Coal resource scheduling method and system

Abstract

The invention relates to the technical field of coal resource scheduling, in particular to a coal resource scheduling method and system. Comprising the steps that an operation state signal, the theoretical rotating speed of a driving motor, the theoretical belt speed obtained through conversion, the instantaneous material weight per unit length output by a weighing device and economic parameters are obtained; according to the running state signal, the actual running speed of the conveying belt is calculated; based on the actual operation speed and the instantaneous weight of the material per unit length, the conveying capacity deviation is calculated; extra energy consumption in the monitoring period is calculated; calculating the consumption of the environment-friendly agent, and accounting the downstream compensation cost in combination with economic parameters; and generating an economic benefit and energy consumption loss report, and outputting a maintenance suggestion or a scheduling correction instruction. The method aims at solving the problems that in an existing coal resource scheduling method, due to the fact that conveying capacity deviation cannot be recognized in time, calculation errors of the instantaneous conveying capacity are caused, and finally the optimization effect of a scheduling system and clean and efficient utilization of coal are affected.

Description

Technical Field

[0001] This application relates to the field of coal resource scheduling technology, and more specifically, to a coal resource scheduling method and system. Background Technology

[0002] In large-scale industrial production, coal resource scheduling relies on the premise of stable and predictable maximum conveying capacity of key equipment (especially the main conveyor belt). Based on this, the scheduling and optimization model calculates the theoretical belt speed using the drive motor speed, and combines it with weighing data to plan transportation time, mixing ratio, and material rhythm, assuming that the actual belt speed equals the theoretical speed.

[0003] However, prolonged heavy-load operation causes uneven wear on the rubber coating of the drive roller, reducing friction between the roller and the belt and resulting in slight and continuous slippage. This causes the actual belt speed to be lower than the theoretical speed. Current monitoring relies heavily on motor current and speed, lacking both independent belt surface speed measurement and a path to identify small, persistent deviations, making it difficult to detect this sub-optimal condition in a timely manner. This leads to a chain reaction of errors: the scheduling system calculates instantaneous delivery volume using an inflated theoretical speed, resulting in a systematically overestimated delivery data; the optimization program allocates slightly overcapacity planned tasks accordingly; downstream mixing silos / boiler silos experience material shortages, forcing operators to bypass optimization strategies and make experience-based over-delivery and early shipments, disrupting the overall rhythm, undermining clean and efficient goals, and potentially increasing energy consumption, fuel, and environmental agent costs.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] This application discloses a coal resource scheduling method and system, aiming to solve the problem in existing coal resource scheduling methods where uneven wear of the conveyor belt drive roller rubber layer causes belt slippage, which in turn leads to the failure to identify the deviation in conveying capacity in a timely manner, resulting in errors in the calculation of instantaneous conveying volume, and ultimately affecting the optimization effect of the scheduling system and the clean and efficient utilization of coal.

[0006] The technical solution of this application is as follows: Firstly, this application discloses a coal resource scheduling method, comprising the following steps: The system acquires the operating status signal of the conveyor belt drive roller area, the theoretical speed of the drive motor, and the theoretical belt speed, the instantaneous weight of material per unit length output by the weighing device, and economic parameters. Based on the operating status signal, obtain the belt slippage related characteristics, combine the belt slippage related characteristics with the theoretical belt speed and the instantaneous material weight per unit length, and calculate the actual operating speed of the conveyor belt according to the pre-calibrated mapping relationship; The corrected instantaneous material conveying capacity is calculated based on the actual operating speed and the instantaneous weight of material per unit length, and the deviation of conveying capacity is characterized by the relative difference between the theoretical belt speed and the actual operating speed. Monitor the real-time power of the drive motor and calculate the additional energy consumption during the monitoring period based on the baseline power established under no slippage conditions; Monitor the operating status and energy consumption of downstream related equipment, the fuel consumption of downstream coal-using equipment, and the consumption of environmental protection agents in flue gas treatment devices, and calculate the downstream compensation costs caused by the deviation in conveying capacity in combination with economic parameters. Summarize the additional energy consumption and downstream compensation costs and calculate the cumulative economic losses to generate an economic benefit and energy loss report; When the cumulative economic loss and / or the deviation in transport capacity reaches a preset threshold, a maintenance suggestion or a scheduling correction instruction will be output.

[0007] Furthermore, based on the operating status signals, belt slippage-related characteristics are obtained. These characteristics are then combined with the theoretical belt speed and the instantaneous weight of material per unit length. Based on a pre-calibrated mapping relationship, the actual operating speed of the conveyor belt is calculated, including: The operating status signal includes a multi-channel acoustic data stream collected by a multi-channel acoustic sensor array deployed in the area of ​​the conveyor belt drive roller. The multi-channel acoustic data stream is spatially located and its acoustic characteristics are calibrated to obtain the spatial direction of the slippage sound source and at least one noise source, as well as calibration parameters for spatial filtering. Based on spatial orientation and calibration parameters, spatial filtering is performed on the multi-channel acoustic data stream to enhance the signal from the slippage sound source direction and attenuate the signal from the noise source direction, thereby obtaining a focused signal. Extract belt slippage-related features from the focused signal; By combining the characteristics of belt slippage with the theoretical belt speed and the instantaneous weight of material per unit length, the actual operating speed of the conveyor belt is calculated based on the mapping relationship.

[0008] Furthermore, spatial localization is performed on the multi-channel acoustic data stream, and acoustic characteristic calibration is conducted to obtain the spatial orientation of the slippage sound source and at least one noise source, as well as calibration parameters for spatial filtering, including: Based on the multi-channel acoustic data stream combined with the conveyor belt operating status, dust suppression system start / stop status and noise-related equipment operating status information, specific time periods where only slippage sound sources or only noise sources exist are identified. Within a specific time period, the current spatial orientation of the slippage sound source and at least one noise source relative to the multi-channel acoustic sensor array is determined using the time difference of arrival and / or a generalized cross-correlation algorithm. Within a specific time period, analyze the spectral characteristics, energy distribution, and directional characteristics of slippage sound sources and noise sources to obtain their current calibration parameters; When a new noise source is detected or the spatial orientation of an existing noise source changes, spatial localization and acoustic characteristic calibration of the new or changed noise source are triggered to update the spatial orientation and calibration parameters. Based on the real-time determined spatial orientation and calibration parameters, update the parameters of the spatial filtering process, including beamforming weights and / or null orientation, to maintain the signal-to-noise ratio of the focused signal and continuously enhance the signal from the slippage sound source direction and attenuate the signal from the noise source direction.

[0009] Furthermore, based on the real-time determined spatial orientation and calibration parameters, the parameters of the spatial filtering process are updated, including beamforming weights and / or null directions, including: The movement trajectory and transient acoustic characteristics of the noise source are monitored in real time. The movement trajectory is determined by the change of the spatial orientation of the noise source relative to the multi-channel acoustic sensor array over time, and the transient acoustic characteristics are determined by the temporal changes of the spectrum and energy distribution of the multi-channel acoustic data stream. Based on the movement trajectory, predict the spatial orientation of the noise source in the next time period; Based on transient acoustic characteristics, the calibration parameters of the noise source are predicted in the next time period; Based on the predicted spatial orientation and calibration parameters, the parameters for spatial filtering in the next time period are pre-calculated within the current time period. The parameters for spatial filtering include beamforming weights and / or null orientations.

[0010] Furthermore, when the cumulative economic losses and / or transmission capacity deviations reach a preset threshold, maintenance recommendations or scheduling correction instructions are output, including: Based on the economic benefits and energy consumption loss report, calculate the growth rate of cumulative economic losses and the trend indicators of transmission capacity deviation. A cost forecasting model for the planning period is established by combining economic parameters to predict the changes in the unit price of electricity, fuel, and environmental protection agents during the preset planning period, thereby obtaining a cost forecasting sequence. Under various pre-defined scenarios, the total cost during the planning period is estimated based on the cost forecast sequence, growth rate, and trend indicators. The total cost during the planning period includes the expected additional energy consumption and downstream compensation costs, as well as maintenance costs and downtime loss costs. Determine the candidate maintenance time point that minimizes the difference in total cost during the planning period and the dynamic adjustment amount of the preset threshold. Define maintenance conditions, which include at least the following: at any candidate maintenance time point, the difference in total cost during the planning period under multiple scenarios is not less than the preset maintenance trigger threshold, and / or the growth rate of cumulative economic loss and the trend indicator of the deviation of transport capacity meet the preset joint threshold. When the maintenance conditions are met, maintenance recommendations are generated. When maintenance conditions are not met, a scheduling correction instruction is generated. The scheduling correction instruction includes at least: adjusting the operating time and start-up / shutdown sequence of downstream related equipment, adjusting the fuel consumption plan of downstream coal-using equipment, and adjusting the environmental agent dosing strategy of flue gas treatment device.

[0011] Furthermore, real-time monitoring of the noise source's movement trajectory and transient acoustic characteristics includes: During the initialization phase or when a change in a noise source is detected, spatial localization and acoustic characteristic calibration are performed for each noise source to obtain its initial spatial orientation and calibration parameters. By applying a multi-target sound source separation algorithm and combining the initial spatial orientation and calibration parameters of each noise source, the signal components corresponding to each noise source in the multi-channel acoustic data stream are separated into independent acoustic signals corresponding to each noise source. Time-frequency analysis was performed on each separated independent acoustic signal to extract its transient acoustic features; Using each independent acoustic signal, and employing time difference of arrival and / or generalized cross-correlation algorithms, the current spatial orientation of each noise source relative to the multi-channel acoustic sensor array is determined and continuously updated to construct the movement trajectory of each noise source.

