A mining intrinsically safe LED lighting sensor system

Abstract

This invention relates to the field of sensor lighting technology, and more particularly to an intrinsically safe LED lighting sensor system for mining applications. The system includes: a data acquisition module, a marker generation module, an aging determination module, an adjustment module, and a correction module. This invention, by simultaneously introducing multi-dimensional operating parameters and environmental parameters for time-scale differentiation and joint analysis, can distinguish between short-term visibility fluctuations caused by changes in the tunnel environment and operational anomalies caused by changes in the internal state of the lighting fixtures. By quantifying the coupling relationship between these two types of parameters at different time scales, it avoids misjudging environmental disturbances as equipment aging or misattributing electrical anomalies to external obstruction factors. Thus, while ensuring the sensitivity of the lighting response, it suppresses unnecessary long-term high-load operation, effectively solving the problem of excessively long periods of false triggering and high-load operation caused by the difficulty in distinguishing between transient environmental disturbances and equipment condition degradation.

Description

Technical Field

[0001] This invention relates to the field of sensor lighting technology, and more particularly to an intrinsically safe LED lighting sensor system for mining applications. Background Technology

[0002] As coal mine operating environments become increasingly demanding and complex, the increased frequency of personnel activity, dust disturbances, and structural uncertainties within roadways necessitate that lighting systems maintain sufficient responsiveness while operating stably under constrained loads and high safety requirements. However, existing mine lighting equipment often struggles to distinguish between short-term fluctuations in lighting demand caused by environmental changes and the accumulated risks resulting from equipment degradation. This leads to frequent false triggers, prolonged high-load operation, or rigid parameter settings, accelerating component aging and potentially weakening the overall system safety margin. Therefore, effectively differentiating between environmental changes and equipment operating conditions in complex roadway environments, while ensuring lighting needs are met and suppressing the evolution of potential risks, has become a key challenge restricting the reliability and lifespan improvement of mine lighting systems.

[0003] Chinese Patent Application Publication No. CN117450487A discloses an intelligent explosion-proof and intrinsically safe LED star chain lamp and intelligent lighting system for coal mines. The lamp includes: a lighting unit comprising a main lamp and multiple auxiliary lamps, each equipped with an LED light source board; an integrated controller within the main lamp, electrically connected to the LED light source board in the main lamp, which integrates communication and drives the main lamp, and automatically adjusts the illumination brightness of the LED light source board in the main lamp; LED light source boards in the auxiliary lamps are electrically connected to the integrated controller in the main lamp via Ethernet cables, which integrates communication and drives each auxiliary lamp, and automatically adjusts the illumination brightness of the corresponding LED light source board in the auxiliary lamp; and microwave sensing devices are installed on both the main lamp and auxiliary lamps, with the microwave sensing device in the main lamp electrically connected to the integrated controller, and the microwave sensing device in the auxiliary lamps electrically connected to the integrated controller in the main lamp via Ethernet cables.

[0004] Therefore, the existing technology has the following problems: it mainly relies on microwave induction to trigger lighting adjustment, making it difficult to distinguish between transient changes in lighting demand caused by short-term human activity or dust disturbance and the risk accumulation caused by the deterioration of the lighting equipment's own operating status; its fault detection focuses on the power-on state and explicit fault feedback, lacking the ability to analyze the evolution trend of the lighting unit's operating status, making it difficult to identify potential risks in advance and take proactive measures; its lighting brightness control is mainly based on centralized or fixed logic, which does not adequately consider the coupling relationship between environmental changes and equipment operating status, making it difficult to suppress the impact of long-term high-load operation on system reliability and lifespan while meeting lighting needs. Summary of the Invention

[0005] To address this, the present invention provides an intrinsically safe LED lighting sensor system for mining applications, which overcomes the problems of high false triggering and excessively long high-load operation time caused by the difficulty in distinguishing between transient environmental disturbances and equipment condition deterioration in the prior art by jointly identifying and hierarchically controlling the characteristics of environmental changes and the operating status of lighting equipment.

[0006] To achieve the above objectives, the present invention provides an intrinsically safe LED lighting sensor system for mining applications, comprising:

[0007] The acquisition module is used to collect multi-dimensional operating data and roadway environment data of the intrinsically safe LED lighting sensor lamps for mining under the control of the reference trigger parameters in real time. The multi-dimensional operating data includes the working current ripple coefficient of the lamp body, the temperature of the driver chip, and the duration of continuous lighting. The roadway environment data includes the movement speed of personnel near the lamp installation point, dust concentration, and the cross-sectional change rate characterizing the roadway structure.

[0008] The marker generation module is used to determine whether a structurally sensitive state has been entered based on the cross-sectional abrupt change rate and a preset disturbance threshold. Based on the entry determination result, and combined with the change trend of the personnel movement speed and the dust concentration within a preset first time window, a suspected visibility deterioration marker is generated.

[0009] An aging determination module is used to determine whether a potential aging mode has been entered based on the timing combination characteristics of the operating current ripple coefficient and the temperature of the driver chip. The potential aging modes include circuit disturbance mode and overload risk mode.

[0010] The adjustment module is used to determine the abnormal coupling index and confidence level based on the suspected visibility deterioration indicator and the potential aging mode, according to the multi-dimensional operating data and the tunnel environment data, and adjust the benchmark triggering parameters based on the threshold comparison result of the abnormal coupling index and the potential aging mode.

[0011] The correction module is used to correct the preset disturbance threshold based on the fluctuation characteristics of the confidence level and the cross-sectional mutation rate within a preset observation period after adjusting the benchmark trigger parameters.

[0012] Furthermore, the flag generation module includes:

[0013] A sensitive state determination unit is used to determine that the structure has entered a sensitive state when the cross-sectional abrupt change rate is greater than the preset disturbance threshold.

