Iron ore multi-source detection data fusion and risk management and control method and system

Abstract

The present application relates to the field of bulk cargo compliance flow control, and discloses a method and system for iron ore multi-source detection data fusion and risk management and control, comprising: calling contract data and regulatory standards to construct a dynamic supervision baseline; obtaining an asynchronous supervision evidence stream composed of a high-frequency radioactive dose rate stream, an image foreign matter identification parameter stream, and a time-lag spectral grade analysis parameter stream; when the high-frequency radioactive dose rate stream or the image foreign matter identification parameter stream touches the red line, abandoning the waiting for a complete set and triggering a high-priority early warning response mechanism to output flow control instructions; within the safety interval, extracting the image foreign matter identification parameter stream residual within the synchronous time window as a dynamic penalty factor to tighten the baseline threshold, the present application reconstructs the asynchronous data decision boundary, eliminates the information timing block caused by the waiting time-lag data, and improves the risk decision state deduction accuracy under the continuous flow condition.

Description

Technical Field

[0001] This invention relates to a method and system for multi-source detection data fusion and risk management of iron ore, belonging to the field of bulk cargo compliance circulation control technology. Background Technology

[0002] Current data flow control systems generally adopt multi-channel data acquisition architecture and static risk mapping rules to process material characteristic flow in order to maintain centralized control and anomaly identification of material conveying status. In the high-frequency flow conveying state of high-throughput material conveying systems, safety and environmental protection detection data have transient and sudden characteristics, and grade analysis chemical characteristics have inherent reaction lag and time delay. Existing control systems forcibly require heterogeneous characteristics to be matched at the same time section before risk can be determined. This causes high-throughput conveying lines to frequently generate logic lockouts and conveying interruptions due to waiting for low-frequency spectral characteristics, resulting in technical conflicts between information flow timing misalignment and the continuous requirements of automated control.

[0003] Increasing the number of on-site physical detection sensors or improving the clock speed of local computing nodes not only fails to eliminate the inherent time delay required for the interaction between materials and detection rays, but also exponentially increases the concurrent redundancy of heterogeneous signals and the systemic cost of spatiotemporal alignment. This makes conventional improvement paths difficult to apply under high-frequency material conveying conditions. Mechanical improvements to the physical configuration of conveyor rollers, along with corresponding control methods, are insufficient when dealing with high-velocity characteristic flows. For example, Chinese invention patent CN110586517B discloses a method for radioactive grain separation based on radio frequency identification technology. The sorting system uses multi-level detectors to measure the trend of material radiation energy changes and adjusts the flow response of the conveying mechanism. This scheme is based on the temporal assumption of discrete packages and fixed physical spacing, requiring the materials to have clear spatiotemporal independence. In the continuous flow of industrial bulk materials applied in this invention, the materials are in a high-frequency, dense, continuous, and non-discrete state on the conveying line with a horizontal physical scale of more than 2m and a forward movement speed of more than 2.5m / s. The existing technology, based on the control logic of discrete package temporal scaling, produces a fundamental mismatch and cannot cope with the asynchronous temporal blockage of multi-source parameters, resulting in control lockout and conveying stagnation.

[0004] Therefore, how to dynamically reconstruct the adjudication boundary of the asynchronous evidence stream according to the management protocol, and how to use high-frequency features to realize the adaptive control state deduction of the transport system under non-homogeneous sampling conditions, has become the technical problem to be solved by this invention. Summary of the Invention

[0005] To address the problems in the background art, the technical solution of the present invention is as follows: A method for multi-source detection data fusion and risk management of iron ore, comprising the following steps: Step S1: Retrieve the trade settlement agreement data and environmental regulatory standards for the current batch of iron ore, and analyze the grade deviation tolerance, foreign matter limit and absolute radioactivity red line to construct a dynamic monitoring baseline specific to this batch of iron ore. Step S2: Obtain the monitoring evidence stream that arrives asynchronously under the flow condition. The monitoring evidence stream consists of a high-frequency radioactive dose rate continuous acquisition value stream, an image foreign object identification parameter stream, and a spectral grade analysis parameter stream with time lag. Step S3: When at least one of the high-frequency radioactive dose rate continuous acquisition value stream and the image foreign object identification parameter stream touches the absolute radioactive red line and the target red line corresponding to the upper limit of foreign object quantity, discard the full wait for the spectral grade analysis parameter stream with time lag and call the high-priority early warning response mechanism. The high-priority early warning response mechanism directly outputs the iron ore flow control instruction in the form of interruption or diversion. Step S4: When both the high-frequency radioactive dose rate continuous acquisition value stream and the image foreign object identification parameter stream are within the safe range, the spectral grade analysis parameter stream with time lag is continuously accumulated and the grade analysis mean is periodically calculated. When the grade analysis mean enters the preset critical range of the allowable grade deviation tolerance, the image foreign object identification parameter stream residual within the synchronous time window is extracted as a dynamic penalty factor. The dynamic penalty factor is used to tighten the baseline threshold of the attention level and output the iron ore flow control instruction for adjusting the routing status of the controlled peripheral terminal.

[0006] Preferably, after outputting the iron ore flow control instruction for adjusting the routing status of the controlled peripheral terminal, the method further includes the following steps: Step S201, pairing and associating the currently generated supervisory evidence stream with the iron ore flow control instruction to generate incremental training samples; Step S202, calling the incremental training samples to perform mean correction and update on the pattern analysis and recognition matrix used to generate the image foreign object recognition parameter stream and the spectral calibration function used to generate the spectral grade analysis parameter stream with time lag.

[0007] Preferably, step S1 includes the following sub-steps: Step S11, extracting the core component yield standard and the maximum allowable proportion of associated impurities of iron ore corresponding to different trade batches from the trade settlement agreement data, and mapping the core component yield standard and the maximum allowable proportion of associated impurities to the allowable grade deviation tolerance; Step S12, retrieving the non-point source pollution emission limit value and solid waste control level of bulk cargo ports from the local environmental protection regulatory standards, and marking the non-point source pollution emission limit value and solid waste control level of bulk cargo ports as the upper limit of foreign matter inventory and the absolute red line of radioactivity, respectively.

[0008] Preferably, step S4 tightens the baseline threshold for the attention level using a dynamic penalty factor, including the following sub-steps: Step S41, calculate the current dynamic risk level of material compliance based on the real-time difference between the mean of grade analysis and the allowable grade deviation tolerance; Step S42, adjust the baseline threshold for triggering the attention level using the dynamic penalty factor, so that the adjusted baseline threshold is equal to the initially set baseline threshold minus the product of the control weight coefficient and the absolute value of the image foreign object identification parameter flow residual, wherein the control weight coefficient is a dimensionless coefficient preset based on the historical grade fluctuation variance characteristics, and the adjusted baseline threshold, the initially set baseline threshold, and the absolute value of the image foreign object identification parameter flow residual are all represented by dimensionless normalized pure numbers.