[0012] Furthermore, by applying a multi-target sound source separation algorithm, and combining the initial spatial orientation and calibration parameters of each noise source, the signal components corresponding to each noise source in the multi-channel acoustic data stream are separated into independent acoustic signals corresponding to each noise source, including: By combining multi-channel acoustic data streams, conveyor belt operating status, corrected instantaneous material conveying volume, and real-time power of drive motors, it is determined whether transient changes occur in the acoustic scene. When a transient change is detected in the acoustic scene, sound source activity is detected in the multi-channel acoustic data stream to identify potential active sound source areas; The acoustic characteristics of potential active sound source areas are compared with a pre-set fingerprint database of slipping sound sources and an environmental background noise fingerprint database to obtain category determination and / or similarity index. For sound source regions whose similarity to the slip sound source fingerprint database or the environmental background noise fingerprint database is greater than a preset similarity threshold, in the multi-target sound source separation algorithm, a lower noise source confidence weight is assigned according to the preset weighting rules and / or they are excluded from the noise source separation target. For a sound source region whose similarity to the slipping sound source fingerprint database or the environmental background noise fingerprint database is less than a preset similarity threshold, and whose similarity to at least one noise source fingerprint in the preset noise source fingerprint database is greater than a preset noise threshold, it is added as a new noise source to the noise source separation target and given a higher noise source confidence weight according to the preset weight rules in the multi-target sound source separation algorithm. During the execution of the multi-target sound source separation algorithm, the separation strategy is dynamically adjusted based on the confidence weight of the noise source. The separated independent acoustic signals are cross-validated. Cross-validation includes comparing them with historical noise source movement trajectories, acoustic fingerprints, and / or the operating status information of noise-associated devices to confirm whether they are real noise sources.

[0013] Furthermore, the separated independent acoustic signals are cross-validated. Cross-validation includes comparing them with historical noise source movement trajectories, acoustic fingerprints, and / or operational status information of noise-associated devices to confirm whether they are genuine noise sources. Obtain the operating status information of noise-related devices, including the start / stop status and power consumption of the noise-related devices; Based on the preset multi-dimensional verification rule set, cross-validation logic judgment is performed on transient acoustic features, movement trajectory and operating status information of noise-related devices to obtain cross-validation judgment results. Based on the cross-validation results, it is confirmed whether each independent acoustic signal corresponds to a real noise source, and misjudgments or omissions are corrected for cases that do not meet the judgment conditions.

[0014] Furthermore, based on the cross-validation results, it is confirmed whether each independent acoustic signal corresponds to a real noise source, and misjudgments or omissions that do not meet the judgment criteria are corrected, including: When the cross-validation result indicates that the transient acoustic characteristics of an independent acoustic signal match the preset noise source fingerprint database, but the operating status information of the noise-associated device is inconsistent or abnormal, the corresponding independent acoustic signal is marked as pending confirmation. For the pending status, anomaly pattern analysis is performed. Within a preset time window, the historical correlation between transient acoustic features, movement trajectory and the operating status information of noise-related equipment is compared to obtain the historical correlation analysis results. When the historical correlation analysis results show that the independent acoustic signal appears when the noise correlation device is abnormal or not started, and matches the transient acoustic characteristics collected at the end of the preset time window, the independent acoustic signal is identified as the real noise source. When the historical correlation analysis results show that the independent acoustic signal does not appear when the noise correlation device is abnormal or not started, and / or its transient acoustic characteristics do not match the transient acoustic characteristics collected at the end of the preset time window, the independent acoustic signal is marked as a non-real noise source. When the operating status information of the noise-related equipment returns to normal, the independent acoustic signals in the pending confirmation state are cross-validated again according to the multi-dimensional verification rule set, and the confirmation conclusion of whether they are real noise sources is updated.

[0015] Secondly, this application also discloses a coal resource dispatching system, comprising: The signal acquisition module is used to acquire the operating status signal of the conveyor belt drive roller area, the theoretical speed of the drive motor and the theoretical belt speed calculated accordingly, the instantaneous weight of material per unit length output by the weighing device, and economic parameters. The speed calculation module is used to obtain belt slippage-related characteristics based on the operating status signal, combine the belt slippage-related characteristics with the theoretical belt speed and the instantaneous material weight per unit length, and calculate the actual operating speed of the conveyor belt according to the pre-calibrated mapping relationship. The conveying capacity calculation module is used to calculate the corrected instantaneous material conveying capacity based on the actual operating speed and the instantaneous weight of material per unit length, and to characterize the conveying capacity deviation by the relative difference between the theoretical belt speed and the actual operating speed. The energy consumption monitoring module is used to monitor the real-time power of the drive motor and calculate the additional energy consumption during the monitoring period based on the baseline power established under no slippage conditions. The cost accounting module is used to monitor the operating status and energy consumption of downstream related equipment, the fuel consumption of downstream coal-using equipment, and the environmental protection agent consumption of flue gas treatment devices, and calculate the downstream compensation cost caused by the deviation in conveying capacity in combination with economic parameters. The report generation module is used to summarize additional energy consumption and downstream compensation costs and calculate cumulative economic losses, generating an economic benefit and energy loss report. The decision guidance module is used to output maintenance suggestions or scheduling correction instructions when the cumulative economic losses and / or transmission capacity deviations reach a preset threshold. Beneficial effects

[0016] This application effectively solves the problem in existing technologies where slight belt slippage caused by uneven wear of the drive roller's rubber coating leads to undetected conveying capacity deviations, resulting in systematic errors in instantaneous conveying capacity calculations, downstream material shortages, and manual intervention, ultimately affecting the optimization of coal resource scheduling and the clean and efficient utilization of coal. This application addresses the lack of direct measurement of actual belt speed in existing systems by introducing a mechanism for estimating actual operating speed, thus accurately identifying and quantifying conveying capacity deviations. Furthermore, by comprehensively considering additional energy consumption and downstream compensation costs, it closely links minor physical faults with economic benefits, achieving a quantitative assessment of hidden losses. Based on this, the system can generate accurate economic benefit and energy loss reports and output maintenance suggestions or scheduling correction instructions in a timely manner, thereby avoiding unplanned manual intervention, optimizing the overall scheduling plan, significantly improving the level of refined management and economic benefits of coal resource scheduling, and achieving the clean and efficient utilization of coal. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a coal resource scheduling method provided in this application.

[0018] Figure 2 A flowchart of a coal resource scheduling system provided in this application.

[0019] In the diagram: 1. Signal acquisition module; 2. Speed ​​calculation module; 3. Transport volume calculation module; 4. Energy consumption monitoring module; 5. Cost accounting module; 6. Report generation module; 7. Decision guidance module. Detailed Implementation

[0020] 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.

[0021] Reference Figure 1 This application proposes a coal resource scheduling method, including: S1000: Acquires the operating status signal of the conveyor belt drive roller area, the theoretical speed of the drive motor, and the theoretical belt speed, the instantaneous weight of material per unit length output by the weighing device, and economic parameters. S2000: Based on the operating status signal, obtain the belt slippage related characteristics, combine the belt slippage related characteristics with the theoretical belt speed and the instantaneous material weight per unit length, and calculate the actual operating speed of the conveyor belt according to the pre-calibrated mapping relationship; S3000: The corrected instantaneous material conveying capacity is calculated based on the actual operating speed and the instantaneous weight of material per unit length, and the conveying capacity deviation is characterized by the relative difference between the theoretical belt speed and the actual operating speed. S4000: Monitors the real-time power of the drive motor and calculates the additional energy consumption during the monitoring period based on the baseline power established under no slippage conditions; S5000: Monitors the operating status and energy consumption of downstream related equipment, the fuel consumption of downstream coal-using equipment, and the consumption of environmental protection agents in flue gas treatment devices, and calculates the downstream compensation costs caused by the deviation in conveying capacity in combination with economic parameters. S6000: Summarizes additional energy consumption and downstream compensation costs and calculates cumulative economic losses, generating an economic benefit and energy loss report; S7000: When the cumulative economic loss and / or the deviation in transport capacity reaches the preset threshold, output maintenance suggestions or scheduling correction instructions.

[0022] To make the technical solution of this application easier and clearer to understand, some key terms involved will be explained first.

[0023] Operating status signals refer to various physical quantity signals that can reflect the working status of the conveyor belt drive roller area, such as acoustic signals, vibration signals, temperature signals, etc.

[0024] Theoretical speed refers to the speed that a drive motor should reach under ideal no-load or rated load conditions, based on its design parameters or control commands.

[0025] The theoretical belt speed is the expected operating speed of the conveyor belt under non-slip conditions, calculated based on the theoretical rotational speed of the drive roller and the roller diameter.

[0026] Instantaneous weight per unit length refers to the weight of material carried per unit length on the conveyor belt at a certain moment, which is usually obtained by real-time measurement by a weighing device.

[0027] Economic parameters refer to various cost and benefit-related data involved in the coal resource scheduling process, such as the unit price of electricity, the unit price of fuel, the unit price of environmental protection agents, equipment maintenance costs, and downtime loss costs.

[0028] Belt slippage-related characteristics refer to the physical characteristics that can characterize the relative sliding phenomenon between the conveyor belt and the drive drum, such as acoustic signals at specific frequencies and changes in vibration modes.

[0029] The mapping relationship refers to the pre-established mathematical or logical correspondence between belt slippage-related characteristics, theoretical belt speed, instantaneous material weight per unit length, and actual conveyor belt speed. It can be obtained through experimental calibration, machine learning model training, and other methods.

[0030] The corrected instantaneous material conveying capacity refers to the actual conveying capacity obtained by correcting the material weight measured by the weighing device after taking into account the actual running speed of the belt.

