which, over long-term operation, will lead to accelerated gearbox wear, reduced energy recovery efficiency, decreased ride comfort, and potential safety hazards. In summary, in complex electromagnetic environments, the speed data of mining lithium battery monorail cranes is inaccurate due to interference with the motor speed encoder signal, severely impacting deceleration control accuracy, energy recovery efficiency, equipment lifespan, and ride comfort. Summary of the Invention

[0005] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes an adaptive constant deceleration control method and system for mining lithium battery monorails, aiming to solve the technical problem that in complex electromagnetic environments, interference with the motor speed encoder signal leads to inaccurate speed data, which in turn affects deceleration control accuracy, energy recovery efficiency, equipment lifespan, and passenger comfort.

[0006] In a first aspect, this application provides an adaptive constant deceleration control method for a mining lithium battery monorail crane, comprising the following steps:

[0007] Continuously acquire multi-source speed information to characterize the speed of the monorail, including a first speed estimated by an inertial measurement unit, a second speed estimated by a drive motor controller, a third speed estimated by track markers, and a fourth speed measured by a motor speed encoder;

[0008] Obtain a purity score for evaluating the signal purity of the fourth speed, an operating status score for characterizing the monorail's operating status, and a consistency score for evaluating the consistency of each speed in the multi-source speed information;

[0009] The contribution weights of each velocity in the multi-source velocity information are adjusted based on the purity score, operational status score, and consistency score.

[0010] Based on the respective contribution weights, the velocities in the multi-source velocity information are weighted and fused to obtain a fused velocity value.

[0011] Based on the fusion speed value, the monorail is controlled to perform adaptive constant deceleration.

[0012] According to some embodiments of this application, the steps of obtaining a purity score for evaluating the signal purity of the fourth speed, an operating state score for characterizing the monorail's operating state, and a consistency score for evaluating the consistency of each speed in the multi-source speed information include:

[0013] Identify the periodic interference components in the fourth speed that are related to the operating frequency of the drive motor, so as to evaluate the signal purity of the fourth speed and obtain a purity score;

[0014] Determine the current operating status of the monorail and obtain an operating status score;

[0015] A consistency score is obtained by comparing the consistency among the velocities in the multi-source velocity information.

[0016] According to some embodiments of this application, the step of identifying periodic interference components in the fourth speed that are related to the operating frequency of the drive motor, in order to evaluate the signal purity of the fourth speed and obtain a purity score, includes:

[0017] The original pulse signal of the fourth velocity is sampled at high frequency;

[0018] The morphological features are obtained by analyzing the original pulse signal obtained from high-frequency sampling, and the short-term statistics of the morphological features are calculated.

[0019] The short-term statistics are compared with the reference statistics, which are the initial baseline values ​​learned and stored during operation in a clean electromagnetic environment tunnel section after the first installation of the monorail.

[0020] When the deviation of the short-term statistic from the reference statistic exceeds a preset comparison threshold, and the deviation exhibits a periodic pattern synchronized with the real-time operating frequency of the drive motor, a periodic interference component related to the operating frequency of the drive motor is identified, and a purity score is obtained based on the identified periodic interference component.

[0021] According to some embodiments of this application, the step of analyzing the original pulse signal obtained from high-frequency sampling to obtain morphological features and calculating the short-term statistics of the morphological features includes:

[0022] Based on the original pulse signal obtained by high-frequency sampling, the morphological characteristics are obtained by analyzing the slope changes of the rising and falling edges, the pulse width, and the small voltage fluctuations in the flat region of the pulse signal.

[0023] Calculate the short-term statistics of the morphological features.

[0024] According to some embodiments of this application, the step of determining the current operating state of the monorail and obtaining an operating state score includes:

[0025] Obtain the geographical information of the mine roadway where the monorail is located, including the track centerline, gradient change points, and curve curvature;

[0026] Obtain the real-time position coordinates and vehicle attitude information of the monorail;

[0027] Obtain the real-time load value and vehicle tilt angle of the monorail;

[0028] The real-time location coordinates, vehicle posture information, real-time load value, vehicle tilt angle, and geographic information are matched with predefined operating conditions.

[0029] The running status score is obtained based on the matched running condition mapping.

[0030] According to some embodiments of this application, the step of comparing the consistency between the velocities in the multi-source velocity information to obtain a consistency score includes:

[0031] Calculate the median of the first, second, third, and fourth velocities;

[0032] Calculate the deviation between each velocity in the multi-source velocity information and the median;

[0033] The detection result is obtained by detecting whether the deviation exceeds a preset threshold and continues for a preset duration;

[0034] Based on the detection results, a consistency score is output, which characterizes the consistency of each velocity in the multi-source velocity information.

[0035] According to some embodiments of this application, the step of adjusting the contribution weight of each velocity in the multi-source velocity information based on the purity score, operating status score, and consistency score includes:

[0036] The purity score, operational status score, and consistency score are mapped to their contribution to the adjustment of each velocity weight in the multi-source velocity information;

[0037] Based on the contribution level, a preliminary weight is generated for each velocity in the multi-source velocity information;

[0038] The initial weights are normalized to obtain the contribution weights.

[0039] According to some embodiments of this application, the purity score is mapped to the contribution of the fourth velocity weight adjustment, and the contribution of the fourth velocity weight adjustment is positively correlated with the purity score.

[0040] According to some embodiments of this application, the step of weighted fusing of each velocity in the multi-source velocity information according to each of the contribution weights to obtain a fused velocity value includes:

[0041] The adjusted contribution weights and the contribution weights recorded in the previous period are subjected to time-series smoothing filtering to obtain the fusion weights used for the fusion calculation in the current period.

[0042] Based on the fusion weights used for the current cycle fusion calculation, the velocities in the multi-source velocity information are weighted and fused to obtain the fused velocity value.

[0043] Secondly, this application also provides an adaptive constant deceleration control system for a mining lithium battery monorail crane, comprising:

[0044] The speed acquisition module is used to continuously acquire multi-source speed information to characterize the speed of the monorail. The multi-source speed information includes a first speed estimated by an inertial measurement unit, a second speed estimated by a drive motor controller, a third speed estimated by track markers, and a fourth speed estimated by a motor speed encoder.

[0045] The score acquisition module is used to acquire a purity score for evaluating the signal purity of the fourth speed, an operating status score for characterizing the monorail's operating status, and a consistency score for evaluating the consistency of each speed in the multi-source speed information.

