Crushing port cooperative unloading method and device, storage medium and electronic equipment

Abstract

This invention relates to the field of dump truck technology, specifically disclosing a method, apparatus, storage medium, and electronic device for coordinated unloading at a crushing point. The method includes: acquiring multi-source sensor data and performing time synchronization and robust preprocessing on the multi-source sensor data to obtain multi-source sensor state variables that meet a pre-set confidence level; performing congestion assessment and prediction based on the multi-source sensor state variables and the expected arrival time series of dump trucks, obtaining congestion assessment and prediction results; constructing a target optimization function based on the congestion assessment and prediction results, dump truck queue information, and target constraint information, obtaining vehicle optimization command information and crushing point equipment setpoints; performing hierarchical control based on the vehicle optimization command information and crushing point equipment setpoints to obtain control feedback results; and performing state management based on the control feedback results. The coordinated unloading method at a crushing point provided by this invention can achieve coordinated control of material unloading at the crushing point.

Description

Technical Field

[0001] This invention relates to the field of dump truck technology, and in particular to a method for co-unloading through a break-in opening, a device for co-unloading through a break-in opening, a storage medium, and electronic equipment. Background Technology

[0002] In the primary crushing stage of the mining and aggregate industry, materials are typically delivered to the crushing inlet (above the hopper / funnel / grate screen) by dump trucks (or loaders), and then continuously conveyed to jaw, impact, or cone crushers via feeders and belt conveyors. This stage is characterized by intermittent feeding, strong impact loads, and large fluctuations in operating conditions: vehicle arrivals are both batch-based and random, and the particle size and mud content fluctuate significantly, leading to frequent problems such as short-term overloading at the crushing inlet, material column bridging, feeder slippage, and severe fluctuations in crusher current. On the other hand, the working environment is characterized by high dust levels, strong vibrations, and poor visibility. Human intervention and driver experience inevitably result in lag in response and inconsistent coordination, making it difficult to balance high-yield steady-state operation with equipment safety.

[0003] Existing production lines mostly adopt a "segmented optimization, local control" approach: vehicles queue along fixed routes, guided by weighbridges or walkie-talkies; level gauges or radar ranging are installed above the crushing inlet; the feeder and belt conveyor are controlled by PLC + frequency converter according to thresholds or simple PID; the crusher side uses current, vibration, or power as protection and load limiting signals. Although the above solutions can achieve basic interlocking and protection at the equipment level, due to the lack of prediction and coordination of the overall "vehicle-machine-material" process, common phenomena are: the silo is full of material when a vehicle arrives and empty when the vehicle leaves. The system fluctuates in a cycle of "overload-current limiting-starvation-feeding", making it difficult to optimize hourly throughput and unit energy consumption.

[0004] Furthermore, congestion and blockage at the crushing inlet exhibit significant characteristics of "random triggering + continuous state": when the feed particle size exceeds the limit or the fine material content is insufficient, the grate screen is prone to bridging; when the particle size is concentrated and the instantaneous flow rate is too large, the impact of the material column causes the machine to stall; when the feeding and discharging cycle is mismatched with the vehicle unloading cycle, the filling degree in the bin rapidly exceeds the safe range. Traditional single-threshold alarm strategies often only trigger flow restriction or shutdown after an "abnormality has occurred," lacking a forward-looking assessment of the congestion index, blockage probability, and short-term capacity, which both delays the rescue window and increases the frequency of clearing blockages and downtime losses.

[0005] With the development of the Industrial Internet of Things (IIoT) and edge computing, production lines are gradually incorporating sensing technologies such as level radar, belt scales, current / vibration sensors, video analytics, and UWB / GNSS positioning, as well as communication and control infrastructure such as 5G / Industrial Ethernet, inverter buses, and SCADA systems. However, these sensors and subsystems often exist in "information silos": vehicle scheduling systems and equipment control systems lack a unified clock and data semantics, making it difficult to integrate ETA (Estimated Time of Arrival), loading quality, and granular information into the control loop; video recognition and vibration diagnosis operate independently, failing to effectively couple with feeding control; and SCADA systems are primarily used for reporting and monitoring, unable to support real-time optimal decision-making and rolling scheduling.

[0006] At the scheduling level, simplified rules such as "first-come, first-served" or "fixed lanes / timed windows" are typically adopted on-site, without considering the instantaneous accepting capacity of the crusher, the changing trend of the bin's filling degree, bottlenecks in the discharge system, and the receiving capacity of the downstream stockpile. The lack of a global cost function aimed at "stable flow" leads to vehicle congestion at the crushing outlet, unnecessary idling and waiting, increased fuel consumption and carbon emissions, and increased safety hazards caused by lane-jumping and sudden stops. Even when a few projects attempt to use rules of thumb to constrain "intermittent entry and limited unloading," the lack of adaptive and predictive capabilities makes it difficult to adapt to seasonal changes in ore properties, equipment degradation, or differences in shift operations.

[0007] At the control level, feeders and belt conveyors mainly rely on PID or segmented logic control, with setpoints subjectively adjusted manually based on current and material level. This makes it difficult to simultaneously meet the multi-objective requirements of "stable feeding, anti-blocking, and maximum throughput." PID has limited effectiveness in conditions with strong time delays, strong nonlinearity, and constraint saturation, and is prone to integral saturation, frequent start-stops, and oscillations. Even advanced control methods such as model predictive control (MPC), even when implemented in some projects, often suffer from model mismatch and insufficient robustness due to the lack of predictable inputs and granularity distribution of upstream disturbances, making it difficult to continuously leverage their advantages.

[0008] In summary, existing technologies generally suffer from the following common shortcomings: First, they lack an integrated coordination mechanism across vehicles, crushing outlets, and feeding and discharging equipment, making it impossible to incorporate transportation scheduling and process control into the same optimization framework; second, they lack forward-looking modeling and quantitative indicators for congestion and blockage risks, with control actions mostly being ex-post interventions; third, although data can be collected, it is difficult to integrate, making it impossible to support rolling prediction and closed-loop optimization; fourth, the execution side lacks coordination strategies and state machine designs oriented towards the goals of "stable flow - anti-blockage - high production," resulting in the system operating in a suboptimal state for a long time.