[0031] Conveying capacity deviation refers to the difference between the actual conveying capacity and the theoretical conveying capacity of a conveyor belt, which is usually characterized by the relative difference between the actual belt speed and the theoretical belt speed.

[0032] Additional energy consumption refers to the portion of energy required by the drive motor that exceeds the energy consumption required under normal, slip-free operating conditions, caused by abnormal working conditions such as belt slippage.

[0033] Downstream compensation costs refer to the additional costs incurred to compensate for the abnormal operation or reduced efficiency of downstream equipment caused by deviations in transport capacity. These costs include increased fuel consumption, increased consumption of environmental protection agents, and labor intervention costs.

[0034] Cumulative economic loss refers to the sum of additional energy consumption and downstream compensation costs within a certain monitoring period.

[0035] The preset threshold refers to the critical value for cumulative economic losses and / or transport capacity deviations used to determine whether maintenance or scheduling correction measures are needed.

[0036] Maintenance recommendations refer to measures such as repair and replacement of parts proposed in response to equipment failure or performance degradation.

[0037] Dispatch correction instructions refer to adjustments made to the existing coal resource dispatch plan to adapt to changes in actual transportation capacity.

[0038] The coal resource scheduling method provided in this application takes the refined perception of the conveyor belt's operating status and the multi-dimensional economic benefit assessment as its core, and realizes intelligent optimization and decision-making for the coal transportation process.

[0039] Specifically, in acquiring the operating status signals of the conveyor belt drive roller area, the theoretical speed of the drive motor and the theoretical belt speed calculated accordingly, the instantaneous weight of material per unit length output by the weighing device, and economic parameters, the operating status signals can be obtained by deploying vibration sensors, temperature sensors, acoustic sensors, etc., in the drive roller area; the theoretical speed can be obtained from the motor controller or equipment nameplate, and the theoretical belt speed can be calculated in combination with the roller diameter; the instantaneous weight of material per unit length can be measured in real time using an electronic belt scale installed on the conveyor belt; and economic parameters can be obtained from Enterprise Resource Planning (ERP), market price databases, or manually entered.

[0040] Secondly, features related to belt slippage are extracted from the operating status signals and input into a pre-calibrated mapping relationship along with the theoretical belt speed and the instantaneous weight of material per unit length to estimate the actual operating speed of the conveyor belt. When the operating status signal is a vibration signal, spectral analysis can be performed to extract energy changes in specific frequency bands as features related to belt slippage; when it is an acoustic signal, friction sounds or whistling sounds related to slippage can be identified. These features, along with the theoretical belt speed and the instantaneous weight of material per unit length, are input into a machine learning model (e.g., support vector machine, neural network) trained on historical data to obtain a real-time estimate of the actual operating speed.

[0041] Furthermore, the corrected instantaneous material conveying capacity is calculated based on the actual operating speed and the instantaneous weight of material per unit length, and the conveying capacity deviation is characterized by the relative difference between the theoretical belt speed and the actual operating speed. For example, if the instantaneous weight of material per unit length is X tons / meter and the actual operating speed is Y meters / second, then the corrected instantaneous material conveying capacity is X × Y tons / second; the conveying capacity deviation can be expressed as: Deviation = (Theoretical belt speed...) (Actual belt speed) Calculated by multiplying the theoretical belt speed by 100%.

[0042] In addition, the real-time power of the drive motor is monitored, and the additional energy consumption during the monitoring period is calculated based on the baseline power established under no-slip conditions. Specifically, motor power data is collected under normal operating conditions without slippage, and a baseline power model that can vary with load and theoretical speed is established; the additional energy consumption during the monitoring period is the positive cumulative portion of the difference between the real-time power and the baseline power.

[0043] Furthermore, the operating status and energy consumption of downstream related equipment, the fuel consumption of downstream coal-fired equipment, and the consumption of environmental protection agents by flue gas treatment devices are monitored, and the downstream compensation costs caused by the deviation in conveying capacity are calculated in conjunction with economic parameters. When there is a material shortage, downstream equipment may need to extend its operating time, increase its load, or change its operating conditions to replenish the materials, resulting in additional consumption of electricity, fuel, and environmental protection agents; these costs can be quantified as downstream compensation costs by combining electricity prices, fuel prices, and agent prices.

[0044] Subsequently, the additional energy consumption and downstream compensation costs are summarized and the cumulative economic losses are calculated to form an economic benefit and energy loss report. The report can show the details of energy consumption losses, the composition of compensation costs, and the time trend of cumulative economic losses, which facilitates management decision-making.

[0045] Finally, when the cumulative economic loss and / or conveying capacity deviation reaches a preset threshold, maintenance suggestions or scheduling correction instructions are output. For example, if the conveying capacity deviation continues to exceed a set percentage, or the cumulative economic loss reaches a set amount, maintenance suggestions can be generated to prompt the inspection or repair of the conveyor belt drive rollers; if the maintenance level has not yet been reached but downstream scheduling has been affected, scheduling correction instructions can be generated, such as adjusting subsequent material conveying plans or optimizing the operating sequence of downstream equipment, in order to reduce economic losses.

[0046] In another embodiment of this application, S2000 is further proposed to include: S2100: The operating status signal includes a multi-channel acoustic data stream collected by a multi-channel acoustic sensor array deployed in the conveyor belt drive roller area. The multi-channel acoustic data stream is spatially located and its acoustic characteristics are calibrated to obtain the spatial direction of the slippage sound source and at least one noise source, as well as calibration parameters for spatial filtering. S2200: Based on spatial orientation and calibration parameters, it performs spatial filtering on multi-channel acoustic data streams to enhance signals from the direction of slippage sound sources and attenuate signals from the direction of noise sources, thereby obtaining a focused signal; S2300: Extract belt slippage-related features from the focused signal; S2400: Combines belt slippage characteristics with theoretical belt speed and instantaneous material weight per unit length, and calculates the actual operating speed of the conveyor belt based on the mapping relationship.

[0047] Specifically, the multi-channel acoustic sensor array consists of multiple acoustic sensors arranged in geometric configurations such as linear, circular, or planar arrays. It is used to simultaneously acquire acoustic signals from different spatial locations within the drive roller area to obtain spatial information about the sound source. The multi-channel acoustic data stream comprises multiple digital or analog acoustic signals acquired by the array over a continuous time period.

[0048] Spatial localization of multi-channel acoustic data streams involves using array signal processing methods such as Time Difference of Arrival (TDOA), Generalized Cross-Correlation (GCC), or Beamforming to determine the spatial position or orientation of slippage and noise sources relative to the array. Acoustic characteristic calibration involves analyzing and parameterizing acoustic properties such as spectrum, energy, and directivity to create a reusable acoustic fingerprint or model, thereby obtaining the spatial orientation of the slippage and at least one noise source, as well as calibration parameters for spatial filtering.

[0049] In practical applications, spatial filtering of multi-channel acoustic data streams based on spatial orientation and calibration parameters refers to using techniques such as beamforming, null steering, or independent component analysis (ICA) to design spatial filters according to known spatial orientation and acoustic characteristics. This selectively enhances signals from the direction of slipping sound sources and suppresses interference from the direction of noise sources, resulting in a focused signal with a significantly improved signal-to-noise ratio.

[0050] Furthermore, extracting belt slippage-related features from the focused signal involves identifying and quantifying indicators directly related to slippage in the time domain, frequency domain, or time-frequency domain. These indicators include specific frequency energy, harmonic structure, transient impact sound, and the spectral characteristics and time-varying patterns of friction sound. These belt slippage-related features are then combined with the theoretical belt speed and the instantaneous weight per unit length of material to calculate the actual operating speed of the conveyor belt based on a pre-calibrated mapping relationship. This mapping relationship is established through experiments, simulations, or training with historical data and is used to quantify the nonlinear relationship between the features and the actual speed.

[0051] This application's solution introduces spatial dimension information through a multi-channel acoustic sensor array, enabling spatial positioning and acoustic characteristic calibration. Based on this, spatial filtering is implemented to obtain a high signal-to-noise ratio focused signal, significantly reducing the interference of environmental noise on feature extraction. The belt slippage-related features obtained from the focused signal are purer, and combined with the mapping relationship, the actual operating speed can be more accurately estimated. This provides reliable input for the corrected instantaneous material conveying capacity, additional energy consumption assessment, and downstream compensation cost calculation, overcoming the limitation of traditional methods in stably obtaining slippage features under complex noise environments.

[0052] In some preferred embodiments, the following specific example illustrates the situation: Taking a large coal mine conveyor belt system as an example: Near the drive drum area, there are crushers, ventilators, and other auxiliary equipment. A multi-channel acoustic sensor array consisting of eight microphones is deployed to continuously collect multi-channel acoustic data streams. First, spatial positioning and acoustic characteristic calibration are performed. The spatial orientation of the crusher, ventilator, and auxiliary equipment relative to the array is determined by the time difference of arrival, and their respective spectra and energy distributions are extracted to obtain calibration parameters. Simultaneously, the typical spatial orientation and acoustic characteristics of the belt slippage sound source are pre-calibrated. Then, based on the determined spatial orientation and calibration parameters, spatial filtering is implemented. Beamforming is used to align the main lobe with the slippage sound source direction, and nulls are set in the directions of the crusher, ventilator, and auxiliary equipment to maximize the amplification of the slippage sound source signal and suppress interference. Finally, belt slippage-related features are extracted from the focused signal, such as the energy of specific frequency bands of friction sound and the intensity of periodic impact sound. Combined with the theoretical belt speed and the instantaneous weight of material per unit length, a mapping model is input to accurately calculate the actual operating speed. When the friction sound energy increases, the model gives the decrease in actual belt speed relative to the theoretical belt speed. Even under the high noise environment of crushers and fans, it can still stably identify slippage and obtain reliable actual operating speed, providing reliable data support for coal resource scheduling.