[0009] Preferably, step S3 includes the following sub-steps: Step S31, when calling the high-priority early warning response mechanism, the corresponding emergency braking control word or path rerouting control word is retrieved and matched in the closed-block risk control instruction library according to the severity of the anomaly; Step S32, the emergency braking control word or path rerouting control word is written into the bearer register of the controlled peripheral terminal, and the monitoring evidence flow converges to the safe throughput threshold on the flow throughput section by adjusting the bearer rate parameter of the controlled peripheral terminal or changing the physical limit state of the flow routing switch.

[0010] Preferably, step S4 includes the following sub-steps: Step S43, read the current carrying rate parameter of the controlled peripheral terminal and obtain the physical flow span between the grade spectral analysis sensing point and the image acquisition sensing point; Step S44, perform reverse calculation of the time axis based on the current carrying rate parameter and the physical flow span, lock the historical time section of the iron ore sample corresponding to the spectral grade analysis parameter flow with time lag when it passes through the image acquisition sensing point, and extend the historical time section by a preset time half-width to synthesize a synchronization time window.

[0011] Preferably, after step S2, the central control scheduling system further includes the following steps: Step S701, sequentially cache the spectral grade analysis parameter stream with time lag within a continuous time period, and calculate the first derivative of the spectral grade analysis parameter stream with time lag over time, as well as the historical variance envelope boundary line formed by the variance statistics calculated based on the sliding time window, so as to objectively quantify the dynamic evolution trend of grade drift; Step S702, when the first derivative or the historical variance envelope boundary line overflows the preset steady-state control domain, add a target correction variable in advance within the dynamic monitoring baseline, which is used to adjust the trigger judgment limit of the corresponding monitoring parameter in step S4.

[0012] Preferably, after step S2, the central control scheduling system further includes the following steps: Step S801, statistically analyzing the amplitude of the random glitch noise residual of the continuous high-frequency radioactive dose rate acquisition stream and the confidence oscillation range of the image foreign object identification parameter stream within a set time period; Step S802, dynamically scaling the length of the time-series filtering sliding window of the high-priority early warning response mechanism based on the amplitude of the random glitch noise residual and the confidence oscillation range, to adjust the filtering smoothness of the continuous high-frequency radioactive dose rate acquisition stream and the image foreign object identification parameter stream in step S3.

[0013] Preferably, step S4 outputs an iron ore flow control instruction for adjusting the routing status of the controlled peripheral terminal, including the following sub-steps: Step S45, converting the comprehensive flow risk level matrix containing the level of concern into bus communication protocol standard control level code; Step S46, injecting the bus communication protocol standard control level code into the physical interface layer of the central control scheduling system to drive the flow routing switch to switch the current deflection angle. The method is applied to the compliance supervision and control system of the continuous flow process of industrial bulk materials. When running step S2, the capture of asynchronous supervision evidence flow is carried out along with the continuous flow process of materials when the horizontal physical scale of the channel of the controlled peripheral terminal is more than 2m and the forward movement speed of the material is more than 2.5m / s. The dynamic penalty factor is used to perform asymmetric time-dependent constraint control on the spectral grade analysis parameter flow with time lag and the high-frequency radioactive dose rate continuous acquisition value flow.

[0014] A multi-source detection data fusion and risk management system for iron ore, used to implement a method for multi-source detection data fusion and risk management of iron ore, comprising: The baseline construction module is used to retrieve the trade settlement agreement data and environmental regulatory standards of the current batch of iron ore, and to extract the grade deviation tolerance, foreign matter limit and absolute radioactivity red line from them to construct a dynamic monitoring baseline specific to this batch of iron ore. The evidence acquisition module is used to acquire the supervisory evidence stream that arrives asynchronously under the flow conditions. The supervisory evidence stream consists of a high-frequency radioactive dose rate continuous acquisition value stream, an image foreign object identification parameter stream, and a spectral grade analysis parameter stream with time lag. The streaming arbitration module, connected to the baseline construction module and the evidence acquisition module, is used to discard the waiting for the time-lag spectral grade analysis parameter stream and call the high-priority early warning response mechanism when at least one of the high-frequency radioactive dose rate continuous acquisition value stream and the image foreign object identification parameter stream touches the absolute radioactive red line and the target red line corresponding to the upper limit of foreign object quantity. The high-priority early warning response mechanism directly outputs the iron ore flow control command in the form of interruption or diversion. The dynamic collaborative control module, connected to the baseline construction module, evidence acquisition module, and streaming arbitration module, is used to continuously accumulate the spectral grade analysis parameter stream with time lag and periodically calculate the grade analysis mean when both the high-frequency radioactive dose rate continuous acquisition value stream and the image foreign object identification parameter stream are within the safe range. When the grade analysis mean enters the preset critical range of the allowable grade deviation tolerance, the residual of the image foreign object identification parameter stream within the synchronization time window is extracted as a dynamic penalty factor. The dynamic penalty factor is used to tighten the baseline threshold of the attention level and output the iron ore flow control command for adjusting the routing status of the controlled peripheral terminal.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. In the fusion of multi-source detection data for iron ore, the allowable grade deviation tolerance, foreign matter limit, and absolute radioactivity red line are analyzed based on the target settlement agreement data and regulatory standards to establish a dynamic monitoring baseline. The continuous acquisition values ​​of high-frequency radioactive dose rate, image foreign matter identification parameters, and spectral grade analysis parameters with time lag are captured and used as asynchronous monitoring evidence streams. When the radioactive dose rate or image foreign matter identification parameters touch the red line of the dynamic monitoring baseline, the high-priority early warning logic is directly triggered regardless of the reception status of the spectral grade analysis parameters. This changes the dependence of data-level fusion on the spatiotemporal alignment of multi-source features and eliminates the information processing deadlock caused by waiting for low-frequency spectral data to be matched.

[0016] 2. When the radiation dose rate and the image foreign object identification parameters are within the normal range, the system compares the cumulative average value of the spectral grade analysis parameters with the allowable grade deviation tolerance in real time. When the deviation approaches the extreme value of the tolerance, the residual of the image foreign object identification parameters within the synchronous time window is extracted as a penalty factor for cross-validation. Based on this, the baseline threshold for triggering the level of concern is dynamically tightened, so that the low-frequency time delay features and high-frequency flow cytometry features can produce deep synergy under critical conditions, thereby improving the accuracy of the control state inference of multi-source features in the continuous transport process.