[0046] The weighting adjustment module is used to adjust the contribution weight of each velocity in the multi-source velocity information based on the purity score, operating status score, and consistency score.

[0047] The speed fusion module is used to perform weighted fusion of each speed in the multi-source speed information according to the contribution weights, so as to obtain a fused speed value;

[0048] The deceleration control module is used to control the monorail to perform adaptive constant deceleration based on the fused speed value.

[0049] The technical solution according to the embodiments of this application has at least the following beneficial effects:

[0050] This application discloses an adaptive constant deceleration control method for a mining lithium battery monorail. By introducing multi-source speed information, including a first speed, a second speed, a third speed, and a fourth speed, a redundant and complementary speed measurement system is constructed. Simultaneously, three key evaluation indicators—purity score, operating state score, and consistency score—are introduced, and the contribution weight of each speed is dynamically adjusted based on these indicators. This reduces the negative impact of inaccurate speed data caused by interference with the motor speed encoder signal in complex electromagnetic environments. Furthermore, by considering the monorail's operating state (such as load, gradient, and curves) and the consistency between speeds, this application can more comprehensively and accurately assess the reliability of the current speed data and optimize the fusion result accordingly. This dynamic and intelligent fusion strategy enables the application to output a highly accurate and stable fused speed value, thus providing a solid foundation for the adaptive constant deceleration control of the monorail, improving the system's deceleration control accuracy, energy recovery efficiency, and ride comfort, while reducing the impact on equipment lifespan.

[0051] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0052] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0053] Figure 1 This is a flowchart illustrating an adaptive constant deceleration control method for a mining lithium battery monorail crane, as provided in an embodiment of this application.

[0054] Figure 2 This is a schematic diagram of the architecture of an adaptive constant deceleration control system for a mining lithium battery monorail crane, provided in an embodiment of this application. Detailed Implementation

[0055] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application 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 this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this 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.

[0056] 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 application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0057] Traditional lithium-ion battery-powered monorail systems for mining operate in mine tunnels where coal dust and moisture can damage the insulation and electromagnetic shielding of the motor speed encoder and its signal transmission path. This leads to high-frequency electromagnetic interference generated by the drive motor inverter inducing periodic noise related to the drive motor's operating frequency on the motor speed encoder signal cable via electromagnetic coupling. This results in a slight, periodic error in the "real-time speed" data received by the speed closed-loop control system. This error, in turn, affects the accuracy of braking force distribution, causing a slight "pulsation" in the actual deceleration curve, accelerating gear wear, reducing energy recovery efficiency, and impacting ride comfort.

[0058] For this, please refer to Figure 1 This application discloses an adaptive constant deceleration control method for a mining lithium battery monorail crane, comprising the following steps:

[0059] S110, continuously acquire multi-source speed information to characterize the speed of the monorail, the multi-source speed information including a first speed estimated by an inertial measurement unit, a second speed estimated by a drive motor controller, a third speed estimated by track markers, and a fourth speed measured by a motor speed encoder;

[0060] S120, obtain a purity score for evaluating the signal purity of the fourth speed, an operating state score for characterizing the monorail's operating state, and a consistency score for evaluating the consistency of each speed in the multi-source speed information;

[0061] S130, adjust the contribution weight of each velocity in the multi-source velocity information according to the purity score, operating status score and consistency score;

[0062] S140, according to the contribution weights, the velocities in the multi-source velocity information are weighted and fused to obtain the fused velocity value;

[0063] S150, based on the fusion speed value, controls the monorail to perform adaptive constant deceleration.

[0064] It should be noted that, in order to better understand the control method proposed in this application, some key terms and implementation environments involved will be explained first.

[0065] "Multi-source speed information" refers to a collection of speed data acquired from different types of sensors or controllers on the monorail. Its purpose is to improve the accuracy and robustness of speed measurement through information redundancy and complementarity. Specifically, the first speed is estimated by an inertial measurement unit (IMU), which typically includes an accelerometer and gyroscope. Speed ​​is estimated by integrating the acceleration signal. Its advantage is that it does not rely on external references, but integration drift may occur. The second speed is estimated by the drive motor controller. This speed is usually calculated based on the motor's speed and transmission ratio, offering high real-time performance, but may be affected by motor load and electromagnetic interference. The third speed is estimated through track markers, such as by identifying preset markers on the track using visual sensors or RFID readers mounted on the monorail and combining this with time information to calculate the speed. Its advantage is high positioning accuracy, but it may be affected by marker density and identification accuracy. The fourth speed is measured by a motor speed encoder. The encoder directly measures the motor shaft speed, offering high accuracy and is a commonly used speed source in traditional control systems, but it is susceptible to electromagnetic interference.

[0066] The "purity score" is an indicator used to quantify the degree of interference to the fourth velocity signal. The higher the score, the purer the signal and the less interference.

[0067] "Operating status score" is an indicator used to characterize the current operating condition of a monorail, such as no load / full load, uphill / downhill, straight / curved road, etc. Different operating statuses may have different effects on the reliability of speed measurement.

[0068] The "consistency score" is an indicator used to evaluate the degree of consistency between velocities in multi-source velocity information. The higher the score, the better the consistency between velocities and the higher the data reliability.

[0069] "Contribution weight" is a weighting coefficient assigned to each velocity in multi-source velocity information, used to reflect the relative importance and reliability of each velocity during the fusion process.

[0070] The "fusion velocity value" is the final velocity estimate obtained after weighted fusion. This value combines the advantages of multi-source velocity information and has higher accuracy and stability.

[0071] The implementation environment of this application is a mining lithium battery monorail system. This system operates in mine roadways, where the environment is complex and may be affected by various factors such as electromagnetic interference, coal dust, water vapor, slope changes, and curves.

[0072] The specific workflow of the adaptive constant deceleration control method for a mining lithium battery monorail proposed in this application is as follows.

[0073] First, multi-source speed information is continuously acquired. For example, the inertial measurement unit (IMU) can use MEMS (Micro-Electro-Mechanical Systems) inertial sensors to estimate the speed of the monorail by performing Kalman filtering or complementary filtering on acceleration and angular velocity signals, thus calculating the first speed. The drive motor controller can estimate the motor speed by monitoring the motor's back electromotive force or Hall sensor signals, thus calculating the second speed. Track markers can be pre-coded with QR codes or RFID tags on the track; the monorail's vision sensors or RFID readers record timestamps and position information when passing these markers, thereby calculating the third speed. The motor speed encoder can be an optical encoder or a magnetic encoder, directly measuring the motor shaft rotation speed and converting it into pulse signals; the fourth speed is calculated by counting these pulses. This speed information can be acquired at different sampling frequencies and data formats and transmitted to the central control unit for further processing.