[0053] In another embodiment of this application, spatial localization of the multi-channel acoustic data stream and acoustic characteristic calibration are further proposed to obtain the spatial orientation of the slippage sound source and at least one noise source, as well as calibration parameters for spatial filtering, specifically including: S2110: Based on the multi-channel acoustic data stream combined with the conveyor belt operating status, dust suppression system start / stop status and noise-related equipment operating status information, identify specific time periods where only slippage sound sources or only noise sources exist. S2120: Within a specific time period, using the time difference of arrival and / or a generalized cross-correlation algorithm, determine the current spatial orientation of the slippage sound source and at least one noise source relative to the multi-channel acoustic sensor array; S2130: Within a specific time period, analyze the spectral characteristics, energy distribution, and directional characteristics of slippage sound sources and noise sources to obtain their current calibration parameters; S2140: When a new noise source is detected or the spatial orientation of an existing noise source changes, spatial localization and acoustic characteristic calibration of the new or changed noise source are triggered to update the spatial orientation and calibration parameters. S2150: Based on the real-time determined spatial orientation and calibration parameters, update the parameters of the spatial filtering process, including beamforming weights and / or null directions, to maintain the signal-to-noise ratio of the focused signal and continuously enhance the signal from the slippage sound source direction and attenuate the signal from the noise source direction.

[0054] Specifically, identifying specific time periods containing only slippage or only noise sources involves the system analyzing multi-channel acoustic data streams while simultaneously considering the conveyor belt's operating status, the dust suppression system's on / off status, and the operating status of noise-related equipment to determine whether the current acoustic environment is a single-source scenario. For example, if a specific acoustic signal is detected when the conveyor belt is empty and no other equipment is running, it can be identified as a specific time period containing only a slippage sound source; conversely, if the conveyor belt is stopped and a noise-related device is activated, it can be identified as a specific time period containing only a noise source. This step provides a clean sample for subsequent spatial positioning and acoustic characteristic calibration.

[0055] The Time Difference of Arrival (TDOA) and / or Generalized Cross-Correlation (GCC) algorithms are used to calculate the spatial orientation of the sound source relative to the multi-channel acoustic sensor array based on the time difference between different sensors receiving the same sound source, combined with the sound velocity and array geometry; beamforming is used when necessary to improve the stability of the orientation estimation. This provides the basic localization information of the spatial orientation of the slippage sound source and the noise source.

[0056] In practical applications, the spectral characteristics, energy distribution, and directionality of the located sound source are analyzed to obtain the current calibration parameters. Calibration parameters may include center frequency, bandwidth, and instantaneous power spectral density. Slippage friction noise typically exhibits a sustained energy increase in the mid-to-high frequency range and shows a bandwidth expansion characteristic with load; fan noise commonly shows narrow-band fundamental and octave peaks; crusher impact noise often manifests as broadband pulsed energy accompanied by directional periodic changes. The above analysis results are used for the parameterization description of subsequent spatial filtering processing.

[0057] Furthermore, when a new noise source is detected or the spatial orientation of an existing noise source changes, the system triggers spatial localization and acoustic characteristic calibration of the new or changed noise source, updating the spatial orientation and calibration parameters in a timely manner to adapt to the dynamic acoustic environment of the industrial site. For example, when a fresh air unit is put into operation, its spatial orientation and calibration parameters need to be added immediately; when mobile equipment passes near the sensor array, its spatial orientation migrates over time, and the system needs to automatically relocate and update the parameters.

[0058] Therefore, based on the real-time determined spatial direction and calibration parameters, the parameters of the spatial filtering process are updated, including beamforming weights and null directions, to maintain the signal-to-noise ratio of the focused signal and continuously enhance the signal from the slippage sound source direction while attenuating the signal from the noise source direction. In practice, the main lobe of the beam can be pointed towards the slippage sound source direction, while nulls are set in the directions of the fan and the crusher; when the intermittent operation of the crusher causes a change in radiation directionality, the null directions are adjusted synchronously according to the time sequence to prevent noise leakage into the focused signal.

[0059] In some preferred embodiments: Near the drive roller area are crushers, fans, and other auxiliary equipment. A multi-channel acoustic sensor array consisting of eight microphones is deployed on-site. During a certain period, the conveyor belt runs unloaded while the crusher, fan, and auxiliary equipment are all stopped. The system identifies this as a specific time period where only slippage noise exists. TDOA and GCC are used to determine the spatial direction of the slippage noise source relative to the array, and calibration parameters for the slippage noise source are obtained through spectral characteristics, energy distribution, and directional characteristics analysis. Subsequently, the fan starts, creating a new noise source. The system immediately triggers spatial localization and acoustic characteristic calibration of this noise source, updating its spatial direction and corresponding calibration parameters. Based on this, the parameters of spatial filtering are dynamically adjusted, beamforming weights are adjusted to point the main lobe towards the slippage noise source, and null directions are set in the directions of the crusher, fan, and auxiliary equipment. After the updated spatial filtering, a high signal-to-noise ratio focused signal is obtained, from which belt slippage-related features are accurately extracted. Combined with the theoretical belt speed and instantaneous material weight per unit length, the actual operating speed of the conveyor belt is calculated based on the mapping relationship, maintaining the accuracy of the calculation results even in strong noise environments.

[0060] In another embodiment of this application, it is further proposed that the parameters of spatial filtering processing, including beamforming weights and / or null directions, be updated based on the spatial orientation and calibration parameters determined in real time, specifically including: S2151: Real-time monitoring of the movement trajectory and transient acoustic characteristics of noise sources. The movement trajectory is determined by the change in the spatial orientation of the noise source relative to the multi-channel acoustic sensor array over time, and the transient acoustic characteristics are determined by the temporal changes in the spectrum and energy distribution of the multi-channel acoustic data stream. S2152: Based on the movement trajectory, predict the spatial orientation of the noise source in the next time period; S2153: Based on transient acoustic characteristics, predict the calibration parameters of a noise source in the next time period; S2154: Based on the predicted spatial orientation and calibration parameters, pre-calculate the parameters for spatial filtering in the next time period within the current time period. The parameters for spatial filtering include beamforming weights and / or null orientations.

[0061] Specifically, real-time monitoring of the movement trajectory and transient acoustic characteristics of noise sources refers to the system continuously tracking the positional changes and acoustic property changes of various noise sources in the environment. The movement trajectory of a noise source refers to the continuous recording of its spatial orientation relative to the multi-channel acoustic sensor array over time. This can be achieved by using continuous Time Difference of Arrival (TDOA) or Generalized Cross-Correlation (GCC) to determine the azimuth and elevation angles at each moment and connecting them to form the trajectory. Transient acoustic characteristics are determined by the changes in the spectrum and energy distribution of the multi-channel acoustic data stream over time. These characteristics, such as the dominant frequency, bandwidth, and energy, can be extracted using Short-Time Fourier Transform (STFT) or other time-frequency analysis methods.

[0062] Furthermore, based on the movement trajectory, the spatial orientation of the noise source in the next time period is predicted. Kalman filtering, particle filtering, or machine learning models based on historical trajectory data (such as recurrent neural networks) can be used to infer the displacement trend within a short time window according to the existing motion pattern, thereby obtaining the spatial orientation prediction result for the next time period.

[0063] Simultaneously, based on transient acoustic characteristics, the calibration parameters of the noise source in the next time period are predicted. Calibration parameters may include spectral characteristics, energy distribution, directional patterns, etc., and can be learned using time series analysis models (such as ARIMA), Hidden Markov Models (HMMs), or deep learning models to predict historical changes and provide acoustic property predictions for future moments.

[0064] Therefore, based on the predicted spatial direction and calibration parameters, the parameters for spatial filtering in the next time period are pre-calculated within the current time period. The spatial filtering parameters include beamforming weights and / or null directions: the former adjusts the amplitude and phase of each channel signal to form a beam pointing towards the target direction, while the latter creates nulls in the direction of the noise source to attenuate the signal in that direction to the maximum extent. The purpose of pre-calculation is to directly apply the optimal parameters when the next time period arrives, avoiding the lag caused by calculating after real-time detection of changes.

[0065] In a typical scenario, a mobile cleaning device serves as the primary noise source in the conveyor belt drive roller area. This device moves along the side of the conveyor belt and its operating mode changes periodically, causing transient acoustic characteristics to fluctuate over time. The system continuously acquires multi-channel acoustic data streams using a multi-channel acoustic sensor array, monitors its movement trajectory in real time, and uses Kalman filtering to smooth and predict historical spatial direction data to obtain the spatial direction within the next few seconds. Simultaneously, the system analyzes the device's spectrum and energy distribution, extracts transient acoustic characteristics, and uses a Long Short-Term Memory (LSTM) network to predict the dominant frequency and energy changes for the next time period. Based on these predictions, the system pre-calculates the beamforming weights and null directions for the next time period in the current time period; these are directly applied when the next time period arrives, ensuring the sound beam continuously points towards the slippage noise source and forms a null in the direction of the cleaning device. Even as the device's position and acoustic characteristics continuously change, the parameters of the spatial filtering process remain highly matched to its state, continuously suppressing noise and maintaining a clear focused signal, thus ensuring the accuracy of the conveyor belt's actual operating speed calculation.