[0009] Therefore, how to provide a method for coordinated unloading at the crushing outlet to achieve coordinated matching of vehicle entry, unloading cycle and feeding / discharging capacity has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0010] This invention provides a method, device, storage medium, and electronic equipment for coordinated unloading of crushed material outlets, which solves the problem in related technologies where coordinated unloading of crushed material outlets leads to a high risk of unloading blockage and affects unloading efficiency.

[0011] As a first aspect of the present invention, a method for coordinated unloading of a breakout is provided, comprising:

[0012] Acquire multi-source sensor data information, and perform time synchronization and robust preprocessing on the multi-source sensor data information to obtain multi-source sensor state quantities that meet the preset confidence level. The multi-source sensor data information includes at least: state data inside the crushing outlet hopper, state data of the crushing outlet equipment, and dynamic data of the dump truck.

[0013] Congestion assessment and prediction are performed based on the multi-source sensor state quantities and the expected arrival time series of dump trucks to obtain congestion assessment and prediction results. The congestion assessment and prediction results include at least the congestion risk information for the next preset time period and the maximum allowable unloading rate at the preset time.

[0014] Based on the congestion assessment and prediction results, dump truck queue information, and target constraint information, a target optimization function is constructed to obtain vehicle optimization instruction information and crushing equipment setting values. The target constraint information includes at least the crushing hopper capacity safety constraint, vehicle safety constraint, unloading impact suppression constraint, and main unit protection constraint.

[0015] Based on the vehicle optimization instruction information and the set value of the crushing equipment, hierarchical control is performed to obtain control feedback results. The hierarchical control includes outer loop control and inner loop control. The outer loop control is used to realize vehicle operation control, and the inner loop control is used to realize crushing equipment control.

[0016] Status management is performed based on the control feedback results to achieve coordinated unloading of dump trucks at the crushing outlet.

[0017] Furthermore, the multi-source sensor data information is time-synchronized and robustly preprocessed to obtain multi-source sensor state quantities that meet a preset confidence level, including:

[0018] The multi-source sensor data information is synchronized in time according to a reference clock;

[0019] Denoising and compensation processing is performed on the time-synchronized crushing outlet silo status data, window filtering processing is performed on the crushing outlet equipment status data, and adaptive correction processing is performed on the dump truck dynamic data to obtain robust preprocessing results.

[0020] The robust preprocessing results are fused from multiple sources and jointly estimated based on adaptive particle filtering. The observation weights are dynamically adjusted based on the data quality and observation noise of the multi-source sensor data to obtain multi-source sensor state quantities that meet the preset confidence level. The multi-source sensor state quantities include at least the crushing inlet hopper filling degree, filling change rate, mass flow rate, 50% particle size, and crushing host load margin.

[0021] Furthermore, congestion assessment and prediction are performed based on the multi-source sensor state variables and the expected arrival time series of dump trucks, including:

[0022] The congestion index is calculated based on multi-source sensor state variables and the expected arrival time series of dump trucks.

[0023] Congestion assessment and prediction are performed based on the basic trend prediction model and the perturbation correction model to obtain congestion risk information for the next preset time period and the maximum allowable unloading rate at the preset time. The basic trend prediction model is used to predict the periodic vehicle arrival pattern, and the perturbation correction model is used to input the rate of change of the congestion index and the granularity anomaly label into the LSTM network for perturbation correction to obtain the future fill degree sequence and host load.

[0024] Furthermore, the formula for calculating the congestion index is as follows:

[0025] ,

[0026] Where CI represents the congestion index. Indicates the probability of congestion. This represents the estimated fill factor. This represents the estimated mass flow rate. , , and All represent weights. This indicates the maximum allowable filling degree threshold or the filling degree corresponding to the maximum effective silo capacity of the crushing feed silo under safe operating conditions. This indicates the rated mass flow rate allowed by the crushing outlet feeding system or the crushing host under rated operating conditions. This represents the vehicle disturbance coefficient determined based on the expected arrival time series of dump trucks.

[0027] Furthermore, based on the congestion assessment and prediction results, dump truck queue information, and target constraint information, a target optimization function is constructed, including:

[0028] The objective optimization function is constructed based on the congestion assessment and prediction results and the dump truck queue information.

[0029] The objective optimization function is solved in descending order of priority according to the objective constraint information. The preset solution order includes, in descending order of priority, a vehicle scheduling sub-problem for determining the entry order and an equipment control sub-problem for determining the vehicle trajectory.

[0030] Based on the solution of the objective optimization function, output vehicle optimization command information and crushing equipment setting values.

[0031] Furthermore, based on the vehicle optimization instruction information and the set value of the crushing equipment, hierarchical control is performed to obtain control feedback results, including:

[0032] Perform outer ring optimization based on the vehicle optimization instruction information to trigger vehicle driving instructions;

[0033] Inner loop control is performed based on the set values ​​of the crushing equipment to achieve the setting of the crushing equipment parameters;

[0034] The frequency converter command, gate servo motor control signal, and human-machine interaction command are obtained based on the outer loop optimization and the inner loop control.

[0035] Furthermore, state management is performed based on the control feedback results, including:

[0036] Based on the control feedback results, a matching state machine is invoked for state management, wherein the state machine includes: executing optimization instructions in normal steady flow state; limiting flow and slowing down vehicles in mild congestion state; closing some gates, prohibiting new vehicles from entering the site, and starting the impact mechanism in severe congestion state; and using historical average particle size, fixed crushing equipment frequency, and manual vehicle scheduling in degraded operation state.