[0074] Secondly, purity score, operational status score, and consistency score are obtained. For example, the purity score can be obtained by analyzing the waveform characteristics of the raw pulse signal of the fourth speed, such as by detecting the jitter amplitude of the rising and falling edges of the pulse signal or the stability of the pulse width. The operational status score can be comprehensively judged by integrating the load sensor, tilt sensor, GPS / BeiDou positioning module of the monorail, and a preset roadway geographic information database. For example, when the monorail is in a fully loaded uphill curve condition, a lower operational status score can be assigned, indicating that speed measurement may face greater challenges under this condition. The consistency score can be obtained by comparing the differences between the speeds in the multi-source speed information, such as by calculating the variance or standard deviation between the speeds, or by comparing them pairwise. When the deviation of a speed from other speeds exceeds a preset threshold, its consistency score is reduced.

[0075] Secondly, adjust the contribution weights of each speed in the multi-source speed information. For example, a weight adjustment rule table can be preset, which dynamically adjusts the weights of the first, second, third, and fourth speeds based on different combinations of purity scores, operating status scores, and consistency scores. When the purity score of the fourth speed is low, its contribution weight can be reduced while the weights of other speeds are increased. When the monorail is in a complex operating state (e.g., steep gradient, heavy load), the weights of all speeds can be appropriately reduced while the weights of more reliable speed sources (such as the third speed) can be increased. When the consistency scores among the speeds are low, the weights of all speeds can be reduced, or speeds with larger deviations can be assigned lower weights.

[0076] Then, based on their respective contribution weights, the velocities from the multi-source velocity information are weighted and fused to obtain a fused velocity value. For example, a linear weighted average method can be used for fusion, where the fused velocity value equals the sum of the products of each velocity and its corresponding contribution weight. For instance, the fused velocity value = W1×V1 + W2×V2 + W3×V3 + W4×V4, where W1, W2, W3, and W4 are the contribution weights of the first, second, third, and fourth velocities, respectively, and V1, V2, V3, and V4 are the corresponding velocity values. Before weighted fusion, the units of each velocity can be standardized and the time synchronized to ensure the accuracy of the fusion.

[0077] Finally, based on the merged speed value, the monorail is controlled to perform adaptive constant deceleration. For example, the merged speed value is used as the input to the speed closed-loop control system and compared with a preset deceleration curve to calculate the required braking force. Then, based on the calculated braking force, the regenerative braking power of the motor is adjusted by the drive motor controller, and the braking force of the mechanical brake is adjusted by the braking system controller, thereby achieving adaptive constant deceleration of the monorail. During deceleration, the control system continuously monitors the merged speed value and adjusts the braking force in real time according to the deviation between the actual speed and the target deceleration curve to ensure that the monorail stops smoothly with a constant deceleration.

[0078] This application discloses an adaptive constant deceleration control method for a mining lithium battery monorail. By introducing multi-source speed information, including a first speed, a second speed, a third speed, and a fourth speed, a redundant and complementary speed measurement system is constructed. Simultaneously, three key evaluation indicators—purity score, operating state score, and consistency score—are introduced, and the contribution weight of each speed is dynamically adjusted based on these indicators. This reduces the negative impact of inaccurate speed data caused by interference with the motor speed encoder signal in complex electromagnetic environments. Furthermore, by considering the monorail's operating state (such as load, gradient, and curves) and the consistency between speeds, this application can more comprehensively and accurately assess the reliability of the current speed data and optimize the fusion result accordingly. This dynamic and intelligent fusion strategy enables the application to output a highly accurate and stable fused speed value, thus providing a solid foundation for the adaptive constant deceleration control of the monorail, improving the system's deceleration control accuracy, energy recovery efficiency, and ride comfort, while reducing the impact on equipment lifespan.

[0079] It should be noted that, in some embodiments of this application, the steps of obtaining the purity score for evaluating the signal purity of the fourth speed, the operating state score for characterizing the monorail's operating state, and the consistency score for evaluating the consistency of each speed in the multi-source speed information preferably include:

[0080] Identify the periodic interference components in the fourth speed that are related to the operating frequency of the drive motor, so as to evaluate the signal purity of the fourth speed and obtain a purity score;

[0081] Determine the current operating status of the monorail and obtain an operating status score;

[0082] A consistency score is obtained by comparing the consistency among the velocities in the multi-source velocity information.

[0083] Specifically, identifying periodic interference components related to the drive motor's operating frequency in the fourth speed signal allows for signal purity assessment, resulting in a purity score. This involves analyzing the signal characteristics of the fourth speed, particularly its susceptibility to periodic noise caused by the drive motor's operating frequency, to quantify the purity of the speed signal. For example, when the drive motor operates at a certain frequency, it may introduce periodic fluctuations or noise into the fourth speed measured by the motor speed encoder, reducing its accuracy. By identifying and quantifying these interference components, the signal quality of the fourth speed can be objectively evaluated, providing a basis for subsequent weight adjustments.

[0084] Determining the current operating status of a monorail and obtaining an operating status score can be understood as classifying and scoring its operating status based on the actual working conditions of the monorail. Different operating states, such as straight-line travel, curve travel, uphill travel, downhill travel, and whether it is unloaded or heavily loaded, will affect the reliability of different speed information. Accurately determining the current operating status provides a basis for subsequent weight adjustments, ensuring that reliable speed information is selected for fusion under different working conditions.

[0085] A consistency score is obtained by comparing the consistency of velocity information from multiple sources. Specifically, this involves comparing velocity information from four different sources (first, second, third, and fourth velocities) and assessing the degree of difference between them. Significant deviations between velocity information may indicate a malfunction or data anomaly in one or more sensors. Quantifying this consistency allows for the timely detection of potential data problems, enabling adjustments to the contribution weights of each velocity to prevent anomalous data from negatively impacting the fused velocity value.