[0066] In another embodiment of this application, when the cumulative economic loss and / or transmission capacity deviation reaches a preset threshold, a maintenance suggestion or scheduling correction instruction is output, including: S7100: Based on economic benefits and energy loss reports, calculate the growth rate of cumulative economic losses and trend indicators of transmission capacity deviation; S7200: Combine economic parameters to establish a cost forecasting model for the planning period, predict the changes in the unit price of electricity, fuel and environmental protection agents during the preset planning period, and obtain the cost forecasting sequence; S7300: Under multiple preset scenarios, based on cost forecast sequences and growth rate and trend indicators, the total cost during the planning period is estimated. The total cost during the planning period includes the expected additional energy consumption and downstream compensation costs, as well as maintenance costs and downtime loss costs. S7400: Determine the dynamic adjustment amount between the candidate maintenance time point that minimizes the difference in total cost during the planning period and the preset threshold, and define maintenance conditions. The maintenance conditions include at least the following: at any candidate maintenance time point, the difference in total cost during the planning period under multiple scenarios is not less than the preset maintenance trigger threshold, and / or the growth rate of cumulative economic losses and the trend indicator of the deviation of transport capacity meet the preset joint threshold. S7500: Generates maintenance recommendations when maintenance conditions are met; S7600: When maintenance conditions are not met, a scheduling correction instruction is generated. The scheduling correction instruction includes at least: adjusting the operating time and start-up / shutdown sequence of downstream related equipment, adjusting the fuel consumption plan of downstream coal-using equipment, and adjusting the environmental agent dosing strategy of flue gas treatment device.

[0067] Specifically, upon receiving reports on economic benefits and energy consumption losses, a time-series analysis is first performed on the cumulative economic losses to calculate their growth rate. Methods such as moving averages, exponential smoothing, or regression analysis can be used to quantify the rate of loss accumulation. Simultaneously, trend analysis is conducted on historical data regarding transmission capacity deviations. Linear regression or nonlinear fitting is used to predict future trends and magnitudes, yielding trend indicators of transmission capacity deviations to provide input for subsequent forecasting and decision-making.

[0068] Furthermore, by incorporating economic parameters (including historical market prices, policy changes, and seasonal factors), a cost forecasting model for the planning period is established to predict changes in the unit price of electricity, fuel, and environmental protection agents within the preset planning period, thus forming a cost forecast sequence. The model can employ time series methods (such as ARIMA and Prophet), machine learning methods (such as support vector regression and neural networks), or expert rules.

[0069] Based on this, and to address uncertainty, under multiple pre-set scenarios (such as optimistic, neutral, and pessimistic), the total cost for the planning period is estimated based on cost forecast sequences, the growth rate of cumulative economic losses, and trend indicators of transmission capacity deviation. The total cost for the planning period also includes anticipated additional energy consumption, downstream compensation costs, maintenance expenses, and downtime losses to comprehensively assess the economic impact of different decision-making paths.

[0070] As a preferred implementation, the candidate maintenance time point with the smallest difference is identified, and the preset threshold is dynamically adjusted accordingly. Simultaneously, maintenance conditions are defined, including at least: at any candidate maintenance time point, the difference in the total planned cost across multiple scenarios is not less than a preset maintenance trigger threshold, and / or the growth rate of cumulative economic losses and the trend indicator of the deviation from the transport capacity meet a preset joint threshold. The former ensures economic rationality, while the latter ensures technical necessity.

[0071] When maintenance conditions are met, maintenance recommendations are generated, such as conducting overhauls, replacing parts, or implementing preventative maintenance on specific dates. When maintenance conditions are not met, scheduling correction instructions are generated, including at least: adjusting the operating time and start-up / shutdown sequence of downstream related equipment, adjusting the fuel consumption plan of downstream coal-using equipment, and adjusting the environmental agent dosing strategy of flue gas treatment devices, in order to reduce total costs while meeting environmental and capacity constraints.

[0072] By using cumulative economic loss growth rate and transmission capacity deviation trend indicators (e.g., monthly growth rate fitted with exponential smoothing / rolling regression; trend indicators fitted with linear / nonlinear fitted predicted values ​​or slopes), risks and loss trends can be identified in advance. Through planning period cost prediction models and multi-scenario analysis, future fluctuations in the unit prices of electricity, fuel, and environmental protection agents are taken into account. The total cost difference during the planning period is used as the optimization target and the threshold is dynamically adjusted to make decisions more economically robust and achieve a balance between maintenance costs, downtime losses, and additional energy consumption and downstream compensation costs generated by continuous operation. When maintenance conditions are not met, flexible optimization on the operation side is achieved through scheduling correction instructions to minimize losses and maintain continuous production.

[0073] In a typical scenario, a transportation system monitors a continuous increase in transportation capacity deviation, leading to a steady rise in cumulative economic losses. The system first calculates a monthly growth rate of 5% for cumulative economic losses, predicting that the trend indicator of transportation capacity deviation will reach 20% after three months. Subsequently, a cost prediction model is established with a six-month planning period: the unit price of electricity is expected to increase by 10%, the unit price of fuel remains relatively stable, and the unit price of environmental protection agents is expected to decrease by 5%. Under optimistic, neutral, and pessimistic scenarios, the estimated total cost for the planning period is 1 million yuan, 1.2 million yuan, and 1.5 million yuan, respectively. Using the difference in total cost across scenarios as the optimization objective, the algorithm determines the end of the third month as the candidate maintenance point with the smallest difference, and dynamically adjusts the preset threshold accordingly. Simultaneously, maintenance conditions are set: if maintenance is performed at the end of the third month, the difference in total cost across scenarios must not be less than the preset maintenance trigger threshold (e.g., 200,000 yuan), and the growth rate of cumulative economic losses and the trend indicator of transportation capacity deviation must meet the preset joint threshold. If the maintenance conditions are met, the system outputs maintenance recommendations, such as conducting a comprehensive overhaul of the conveyor belt drive rollers and replacing worn parts at the end of the third month. If the maintenance conditions are not met, a scheduling correction instruction is generated, such as adjusting the operating time and start-up / shutdown sequence of downstream related equipment, optimizing the fuel consumption plan of downstream coal-using equipment, and optimizing the environmental agent dosing strategy of the flue gas treatment device under the premise of meeting standards. Through this process, even without immediate maintenance, economic losses can be reduced and production can be stabilized through refined scheduling.

[0074] In another embodiment of this application, real-time monitoring of the movement trajectory and transient acoustic characteristics of a noise source is further proposed, including: S2151-1: During the initialization phase or when a change in a noise source is detected, spatial localization and acoustic characteristic calibration are performed for each noise source to obtain its initial spatial orientation and calibration parameters. S2151-2: Apply a multi-target sound source separation algorithm, combining the initial spatial direction and calibration parameters of each noise source, to separate the signal components corresponding to each noise source in the multi-channel acoustic data stream into independent acoustic signals corresponding to each noise source. S2151-3: Perform time-frequency analysis on each separated independent acoustic signal to extract its transient acoustic features; S2151-4: Using each independent acoustic signal, the current spatial orientation of each noise source relative to the multi-channel acoustic sensor array is determined and continuously updated using the time difference of arrival and / or generalized cross-correlation algorithm, so as to construct the movement trajectory of each noise source.

[0075] Specifically, the initialization phase refers to the initial identification and parameter acquisition of existing noise sources in the environment when the system is first started or undergoes a large-scale configuration update. Noise source change refers to the discovery of new noise sources through acoustic scene analysis or anomaly detection, or significant changes in the acoustic characteristics and location of existing noise sources. Spatial localization and acoustic characteristic calibration are performed for each noise source to obtain calibration parameters such as its spatial orientation, spectrum, and energy distribution relative to the multi-channel acoustic sensor array, providing accurate initial values ​​for subsequent sound source separation and tracking.

[0076] Multi-target acoustic source separation algorithms are used to separate the signal components corresponding to each noise source in a multi-channel acoustic data stream into their respective independent acoustic signals. By combining the initial spatial orientation and calibration parameters of each noise source, independent component analysis (ICA), non-negative matrix factorization (NMF), or beamforming-based separation techniques can be used to improve the effectiveness and stability of the separation.

[0077] Time-frequency analysis is performed on each isolated acoustic signal to reveal its energy distribution and variation patterns in the time and frequency dimensions, and to extract transient acoustic features. These transient acoustic features may include short-time energy, zero-crossing rate, spectral centroid, spectral bandwidth, Mel-frequency cepstral coefficients (MFCC), etc., to characterize the transient properties of the noise source and support subsequent identification and prediction.

[0078] By utilizing individual acoustic signals and employing Time Difference of Arrival (TDOA) and / or Generalized Cross-Correlation (GCC) algorithms, the current spatial orientation of each noise source relative to a multi-channel acoustic sensor array is accurately determined. TDOA calculates the sound source location by measuring the time difference of arrival between multiple sensors, while GCC estimates the time delay and indirectly obtains TDOA by using the cross-correlation peak value. Continuous updates to the spatial orientation allow the construction of a time-evolving trajectory, enabling dynamic tracking of the noise source.

[0079] This application's solution integrates initialization / change detection, spatial positioning and acoustic characteristic calibration, multi-target sound source separation, time-frequency analysis to extract transient acoustic features, and TDOA / GCC continuous positioning and trajectory construction into a unified process. This ensures cleaner separation of independent acoustic signals and allows for real-time and accurate updates to the spatial direction and trajectory of noise sources. Consequently, subsequent spatial filtering can obtain timely and accurate input parameters, improving the enhancement of slippage sound sources and noise attenuation. This, in turn, enhances the accuracy and robustness of conveyor belt actual operating speed estimation, optimizing the overall efficiency and reliability of coal resource scheduling.