[0037] As another aspect of the present invention, a fracture joint unloading device is provided for implementing the fracture joint unloading method described above, wherein the device includes:

[0038] The sensing module is used to acquire multi-source sensor data information, and to perform time synchronization and robust preprocessing on the multi-source sensor data information to obtain multi-source sensor state quantities that meet the preset confidence level. The multi-source sensor data information includes at least: state data inside the crushing outlet hopper, state data of the crushing outlet equipment, and dynamic data of the dump truck.

[0039] The state assessment and prediction module is used to perform congestion assessment and prediction based on the multi-source sensor state quantities and the expected arrival time series of dump trucks, and obtain congestion assessment and prediction results. The congestion assessment and prediction results include at least the congestion risk information for the next preset time period and the maximum allowable unloading rate at the preset time.

[0040] The collaborative optimization and decision-making module is used to construct a target optimization function based on the congestion assessment and prediction results, dump truck queue information and target constraint information, and obtain vehicle optimization instruction information and crushing equipment setting value. The target constraint information includes at least the crushing hopper capacity safety constraint, vehicle safety constraint, unloading impact suppression constraint and host protection constraint.

[0041] The execution and feedback module is used to obtain control feedback results by performing hierarchical control based on the vehicle optimization instruction information and the set value of the crushing equipment. The hierarchical control includes an outer loop control and an inner loop control. The outer loop control is used to realize the vehicle operation control, and the inner loop control is used to realize the crushing equipment control.

[0042] The status management module is used to manage the status based on the control feedback results, so as to realize the coordinated unloading of dump trucks at the crushing outlet.

[0043] As another aspect of the invention, a storage medium is provided for storing a computer program that is executed by a processor to implement the aforementioned collaborative unloading method for the breakout.

[0044] As another aspect of the present invention, an electronic device is provided, comprising a memory and a processor, the processor being communicatively connected to the memory, the memory being used to store a computer program, and the processor being used to load and execute the computer program to implement the aforementioned collaborative unloading method for the broken opening.

[0045] The collaborative unloading method at the crushing point provided by this invention acquires multi-source sensor data, performs congestion assessment and prediction based on this data, then implements hierarchical control based on the congestion assessment and prediction results, and finally performs state management based on the control feedback results, thereby achieving collaborative unloading of dump trucks at the crushing point. This collaborative unloading method at the crushing point constructs a five-layer closed-loop architecture of perception-assessment-decision-execution-feedback, enabling coordinated matching control of vehicle entry, unloading cycle time, and feeding / discharging capacity. Attached Figure Description

[0046] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the following detailed description to explain the invention, but do not constitute a limitation thereof.

[0047] Figure 1 The flowchart of the collaborative unloading method for the broken opening provided by the present invention.

[0048] Figure 2 This invention provides a flowchart for time synchronization and robust preprocessing of multi-source sensor data.

[0049] Figure 3 The flowchart for congestion assessment and prediction provided by this invention.

[0050] Figure 4 A flowchart for constructing the target optimization function provided by the present invention.

[0051] Figure 5 A flowchart for hierarchical control provided by the present invention.

[0052] Figure 6 This is a structural block diagram of the crushing port co-unloading device provided by the present invention.

[0053] Figure 7 This is a structural block diagram of the electronic device provided by the present invention. Detailed Implementation

[0054] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0055] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0056] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of the invention described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0057] This embodiment provides a method for collaborative unloading at the breakout point. Figure 1 This is a flowchart of the collaborative unloading method for the fracture opening provided according to an embodiment of the present invention, such as... Figure 1 As shown, it includes:

[0058] S100. Acquire multi-source sensor data information, and perform time synchronization and robust preprocessing on the multi-source sensor data information to obtain multi-source sensor state quantities that meet the preset confidence level. The multi-source sensor data information includes at least: state data inside the crushing outlet hopper, state data of the crushing outlet equipment, and dynamic data of the dump truck.

[0059] In this embodiment of the invention, the multi-source sensing data information can be obtained through multi-source sensors. Specifically, the status data inside the crushing outlet hopper can include the filling height sequence provided by the material level radar, the instantaneous mass flow rate collected by the belt scale, the block size distribution histogram and mud content image captured by the industrial camera, the status data of the crushing outlet equipment can include the crusher current / vibration sensor signal and the frequency feedback of the feeder frequency converter, and the status data of the dump truck can include the position / speed information provided by the UWB positioning tag and the load and estimated arrival time uploaded by the vehicle terminal.

[0060] S200. Congestion assessment and prediction are performed based on the multi-source sensor state quantities and the expected arrival time series of dump trucks to obtain congestion assessment and prediction results. The congestion assessment and prediction results include at least the congestion risk information for the next preset time period and the maximum allowable unloading rate at the preset time.

[0061] In this embodiment of the invention, a congestion index is calculated based on the multi-source sensor data information obtained above, and then unloading prediction is made based on the congestion index calculation result.

[0062] S300. Based on the congestion assessment and prediction results, dump truck queue information and target constraint information, construct a target optimization function to obtain vehicle optimization instruction information and crushing equipment setting value. The target constraint information includes at least the crushing hopper capacity safety constraint, vehicle safety constraint, unloading impact suppression constraint and main unit protection constraint.

[0063] In this embodiment of the invention, a target optimization function is constructed based on the above congestion assessment and prediction results, and then optimization instructions and equipment settings are obtained based on the target optimization function.

[0064] S400. Based on the vehicle optimization instruction information and the set value of the crushing equipment, hierarchical control is performed to obtain control feedback results. The hierarchical control includes outer loop control and inner loop control. The outer loop control is used to realize vehicle operation control, and the inner loop control is used to realize crushing equipment control.

[0065] In this embodiment of the invention, the vehicle and equipment are controlled accordingly based on the vehicle optimization instruction information and the set value of the crushing equipment, and the control feedback results of the vehicle and equipment are obtained.

[0066] S500. Perform state management based on the control feedback results to achieve coordinated unloading of the dump truck at the crushing outlet.

[0067] It should be understood that state management can be achieved based on the above control feedback results, and safe mode switching instructions and complete event logs can be obtained.