[0086] This application's solution refines the steps for obtaining purity scores, operational status scores, and consistency scores, enabling a more comprehensive and accurate assessment of the quality and applicability of multi-source speed information. Specifically, by identifying periodic interference components in the fourth speed related to the drive motor's operating frequency, distortion of the fourth speed caused by motor noise can be effectively avoided, thus ensuring the objectivity of the purity score. Simultaneously, by determining the current operational status of the monorail, the system can dynamically adjust the level of trust in different speed information based on actual operating conditions; for example, when traveling on curves or slopes, some speed sources may be more reliable than others. Furthermore, by comparing the consistency between speeds in the multi-source speed information, abnormal data can be promptly identified and eliminated, preventing a single fault source from severely impacting the overall speed estimation. Therefore, these refined assessment steps work together to provide a more solid and reliable data foundation for subsequent weighted fusion.

[0087] In a specific embodiment of this application, the step of identifying periodic interference components related to the operating frequency of the drive motor in the fourth speed, and evaluating the signal purity of the fourth speed to obtain a purity score, preferably includes:

[0088] The original pulse signal of the fourth velocity is sampled at high frequency; the morphological features of the original pulse signal obtained by high-frequency sampling are analyzed, and the short-term statistics of the morphological features are calculated; the short-term statistics are compared with reference statistics, which are initial benchmark values ​​learned and stored during operation in a clean electromagnetic environment tunnel section after the first installation of the monorail; when the deviation of the short-term statistics relative to the reference statistics exceeds a preset comparison threshold, and the deviation exhibits a periodic pattern synchronized with the real-time operating frequency of the drive motor, periodic interference components related to the operating frequency of the drive motor are identified, and a purity score is obtained based on the identified periodic interference components.

[0089] The raw pulse signal for the fourth speed refers to the raw electrical signal measured by the motor speed encoder. It is typically output as a pulse sequence to characterize the rotational speed of the drive motor, thus reflecting the operating speed of the monorail. High-frequency sampling of this raw pulse signal aims to capture subtle changes and potential interference information, ensuring the accuracy of subsequent analysis. High-frequency sampling can be understood as digitizing the signal at a sampling rate much higher than the Nyquist frequency to preserve the integrity and detail of the signal.

[0090] The morphological characteristics of the original pulse signal obtained from high-frequency sampling are analyzed, and short-term statistics of these morphological characteristics are calculated. Morphological characteristics may include, but are not limited to, changes in the slope of the pulse's rising and falling edges, pulse width, and minute voltage fluctuations in the flat region of the pulse. These morphological characteristics can reflect the signal quality and the presence of anomalies. Short-term statistics involve statistically analyzing the values ​​of these morphological characteristics within a short time window, such as calculating the mean, variance, kurtosis, and skewness, to quantify the instantaneous characteristics of the signal.

[0091] The short-term statistics will be compared with reference statistics. These reference statistics are initial baseline values ​​learned and stored during operation in a clean electromagnetic environment within a tunnel section after the monorail's initial installation. These baseline values ​​represent the normal morphological characteristics of the original pulse signal of the fourth velocity under ideal or near-ideal operating conditions. By comparing these values ​​with the baseline values, abnormal signal conditions under the current operating state can be effectively identified.

[0092] When the deviation of the short-term statistic from the reference statistic exceeds a preset comparison threshold, and the deviation exhibits a periodic pattern synchronized with the real-time operating frequency of the drive motor, a periodic interference component related to the drive motor's operating frequency can be identified. The preset comparison threshold is used to define the significance of the deviation, while the synchronization of the periodic pattern with the real-time operating frequency of the drive motor is the key basis for determining the source of the interference. Once such a periodic interference component is identified, a purity score can be obtained based on its intensity, frequency, duration, and other characteristics. The purity score can be a quantitative indicator; for example, the more significant the interference, the lower the purity score, and vice versa.

[0093] This application's solution employs high-frequency sampling of the raw pulse signal of the fourth speed and in-depth analysis of its morphological characteristics and short-term statistics to precisely capture abnormal fluctuations in the signal. By comparing these short-term statistics with reference statistics established under ideal conditions, the degree of signal deviation can be effectively detected. More importantly, when this deviation not only exceeds a preset threshold but also exhibits a periodic pattern synchronized with the real-time operating frequency of the drive motor, this solution can accurately identify periodic interference generated by the drive motor itself. This interference is often caused by factors such as motor commutation and electromagnetic coupling, affecting the accuracy of speed measurement. Through this mechanism, this solution can isolate motor-related interference from complex signals, thereby objectively and accurately assessing the signal purity of the fourth speed and providing a more reliable input for subsequent speed fusion.

[0094] In a further embodiment of this application, the step of analyzing the original pulse signal obtained from high-frequency sampling to obtain morphological features and calculating the short-term statistics of the morphological features preferably includes:

[0095] Based on the original pulse signal obtained by high-frequency sampling, the morphological characteristics are obtained by analyzing the slope changes of the rising and falling edges, the pulse width, and the small voltage fluctuations in the flat region of the pulse signal.

[0096] Calculate the short-term statistics of the morphological features.

[0097] The morphological characteristics refer to the patterns or attributes extracted from the high-frequency sampling of the original pulse signal of the fourth velocity through in-depth analysis of these sampled data, which reflect signal quality and interference. Specifically, the slope changes of the rising and falling edges can reflect the transient response characteristics of the signal and the presence of glitches; the pulse width is related to the signal duration, and abnormal pulse widths may indicate signal distortion; small voltage fluctuations in the flat region of the pulse can reveal the noise level of the signal in a steady state. Comprehensive analysis of these morphological characteristics helps to fully assess the purity of the signal.

[0098] The short-term statistics are statistical indicators that quantify the aforementioned morphological characteristics within a short time window. For example, the mean, variance, kurtosis, skewness, etc., of these morphological characteristics can be calculated, or methods such as moving averages and exponential smoothing can be used to obtain their short-term trends. These short-term statistics can reflect the quality changes of the original pulse signal in real time and dynamically, providing a quantitative basis for subsequent interference identification.

[0099] This application's solution, through detailed morphological feature analysis of the original pulse signal obtained from high-frequency sampling and calculation of the short-term statistics of these morphological features, can more accurately capture possible abnormal fluctuations and periodic interference in the fourth velocity signal. By analyzing the slope changes of the rising and falling edges, pulse width, and minute voltage fluctuations in the pulse flat region, the fine structure of the signal can be characterized from multiple dimensions, thereby more sensitively detecting periodic interference components caused by the operating frequency of the drive motor. These short-term statistics of morphological features provide real-time, quantitative signal quality indicators, providing a solid data foundation for subsequent comparison with reference statistics, making interference identification more accurate and reliable.