[0080] In another embodiment of this application, S2151-2 further includes: S2151-21: By combining multi-channel acoustic data streams, conveyor belt operating status, corrected instantaneous material conveying volume, and real-time power of drive motors, determine whether transient changes occur in the acoustic scene; S2151-22: When it is determined that a transient change has occurred in the acoustic scene, sound source activity detection is performed on the multi-channel acoustic data stream to identify potential active sound source areas; S2151-23: Compare the acoustic characteristics of potential active sound source areas with a preset slippage sound source fingerprint database and an environmental background noise fingerprint database to obtain a category determination and / or similarity index. S2151-24: For sound source regions whose similarity to the slipping sound source fingerprint database or the environmental background noise fingerprint database is greater than a preset similarity threshold, in the multi-target sound source separation algorithm, a lower noise source confidence weight is assigned according to the preset weighting rules and / or they are excluded from the noise source separation target. S2151-25: For a sound source region whose similarity to the slipping sound source fingerprint database or the environmental background noise fingerprint database is less than a preset similarity threshold, and whose similarity to at least one noise source fingerprint in the preset noise source fingerprint database is greater than a preset noise threshold, it is added as a new noise source to the noise source separation target and given a higher noise source confidence weight according to the preset weighting rules in the multi-target sound source separation algorithm. S2151-26: During the execution of the multi-target sound source separation algorithm, the separation strategy is dynamically adjusted according to the confidence weight of the noise source. S2151-27: Cross-validate each of the separated independent acoustic signals. Cross-validation includes comparing them with historical noise source movement trajectories, acoustic fingerprints, and / or the operating status information of noise-associated devices to confirm whether they are real noise sources.

[0081] Specifically, determining whether a transient change has occurred in the acoustic environment involves comprehensively analyzing multi-dimensional data such as multi-channel acoustic data streams, conveyor belt operating status, corrected instantaneous material conveying volume, and real-time power of the drive motor. This data is used to identify whether significant and unexpected changes have occurred in the current acoustic environment, such as the appearance of new noise sources, sudden changes in the intensity of existing noise sources, or their disappearance. The purpose is to trigger a more refined sound source processing procedure in a timely manner.

[0082] Sound source activity detection refers to the use of energy detection, spectral analysis, or machine learning methods to locate specific time-frequency or spatial regions where sound source activity exists within a multi-channel acoustic data stream, thereby identifying potentially active sound source areas. Its purpose is to narrow down the analysis scope and focus on signal segments that may contain noise sources or slippage sources.

[0083] The slippage sound source fingerprint database, the environmental background noise fingerprint database, and the noise source fingerprint database are pre-established databases that contain sets of acoustic characteristics of various typical sound sources, such as spectrum, energy distribution, and time-domain waveform characteristics. By comparing the acoustic characteristics of potential active sound source areas with the above fingerprint databases, category determination and / or similarity index can be obtained, thereby initially determining the sound source type and providing a basis for subsequent allocation of noise source confidence weights.

[0084] Noise source confidence weights are used to guide multi-target noise source separation algorithms. Higher weights indicate that the region is more likely to be a real noise source, and the separation algorithm will tend to separate it as an independent noise source; lower weights or exclusion from the separation target indicate that the region is more likely to correspond to slippage noise sources or unimportant background noise. The purpose is to optimize separation performance and avoid mistakenly including slippage noise sources or background noise in complex separations.

[0085] Dynamically adjusting the separation strategy refers to the process by which a multi-target sound source separation algorithm adjusts its internal parameters and processing flow in a timely manner based on the real-time updated confidence weights of the noise sources. This includes adjusting the beamforming direction, filter coefficients, or the objective function in the iterative optimization process to adapt to the ever-changing acoustic environment and improve separation accuracy and robustness.

[0086] Cross-validation refers to conducting multi-dimensional and multi-source verification on each isolated acoustic signal, including comparing it with historical noise source movement trajectories, acoustic fingerprints, and / or the operating status information of noise-related devices, in order to further confirm whether it corresponds to the real noise source, thereby reducing the risk of misjudgment and missed judgment.

[0087] In some preferred embodiments: If a new high-frequency noise suddenly appears in the area of ​​the conveyor belt drive roller, the system will combine multi-channel acoustic data streams, conveyor belt operating status (e.g., no load or full load), corrected instantaneous material conveying volume, and real-time power of the drive motor to determine whether it is a transient acoustic event. Once confirmed, sound source activity detection is immediately performed to locate the potential active sound source area of ​​the high-frequency noise. Subsequently, its acoustic characteristics are compared with the slippage sound source fingerprint database, the environmental background noise fingerprint database, and various noise source fingerprint databases: if the similarity with the slippage sound source fingerprint database or the environmental background noise fingerprint database is high (e.g., above a preset similarity threshold), a lower noise source confidence weight is assigned or it is excluded; if the similarity with the above two types of fingerprints is low, but the similarity with a specific noise source fingerprint (e.g., above a preset noise threshold) is high, it is added as a new noise source to the separation target and assigned a higher weight. The multi-target noise source separation algorithm dynamically adjusts the beamforming direction and intensity accordingly, more effectively separating the high-frequency noise. After separation, it is cross-validated with historical noise source movement trajectories, acoustic fingerprints, and the start / stop status and power consumption of corresponding equipment to ultimately confirm whether the high-frequency noise is indeed the true noise source of the corresponding fan or vibrating screen. Through this approach, the accuracy of noise source separation is ensured, and misidentification of slippage sources as noise sources is avoided, thereby improving the reliability of belt slippage detection.

[0088] In another embodiment of this application, the step of cross-validating the separated independent acoustic signals includes: S2151-271: Obtain the operating status information of noise-related equipment, including the start / stop status and power consumption of the noise-related equipment; S2151-272: Based on the preset multi-dimensional verification rule set, cross-validation logic judgment is performed on transient acoustic features, movement trajectory and operating status information of noise-related equipment to obtain cross-validation judgment results; S2151-273: Based on the cross-validation results, confirm whether each independent acoustic signal corresponds to a real noise source, and correct any misjudgments or omissions that do not meet the judgment conditions.

[0089] Specifically, the operational status information of noise-related equipment refers to the real-time operating data of equipment associated with potential noise sources. This information can include the start / stop status and power consumption of the noise-related equipment. Obtaining this information can provide external evidence for determining the authenticity of acoustic signals. For example, if an acoustic signal is identified as noise from a fan, but the fan is shut down or has abnormal power consumption, further verification is required.

[0090] The multi-dimensional verification rule set is a set of pre-defined logical judgment rules used to comprehensively analyze transient acoustic characteristics, movement trajectories, and the operating status information of noise-related devices. These rules can be formed based on expert experience, historical data analysis, or machine learning models, with the goal of identifying correlations and inconsistencies between multi-dimensional information. For example, the rules might stipulate that: if the acoustic fingerprint matches a known noise source and the corresponding device's power consumption is within the normal range, it is determined to be a real noise source; if the acoustic fingerprint matches but the device is in a shutdown state, it is determined to be an abnormal situation and proceeds to subsequent processing.

[0091] In practical applications, based on the cross-validation results, it is confirmed whether each independent acoustic signal corresponds to a real noise source, and misjudgments or omissions that do not meet the judgment criteria are corrected. In other words, when the judgment is ambiguous, contradictory, or erroneous, the system will not simply accept or reject it, but will activate a correction mechanism to improve the accuracy and robustness of the recognition.

[0092] This application's solution addresses the inaccuracy of traditional cross-validation in complex industrial environments by introducing a multi-dimensional verification rule set and a correction mechanism for misjudgments or omissions. First, it acquires operational status information such as the start / stop status and power consumption of noise-related equipment, providing crucial non-acoustic data. Second, it utilizes a multi-dimensional verification rule set to comprehensively and logically judge transient acoustic features, movement trajectories, and equipment operating status, avoiding biases caused by single dimensions. For example, when an acoustic signal matches a known noise source fingerprint but the corresponding equipment is not running or has abnormal power, the rule can identify this inconsistency and trigger a correction process. Thus, based on the cross-validation results, the system can both confirm the actual noise source and correct situations that do not meet the judgment criteria, such as through further analysis or entering anomaly handling procedures, significantly improving the accuracy and reliability of noise source identification and avoiding scheduling decision errors caused by misjudgments or omissions.

[0093] In some preferred embodiments, it is assumed that a multi-channel acoustic sensor array in the conveyor belt drive roller area detects a new independent acoustic signal. Preliminary analysis shows that the transient acoustic characteristics of this signal highly match a preset noise fingerprint database for a certain fan, but the operating status information of the noise-associated equipment shows that the fan is currently shut down. Relying solely on acoustic fingerprint matching is prone to misjudgment. The solution in this application uses a multi-dimensional verification rule set for cross-validation logic: when the fingerprint matches but the equipment is shut down, the independent acoustic signal is marked as pending confirmation; subsequently, anomaly pattern analysis is performed, comparing the transient acoustic characteristics of the independent acoustic signal with the movement trajectory and the fan's historical operating status information within a preset time window. For example, if historical data shows that the fan has never generated such an acoustic signal when shut down, the independent acoustic signal can be marked as a non-real noise source; conversely, if history shows that the fan occasionally generates similar noise due to inertia or malfunction when shut down, and the current acoustic characteristics match historical anomaly patterns, it can be confirmed as a real noise source and trigger corresponding maintenance recommendations. When the wind turbine resumes normal operation, the system will re-perform cross-validation logic judgment on the independent acoustic signals in the pending confirmation state according to the multi-dimensional verification rule set, and update the confirmation conclusion of whether they are real noise sources to ensure the dynamic accuracy of the identification results.