[0068] Therefore, the collaborative unloading method at the breakout point provided by this invention acquires multi-source sensor data, performs congestion assessment and prediction based on this data, then performs hierarchical control based on the congestion assessment and prediction results, and finally performs state management based on the control feedback results, thereby achieving collaborative unloading of dump trucks at the breakout point. This collaborative unloading method at the breakout point, by constructing a five-layer closed-loop architecture of perception-assessment-decision-execution-feedback, can achieve coordinated matching control of vehicle entry, unloading cycle time, and feeding / discharging capacity.

[0069] In this embodiment of the invention, the multi-source sensor data information is time-synchronized and robustly preprocessed to obtain multi-source sensor state quantities that satisfy a preset confidence level, such as... Figure 2 As shown, it includes:

[0070] S110. Synchronize the multi-source sensor data information according to the reference clock;

[0071] Specifically, the edge nodes use the PTP protocol as the reference clock and add nanosecond-level timestamps to all sensor data to achieve time synchronization.

[0072] S120. The status data of the crushing outlet silo after time synchronization is denoised and compensated, the status data of the crushing outlet equipment is window filtered, and the dynamic data of the dump truck is adaptively corrected to obtain robust preprocessing results.

[0073] Specifically, wavelet denoising and temperature drift compensation are applied to the material level data, adaptive brightness correction and occlusion discrimination are performed on the visual data (combined with UWB position verification), and sliding window standard deviation filtering is applied to the current / vibration signal to eliminate transient interference from mechanical impact.

[0074] S130. Perform multi-source fusion and joint state estimation on the robust preprocessing results according to the adaptive particle filter, and dynamically adjust the observation weights based on the data quality and observation noise of the multi-source sensor data information to obtain multi-source sensor state quantities that meet the preset confidence level. The multi-source sensor state quantities include at least the crushing outlet hopper filling degree, filling change rate, mass flow rate, 50% particle size, and crushing host load margin.

[0075] Specifically, joint state estimation is performed using adaptive particle filtering to establish a state vector.

[0076] ,

[0077] Where F represents the fill degree, Ḟ represents the fill change rate, Q represents the mass flow rate, G50 represents the 50% particle size, Lmargin represents the host load margin, and the observation weights are dynamically adjusted (when the visual data is valid and the vibration characteristics are stable, the weight allocation is 0.7 visual particle size + 0.3 current harmonic analysis; otherwise, 0.9 belt scale flow rate + 0.1 historical trend model is used), and high-confidence state quantities are output. And data quality labels ranging from 0 to 100, including high-confidence state quantities. for:

[0078] .

[0079] In this embodiment of the invention, congestion assessment and prediction are performed based on the multi-source sensor state variables and the expected arrival time series of dump trucks, such as... Figure 3 As shown, it includes:

[0080] S210. Calculate the congestion index based on the multi-source sensor state variables and the expected arrival time series of dump trucks.

[0081] Specifically, based on the aforementioned high-confidence state quantity The congestion index is calculated based on the ETA (Estimated Time of Arrival) sequence of dump trucks. Specifically, the formula for calculating the congestion index is as follows:

[0082] ,

[0083] Where CI represents the congestion index. Indicates the probability of congestion. This represents the estimated fill factor. This represents the estimated mass flow rate. , , and All represent weights. This indicates the maximum allowable filling degree threshold or the filling degree corresponding to the maximum effective silo capacity of the crushing feed silo under safe operating conditions. This indicates the rated mass flow rate allowed by the crushing outlet feeding system or the crushing host under rated operating conditions. This represents the vehicle disturbance coefficient determined based on the expected arrival time series of dump trucks.

[0084] It should be noted that, The (congestion probability) is output by a lightweight XGBoost model (inputs are G50, est, current fluctuation entropy, and vibration spectrum peak value), and the weights (α,β,γ,δ) are self-tuned online through a reinforcement learning reward function based on historical CI and actual congestion events.

[0085] S220. Congestion assessment and prediction are performed based on the basic trend prediction model and the perturbation correction model to obtain congestion risk information for the next preset time period and the maximum allowable unloading rate at the preset time. The basic trend prediction model is used to predict the periodic vehicle arrival pattern. The perturbation correction model is to input the rate of change of the congestion index and the granularity anomaly label into the LSTM network for perturbation correction to obtain the future fill degree sequence and host load.

[0086] In this embodiment of the invention, the rolling predictor (300-second window) employs a dual-mode prediction strategy: the basic trend is handled by the Prophet model to process periodic traffic patterns, and the perturbation correction is completed by an LSTM network (inputs are real-time CI change rate and granular anomaly markers), outputting the future fill degree sequence F̂(t+k) and host load L̂(t+k). Finally, a congestion risk heatmap for the next time period (5 minutes) is obtained. And the key feedforward quantity, namely the maximum allowable unloading rate at time t. :

[0087] .

[0088] In this embodiment of the invention, a target optimization function is constructed based on the congestion assessment and prediction results, dump truck queue information, and target constraint information, such as... Figure 4 As shown, it includes:

[0089] S310. Construct the objective optimization function based on the congestion assessment and prediction results and the dump truck queue information;

[0090] S320. Solve the target optimization function according to the target constraint information in descending order of priority according to the preset solution order, wherein the preset solution order includes, in descending order of priority, a vehicle scheduling sub-problem for determining the entry order and an equipment control sub-problem for determining the vehicle trajectory.

[0091] S330. Output vehicle optimization command information and crushing equipment setting value based on the solution result of the objective optimization function.

[0092] In this embodiment of the invention, based on the above congestion assessment and prediction results... , and vehicle queue Construct the objective optimization function:

[0093] ,

[0094] in, Includes vehicle entry sequence and single vehicle unloading limit The parameters include the gate opening curve, feeder frequency fg(t), and belt speed vb(t). Specifically, for this objective optimization function, four types of hard constraints are strictly enforced: silo capacity safety constraint (Fmin≤F̂(t+k)≤Fmax), host protection constraint (L̂(t+k)≤Lsafe), vehicle safety constraint (Tdepart,i-Tarr,i≥ΔTsafe), and unloading impact suppression constraint (dQdump / dt≤η).