[0100] It should be noted that, in some embodiments of this application, the step of determining the current operating state of the monorail and obtaining an operating state score preferably includes:

[0101] Obtain the geographical information of the mine roadway where the monorail is located, including the track centerline, gradient change points, and curve curvature;

[0102] Obtain the real-time position coordinates and vehicle attitude information of the monorail;

[0103] Obtain the real-time load value and vehicle tilt angle of the monorail;

[0104] The real-time location coordinates, vehicle posture information, real-time load value, vehicle tilt angle, and geographic information are matched with predefined operating conditions.

[0105] The running status score is obtained based on the matched running condition mapping.

[0106] This involves acquiring the geographical information of the mine roadway where the monorail is located. This geographical information includes the track centerline, gradient change points, and curve curvature. It refers to obtaining data describing the static characteristics of the monorail's operating environment through pre-surveyed mapping or real-time data collection using onboard sensors. The track centerline precisely defines the monorail's travel path, gradient change points indicate the undulations of the roadway terrain, and curve curvature reflects the degree of sharpness of track turns. This geographical information is fundamental data for assessing the complexity of the monorail's operating environment.

[0107] Obtaining the real-time position coordinates and vehicle attitude information of a monorail refers to using a Global Positioning System (GPS), Inertial Navigation System (INS), or track marker-based positioning technology to acquire the precise three-dimensional position of the monorail in the tunnel. Simultaneously, sensors such as Inertial Measurement Units (IMUs) are used to acquire the real-time attitude of the monorail in three-dimensional space, such as pitch, roll, and yaw angles. This real-time data is crucial for dynamically assessing the operational status of the monorail.

[0108] Obtaining the real-time load value and tilt angle of a monorail involves monitoring the weight of materials or personnel carried by the monorail in real time using equipment such as load cells installed on the monorail. Simultaneously, tilt sensors are used to obtain the tilt angle of the monorail body relative to the horizontal plane. The load value and tilt angle directly affect the monorail's center of gravity position and stability, and are key parameters for assessing its operational risks.

[0109] Matching the real-time location coordinates, vehicle posture information, real-time load value, vehicle tilt angle, and geographical information with predefined operating conditions involves comparing the multi-dimensional real-time data with a series of pre-defined typical operating modes. These predefined operating conditions can cover various scenarios such as unloaded level roadway operation, heavy-load uphill operation, unloaded sharp curve operation, and heavy-load downhill operation. Through pattern recognition or machine learning algorithms, the current real-time parameters of the monorail are associated with the most suitable predefined operating conditions.

[0110] The operation status score is obtained by mapping the matched operating conditions. This means converting the identified specific operating conditions into a quantified operation status score based on the matching results. This score aims to characterize the complexity, potential risks, or weight of the current operating status on the deceleration control strategy. For example, a stable, low-risk operating condition may correspond to a higher operation status score, while a complex, high-risk operating condition may correspond to a lower score.

[0111] This application's solution comprehensively acquires the geographical information of the mine roadway where the monorail is located, the real-time position coordinates and vehicle attitude information of the monorail, the real-time load value and vehicle tilt angle of the monorail, and matches them with predefined operating conditions. This enables a comprehensive and accurate identification of the specific operating environment and load conditions of the monorail. Therefore, it avoids the limitations of judging the operating status based on only a single or limited parameter. For example, considering only speed or acceleration may not fully reflect the actual operating risks of the monorail under complex terrain or heavy load conditions. By mapping the matched operating conditions to operating status scores, a refined basis is provided for adjusting the contribution weight of each speed in the subsequent multi-source speed information, allowing the deceleration control strategy to better adapt to the actual operating conditions of the monorail.

[0112] In some embodiments of this application, the step of comparing the consistency between the velocities in the multi-source velocity information to obtain a consistency score preferably includes:

[0113] Calculate the median of the first, second, third, and fourth velocities;

[0114] Calculate the deviation between each velocity in the multi-source velocity information and the median;

[0115] The detection result is obtained by detecting whether the deviation exceeds a preset threshold and continues for a preset duration;

[0116] Based on the detection results, a consistency score is output, which characterizes the consistency of each velocity in the multi-source velocity information.

[0117] Specifically, after acquiring multi-source velocity information, the median of the first, second, third, and fourth velocities is first calculated. The median, as a robust measure of central tendency, effectively avoids interference from extreme outliers in the overall assessment. Compared to the average, it is less sensitive to outliers and is more suitable for consistency assessment of multi-source heterogeneous data. Subsequently, the deviation between each velocity in the multi-source velocity information and the median is calculated. This deviation quantifies the degree of deviation of each velocity value from the overall trend. Further, the deviation is detected to determine whether it exceeds a preset threshold and persists for a preset duration. The preset threshold defines the acceptable range of velocity differences, while the preset duration distinguishes between instantaneous fluctuations and persistent anomalies, avoiding misjudgments of consistency due to brief signal interference or data jumps. Thus, by comprehensively considering the magnitude and duration of the deviation, a more reliable detection result can be obtained. Finally, based on the detection results, a consistency score characterizing the consistency of each velocity in the multi-source velocity information is output. For example, a high consistency score can be output when the detection results show that the deviations of all speeds are within the threshold and do not continuously exceed the limit; otherwise, a low consistency score is output if there are deviations that continuously exceed the limit.

[0118] This application's solution effectively addresses the robustness limitations of traditional simple comparison methods when facing complex mining environments and multi-source sensor data by introducing the median as a benchmark and combining it with the magnitude and duration of deviations for detection. Specifically, the use of the median makes the consistency assessment less susceptible to the influence of momentary failures or large outliers in a single or a few sensors, thereby improving the stability of the assessment. Simultaneously, by setting preset thresholds and preset durations, short-term, minor data fluctuations can be effectively filtered out, identifying only persistent and significant inconsistencies, avoiding misjudgments, and ensuring that only when there is indeed a persistent inconsistency in multi-source velocity information will it be accurately reflected in the consistency score. This mechanism guarantees that the obtained consistency score can more realistically and accurately reflect the overall reliability of multi-source velocity information, providing high-quality input for subsequent contribution weight adjustment and weighted fusion.

[0119] The following is a specific example to illustrate this.

[0120] At a certain moment during the operation of the monorail, the first speed estimated by the inertial measurement unit is 10.2 m / s, the second speed estimated by the drive motor controller is 10.0 m / s, the third speed estimated by the track marker is 10.1 m / s, and the fourth speed measured by the motor speed encoder is 10.0 m / s.