[0094] In another embodiment of this application, a scheme is further proposed to confirm whether each independent acoustic signal corresponds to a real noise source based on the cross-validation results, and to correct misjudgments or omissions in cases that do not meet the judgment conditions. Specifically, this includes: A1: When the cross-validation result indicates that the transient acoustic characteristics of the independent acoustic signal match the preset noise source fingerprint database, but the operating status information of the noise-associated device is inconsistent or abnormal, the corresponding independent acoustic signal will be marked as pending confirmation. A2: For the pending status, perform anomaly pattern analysis, compare the historical correlation between transient acoustic features, movement trajectory and noise-related equipment operating status information within a preset time window, and obtain the historical correlation analysis results; A3: When the historical correlation analysis results show that the independent acoustic signal appears when the noise correlation device is abnormal or not started, and matches the transient acoustic characteristics collected at the end of the preset time window, the independent acoustic signal is confirmed as the real noise source. A4: When the historical correlation analysis results show that the independent acoustic signal does not appear when the noise correlation device is abnormal or not started, and / or its transient acoustic characteristics do not match the transient acoustic characteristics collected at the end of the preset time window, the independent acoustic signal will be marked as a non-real noise source. A5: When the operating status information of the noise-related equipment returns to normal, cross-validation logic is re-performed on the independent acoustic signals in the pending confirmation state according to the multi-dimensional verification rule set, and the confirmation conclusion on whether they are real noise sources is updated.

[0095] Specifically, when the cross-validation result indicates that the transient acoustic characteristics of an independent acoustic signal match the preset noise source fingerprint database, but the operating status information of the noise-associated device is inconsistent or abnormal (e.g., the device is not started but its characteristic acoustic signal is detected, or the device is running but the acoustic characteristics do not match), the system does not immediately draw a conclusion, but marks the independent acoustic signal as pending confirmation, in order to avoid misjudgment caused by transient abnormalities or information mismatch.

[0096] For states awaiting confirmation, the system performs anomaly pattern analysis. Within a preset time window, it compares and evaluates the historical correlation between transient acoustic characteristics, movement trajectories, and the operational status information of noise-related equipment. For example, it traces whether the signal has a stable correspondence with the start / stop status of the corresponding equipment in past periods, or whether it occurs frequently during equipment malfunctions, thus more comprehensively determining the authenticity of the signal.

[0097] If the historical correlation analysis results indicate that the independent acoustic signal appears when the noise-correlated device is malfunctioning or not started, and matches the transient acoustic characteristics collected at the end of the preset time window, it is identified as a real noise source; otherwise, if the signal does not appear when the device is malfunctioning or not started, and / or its transient acoustic characteristics do not match the transient acoustic characteristics collected at the end of the preset time window, it is marked as a non-real noise source to exclude occasional interference or misidentification.

[0098] In addition, when the operating status information of the noise-related equipment returns to normal, the system re-executes the cross-validation logic judgment on the independent acoustic signals in the pending confirmation state according to the multi-dimensional verification rule set, updates the confirmation conclusion of whether it is a real noise source, and realizes dynamic verification based on state recovery.

[0099] By introducing analysis of unconfirmed states and abnormal patterns, and combining this with historical correlation analysis, this solution effectively reduces the risk of misjudgment and omission in traditional cross-validation when there is inconsistency or anomaly in the operating status information of noise-related equipment. Multi-dimensional, time-series joint analysis enables the system to more accurately distinguish between real noise sources and interference signals, thereby improving the reliability of extracting belt slippage-related features and estimating the actual operating speed of the conveyor belt. This provides a more robust data foundation for coal resource scheduling, reduces additional energy consumption and downstream compensation costs, and improves overall economic efficiency.

[0100] In some preferred embodiments, the following scenario can be considered: A new, independent acoustic signal is detected in the conveyor belt drive roller area. Its transient acoustic characteristics highly match the noise fingerprint of a certain fan, but the operating status information shows that the fan is stopped or its power consumption is significantly low. In this case, the system marks the signal as pending confirmation and performs anomaly mode analysis within a preset time window: comparing the transient acoustic characteristics of the signal with its movement trajectory, and the historical operating status of the fan. If the historical record shows that the fan has never produced such an acoustic signal during the stop phase, or if the current characteristics do not match the transient acoustic characteristics collected at the end of the preset time window, the signal is marked as a non-real noise source to avoid misjudging external interference or momentary sensor malfunctions as equipment noise. Conversely, if the historical record indicates that the fan occasionally generates similar noise when it is abnormal or not running, and the current characteristics match the transient acoustic characteristics collected at the end of the preset time window, it is confirmed as a real noise source, and corresponding maintenance suggestions can be triggered. Subsequently, when the wind turbine resumes normal operation, the system again performs cross-validation logic judgment based on the multi-dimensional verification rule set. If the acoustic characteristics are consistent with the normal operation status, the confirmation conclusion is updated to ensure the dynamic accuracy of the identification results.

[0101] Reference Figure 2 This application proposes a coal resource scheduling system, comprising: Signal acquisition module 1 is used to acquire the operating status signal of the conveyor belt drive roller area, the theoretical speed of the drive motor and the theoretical belt speed calculated accordingly, the instantaneous weight of material per unit length output by the weighing device, and economic parameters. Speed ​​calculation module 2 is used to obtain belt slippage related characteristics based on the operating status signal, combine the belt slippage related characteristics with the theoretical belt speed and the instantaneous material weight per unit length, and calculate the actual operating speed of the conveyor belt according to the pre-calibrated mapping relationship. The conveying capacity calculation module 3 is used to calculate the corrected instantaneous material conveying capacity based on the actual operating speed and the instantaneous weight of material per unit length, and to characterize the conveying capacity deviation by the relative difference between the theoretical belt speed and the actual operating speed. Energy consumption monitoring module 4 is used to monitor the real-time power of the drive motor and calculate the additional energy consumption within the monitoring period based on the baseline power established under non-slip conditions. Cost accounting module 5 is used to monitor the operating status and energy consumption of downstream related equipment, the fuel consumption of downstream coal-using equipment, and the consumption of environmental protection agents of flue gas treatment devices, and to calculate the downstream compensation cost caused by the deviation in conveying capacity in combination with economic parameters. Report generation module 6 is used to summarize additional energy consumption and downstream compensation costs and calculate cumulative economic losses, generating an economic benefit and energy loss report; The decision guidance module 7 is used to output maintenance suggestions or scheduling correction instructions when the cumulative economic loss and / or the deviation of the transmission capacity reaches a preset threshold.

[0102] The coal resource dispatching system of this application integrates multiple functional modules to achieve refined perception of the conveyor belt's operating status, accurate calculation of actual conveying capacity, quantitative assessment of multi-dimensional economic losses, and intelligent decision support.

[0103] The above are merely embodiments of this application and are 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. A coal resource scheduling method, characterized in that, include: The system acquires the operating status signal of the conveyor belt drive roller area, the theoretical speed of the drive motor, and the theoretical belt speed, the instantaneous weight of material per unit length output by the weighing device, and economic parameters. Based on the operating status signal, belt slippage-related characteristics are obtained. These characteristics are then combined with the theoretical belt speed and the instantaneous weight of material per unit length. The actual operating speed of the conveyor belt is calculated based on a pre-calibrated mapping relationship. The corrected instantaneous material conveying capacity is calculated based on the actual operating speed and the instantaneous weight of material per unit length, and the relative difference between the theoretical belt speed and the actual operating speed is used to characterize the conveying capacity deviation. Monitor the real-time power of the drive motor and calculate the additional energy consumption during the monitoring period based on the baseline power established under no slippage conditions; Monitor the operating status and energy consumption of downstream related equipment, the fuel consumption of downstream coal-using equipment, and the environmental protection agent consumption of flue gas treatment devices, and calculate the downstream compensation cost caused by the deviation in the conveying capacity based on the economic parameters. Summarize the additional energy consumption and the downstream compensation costs, calculate the cumulative economic loss, and generate an economic benefit and energy loss report; When the cumulative economic loss and / or the deviation in transport capacity reach a preset threshold, a maintenance suggestion or a scheduling correction instruction is output.

2. The coal resource scheduling method according to claim 1, characterized in that, Based on the operating status signal, belt slippage-related characteristics are obtained. These characteristics are then combined with the theoretical belt speed and the instantaneous weight of material per unit length. The actual operating speed of the conveyor belt is calculated based on a pre-calibrated mapping relationship, including: The operating status signal includes a multi-channel acoustic data stream collected by a multi-channel acoustic sensor array deployed in the conveyor belt drive roller area. The multi-channel acoustic data stream is spatially located and its acoustic characteristics are calibrated to obtain the spatial direction of the slippage sound source and at least one noise source, as well as calibration parameters for spatial filtering. Based on spatial orientation and calibration parameters, the multi-channel acoustic data stream is spatially filtered to enhance the signal from the slippage sound source direction and attenuate the signal from the noise source direction, thereby obtaining a focused signal. Extract the belt slippage-related features from the focused signal; By combining the belt slippage characteristics with the theoretical belt speed and the instantaneous weight of material per unit length, the actual operating speed of the conveyor belt is calculated based on the mapping relationship.