[0017] 3. By replacing the static risk assessment matrix with discrete state arbitration logic centered on management state transitions, differentiated management weights are assigned to high-frequency transient characteristics and low-frequency delay characteristics. In transient conditions with missing data, a unique management downgrade or blocking command is pre-deduced based on the currently reached baseline boundary. This avoids interference and lag in the transport control decision caused by the inherent time delay of the spectral detection hardware, and maintains the continuity of the control logic of the bulk cargo continuous transport system under non-kit sampling conditions. Attached Figure Description

[0018] Figure 1 This is the overall flowchart of the iron ore multi-source detection data fusion and risk management method of the present invention; Figure 2This is the logic diagram for the asynchronous monitoring evidence flow fusion and early warning of iron ore in this invention; Figure 3 This is a diagram of the architecture of the multi-source detection and dynamic collaborative control system for iron ore according to the present invention.

[0019] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0021] A method for multi-source detection data fusion and risk management of iron ore, the core process control and time-series convergence process are as follows: Figure 1 As shown, it includes the following steps: Step S1: Retrieve the trade settlement agreement data and environmental regulatory standards for the current batch of iron ore, and analyze the grade deviation tolerance, foreign matter limit and absolute radioactivity red line to construct a dynamic monitoring baseline specific to this batch of iron ore. Step S2: Obtain the monitoring evidence stream that arrives asynchronously under the flow condition. The monitoring evidence stream consists of a high-frequency radioactive dose rate continuous acquisition value stream, an image foreign object identification parameter stream, and a spectral grade analysis parameter stream with time lag. Step S3: When at least one of the high-frequency radioactive dose rate continuous acquisition value stream and the image foreign object identification parameter stream touches the absolute radioactive red line and the target red line corresponding to the upper limit of foreign object quantity, discard the full wait for the spectral grade analysis parameter stream with time lag and call the high-priority early warning response mechanism. The high-priority early warning response mechanism directly outputs the iron ore flow control instruction in the form of interruption or diversion. Step S4: When both the high-frequency radioactive dose rate continuous acquisition value stream and the image foreign object identification parameter stream are within the safe range, the spectral grade analysis parameter stream with time lag is continuously accumulated and the grade analysis mean is periodically calculated. When the grade analysis mean enters the preset critical range of the allowable grade deviation tolerance, the image foreign object identification parameter stream residual within the synchronous time window is extracted as a dynamic penalty factor. The dynamic penalty factor is used to tighten the baseline threshold of the attention level and output the iron ore flow control instruction for adjusting the routing status of the controlled peripheral terminal.

[0022] Preferably, after outputting the iron ore flow control instruction for adjusting the routing status of the controlled peripheral terminal, the method further includes the following steps: Step S201, pairing and associating the currently generated supervisory evidence stream with the iron ore flow control instruction to generate incremental training samples; Step S202, calling the incremental training samples to perform mean correction and update on the pattern analysis and recognition matrix used to generate the image foreign object recognition parameter stream and the spectral calibration function used to generate the spectral grade analysis parameter stream with time lag.

[0023] Preferably, step S1 includes the following sub-steps: Step S11, extracting the core component yield standard and the maximum allowable proportion of associated impurities of iron ore corresponding to different trade batches from the trade settlement agreement data, and mapping the core component yield standard and the maximum allowable proportion of associated impurities to the allowable grade deviation tolerance; Step S12, retrieving the non-point source pollution emission limit value and solid waste control level of bulk cargo ports from the local environmental protection regulatory standards, and marking the non-point source pollution emission limit value and solid waste control level of bulk cargo ports as the upper limit of foreign matter inventory and the absolute red line of radioactivity, respectively.

[0024] Preferably, step S4 tightens the baseline threshold for the attention level using a dynamic penalty factor, including the following sub-steps: Step S41, calculate the current dynamic risk level of material compliance based on the real-time difference between the mean of grade analysis and the allowable grade deviation tolerance; Step S42, adjust the baseline threshold for triggering the attention level using the dynamic penalty factor, so that the adjusted baseline threshold is equal to the initially set baseline threshold minus the product of the control weight coefficient and the absolute value of the image foreign object identification parameter flow residual, wherein the control weight coefficient is a dimensionless coefficient preset based on the historical grade fluctuation variance characteristics, and the adjusted baseline threshold, the initially set baseline threshold, and the absolute value of the image foreign object identification parameter flow residual are all represented by dimensionless normalized pure numbers.

[0025] Preferably, step S3 includes the following sub-steps: Step S31, when calling the high-priority early warning response mechanism, the corresponding emergency braking control word or path rerouting control word is retrieved and matched in the closed-block risk control instruction library according to the severity of the anomaly; Step S32, the emergency braking control word or path rerouting control word is written into the bearer register of the controlled peripheral terminal, and the monitoring evidence flow converges to the safe throughput threshold on the flow throughput section by adjusting the bearer rate parameter of the controlled peripheral terminal or changing the physical limit state of the flow routing switch.

[0026] Preferably, step S4 includes the following sub-steps: Step S43, read the current carrying rate parameter of the controlled peripheral terminal and obtain the physical flow span between the grade spectral analysis sensing point and the image acquisition sensing point; Step S44, perform reverse calculation of the time axis based on the current carrying rate parameter and the physical flow span, lock the historical time section of the iron ore sample corresponding to the spectral grade analysis parameter flow with time lag when it passes through the image acquisition sensing point, and extend the historical time section by a preset time half-width to synthesize a synchronization time window.

[0027] Preferably, after step S2, the central control scheduling system further includes the following steps: Step S701, sequentially cache the spectral grade analysis parameter stream with time lag within a continuous time period, and calculate the first derivative of the spectral grade analysis parameter stream with time lag over time, as well as the historical variance envelope boundary line formed by the variance statistics calculated based on the sliding time window, so as to objectively quantify the dynamic evolution trend of grade drift; Step S702, when the first derivative or the historical variance envelope boundary line overflows the preset steady-state control domain, add a target correction variable in advance within the dynamic monitoring baseline, which is used to adjust the trigger judgment limit of the corresponding monitoring parameter in step S4.

[0028] Preferably, after step S2, the central control scheduling system further includes the following steps: Step S801, statistically analyzing the amplitude of the random glitch noise residual of the continuous high-frequency radioactive dose rate acquisition stream and the confidence oscillation range of the image foreign object identification parameter stream within a set time period; Step S802, dynamically scaling the length of the time-series filtering sliding window of the high-priority early warning response mechanism based on the amplitude of the random glitch noise residual and the confidence oscillation range, to adjust the filtering smoothness of the continuous high-frequency radioactive dose rate acquisition stream and the image foreign object identification parameter stream in step S3.