[0121] First, calculate the median of the four velocity values. Sort the velocity values ​​as: 10.0, 10.0, 10.1, 10.2. The median is (10.0 + 10.1) / 2 = 10.05 m / s.

[0122] Next, the deviation between each speed and the median is calculated.

[0123] Deviation of the first velocity: |10.2 - 10.05| = 0.15 m / s

[0124] The deviation of the second velocity: |10.0 - 10.05| = 0.05 m / s

[0125] The deviation of the third velocity: |10.1 - 10.05| = 0.05 m / s

[0126] The deviation of the fourth velocity: |10.0 - 10.05| = 0.05 m / s

[0127] If the preset threshold is 0.1 m / s and the preset duration is 0.5 seconds, in this example, the deviation of the first speed of 0.15 m / s exceeds the preset threshold of 0.1 m / s. If this exceedance does not last for the preset duration, it may be considered a momentary fluctuation, and the consistency score may remain high. However, if the deviation lasts for more than 0.5 seconds, the detection result will indicate inconsistency. Based on the detection result, the system will output the corresponding consistency score. For example, if all deviations are within the threshold or the exceedance time does not reach the preset duration, the consistency score may be 0.9; if there is a sustained exceedance deviation, the consistency score may decrease to 0.6 to reflect poor consistency of the current speed information.

[0128] In some embodiments of this application, the step of adjusting the contribution weight of each velocity in the multi-source velocity information based on the purity score, operating status score, and consistency score preferably includes:

[0129] The purity score, operational status score, and consistency score are mapped to their contribution to the adjustment of each velocity weight in the multi-source velocity information;

[0130] Based on the contribution level, a preliminary weight is generated for each velocity in the multi-source velocity information;

[0131] The initial weights are normalized to obtain the contribution weights.

[0132] Specifically, when adjusting the contribution weights of each velocity in multi-source velocity information, the first step is to convert the obtained purity score, operational status score, and consistency score into contributions to the weight adjustment of each velocity. The contribution can be understood as the degree of influence each score has on the final weight allocation. For example, the purity score may primarily affect the weight contribution of the fourth velocity, while the operational status score and consistency score may affect the weight contribution of all velocities. This mapping relationship can be predefined, for example, through table lookups, piecewise functions, or rules based on expert experience.

[0133] After obtaining the contribution of each velocity weight adjustment, preliminary weights for each velocity in the multi-source velocity information are generated based on these contributions. These preliminary weights are calculated based on the direct impact of each score on the velocity weights and have not yet undergone uniform proportional adjustment. For example, an initial weight value can be assigned to each velocity based on its contribution; the higher the contribution, the larger the corresponding preliminary weight.

[0134] To ensure that the sum of the contribution weights of all speeds is 1 (or a preset constant), the initial weights need to be normalized. Normalization involves scaling the initial weights according to a certain ratio so that their sum meets a preset condition. For example, all initial weights can be added together, and then each initial weight can be divided by the sum to obtain the final contribution weight. Normalization ensures that the contribution weights of each speed are within a reasonable range and accurately reflect their relative importance.

[0135] This application's solution maps purity scores, operational status scores, and consistency scores to their contribution to the adjustment of each speed weight. Based on this, preliminary weights are generated and normalized, enabling refined and adaptive adjustment of the contribution weights of each speed in multi-source speed information. This step-by-step processing method allows each evaluation score to independently and effectively influence its corresponding speed weight, avoiding the complexity and inaccuracies that may arise from direct calculation. By introducing contribution values, the degree of influence of different scores on different speed weights can be flexibly configured. For example, when the signal purity of the fourth speed is high, it can be assigned a higher contribution value, thus obtaining a greater weight during the fusion process. The normalization process ensures the rationality and stability of the weight allocation, enabling the fused speed value to more accurately reflect the true speed of the monorail.

[0136] In a preferred embodiment of this application, the purity score is mapped to the contribution of the fourth velocity weight adjustment, and the contribution of the fourth velocity weight adjustment is positively correlated with the purity score.

[0137] Specifically, the purity score is an indicator used to evaluate the purity of the fourth velocity signal. A higher purity signal results in a higher purity score, and vice versa. Mapping the purity score to its contribution to the weight adjustment of the fourth velocity means that the purity score directly affects the weight of the fourth velocity in the weighted fusion of multi-source velocity information. A positive correlation indicates that a higher purity score means a greater contribution of the fourth velocity to the fusion process, thus enhancing its influence in the final fused velocity value; conversely, a lower purity score means a smaller contribution and weaker influence. This mapping relationship ensures that the reliability of the fourth velocity matches its practical application value.

[0138] The above technical solution ensures that the weight adjustment of the fourth velocity during the weighted fusion of multi-source velocity information fully reflects its signal purity. This adaptive weight adjustment mechanism significantly improves the accuracy and reliability of the fused velocity values. Especially in the complex electromagnetic environment of mines, it can effectively cope with the periodic interference that may occur in the fourth velocity, thereby providing a more accurate speed input for the adaptive constant deceleration control of the monorail crane and improving the stability and safety of the control system.

[0139] The following is a specific example to illustrate this.

[0140] During the operation of the monorail, by performing high-frequency sampling and morphological feature analysis on the original pulse signal of the fourth speed, the calculated short-term statistics are compared with reference statistics to identify periodic interference components synchronized with the operating frequency of the drive motor, resulting in a decrease in the purity score. For example, the purity score drops from 0.9 to 0.5. According to this embodiment, since the purity score is positively correlated with the contribution of the fourth speed weight adjustment, when the purity score drops from 0.9 to 0.5, the system will correspondingly reduce the contribution weight of the fourth speed in the weighted fusion of multi-source speed information. For example, if the initial weight is 0.4, its contribution weight may be adjusted to 0.2 after the purity score decreases. This adjustment reduces the impact of the fourth speed signal quality on the final fused speed value, thereby avoiding a large deviation in the fused speed value due to the interference of the fourth speed data. Conversely, if the purity score increases from 0.5 to 0.9, the contribution weight of the fourth speed will increase accordingly, allowing it to play a greater role in the fusion and utilize its high-precision recovery characteristics.

[0141] In a specific embodiment of this application, the step of weighted fusion of the velocities in the multi-source velocity information according to their respective contribution weights to obtain the fused velocity value preferably includes:

[0142] The adjusted contribution weights and the contribution weights recorded in the previous period are subjected to time-series smoothing filtering to obtain the fusion weights used for the fusion calculation in the current period.