[0095] The objective optimization function can be solved using a hierarchical strategy: first, solve the vehicle scheduling subproblem (MIQP) to determine the entry order and Wlimit,i; then solve the equipment control subproblem (QP) to generate the trajectory [fg(t),vb(t)]; when the optimization timeout is >500ms, activate the rule base degradation strategy. Finally, the vehicle commands (Vi entry time Tenter,i, speed limit vlimit,i, allowable unloading mass Wlimit,i) and equipment settings (feeder frequency curve fg*(t), gate opening sequence d*(t)) are obtained.

[0096] In this embodiment of the invention, hierarchical control is performed based on the vehicle optimization instruction information and the set value of the crushing equipment to obtain control feedback results, such as... Figure 5 As shown, it includes:

[0097] S410. Perform outer ring optimization based on the vehicle optimization instruction information to trigger a vehicle driving instruction.

[0098] S420. Perform inner loop control according to the set value of the crushing outlet equipment to realize the setting of the crushing outlet equipment parameters;

[0099] S430. Based on the outer loop optimization and the inner loop control, the inverter frequency command, the gate servo motor control signal and the human-machine interaction command are obtained.

[0100] Specifically, based on the above optimization layer instructions and real-time Xest, layered control is adopted: In the outer loop optimization (second-level), CItarget is updated every 2 seconds, and the base value of fg*(t) is dynamically adjusted. When CI>CIth1(0.4), a vehicle slowdown instruction is triggered (vlimit,i←0.5vlimit,i); In the inner loop control (millisecond-level), the feeder MPC controller sets the reference value to: The inverter acceleration is limited to ≤0.5Hz / s and the current to ≤85% of the rated value. The gate implements an anti-impact strategy (the opening degree increases exponentially according to d(t)=dmax·(1-e-kt) when unloading starts, and the opening degree is stepped down by 20% when dF / dt>15% / s); the abnormal feedback mechanism activates the unblocking state machine when Pblock>0.85 and G50,est>800mm. This layer outputs inverter frequency commands, gate servo motor control signals, and human-machine interaction commands (display on the dispatch screen, voice prompts on the intercom).

[0101] In this embodiment of the invention, state management based on the control feedback result includes:

[0102] Based on the control feedback results, a matching state machine is invoked for state management, wherein the state machine includes: executing optimization instructions in normal steady flow state; limiting flow and slowing down vehicles in mild congestion state; closing some gates, prohibiting new vehicles from entering the site, and starting the impact mechanism in severe congestion state; and using historical average particle size, fixed crushing equipment frequency, and manual vehicle scheduling in degraded operation state.

[0103] It should be understood that state management is specifically based on CI value, communication health, and manual emergency stop signals. In this embodiment of the invention, a six-state machine is designed: Normal steady flow state (CI < 0.4) executes optimization instructions; mild congestion state (0.4 ≤ CI < 0.7) limits flow to Qallow × 0.8 and slows vehicle movement by 20%, exiting when CI < 0.35 for 10 seconds; severe congestion state (CI ≥ 0.7 or Pblock > 0.9) closes the gate by 30%, prohibits new vehicle entry, and activates the paving mechanism, exiting only after clearing congestion and CI < 0.5; degraded operation state (critical sensor failure or communication interruption > 3 seconds) uses historical average granularity, a fixed fg = 35Hz, and manual vehicle dispatch, exiting only when communication is restored and data quality > 80 points. Finally, a safe mode switching instruction and a complete event log can be output.

[0104] Therefore, the collaborative unloading method at the crushing outlet provided by this invention can solve the long-standing problems of uncoordinated imbalance and unpredictable congestion in the entire chain of existing crushing production lines, from "vehicle entry—crushing outlet unloading—feeding / discharging conveying—main crushing." Specifically, existing technologies mostly involve point-like or threshold control of each sub-link, lacking unified modeling and prediction of bin filling degree, particle size distribution, instantaneous material flow, main crusher load, and upstream vehicle disturbances. This leads to congestion, bridging, and stalling at the crushing outlet during short-term overfeeding, and material shortages and "starvation" during vehicle flow intervals. The system frequently oscillates between overload and underload, making it difficult to balance hourly throughput, equipment energy efficiency, and stability. Specifically, this invention constructs an integrated congestion index and blockage risk quantification model for "vehicle-machine-material," enabling rolling predictions of future short-term vehicle arrival times, bin conditions, and main crusher capacity. This provides calculable feedforward quantities and optimization targets for vehicle scheduling and feeding / discharging control.

[0105] Secondly, existing vehicle scheduling generally adopts "first-come, first-served" or experience-based flow control, failing to incorporate host constraints, in-warehouse evolution, and downstream bottlenecks into unified optimization, which easily leads to crushing port aggregation, idling, and increased energy consumption. This invention specifically introduces a collaborative optimization mechanism with "stable flow, anti-blocking, and high production" as its core objectives: under constraints such as safe current / vibration, block size limits, equipment start / stop, and traffic rules, it jointly decides "which vehicle enters when, how much is unloaded, and how fast," as well as "how the set values ​​of the feeder / belt conveyor / gate evolve over time," maintaining the in-warehouse filling level and host load within the optimal operating range, significantly reducing the probability of congestion and the frequency of clearing blockages, and minimizing unnecessary idling and carbon emissions.

[0106] Furthermore, the mining environment presents challenges such as high dust levels, strong vibrations, poor line-of-sight, and unstable communication, making it difficult for multi-source sensing to reliably serve real-time control. Simultaneously, project modifications must minimize changes to the host structure and ensure compatibility with heterogeneous sensors and existing PLCs / inverters. This invention provides a systematic and feasible implementation path: At the edge, multi-source data time synchronization and robust fusion are achieved to form interpretable congestion indicators; on the control side, a hierarchical closed-loop architecture of "predictive feedforward + constraint optimization + local PID / MPC" is adopted, with an anomaly / clearing state machine designed to ensure safety; on the engineering side, existing equipment is connected via modular interfaces and standardized protocols, providing "degraded operation / manual takeover / offline replay" capabilities to ensure relatively stable material supply even with communication jitter, single-point failures, or sensor loss.