[0029] Preferably, step S4 outputs an iron ore flow control instruction for adjusting the routing status of the controlled peripheral terminal, including the following sub-steps: Step S45, converting the comprehensive flow risk level matrix containing the level of concern into bus communication protocol standard control level code; Step S46, injecting the bus communication protocol standard control level code into the physical interface layer of the central control scheduling system to drive the flow routing switch to switch the current deflection angle. The method is applied to the compliance supervision and control system of the continuous flow process of industrial bulk materials. When running step S2, the capture of asynchronous supervision evidence flow is carried out along with the continuous flow process of materials when the horizontal physical scale of the channel of the controlled peripheral terminal is more than 2m and the forward movement speed of the material is more than 2.5m / s. The dynamic penalty factor is used to perform asymmetric time-dependent constraint control on the spectral grade analysis parameter flow with time lag and the high-frequency radioactive dose rate continuous acquisition value flow.

[0030] A multi-source detection data fusion and risk management system for iron ore, its hardware-level interaction and signal-level topology connection architecture is as follows: Figure 3 As shown, the system includes: The baseline construction module is used to retrieve the trade settlement agreement data and environmental regulatory standards of the current batch of iron ore, and to extract the grade deviation tolerance, foreign matter limit and absolute radioactivity red line from them to construct a dynamic monitoring baseline specific to this batch of iron ore. The evidence acquisition module is used to acquire the supervisory evidence stream that arrives asynchronously under the flow conditions. The supervisory evidence stream consists of a high-frequency radioactive dose rate continuous acquisition value stream, an image foreign object identification parameter stream, and a spectral grade analysis parameter stream with time lag. The streaming arbitration module, connected to the baseline construction module and the evidence acquisition module, is used to discard the waiting for the time-lag spectral grade analysis parameter stream and call the high-priority early warning response mechanism when at least one of the high-frequency radioactive dose rate continuous acquisition value stream and the image foreign object identification parameter stream touches the absolute radioactive red line and the target red line corresponding to the upper limit of foreign object quantity. The high-priority early warning response mechanism directly outputs the iron ore flow control command in the form of interruption or diversion. The dynamic collaborative control module, connected to the baseline construction module, evidence acquisition module, and streaming arbitration module, is used to continuously accumulate the spectral grade analysis parameter stream with time lag and periodically calculate the grade analysis mean when both the high-frequency radioactive dose rate continuous acquisition value stream and the image foreign object identification parameter stream are within the safe range. When the grade analysis mean enters the preset critical range of the allowable grade deviation tolerance, the residual of the image foreign object identification parameter stream within the synchronization time window is extracted as a dynamic penalty factor. The dynamic penalty factor is used to tighten the baseline threshold of the attention level and output the iron ore flow control command for adjusting the routing status of the controlled peripheral terminal.

[0031] Example 1: The following, in conjunction with the accompanying drawings, illustrates the on-site deployment of the iron ore multi-source detection data fusion and risk management method and system provided by the present invention. Figure 3 As shown, the underlying detection gateway topology relied upon in this embodiment mainly consists of a dock conveyor belt, a radioactive dose rate detector, a visual image recognition and acquisition unit, and a spectral grade analyzer. The radioactive dose rate detector is installed above the belt and outputs a high-frequency data stream characterizing the radioactive dose rate by acquiring the transient gamma radiation intensity of the material surface in real time. The visual image recognition and acquisition unit consists of a high-speed industrial camera that captures images of the material on the belt surface in real time and continuously outputs a data stream characterizing the proportion of foreign matter through a preset foreign matter feature recognition algorithm. The spectral grade analyzer is deployed at the material discharge port at the end of the belt. Due to the inherent physical time consumption of material sample preparation, X-ray excitation, and spectral peak fitting in the spectral data analysis process, its output spectral grade analysis parameters have an inherent time lag of 60 seconds.

[0032] During the collaborative operation of the multi-sensor fusion system, the background algorithm logic adaptively advances the corresponding control methods and steps: First, the system retrieves the trade settlement agreement and local environmental regulatory standards of the current batch of iron ore as a dynamic monitoring baseline. This dynamic monitoring baseline, in system operation, manifests as a multi-dimensional, multi-attribute control threshold dynamic matrix stored in the central control and scheduling system, serving as a reference boundary for composite virtual risk assessment. The maximum permissible proportion of associated impurities in the trade settlement agreement is mapped to a grade deviation tolerance. The grade deviation tolerance refers to the maximum allowable deviation limit for actual detected values ​​based on the nominal standard value of the core component agreed upon in the settlement agreement. Physically, it belongs to a dynamic closed-loop boundary tolerance value within an engineering control system, used to accommodate the trade agreement's shift towards automation. The digital mapping of control thresholds; the solid waste control level and radioactivity limit in the local environmental protection regulatory standards are respectively marked as the upper limit of foreign matter storage and the absolute red line of radioactivity, thus establishing the baseline parameter environment in method step S1. Subsequently, the method drives the sensing gateway to capture materials when the conveyor belt is in a high-throughput operation state. The high-frequency radioactive dose rate data stream and the image foreign matter identification data stream are input to the streaming arbitration module as transient supervision evidence streams. Correspondingly, the streaming data sequences captured by this system that are asynchronously generated by different sensing hardware are uniformly defined as asynchronous supervision evidence streams, which refer to a set of heterogeneous streaming data sequences that are non-homogeneous and arrive asynchronously on the time axis due to different underlying physical measurement mechanisms; this module executes discrete state arbitration logic: such as Figure 2As shown, when any transient monitoring evidence stream touches the corresponding red line, the system determines it to be a compliance administrative blocking condition. It directly skips the kitting check step for spectral grade analysis parameters, invokes the early warning response mechanism, and issues a shutdown command to the terminal control system. Thus, in the non-kitting state where some low-frequency time-delay data has not arrived, the high-priority blocking control process declared in method step S3 is executed first. When all transient monitoring evidence streams are within the safe range, the system, based on the received transient parameters, compares the currently accumulated average value of spectral grade analysis parameters with the grade deviation tolerance in real time. Here, the average grade analysis value refers to the discrete output of the spectral grade analysis hardware within a specific time sliding period or accumulated batch of materials under continuous industrial operation conditions. The statistical scalar value obtained by time-weighted averaging of spectral grade analysis parameters is used to eliminate noise interference from instantaneous jumps in single-point chemical characteristics on global compliance decisions. The dynamic risk level of material compliance is a probabilistic scalar indicator calculated by the system through dimensionless normalization mapping based on the real-time difference between the real-time acquired average grade analysis value and the allowable grade deviation tolerance. This indicator quantifies the overall compliance deterioration trend of the currently circulating iron ore. By extracting the residual of foreign object identification parameters within the corresponding time window as a penalty factor, the threshold for the attention level is dynamically tightened. Here, the baseline threshold for the attention level refers to a pre-warning trigger threshold between the completely safe range and the absolute red line boundary in the multi-level risk warning system of this system. The boundary state threshold is used to instruct the control system to pre-adjust the routing status of controlled peripheral terminals to prevent risk spillover. The extracted dynamic penalty factor, in its physical essence, is a gain control variable used for cross-attribute feedback adjustment of the boundary for determining the microscopic chemical content, based on the dimensionless processing of the discrete residual of macroscopic surface foreign matter fluctuations. Algebraically, it represents a deduction term of the control threshold to proactively and adaptively tighten the system's risk access control boundary. Thus, under conditions of physical detection timing misalignment, management and disposal instructions are deduced in advance based on the reached baseline boundary, eliminating the linkage lock-in phenomenon caused by the inherent time delay of the underlying detection equipment on the upper-level logistics scheduling. Specifically, under continuous iron ore flow conditions, the surface foreign matter inventory... The fluctuation residual essentially reflects the uniformity variation of the original ore vein in geological distribution. When the foreign matter inventory output by image recognition shows high-frequency oscillation and the residual increases, it indicates that the probability of macroscopic non-mineral inclusions in the material increases. Statistically, it has a high physical synergistic correlation with the fluctuation of the content of internal associated impurities. That is, the increased dispersion of surface geometric foreign matter features indicates an increased risk of dilution of internal low-frequency chemical content. Therefore, by transforming the high-frequency visual foreign matter recognition parameter flow residual into a macroscopic control variable to correct the boundary of microscopic chemical content determination, it is possible to achieve cross-attribute synergistic correlation from macroscopic physical shape fluctuations to potential variations in microscopic chemical composition, providing a causal law-driven physical mechanism support for the dynamic tightening of the baseline threshold.