[0143] Based on the fusion weights used for the current cycle fusion calculation, the velocities in the multi-source velocity information are weighted and fused to obtain the fused velocity value.

[0144] The time-series smoothing filtering process refers to applying a weighted average, low-pass filter, or other smoothing method to the adjusted contribution weights calculated in the current cycle and the contribution weights recorded in one or more previous cycles. Its purpose is to eliminate high-frequency noise or instantaneous fluctuations in the contribution weights, making their changes smoother and more stable. For example, first-order lag filtering, moving average filtering, or Kalman filtering can be used to process the contribution weights. Through this processing, fused weights for the current cycle's fusion calculation can be obtained, which have better temporal continuity and stability compared to the unsmoothed contribution weights. The contribution weights recorded in the previous cycle can be understood as contribution weight values ​​that underwent the same adjustment logic and may have been smoothed in the immediately preceding control cycle; these are stored for use in the time-series smoothing filtering calculation of the current cycle.

[0145] The proposed solution effectively addresses the potential for instantaneous fluctuations in contribution weights by introducing time-series smoothing filtering. Specifically, when purity scores, operational status scores, and consistency scores fluctuate rapidly due to external disturbances or instantaneous changes within the system, directly using these volatile weights for speed fusion would lead to unstable jumps in the fused speed value. By applying time-series smoothing filtering to the adjusted contribution weights and those recorded in the previous cycle, such instantaneous fluctuations can be effectively suppressed, resulting in smoother and more continuous changes in the fusion weights used for actual fusion calculations. It is precisely this stability of the fusion weights that makes the final fused speed value more stable, thus providing a more reliable and continuous speed input for the adaptive constant deceleration control of the monorail crane.

[0146] It should be noted that the temporal smoothing filtering process can be implemented using a first-order lag filtering algorithm. Specifically, let the adjusted contribution weight of the current period be V, and the fusion weight recorded in the previous period be W0. Then, the fusion weight W of the current period can be calculated using the following formula: W = α × V + (1 - α) × W0. Here, α is a smoothing coefficient between 0 and 1, and its value can be set according to the actual application scenario and the requirements for response speed and smoothness. For example, when α is small, the smoothing effect is more significant, but the response speed will be relatively slower; when α is large, the response speed is faster, but the smoothing effect will be weakened. In this way, the new fusion weight W not only reflects the currently adjusted contribution weight V, but also considers the historical fusion weight W0, thus achieving temporal smoothing of the weights. Subsequently, based on these smoothed fusion weights, the velocities in the multi-source velocity information are weighted and fused to obtain the final fused velocity value.

[0147] Please refer to Figure 2 This application also discloses an adaptive constant deceleration control system 200 for a mining lithium battery monorail crane, comprising:

[0148] The speed acquisition module 210 is used to continuously acquire multi-source speed information to characterize the speed of the monorail. The multi-source speed information includes a first speed estimated by an inertial measurement unit, a second speed estimated by a drive motor controller, a third speed estimated by track markers, and a fourth speed measured by a motor speed encoder.

[0149] The score acquisition module 220 is used to acquire a purity score for evaluating the signal purity of the fourth speed, an operating status score for characterizing the monorail's operating status, and a consistency score for evaluating the consistency of each speed in the multi-source speed information.

[0150] The weight adjustment module 230 is used to adjust the contribution weight of each velocity in the multi-source velocity information according to the purity score, the operating status score and the consistency score.

[0151] The speed fusion module 240 is used to perform weighted fusion of each speed in the multi-source speed information according to the contribution weights to obtain a fused speed value;

[0152] The deceleration control module 250 is used to control the monorail to perform adaptive constant deceleration based on the fused speed value.

[0153] To better understand the control system proposed in this application, the implementation methods of some key modules involved are explained below.

[0154] The speed acquisition module 210 can be configured as a hardware unit containing multiple physical sensors and data acquisition interfaces. For example, the inertial measurement unit can be a standalone IMU sensor module connected to the central control unit via a CAN bus or Ethernet interface; the drive motor controller can be a frequency converter with integrated speed estimation function, outputting a second speed via an industrial Ethernet or RS485 interface; the track marker estimation device can be a vision sensor or RFID reader, working with corresponding image processing or RFID identification hardware to transmit a third speed data to the system; the motor speed encoder can be a photoelectric encoder or magnetic encoder mounted on the motor shaft, whose pulse signal is converted into a fourth speed via a high-speed counter or dedicated signal conditioning circuit. These hardware units work together to ensure continuous, real-time acquisition of multi-source speed information.

[0155] The score acquisition module 220 can be implemented as a software program running on a central processing unit (e.g., an industrial PC, embedded controller, or high-performance PLC). This program receives raw speed data from the speed acquisition module and performs signal processing, state judgment, and consistency comparison algorithms. For example, for signal purity assessment of the fourth speed, the module can include a high-frequency sampling circuit and a digital signal processor to analyze the morphological characteristics of the raw pulse signal; for acquiring the running state score, the module can integrate a Geographic Information System (GIS) interface, a load sensor interface, and an attitude sensor interface, and run a state matching algorithm; for acquiring the consistency score, the module can perform statistical analysis algorithms such as median calculation and deviation detection.

[0156] The weight adjustment module 230 can be implemented as a decision algorithm or rule engine running on the central processing unit. This module receives the purity score, running status score, and consistency score output by the score acquisition module as input, and dynamically calculates the contribution weight of each velocity in the multi-source velocity information according to a preset weight adjustment strategy or adaptive learning algorithm. For example, a fuzzy logic controller, a neural network model, or an expert-based lookup table can be used to achieve real-time weight adjustment.

[0157] The speed fusion module 240 can be implemented as a data processing algorithm running on the central processing unit. This module receives the contribution weights output by the weight adjustment module and the multi-source speed information provided by the speed acquisition module, and performs a weighted fusion operation. For example, algorithms such as linear weighted averaging, Kalman filtering, extended Kalman filtering, or unscented Kalman filtering can be used to optimize and combine speed data from different sources and with different reliability, generating a more accurate and stable fused speed value.

[0158] The deceleration control module 250 can be implemented as a closed-loop control algorithm running on a dedicated motion controller or main control unit. This module receives the fused speed value output from the speed fusion module and compares it with a preset deceleration curve to calculate the required braking force. Then, the module adjusts the regenerative braking power of the drive motor and the braking force of the mechanical brake by outputting control commands, such as PWM signals or digital communication commands. For example, it can communicate with the frequency converter and brake actuator via CAN bus or Ethernet / IP protocol to achieve precise adaptive constant deceleration control of the monorail.