[0107] Finally, addressing the generalization challenges arising from different main crushers (jaw crusher, impact crusher, cone crusher), different feeding methods (dump truck, loader), and different particle characteristics (mud content, block size spectrum), this invention provides a configurable, transferable, and easily maintainable algorithm and system template. Through parameterized congestion models and scheduling targets, pluggable block size identification and capacity prediction components, and scalable human-machine interfaces and event loops, it enables progressive deployment and reuse from single-point applications to multiple crushing outlets, multiple material lines, and the entire mining area. Ultimately, without changing the main crusher itself, it achieves predictable, measurable, and controllable collaborative unloading of the crushing outlets, thereby increasing hourly throughput, reducing unit energy consumption, reducing downtime for clearing blockages, and improving operational safety.

[0108] In summary, the collaborative unloading method for the breakout point provided by this invention has the following advantages:

[0109] (1) Stabilize flow and prevent blockage, increase production and reduce consumption: With multi-source perception + congestion index + short-term prediction / MPC as the core, the “vehicle entry - unloading cycle - feeding / discharging setting” is optimized in a coordinated manner, turning intermittent impact feeding into continuous steady-state feeding, significantly reducing blockage / clearing and main unit current fluctuations; increasing hourly processing capacity and reducing unit energy consumption and carbon emissions without increasing the installed capacity.

[0110] (2) Vehicle-machine-material integration and safety and reliability: Rolling scheduling of "which vehicle enters when, how much is unloaded, and how fast is unloaded", linking feeder / gate / belt closed-loop control to reduce vehicle idling and on-site congestion; layered closed-loop (outer layer optimization, inner layer PID / MPC) combined with abnormal state machine and degraded operation, still maintains controllability and continuous production in the event of communication jitter, sensor loss or emergency stop.

[0111] (3) Low modification, easy expansion, and data availability: edge computing + standard bus reuse of existing PLC / sensors, with minimal modification intrusion and fast online deployment; parameterized model and pluggable components are compatible with jaw crusher / impact crusher / cone crusher and multi-crushing port collaboration; equipped with digital twin and KPI dashboard, the strategy can be simulated and verified before going online, and can be quantitatively accepted and continuously iterated after going online, with simple maintenance and replicability.

[0112] As another embodiment of the present invention, a fracture joint unloading device 100 is provided for implementing the fracture joint unloading method described above, wherein, as Figure 6 As shown, it includes:

[0113] The sensing module 110 is used to acquire multi-source sensor data information, and to perform time synchronization and robust preprocessing on the multi-source sensor data information to obtain multi-source sensor state quantities that meet the preset confidence level. The multi-source sensor data information includes at least: state data inside the crushing outlet hopper, state data of the crushing outlet equipment, and dynamic data of the dump truck.

[0114] The state assessment and prediction module 120 is used to perform congestion assessment and prediction based on the multi-source sensor state quantities and the expected arrival time series of dump trucks, and obtain congestion assessment and prediction results. The congestion assessment and prediction results include at least the congestion risk information for the next preset time period and the maximum allowable unloading rate at the preset time.

[0115] The collaborative optimization and decision-making module 130 is used to construct a target optimization function based on the congestion assessment and prediction results, dump truck queue information and target constraint information, and obtain vehicle optimization instruction information and crushing equipment setting value. The target constraint information includes at least the crushing hopper capacity safety constraint, vehicle safety constraint, unloading impact suppression constraint and host protection constraint.

[0116] The execution and feedback module 140 is used to perform hierarchical control based on the vehicle optimization instruction information and the set value of the crushing equipment to obtain control feedback results. The hierarchical control includes an outer loop control and an inner loop control. The outer loop control is used to realize the vehicle operation control, and the inner loop control is used to realize the crushing equipment control.

[0117] The status management module 150 is used to perform status management based on the control feedback results, so as to realize the coordinated unloading of dump trucks at the crushing outlet.

[0118] In this embodiment of the invention, the modules are tightly coupled through key data flows: the F̂(t) output by the rolling predictor dynamically adjusts the hard constraint Fmax of the optimization and decision-making module 130; the congestion index CI of the state evaluation and prediction module 120 serves as the objective function term for optimization, the feedforward compensation amount for MPC, and the basis for state machine transitions; when the data quality of the sensing module 110 is <60 points, a degraded operating state is automatically triggered, at which time the optimization and decision-making module 130 is shut down, and the execution and feedback module 140 adopts a conservative rule base (fg=0.7×frated, Wlimit=0.5×Wavg). In this embodiment of the invention, the breakout collaborative unloading device 100 calculates the steady flow index SI=1-(σCI / μCI) every 5 minutes, and triggers parameter self-tuning when SI<0.6, forming a "run-evaluation-optimization" closed loop. In addition, this embodiment of the invention may also include a digital twin module, which automatically downloads the operation log at 02:00 every day, replays abnormal events to generate parameter optimization suggestions (such as adjusting the weights of α and β), and achieves continuous evolution. The entire solution does not rely on host machine modification; all innovations are achieved through edge nodes, and it is compatible with existing PLCs / frequency converters via Modbus TCP / Profinet protocol.

[0119] In this embodiment of the invention, firstly, the integrated implementation of "multi-source sensing - unified state estimation - congestion / blockage modeling" is achieved. Specifically, based on a unified clock and timestamp mechanism, time synchronization, noise reduction, and anomaly removal are performed on multi-source signals such as material level radar / laser, belt scale (or weighing and feeding), host current and vibration, industrial camera block size recognition, UWB / GNSS vehicle positioning, and ETA. Extended Kalman or particle filtering is used to jointly estimate the silo filling degree F, instantaneous mass flow rate Q, host load margin, and granularity spectrum parameters. On this basis, an interpretable congestion index CI is proposed (composed of filling degree ratio, flow rate ratio, blockage probability, and optional trend term weighted, with weights supporting online self-tuning). The blockage probability is given by a lightweight model of "visual block size + load / vibration".