[0033] When establishing a dynamic monitoring baseline, the control weighting coefficients are based on the variance of the grade analysis. Dynamic adjustment, quality analysis variance The variance of the grade of the core components of iron ore in 30 consecutive batches is the standard deviation of the measured values ​​relative to the batch mean. When the variance is not greater than 0.05, the control weighting coefficient is set to 0.3. When the variance is greater than 0.05 and not greater than 0.2, it is determined to be a linear interpolation result between 0.5 and 1.0. When the value is greater than 0.2, it is set to 1.5 to adjust the tightening of the baseline threshold. During this process, to ensure that parameters with different physical dimensions can perform unified algebraic operations, the initially set baseline threshold, control weight coefficient, and image foreign object recognition parameter flow residual are all mapped to their respective maximum design limits using maximum value normalization, thus transforming them into dimensionless pure scalars between zero and one. In this invention, the image foreign object recognition parameter flow residual is used to quantitatively characterize the absolute deviation of surface geometric foreign object features from the preset steady-state benchmark by taking the absolute value or calculating the modulus, thereby ensuring that the dynamic penalty factor is always a non-negative scalar. This drives the monotonically tightening of the baseline threshold at the level of concern in the algebraic subtraction operation, achieving the control intention of adaptively improving risk management sensitivity under critical conditions. (Variance analysis is also included.) The step decision boundary values ​​of 0.05 and 0.2 are derived from the statistical convergence characteristics of historical transport datasets from multi-source mining operations: when the variance... When the value is not greater than 0.05, it indicates that the current mining face is in a single geologically stable ore layer with uniform material composition. A low weighting coefficient of 0.3 is used to maintain threshold stability. When the variance... A value greater than 0.2 indicates that the circulating material belongs to the disorderly blending of multiple mineral sources or the fault-based mixed mining condition, with violent fluctuations in composition. Therefore, a high weighting coefficient of 1.5 is set to forcibly tighten the warning red line significantly, preventing missed detections caused by low-frequency time lags. This achieves self-consistent constraint of parameter boundaries in engineering mathematical logic. The mean correction update of the pattern analysis and recognition matrix and the spectral calibration function is calculated using a first-order incremental moving average update algorithm, and the update smoothing coefficient is updated. The smoothing coefficient represents the weighting of the current incremental data in the matrix iteration, and is used when the continuous transport time of a single mineral source in the material flow path is not less than 24 hours. The smoothing coefficient is set at 0.98 when the mineral source switching frequency is no less than 3 times within 24 hours. The value was set to 0.85 to mitigate measurement drift caused by dust accumulation or background radiation noise fluctuations on the sensor surface. Furthermore, the mean correction update mapping path is implemented as follows: the calculated first-order incremental moving average scalar correction value is used as the error feedback quantity. On one hand, it is directly superimposed onto the feature gain weighting vector of the pattern analysis and recognition matrix, dynamically adjusting the extraction threshold of the foreign object edge contrast through linear mapping transformation of the element-by-element correction matrix. On the other hand, this scalar correction value is used as the intercept compensation quantity, substituted into the polynomial fitting relationship of the spectral calibration function for zero-point drift correction, thereby accurately transforming the one-dimensional scalar calculation result into online parameter reconstruction of the multi-dimensional algorithm matrix and complex function model.

[0034] During the synchronization time window, the central control and scheduling system reads the current load rate parameter and physical flow span of the controlled peripheral terminal, divides the physical flow span by the load rate parameter, and obtains the base time difference of grade lag. Quality lag and basic time difference This is the nominal physical transport time of an iron ore sample from the image acquisition sensor site to the grade spectral analysis sensor site, and it is calculated by subtracting the grade lag baseline time difference in reverse along the time axis when the spectral grade analysis parameter stream is output. The historical time section with a preset half-width is used to lock the iron ore sample at the sensing point of image acquisition. To align the sampling window half-width with the data acquisition timing of heterogeneous sensors, the value is dynamically adjusted based on the load rate parameter and the instantaneous load fluctuation frequency of the material clumps on the belt surface. When the belt conveyor is under stable load conditions and the load rate parameter is 2.5 m / s, the preset half-width time is... The preset half-width time is set to 4.0s. When the variance of the drive motor output power exceeds the power variance threshold of 2kW, the half-width time will be... Extending the time segment to 6.5 seconds, this extended time window is used as the synchronization time window to extract the residual of the image foreign object identification parameter flow. It should be noted that although the variance statistic appears as a scalar value on the discrete time section, in the time-domain analysis under continuous flow conditions of this invention, the historical variance envelope boundary line is dynamically constructed as a time-varying trajectory line by configuring a specific sliding time window within the central control scheduling system and performing online rolling statistics on the sequentially cached spectral grade analysis parameter flow. Specifically, the central control scheduling system moves this sliding time window along the continuous time axis with a preset sampling step size. The system operates within a time window and continuously calculates the instantaneous variance of each sample point relative to the mean. This sequentially strings together a series of time-sequential variance scalars, mapping them to a continuous function curve that dynamically migrates over time. This function curve serves as the historical variance envelope boundary line in this invention, dynamically encapsulating, constraining, and objectively quantifying the fluctuation envelope of grade drift within the time domain. This design completes the engineering mathematical transformation from transient variance scalars to time-series streaming envelope boundary lines, providing continuous and high-precision control limits for subsequent steady-state control domain overflow determination. This is particularly relevant when calling high-priority early warning responses. When the mechanism is activated, the central control and dispatching system matches the control word based on the deviation of the high-frequency radioactive dose rate continuously acquired value stream or image foreign object identification parameter stream touching the target red line. When the deviation does not exceed the preset safety margin threshold, a path rerouting control word is generated and written to the load register of the controlled peripheral terminal, driving the flow routing switch to switch the deflection angle to guide the material into the diversion channel. When the deviation exceeds the preset safety margin threshold, an emergency braking control word is generated and written to the load register, controlling the load rate parameter of the controlled peripheral terminal to reduce the running speed to zero to stop the conveying and prevent continuous flow. As the material throughput at the cross section converges to within the safe throughput threshold, the physical interface layer of the central control and scheduling system uses a built-in hardware driver mapping table to decompose the comprehensive flow risk level matrix containing the above-mentioned concern levels according to the row and column indices, and map it into a preset binary control command frame. According to the industrial general bus protocol standard, the logic state of each bit in the command frame is modulated into a differential voltage signal or level pulse code with the corresponding timing, and sent to the controlled peripheral terminal, thereby realizing a smooth cross-layer conversion from the high-level software algorithm decision matrix to the low-level hardware execution level code.