3. The coal resource scheduling method according to claim 2, characterized in that, Spatial localization of the multi-channel acoustic data stream and acoustic characteristic calibration to obtain the spatial orientation of the slippage sound source and at least one noise source, as well as calibration parameters for spatial filtering, including: Based on the multi-channel acoustic data stream combined with the conveyor belt operating status, dust suppression system start / stop status, and noise-related equipment operating status information, specific time periods where only slippage sound sources or only noise sources exist are identified. Within the specified time period, the current spatial orientation of the slippage sound source and at least one noise source relative to the multi-channel acoustic sensor array is determined using the time difference of arrival and / or a generalized cross-correlation algorithm. Within the specified time period, the spectral characteristics, energy distribution, and directional characteristics of the slippage sound source and the noise source are analyzed to obtain their current calibration parameters; When a new noise source is detected or the spatial orientation of an existing noise source changes, spatial localization and acoustic characteristic calibration of the new or changed noise source are triggered to update the spatial orientation and calibration parameters. Based on the real-time determined spatial orientation and calibration parameters, update the parameters of the spatial filtering process, including beamforming weights and / or null orientation, to maintain the signal-to-noise ratio of the focused signal and continuously enhance the signal from the slippage sound source direction while attenuating the signal from the noise source direction.

4. The coal resource scheduling method according to claim 3, characterized in that, Based on the real-time determined spatial orientation and calibration parameters, the parameters of the spatial filtering process are updated, including beamforming weights and / or null directions, including: The movement trajectory and transient acoustic characteristics of the noise source are monitored in real time. The movement trajectory is determined by the change of the spatial orientation of the noise source relative to the multi-channel acoustic sensor array over time, and the transient acoustic characteristics are determined by the temporal changes of the spectrum and energy distribution of the multi-channel acoustic data stream. Based on the movement trajectory, predict the spatial direction of the noise source in the next time period; Based on the transient acoustic characteristics, the calibration parameters of the noise source in the next time period are predicted; Based on the predicted spatial orientation and calibration parameters, the parameters for the next time period are pre-calculated within the current time period. The parameters for the spatial filtering process include the beamforming weights and / or the null orientation.

5. The coal resource scheduling method according to claim 1, characterized in that, When the cumulative economic loss and / or the transport capacity deviation reaches a preset threshold, a maintenance suggestion or scheduling correction instruction is output, including: Based on the aforementioned economic benefits and energy loss report, calculate the growth rate of the cumulative economic loss and the trend index of the transmission capacity deviation; A cost forecasting model for the planning period is established based on the aforementioned economic parameters to predict the changes in the unit price of electricity, the unit price of fuel, and the unit price of environmental protection agents during the preset planning period, thereby obtaining a cost forecasting sequence. Under various preset scenarios, the total cost for the planning period is estimated based on the cost prediction sequence, the growth rate, and the trend indicators. The total cost for the planning period includes the expected additional energy consumption, the downstream compensation cost, maintenance costs, and downtime loss costs. Determine the dynamic adjustment amount between the candidate maintenance time point that minimizes the difference in the total cost of the planning period and the preset threshold, and define maintenance conditions. The maintenance conditions include at least: at any candidate maintenance time point, the difference in the total cost of the planning period under the multiple scenarios is not less than the preset maintenance trigger threshold, and / or the growth rate of the cumulative economic loss and the trend index of the deviation of the transport capacity meet the preset joint threshold. When the maintenance conditions are met, the maintenance suggestion is generated; When the maintenance conditions are not met, the scheduling correction instruction is generated. The scheduling correction instruction includes at least: adjusting the operating time and start-up / shutdown sequence of the downstream related equipment, adjusting the fuel consumption plan of the downstream coal-using equipment, and adjusting the environmental agent dosing strategy of the flue gas treatment device.

6. The coal resource scheduling method according to claim 4, characterized in that, Real-time monitoring of the movement trajectory and transient acoustic characteristics of the noise source, including: During the initialization phase or when a change in a noise source is detected, spatial localization and acoustic characteristic calibration are performed for each noise source to obtain its initial spatial orientation and calibration parameters. By applying a multi-target sound source separation algorithm and combining the initial spatial orientation and calibration parameters of each noise source, the signal components corresponding to each noise source in the multi-channel acoustic data stream are separated into independent acoustic signals corresponding to each noise source. Time-frequency analysis was performed on each separated independent acoustic signal to extract its transient acoustic features; Using the independent acoustic signals, and employing time difference of arrival and / or generalized cross-correlation algorithms, the current spatial orientation of each noise source relative to the multi-channel acoustic sensor array is determined and continuously updated to construct the movement trajectory of each noise source.

7. The coal resource scheduling method according to claim 6, characterized in that, A multi-target sound source separation algorithm is applied, combining the initial spatial orientation and calibration parameters of each noise source, to separate the signal components corresponding to each noise source in the multi-channel acoustic data stream into independent acoustic signals corresponding to each noise source, including: By combining the multi-channel acoustic data stream, the conveyor belt operating status, the corrected instantaneous material conveying volume, and the real-time power of the drive motor, it is determined whether a transient change has occurred in the acoustic scene. When it is determined that a transient change has occurred in the acoustic scene, sound source activity detection is performed on the multi-channel acoustic data stream to identify potential active sound source regions; The acoustic characteristics of the potential active sound source region are compared with the preset slip sound source fingerprint database and environmental background noise fingerprint database to obtain category determination and / or similarity index. For a sound source region whose similarity to the slipping sound source fingerprint database or the environmental background noise fingerprint database is greater than a preset similarity threshold, a lower noise source confidence weight is assigned and / or it is excluded from the noise source separation target in the multi-target sound source separation algorithm according to a preset weighting rule. For a sound source region whose similarity to the slipping sound source fingerprint database or the environmental background noise fingerprint database is less than a preset similarity threshold, and whose similarity to at least one noise source fingerprint in the preset noise source fingerprint database is greater than a preset noise threshold, it is added as a new noise source to the noise source separation target, and a higher noise source confidence weight is assigned according to the preset weighting rules in the multi-target sound source separation algorithm. During the execution of the multi-target sound source separation algorithm, the separation strategy is dynamically adjusted according to the confidence weight of the noise source; The separated independent acoustic signals are cross-validated, which includes comparing them with historical noise source movement trajectories, acoustic fingerprints, and / or the operating status information of the noise-associated devices to confirm whether they are real noise sources.

8. The coal resource scheduling method according to claim 7, characterized in that, Cross-validation is performed on each of the separated independent acoustic signals. This cross-validation includes comparing the signals with historical noise source movement trajectories, acoustic fingerprints, and / or the operational status information of the noise-associated devices to confirm whether they are genuine noise sources. Obtain the operating status information of the noise-associated device, the operating status information including the start / stop status and power consumption of the noise-associated device; Based on a preset multi-dimensional verification rule set, cross-verification logic is performed on the transient acoustic features, the movement trajectory, and the operating status information of the noise-related device to obtain the cross-verification judgment result. Based on the cross-validation results, it is confirmed whether each independent acoustic signal corresponds to a real noise source, and misjudgments or omissions are corrected for cases that do not meet the judgment conditions.

9. The coal resource scheduling method according to claim 8, characterized in that, Based on the cross-validation results, it is confirmed whether each independent acoustic signal corresponds to a real noise source, and misjudgments or omissions are corrected for cases that do not meet the judgment criteria, including: When the cross-validation result indicates that the transient acoustic characteristics of the independent acoustic signal match the preset noise source fingerprint database, but the operating status information of the noise-associated device is inconsistent or abnormal, the corresponding independent acoustic signal is marked as pending confirmation. For the unconfirmed state, anomaly pattern analysis is performed. Within a preset time window, the historical correlation between the transient acoustic features, the movement trajectory, and the operating status information of the noise-related device is compared to obtain the historical correlation analysis results. When the historical correlation analysis results show that the independent acoustic signal appears when the noise correlation device is abnormal or not started, and matches the transient acoustic characteristics collected at the end of the preset time window, the independent acoustic signal is confirmed as a real noise source. When the historical correlation analysis results show that the independent acoustic signal did not appear when the noise correlation device was abnormal or not started, and / or its transient acoustic characteristics did not match the transient acoustic characteristics collected at the end of the preset time window, the independent acoustic signal was marked as a non-real noise source. When the operating status information of the noise-associated device returns to normal, the independent acoustic signal in the pending confirmation state is cross-validated again according to the multi-dimensional verification rule set, and the confirmation conclusion of whether it is a real noise source is updated.

10. A coal resource dispatching system, characterized in that, include: The signal acquisition module is used to acquire the operating status signal of the conveyor belt drive roller area, the theoretical speed of the drive motor and the theoretical belt speed calculated accordingly, the instantaneous weight of material per unit length output by the weighing device, and economic parameters. The speed calculation module is used to obtain belt slippage related characteristics based on the operating status signal, combine the belt slippage related characteristics with the theoretical belt speed and the instantaneous material weight per unit length, and calculate the actual operating speed of the conveyor belt according to the pre-calibrated mapping relationship. The conveying capacity calculation module is used to calculate the corrected instantaneous material conveying capacity based on the actual operating speed and the instantaneous material weight per unit length, and to characterize the conveying capacity deviation by the relative difference between the theoretical belt speed and the actual operating speed. The energy consumption monitoring module is used to monitor the real-time power of the drive motor and calculate the additional energy consumption during the monitoring period based on the baseline power established under no slippage conditions. The cost accounting module is used to monitor the operating status and energy consumption of downstream related equipment, the fuel consumption of downstream coal-using equipment, and the environmental protection agent consumption of flue gas treatment devices, and calculate the downstream compensation cost caused by the deviation in the conveying capacity in combination with the economic parameters. The report generation module is used to summarize the additional energy consumption and the downstream compensation costs and calculate the cumulative economic loss to generate an economic benefit and energy loss report. The decision guidance module is used to output maintenance suggestions or scheduling correction instructions when the cumulative economic loss and / or the transmission capacity deviation reaches a preset threshold.

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