[0120] Secondly, rolling optimization and hierarchical closed-loop control for vehicle feeding / discharging coordination are implemented. The upper layer uses a 1-5 minute rolling time window to comprehensively predict the arrival cycle time of vehicles, the evolution within the silo, and the host's accepting capacity, jointly deciding on the vehicle entry sequence and timing, and the single-vehicle unloading limit and cycle time. The lower layer uses a 1-2 second control cycle to generate continuously set trajectories for the feeder frequency, gate opening, and belt speed through MPC / PID, forming a two-level structure of "predictive feedforward + constraint optimization + local closed loop". The objective function simultaneously minimizes the deviation of the congestion index from the objective and penalizes drastic changes in control variables, while maximizing throughput. Constraints include vehicle safety distance and passage rules, equipment current / vibration limits, silo upper and lower limits, downstream discharge capacity, and single unloading mass / acceleration limits, etc.

[0121] Furthermore, based on safety interlocks, abnormal state machines, and a deployable edge architecture, it offers advantages in engineering feasibility and security reliability. Specifically, a state machine covering "stable flow, mild congestion, severe congestion, congestion clearing, degraded operation, and manual takeover" is constructed. CI, congestion probability, current / vibration, and communication health are used as transition conditions. Control priorities and actions (such as flow limiting, vehicle slowing / restriction, soft stop, tapping or reversing to clear congestion) are defined for each state. It automatically switches to conservative settings and resumes operation from breakpoints when communication jitter or sensor failure occurs. In engineering, a containerized modular architecture using edge computing and a standard bus is adopted to decouple sensing, estimation, prediction, optimization, control, and HMI. Interconnection is achieved through a unified data model and message bus, and digital twins / offline playback are provided for pre-deployment policy verification and rapid rollback of blue-green switching / shadow instances.

[0122] In summary, the collaborative unloading device at the crushing point provided by this invention acquires multi-source sensor data, performs congestion assessment and prediction based on this data, then implements hierarchical control based on the congestion assessment and prediction results, and finally performs state management based on the control feedback results, thereby achieving collaborative unloading of dump trucks at the crushing point. This collaborative unloading device at the crushing point, by constructing a five-layer closed-loop architecture of perception-assessment-decision-execution-feedback, can achieve coordinated matching control of vehicle entry, unloading cycle time, and feeding / discharging capacity.

[0123] The specific working principle of the crushing joint unloading device provided by the present invention can be referred to the specific description of the crushing joint unloading method in the previous text, and will not be repeated here.

[0124] As another embodiment of the present invention, a storage medium is provided for storing a computer program, which is executed by a processor to implement the aforementioned collaborative unloading method for the broken opening.

[0125] In this embodiment of the invention, a non-transitory computer-readable storage medium is provided. The computer-readable storage medium stores computer-executable instructions that can execute the fracture-port cooperative unloading method in any of the above method embodiments. The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above types of memory.

[0126] As another embodiment of the present invention, an electronic device is provided, comprising a memory and a processor, wherein the processor is communicatively connected to the memory, the memory is used to store a computer program, and the processor is used to load and execute the computer program to implement the aforementioned collaborative unloading method for the broken opening.

[0127] like Figure 7As shown, the electronic device 10 may include: at least one processor 11, such as a CPU (Central Processing Unit), at least one communication interface 13, a memory 14, and at least one communication bus 12. The communication bus 12 is used to enable communication between these components. The communication interface 13 may include a display screen or a keyboard; optionally, the communication interface 13 may also include a standard wired interface or a wireless interface. The memory 14 may be high-speed RAM (Random Access Memory) or non-volatile memory, such as at least one disk drive. Optionally, the memory 14 may also be at least one storage device located remotely from the aforementioned processor 11. The memory 14 stores application programs, and the processor 11 calls the program code stored in the memory 14 to execute any of the aforementioned method steps.

[0128] The communication bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus 12 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0129] The memory 14 may include volatile memory, such as random-access memory (RAM); the memory may also include non-volatile memory, such as flash memory, hard disk drive (HDD) or solid-state drive (SSD); the memory 14 may also include a combination of the above types of memory.

[0130] The processor 11 can be a central processing unit (CPU), a network processor (NP), or a combination of CPU and NP.

[0131] The processor 11 may further include a hardware chip. This hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0132] Optionally, memory 14 is also used to store program instructions. Processor 11 can invoke program instructions to implement the present invention. Figure 1 The embodiment shows the collaborative unloading method at the breakout point.

[0133] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for coordinated unloading at a breakout point, characterized in that, include: Acquire multi-source sensor data information, and perform time synchronization and robust preprocessing on the multi-source sensor data information to obtain multi-source sensor state quantities that meet the preset confidence level. The multi-source sensor data information includes at least: state data inside the crushing outlet hopper, state data of the crushing outlet equipment, and dynamic data of the dump truck. Congestion assessment and prediction are performed based on the multi-source sensor state quantities and the expected arrival time series of dump trucks to obtain congestion assessment and prediction results. The congestion assessment and prediction results include at least the congestion risk information for the next preset time period and the maximum allowable unloading rate at the preset time. Based on the congestion assessment and prediction results, dump truck queue information, and target constraint information, a target optimization function is constructed to obtain vehicle optimization instruction information and crushing equipment setting values. The target constraint information includes at least the crushing hopper capacity safety constraint, vehicle safety constraint, unloading impact suppression constraint, and main unit protection constraint. Based on the vehicle optimization instruction information and the set value of the crushing equipment, hierarchical control is performed to obtain control feedback results. The hierarchical control includes outer loop control and inner loop control. The outer loop control is used to realize vehicle operation control, and the inner loop control is used to realize crushing equipment control. Status management is performed based on the control feedback results to achieve coordinated unloading of dump trucks at the crushing outlet.