[0035] Example 2: This example selects a typical working condition of mixed transportation of iron ore from different sources in a large open-pit mine circulation system for verification. The system constructs a data acquisition and risk management system through a sensor array deployed along the entire conveyor belt. Two representative iron ore samples were selected for the experiment. The background radioactivity dose rate of ore A was 0.08 μSv / h, and the background radioactivity dose rate of ore B was 0.12 μSv / h, both within the safe range. During the experiment, non-ore ceramic foreign objects of different sizes were added to simulate a continuous transportation scenario. The system set the grade deviation tolerance to 1.5%, the upper limit of foreign object inventory to 0.5%, and the absolute red line of radioactivity to 0.3 μSv / h according to the trade settlement agreement.

[0036] The experimental group adopted the technical solution of this invention and continuously monitored the system under the condition of a belt conveyor speed of 3.5 m / s. The system recorded radiation data at a sampling frequency of 100 Hz using a high-frequency radiation dose rate detector, while the visual image recognition and acquisition unit captured images of the belt surface at a rate of 30 frames per second. At the 120th second of the experiment, the system identified that the proportion of surface ceramic foreign matter reached 0.8%, which exceeded the upper limit of 0.5% for foreign matter. At this time, the instantaneous value recorded by the radiation dose rate detector was within the safe range of 0.15 μSv / h. Since the system arbitration logic is set so that any transient indicator touches the red line, the system will proceed with the test. Upon execution, the streaming arbitration module issued a shutdown command at 120.2 seconds to complete the abnormal risk control. To verify the system's adaptability to normal operating conditions, the test group operated after eliminating foreign object interference. At this time, the system extracted the image recognition residual within the time window as a dynamic penalty factor, and calculated that the real-time baseline threshold fluctuation range was 0.05% to 0.12%. When running for 600 seconds, the spectral grade analyzer output grade resolution parameter of 64.2%, corresponding to a batch benchmark of 63.8%, with a grade deviation of 0.4%, which is within the tolerance upper limit of 1.5%. The system maintained normal operation.

[0037] The control group adopted a static fitting evaluation mechanism. In this group of experiments, when the foreign matter content was identified as 0.8%, the system triggered the spectral grade analyzer at the material discharge port to prepare and analyze the sample. Due to the physical time required for material excitation and peak fitting in spectral data analysis, the system was in a suspended waiting state until 180.2 seconds before data fitting was completed and an alarm was output. The experimental data showed that under the high-frequency material conveying cycle, the risk control response delay of the technical solution of this invention was reduced by more than 99%. Moreover, under the premise that the spectral analysis parameters were unavailable, compliance blocking could be achieved through high-frequency transient evidence flow, demonstrating the feasibility of achieving a closed loop of risk control in an asynchronous evidence environment. When the ceramic foreign matter content was increased to 1.5%, the balance point between the system false alarm rate and the system missed detection rate was at the critical value of the optimal process range, indicating that the dynamic baseline adjustment mechanism has engineering robustness under specific impurity concentrations. This embodiment, by comparing the response timing under transient conditions, confirmed the technical effectiveness of the streaming data arbitration logic in ensuring continuous flow and the effectiveness of risk control.

[0038] Example 3: This example relates to a method and system for multi-source detection data fusion and risk management of iron ore. The system is deployed on an ore conveying gateway containing multi-source sensing nodes. Through deep scanning of historical sensor sampling sequences, it defends against malicious data tampering or non-logical event injection triggered by equipment malfunctions. During the operation of the ore flow system, the system constructs a sliding time window. The length of this window is adaptively adjusted according to the instantaneous load fluctuation frequency of the belt conveyor to ensure that the window contains the complete material flow cycle. The system performs feature mapping on the parameters collected by each sensor entering the window, converting the original voltage or pulse signals into numerical sequences in a standardized feature space, and calculates the adjacent differences of these values ​​in the time series. Based on this, the system introduces a feature deviation accumulation model, which compares the detection parameters of the current material cluster with the parameter benchmarks corresponding to the same type of ore in the historical sample library. To obtain real-time feature deviation, the feature deviation accumulation model constructs a discrete integral formula that runs within a sliding time window. It sequentially reads the real-time values ​​of radioactive dose rate, image foreign object ratio, and spectral resolution, calculates the Euclidean distance between each parameter at the current moment and the historical benchmark value, and weights and accumulates the absolute deviations of multi-dimensional features to output a single comprehensive feature deviation scalar. This model does not require changing the topology of the underlying multi-source detection channel. It only quantitatively characterizes the drift degree of the material feature sequence through standardized difference fusion of the data space. In order to eliminate non-logical noise caused by belt misalignment or drive motor start-stop, the system sets a dynamic change threshold for the cumulative deviation value. This threshold is obtained by calculating the variance of the drive motor output power in the current time period and scaling it linearly. Thus, the judgment boundary is automatically relaxed when the motor load fluctuates greatly, and the monitoring is tightened when the load is stable.