[0159] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0160] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An adaptive constant deceleration control method for a mining lithium battery monorail crane, characterized in that, Includes the following steps: Continuously acquire multi-source speed information to characterize the speed of the monorail, including a first speed estimated by an inertial measurement unit, a second speed estimated by a drive motor controller, a third speed estimated by track markers, and a fourth speed measured by a motor speed encoder; Obtain a purity score for evaluating the signal purity of the fourth speed, an operating status score for characterizing the monorail's operating status, and a consistency score for evaluating the consistency of each speed in the multi-source speed information; The contribution weights of each velocity in the multi-source velocity information are adjusted based on the purity score, operational status score, and consistency score. Based on the respective contribution weights, the velocities in the multi-source velocity information are weighted and fused to obtain a fused velocity value; Based on the fusion speed value, the monorail is controlled to perform adaptive constant deceleration.

2. The adaptive constant deceleration control method for a mining lithium battery monorail crane according to claim 1, characterized in that, The steps of obtaining the purity score for evaluating the signal purity of the fourth speed, the operating state score for characterizing the monorail's operating state, and the consistency score for evaluating the consistency of each speed in the multi-source speed information include: Identify the periodic interference components in the fourth speed that are related to the operating frequency of the drive motor, so as to evaluate the signal purity of the fourth speed and obtain a purity score; Determine the current operating status of the monorail and obtain an operating status score; By comparing the consistency among the velocities in the multi-source velocity information, a consistency score is obtained.

3. The adaptive constant deceleration control method for a mining lithium battery monorail crane according to claim 2, characterized in that, The step of identifying periodic interference components in the fourth speed that are related to the operating frequency of the drive motor, and evaluating the signal purity of the fourth speed to obtain a purity score, includes: The original pulse signal of the fourth velocity is sampled at high frequency; The morphological features are obtained by analyzing the original pulse signal obtained from high-frequency sampling, and the short-term statistics of the morphological features are calculated. The short-term statistics are compared with the reference statistics, which are the initial baseline values ​​learned and stored during operation in a clean electromagnetic environment tunnel section after the first installation of the monorail. When the deviation of the short-term statistic from the reference statistic exceeds a preset comparison threshold, and the deviation exhibits a periodic pattern synchronized with the real-time operating frequency of the drive motor, a periodic interference component related to the operating frequency of the drive motor is identified, and a purity score is obtained based on the identified periodic interference component.

4. The adaptive constant deceleration control method for a mining lithium battery monorail crane according to claim 3, characterized in that, The steps of analyzing the original pulse signal obtained from high-frequency sampling to obtain morphological features and calculating the short-term statistics of the morphological features include: Based on the original pulse signal obtained by high-frequency sampling, the morphological characteristics are obtained by analyzing the slope changes of the rising and falling edges, the pulse width, and the small voltage fluctuations in the flat region of the pulse signal. Calculate the short-term statistics of the morphological features.

5. The adaptive constant deceleration control method for a mining lithium battery monorail crane according to claim 2, characterized in that, The step of determining the current operating status of the monorail and obtaining the operating status score includes: Obtain the geographical information of the mine roadway where the monorail is located, including the track centerline, gradient change points, and curve curvature; Obtain the real-time position coordinates and vehicle attitude information of the monorail; Obtain the real-time load value and vehicle tilt angle of the monorail; The real-time location coordinates, vehicle posture information, real-time load value, vehicle tilt angle, and geographic information are matched with predefined operating conditions. The running status score is obtained based on the matched running condition mapping.

6. The adaptive constant deceleration control method for a mining lithium battery monorail crane according to claim 2, characterized in that, The step of comparing the consistency between the velocities in the multi-source velocity information to obtain a consistency score includes: Calculate the median of the first, second, third, and fourth velocities; Calculate the deviation between each velocity in the multi-source velocity information and the median; The detection result is obtained by detecting whether the deviation exceeds a preset threshold and continues for a preset duration; Based on the detection results, a consistency score is output, which characterizes the consistency of each velocity in the multi-source velocity information.

7. The adaptive constant deceleration control method for a mining lithium battery monorail crane according to claim 1, characterized in that, The steps for adjusting the contribution weights of each velocity in the multi-source velocity information based on the purity score, operating status score, and consistency score include: The purity score, operational status score, and consistency score are mapped to their contribution to the adjustment of each velocity weight in the multi-source velocity information; Based on the contribution level, a preliminary weight is generated for each velocity in the multi-source velocity information; The initial weights are normalized to obtain the contribution weights.

8. The adaptive constant deceleration control method for a mining lithium battery monorail crane according to claim 7, characterized in that, The purity score is mapped to the contribution of the fourth velocity weight adjustment, and the contribution of the fourth velocity weight adjustment is positively correlated with the purity score.

9. The adaptive constant deceleration control method for a mining lithium battery monorail crane according to claim 1, characterized in that, The step of weightedly fusing the velocities in the multi-source velocity information according to their respective contribution weights to obtain the fused velocity value includes: The adjusted contribution weights and the contribution weights recorded in the previous period are subjected to time-series smoothing filtering to obtain the fusion weights used for the fusion calculation in the current period. Based on the fusion weights used for the current cycle fusion calculation, the velocities in the multi-source velocity information are weighted and fused to obtain the fused velocity value.

10. An adaptive constant deceleration control system for a mining lithium battery monorail crane, characterized in that, include: The speed acquisition module is used to continuously acquire multi-source speed information to characterize the speed of the monorail. The multi-source speed information includes a first speed estimated by an inertial measurement unit, a second speed estimated by a drive motor controller, a third speed estimated by track markers, and a fourth speed estimated by a motor speed encoder. The score acquisition module is used to acquire a purity score for evaluating the signal purity of the fourth speed, an operating status score for characterizing the monorail's operating status, and a consistency score for evaluating the consistency of each speed in the multi-source speed information. The weighting adjustment module is used to adjust the contribution weight of each velocity in the multi-source velocity information based on the purity score, operating status score, and consistency score. The speed fusion module is used to perform weighted fusion of each speed in the multi-source speed information according to the contribution weights, so as to obtain a fused speed value; The deceleration control module is used to control the monorail to perform adaptive constant deceleration based on the fused speed value.

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