2. The method for coordinated unloading at the breakout point according to claim 1, characterized in that, The process of time synchronization and robust preprocessing of the multi-source sensor data to obtain multi-source sensor state variables that meet a preset confidence level includes: The multi-source sensor data information is synchronized in time according to a reference clock; Denoising and compensation processing is performed on the time-synchronized crushing outlet silo status data, window filtering processing is performed on the crushing outlet equipment status data, and adaptive correction processing is performed on the dump truck dynamic data to obtain robust preprocessing results. The robust preprocessing results are fused from multiple sources and jointly estimated based on adaptive particle filtering. The observation weights are dynamically adjusted based on the data quality and observation noise of the multi-source sensor data to obtain multi-source sensor state quantities that meet the preset confidence level. The multi-source sensor state quantities include at least the crushing inlet hopper filling degree, filling change rate, mass flow rate, 50% particle size, and crushing host load margin.

3. The method for coordinated unloading at the breakout point according to claim 1, characterized in that, Congestion assessment and prediction are performed based on the multi-source sensor state variables and the expected arrival time series of dump trucks, including: The congestion index is calculated based on multi-source sensor state variables and the expected arrival time series of dump trucks. Congestion assessment and prediction are performed based on the basic trend prediction model and the perturbation correction model to obtain congestion risk information for the next preset time period and the maximum allowable unloading rate at the preset time. The basic trend prediction model is used to predict the periodic vehicle arrival pattern, and the perturbation correction model is used to input the rate of change of the congestion index and the granularity anomaly label into the LSTM network for perturbation correction to obtain the future fill degree sequence and host load.

4. The method for coordinated unloading at the breakout point according to claim 3, characterized in that, The formula for calculating the congestion index is as follows: ， Where CI represents the congestion index. Indicates the probability of congestion. This represents the estimated fill factor. This represents the estimated mass flow rate. , , and All represent weights. This indicates the maximum allowable filling degree threshold or the filling degree corresponding to the maximum effective silo capacity of the crushing feed silo under safe operating conditions. This indicates the rated mass flow rate allowed by the crushing outlet feeding system or the crushing host under rated operating conditions. This represents the vehicle disturbance coefficient determined based on the expected arrival time series of dump trucks.

5. The method for coordinated unloading of the breakout point according to claim 1, characterized in that, Based on the congestion assessment and prediction results, dump truck queue information, and target constraint information, a target optimization function is constructed, including: The objective optimization function is constructed based on the congestion assessment and prediction results and the dump truck queue information. The objective optimization function is solved in descending order of priority according to the objective constraint information. The preset solution order includes, in descending order of priority, a vehicle scheduling sub-problem for determining the entry order and an equipment control sub-problem for determining the vehicle trajectory. Based on the solution of the objective optimization function, output vehicle optimization command information and crushing equipment setting values.

6. The method for coordinated unloading at the breakout point according to claim 1, characterized in that, Based on the vehicle optimization instruction information and the set value of the crushing equipment, hierarchical control is performed to obtain control feedback results, including: Perform outer ring optimization based on the vehicle optimization instruction information to trigger vehicle driving instructions; Inner loop control is performed based on the set values ​​of the crushing equipment to achieve the setting of the crushing equipment parameters; The frequency converter command, gate servo motor control signal, and human-machine interaction command are obtained based on the outer loop optimization and the inner loop control.

7. The method for coordinated unloading at the breakout point according to claim 1, characterized in that, State management is performed based on the control feedback results, including: Based on the control feedback results, a matching state machine is invoked for state management, wherein the state machine includes: executing optimization instructions in normal steady flow state; limiting flow and slowing down vehicles in mild congestion state; closing some gates, prohibiting new vehicles from entering the site, and starting the impact mechanism in severe congestion state; and using historical average particle size, fixed crushing equipment frequency, and manual vehicle scheduling in degraded operation state.

8. A crushing joint unloading device for implementing the crushing joint unloading method according to any one of claims 1 to 7, characterized in that, include: The sensing module is used to acquire multi-source sensor data information, and to perform time synchronization and robust preprocessing on the multi-source sensor data information to obtain multi-source sensor state quantities that meet the preset confidence level. The multi-source sensor data information includes at least: state data inside the crushing outlet hopper, state data of the crushing outlet equipment, and dynamic data of the dump truck. The state assessment and prediction module is used to perform congestion assessment and prediction based on the multi-source sensor state quantities and the expected arrival time series of dump trucks, and obtain congestion assessment and prediction results. The congestion assessment and prediction results include at least the congestion risk information for the next preset time period and the maximum allowable unloading rate at the preset time. The collaborative optimization and decision-making module is used to construct a target optimization function based on the congestion assessment and prediction results, dump truck queue information and target constraint information, and obtain vehicle optimization instruction information and crushing equipment setting value. The target constraint information includes at least the crushing hopper capacity safety constraint, vehicle safety constraint, unloading impact suppression constraint and host protection constraint. The execution and feedback module is used to obtain control feedback results by performing hierarchical control based on the vehicle optimization instruction information and the set value of the crushing equipment. The hierarchical control includes an outer loop control and an inner loop control. The outer loop control is used to realize the vehicle operation control, and the inner loop control is used to realize the crushing equipment control. The status management module is used to manage the status based on the control feedback results, so as to realize the coordinated unloading of dump trucks at the crushing outlet.

9. A storage medium, characterized in that, Used to store a computer program, which is executed by a processor to implement the collaborative unloading method for the breakout as described in any one of claims 1 to 8.

10. An electronic device, characterized in that, The device includes a memory and a processor, the processor being communicatively connected to the memory, the memory being used to store a computer program, and the processor being used to load and execute the computer program to implement the collaborative unloading method for the breakout as described in any one of claims 1 to 8.

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