[0039] When the cumulative characteristic deviation value continues to rise within the range of the dynamically changing threshold and exhibits a non-monotonic abrupt jump, the system identifies the injection of implicit negative event records. At this time, the system triggers the record backtracking logic, marks the data block within the preset window length before the current trigger point as an abnormal pending verification state, and pushes an early warning work order containing the timestamp of the abnormal trigger point, the corresponding sensor physical identification code, and the associated power variance data to the management terminal. Simultaneously, the system maintains the flow control of the current belt segment, without directly cutting off the material conveying. Instead, it generates a control logic instruction containing the abnormal characteristic mark and injects it into the subsequent grade analysis module, enabling subsequent processing nodes to perform weighted elimination or independent calibration of the affected material segment based on the mark. This embodiment achieves real-time capture and control of implicit negative events without interrupting production by dynamically embedding the characteristic deviation cumulative model and adaptive changing threshold in the data stream, ensuring that the injection of abnormal data from a single node will not cause the decision-making of the global flow strategy to fail during the multi-source data fusion process.

[0040] Example 4: When the system faces sensor measurement deviations caused by fluctuations in ore composition, or hardware performance degradation caused by continuous mechanical operation, the dynamic benchmark calibration mechanism of the present invention is triggered. The system has a preset calibration test sequence, which is executed during downtime when the conveyor system is idle or during regular maintenance. Before executing the calibration procedure, the system confirms that each radioactive dose rate detector and spectral grade analyzer is in a preheating state to ensure that the stability of the hardware output reaches the preset minimum engineering benchmark. The system measures the response characteristics of the sensor array to the standard material by introducing a set of standard calibration ore samples with known physicochemical properties into the belt conveyor path.

[0041] After acquiring the measured response signal of the standard calibration ore sample, the system compares the theoretical physical values ​​of the standard calibration ore sample with the real-time acquired values, calculates the deviation space between the two, and for the radioactive dose rate detector, the system applies a first-order linear correction function to compensate for the energy gain of the detection window to ensure a stable physical ratio between the radiation count rate and the background dose of the ore under different belt loads; for the spectral grade analyzer, the system uses the known element content in the calibration ore sample to perform discretization weight correction on the spectral peak fitting model to eliminate the grade measurement deviation caused by excitation source attenuation or dust accumulation on the photoelectric sensor. The final output of this calibration procedure is... An updated set of dynamic monitoring baseline parameters is synchronously written into the memory address of the risk control module. During subsequent ore transfers, the risk control module executes data fusion logic based on the latest baseline parameters, effectively suppressing detection drift caused by equipment aging or changes in environmental conditions. When the deviation between the short-term mean of the monitoring parameters and the baseline parameters exceeds the set range multiple times, the system automatically marks the detection unit as needing maintenance and inspection. This mechanism ensures that the risk control judgment logic is always based on the physical data that has been calibrated in real time, eliminating the uncontrollable measurement risks introduced by the deterioration of sensor performance due to long-term operation.

[0042] Example 5: This example relates to an online calibration and baseline model self-repair procedure for multi-source heterogeneous sensing devices in an iron ore transfer system. In the initial deployment of the ore conveying system, the system triggers a full-link physical parameter initialization calibration program. This program, through a preset fixed-point feeding operation, delivers standard lead shielding plates of different densities to the radioactive dose rate detector when the conveyor belt is unloaded. It records the gamma radiation response counts at different attenuation levels and compares them with the reference attenuation curve provided by the detector manufacturer. The program calculates the response deviation correction coefficient for each detection channel, which is stored in the system memory as the basic gain calibration parameter for the physical detection unit. This is then applied to the chemical analysis of the spectral grade analyzer. The composition analysis model involves the system executing a baseline sample verification procedure. The system samples a batch of standard ore materials, which are pre-processed in a physicochemical laboratory to obtain accurate elemental content standard values. These standard values ​​are then used as reference inputs to the detection channel of the spectrometer. By comparing the spectral fitting values ​​output by the instrument with the laboratory standard values, the system generates a linear drift compensation vector for the elemental regression model. This compensation vector undergoes secondary self-correction in subsequent operation based on real-time thermodynamic monitoring data from the spectrometer's internal cooling system. When the ambient temperature changes by more than 5 degrees Celsius per hour (preset), the system calls the built-in temperature compensation mapping table to dynamically calibrate the elemental characteristic peak positions in the spectral regression model.

[0043] During long-term system operation, the online self-healing logic automatically adjusts the baseline based on the periodic detection performance of ore materials. The system tracks the environmental background drift of the radioactive dose rate detector under no-load conditions in real time. If the average environmental background count of a continuous monitoring period deviates from the initial calibration value by a factor that reaches the preset engineering limit, the system immediately triggers the background update procedure. The sliding time window averaging method is used to subtract the environmental radiation noise during that period as the new background, preventing the accumulation of environmental noise from causing compliance misjudgments. This calibration and repair mechanism ensures that the multi-source detection baseline of the system is always maintained at the same technical level as the initial deployment state under non-ideal conditions such as physical hardware aging or drastic changes in the external environment, realizing the robust reproduction of risk control parameters throughout the entire system life cycle.

[0044] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for iron ore multi-source detection data fusion and risk management and control, characterized in that, Includes the following steps: Step S1: Retrieve the trade settlement agreement data and environmental regulatory standards for the current batch of iron ore, and analyze the grade deviation tolerance, foreign matter limit and absolute radioactivity red line to construct a dynamic monitoring baseline specific to this batch of iron ore. Step S2: Obtain the supervisory evidence stream that arrives asynchronously under the flow condition. The supervisory evidence stream consists of a high-frequency radioactive dose rate continuous acquisition value stream, an image foreign object identification parameter stream, and a spectral grade analysis parameter stream with time lag. Step S3: When at least one of the high-frequency radioactive dose rate continuous acquisition value stream and the image foreign object identification parameter stream touches the absolute radioactive red line and the target red line corresponding to the upper limit of foreign object quantity, discard the full wait for the spectral grade analysis parameter stream with time lag and call the high-priority early warning response mechanism. The high-priority early warning response mechanism directly outputs the iron ore flow control instruction in the form of interruption or diversion. Step S4: When both the high-frequency radioactive dose rate continuous acquisition value stream and the image foreign object identification parameter stream are within the safe range, the spectral grade analysis parameter stream with time lag is continuously accumulated and the grade analysis mean value is periodically calculated. When the grade analysis mean value enters the preset critical range of the allowable grade deviation tolerance, the image foreign object identification parameter stream residual within the synchronous time window is extracted as a dynamic penalty factor. The dynamic penalty factor is used to tighten the baseline threshold of the attention level and output the iron ore flow control instruction for adjusting the routing status of the controlled peripheral terminal. Step S4 tightens the baseline threshold for the attention level using a dynamic penalty factor, including the following sub-steps: Step S41, calculate the current dynamic risk level of material compliance based on the real-time difference between the average grade analysis value and the allowable grade deviation tolerance; Step S42, adjust the baseline threshold for triggering the attention level using a dynamic penalty factor, so that the adjusted baseline threshold is equal to the initially set baseline threshold minus the product of the control weight coefficient and the absolute value of the image foreign object identification parameter flow residual, wherein the control weight coefficient is a dimensionless coefficient preset based on the historical grade fluctuation variance characteristics, and the adjusted baseline threshold, the initially set baseline threshold, and the absolute value of the image foreign object identification parameter flow residual are all represented by dimensionless normalized pure numbers; Step S4 includes the following sub-steps: Step S43, read the current carrying rate parameter of the controlled peripheral terminal and obtain the physical flow span between the grade spectral analysis sensing point and the image acquisition sensing point; Step S44, perform reverse calculation of the time axis based on the current carrying rate parameter and the physical flow span, lock the historical time section of the iron ore sample corresponding to the spectral grade analysis parameter flow with time lag when it passes through the image acquisition sensing point, and extend the historical time section by a preset time half-width to synthesize a synchronization time window.

2. The method for multi-source detection data fusion and risk management of iron ore according to claim 1, characterized in that, After outputting the iron ore flow control instruction for adjusting the routing status of the controlled peripheral terminal, the following steps are also included: Step S201, pairing and associating the currently generated supervisory evidence stream with the iron ore flow control instruction to generate incremental training samples; Step S202, calling the incremental training samples to perform mean correction and update on the pattern analysis and recognition matrix used to generate the image foreign object recognition parameter stream and the spectral calibration function used to generate the spectral grade analysis parameter stream with time lag.

3. The method for multi-source detection data fusion and risk management of iron ore according to claim 1, characterized in that, Step S1 includes the following sub-steps: Step S11, extract the core component yield standard and the maximum allowable proportion of associated impurities of iron ore corresponding to different trade batches from the trade settlement agreement data, and map the core component yield standard and the maximum allowable proportion of associated impurities to the allowable grade deviation tolerance; Step S12, retrieve the non-point source pollution emission limit value and solid waste control level of bulk cargo ports from the local environmental protection supervision standards, and mark the non-point source pollution emission limit value and solid waste control level of bulk cargo ports as the upper limit of foreign matter and the absolute red line of radioactivity, respectively.

4. The method for multi-source detection data fusion and risk management of iron ore according to claim 1, characterized in that, Step S3 includes the following sub-steps: Step S31, when calling the high-priority early warning response mechanism, search and match the corresponding emergency braking control word or path rerouting control word in the closed-block risk control instruction library according to the severity of the anomaly. Step S32: Write the emergency braking control word or the path rerouting control word into the bearer register of the controlled peripheral terminal. By adjusting the bearer rate parameter of the controlled peripheral terminal or changing the physical limit state of the flow routing switch, the monitoring evidence flow converges to within the safe throughput threshold on the flow throughput section.

5. The method for multi-source detection data fusion and risk management of iron ore according to claim 1, characterized in that, Following step S2, the central control scheduling system further includes the following steps: Step S701, sequentially cache the spectral grade analysis parameter stream with time lag within a continuous time period, and calculate the first derivative of the spectral grade analysis parameter stream with time lag over time, as well as the historical variance envelope boundary line formed by the variance statistics calculated based on the sliding time window, so as to objectively quantify the dynamic evolution trend of grade drift; Step S702, when the first derivative or the historical variance envelope boundary line overflows the preset steady-state control domain, add a target correction variable in advance within the dynamic monitoring baseline, which is used to adjust the trigger judgment limit of the corresponding monitoring parameter in step S4.

6. The method for multi-source detection data fusion and risk management of iron ore according to claim 1, characterized in that, Following step S2, the central control and scheduling system further includes the following steps: Step S801, statistically analyzing the random glitch noise residual amplitude of the continuous high-frequency radioactive dose rate acquisition stream and the confidence oscillation range of the image foreign object identification parameter stream within a set time period; Step S802, dynamically scaling the time-series filtering sliding window section length of the high-priority early warning response mechanism based on the random glitch noise residual amplitude and the confidence oscillation range, in order to adjust the filtering smoothness of the continuous high-frequency radioactive dose rate acquisition stream and the image foreign object identification parameter stream in step S3.

7. The method for multi-source detection data fusion and risk management of iron ore according to claim 1, characterized in that, Step S4 outputs an iron ore circulation control instruction for adjusting the routing status of the controlled peripheral terminal, including the following sub-steps: Step S45, converts the comprehensive circulation risk level matrix containing the level of concern into bus communication protocol standard control level code; Step S46: Inject the standard control level code of the bus communication protocol into the physical interface layer of the central control scheduling system to drive the flow routing switch to switch the current deflection angle mode. The method is applied to the compliance supervision and control system of the continuous flow process of industrial bulk materials. When running step S2, the capture of asynchronous supervision evidence flow is carried out along with the continuous flow process of materials when the horizontal physical scale of the channel of the controlled peripheral terminal is more than 2m and the forward movement speed of the material is more than 2.5m / s. The dynamic penalty factor is used to perform asymmetric time-dependent constraint control on the spectral grade analysis parameter flow with time lag and the high-frequency radioactive dose rate continuous acquisition value flow.

8. A multi-source detection data fusion and risk management system for iron ore, used to implement the multi-source detection data fusion and risk management method for iron ore as described in claim 1, characterized in that, include: The baseline construction module is used to retrieve the trade settlement agreement data and environmental regulatory standards of the current batch of iron ore, and to extract the grade deviation tolerance, foreign matter limit and absolute radioactivity red line from them to construct a dynamic monitoring baseline specific to this batch of iron ore. The evidence acquisition module is used to acquire the supervisory evidence stream that arrives asynchronously under the flow conditions. The supervisory evidence stream consists of a high-frequency radioactive dose rate continuous acquisition value stream, an image foreign object identification parameter stream, and a spectral grade analysis parameter stream with time lag. The streaming arbitration module, connected to the baseline construction module and the evidence acquisition module, is used to discard the waiting for the time-lag spectral grade analysis parameter stream and call the high-priority early warning response mechanism when at least one of the high-frequency radioactive dose rate continuous acquisition value stream and the image foreign object identification parameter stream touches the absolute radioactive red line and the target red line corresponding to the upper limit of foreign object quantity. The high-priority early warning response mechanism directly outputs the iron ore flow control command in the form of interruption or diversion. The dynamic collaborative control module, connected to the baseline construction module, evidence acquisition module, and streaming arbitration module, is used to continuously accumulate the spectral grade analysis parameter stream with time lag and periodically calculate the grade analysis mean when both the high-frequency radioactive dose rate continuous acquisition value stream and the image foreign object identification parameter stream are within the safe range. When the grade analysis mean enters the preset critical range of the allowable grade deviation tolerance, the residual of the image foreign object identification parameter stream within the synchronization time window is extracted as a dynamic penalty factor. The dynamic penalty factor is used to tighten the baseline threshold of the attention level and output the iron ore flow control command for adjusting the routing status of the controlled peripheral terminal.

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