[0014] The trend feature calculation unit is used to calculate the difference in the movement speed of the personnel corresponding to the first and last moments of the preset first time window when it is determined that the structure is in a sensitive state, so as to obtain the speed change amount; and to calculate the difference in the dust concentration corresponding to the first and last moments of the preset first time window, so as to obtain the concentration change amount.

[0015] A flag generation unit is used to generate the suspected visibility deterioration flag based on the change in velocity and the change in concentration.

[0016] Furthermore, the flag generation unit includes:

[0017] The change comparison subunit is used to compare the change in velocity with a preset velocity change threshold, and to compare the change in concentration with a preset concentration change threshold, so as to obtain the change comparison result;

[0018] A flag generation subunit is used to determine a suspected visibility deterioration when the change comparison result is that the change in speed is less than the preset speed change threshold and the change in concentration is greater than the preset concentration change threshold, so as to generate the suspected visibility deterioration flag.

[0019] Furthermore, the aging determination module includes:

[0020] An aging characteristic calculation unit is used to calculate the sliding beginning and end difference of the working current ripple coefficient and the sliding beginning and end difference of the driving chip temperature respectively within a preset second time window based on a sliding sub-window, so as to obtain the sliding change of the ripple coefficient and the sliding change of the temperature respectively.

[0021] The trend correlation determination unit is used to determine whether the intrinsically safe LED lighting sensor lamp for mining has entered the potential aging mode based on the relationship between the sliding change of the ripple coefficient and the sliding change of the temperature.

[0022] Furthermore, the trend correlation determination unit includes:

[0023] The correlation comparison subunit is used to compare the change direction of the ripple coefficient sliding change with the change direction of the temperature sliding change in order to generate the change direction relationship;

[0024] The associated determination subunit is used to determine that the intrinsically safe LED lighting sensor for mining has entered the circuit disturbance mode when the ripple coefficient sliding change increases and the temperature sliding change does not increase synchronously, and to determine that the intrinsically safe LED lighting sensor for mining has entered the overload risk mode when both the ripple coefficient sliding change and the temperature sliding change show an increasing trend.

[0025] Furthermore, the adjustment module includes:

[0026] The coupling index calculation unit is used to calculate the abnormal coupling index, which characterizes the degree of correlation between environmental changes and equipment status changes, based on the changes in the personnel movement speed and dust concentration within a preset first time window, and the changes in the operating current ripple coefficient and the driver chip temperature within a preset second time window, when the suspected visibility deterioration indicator is generated and the potential aging mode is determined.

[0027] A confidence level determination unit is used to determine the corresponding confidence level based on the degree of matching between the abnormal coupling index and the distribution of historical abnormal samples.

[0028] The parameter adjustment unit is used to adjust the benchmark trigger parameters according to the potential aging mode when the abnormal coupling index is greater than a preset coupling threshold and the confidence level is greater than a preset confidence threshold.

[0029] Furthermore, the confidence determination unit includes:

[0030] The deviation calculation subunit is used to calculate the degree of deviation of the abnormal coupling index from the center value of the historical abnormal sample distribution to obtain the index deviation.

[0031] The stability assessment subunit is used to determine the index stability index based on the fluctuation range of the abnormal coupling index within a preset assessment period.

[0032] A confidence generation subunit is used to comprehensively determine the confidence level corresponding to the abnormal coupling index based on the index deviation and the index stability index.

[0033] Furthermore, the parameter adjustment unit includes:

[0034] A first adjustment subunit is used to reduce a preset sensitivity threshold for triggering continuous illumination in the reference trigger parameters according to a preset first adjustment ratio based on the potential aging mode of the circuit disturbance mode.

[0035] The second adjustment subunit is used to reduce the preset continuous lighting duration upper limit in the reference triggering parameters according to a preset second adjustment ratio based on the potential aging mode of the overload risk mode.

[0036] Furthermore, the correction module includes:

[0037] The confidence fluctuation calculation unit is used to calculate the standard deviation of the confidence level within the preset observation period to obtain the fluctuation characteristics of the confidence level.

[0038] The correlation calculation unit is used to calculate the standard deviation of the cross-sectional mutation rate within the same preset observation period to obtain the fluctuation characteristics of the cross-sectional mutation rate, and to calculate the fluctuation correlation based on the fluctuation characteristics of the cross-sectional mutation rate and the fluctuation characteristics of the confidence level.

[0039] The correction unit is used to correct the preset disturbance threshold according to the fluctuation correlation.

[0040] Furthermore, the correction unit includes:

[0041] A threshold comparison subunit is used to compare the fluctuation correlation with a preset correlation threshold to obtain a threshold comparison result;

[0042] A correction subunit is used to correct the preset disturbance threshold according to the deviation ratio between the fluctuation correlation and the preset correlation threshold when the threshold comparison result is that the fluctuation correlation is less than the preset correlation threshold.

[0043] Compared with existing technologies, the beneficial effects of this invention are as follows: by simultaneously introducing the operating current ripple coefficient characterizing the electrical stability of the luminaire, the driver chip temperature reflecting the thermal load state of the driver, and the continuous lighting duration characterizing the usage intensity, and performing time-scale differentiation and joint analysis with environmental parameters such as personnel movement speed, dust concentration, and abrupt change rate of roadway cross-section, it is possible to distinguish between short-term visibility fluctuations caused by changes in the roadway environment and operational anomalies caused by changes in the internal state of the luminaire. Among them, the coordinated changes of personnel movement speed and dust concentration within a short time window are used to characterize the direct impact of obstruction and dust on visibility, while the evolution trend of the operating current ripple coefficient and driver chip temperature within a longer time window is used to reflect the cumulative effect of the load state and thermal stress of the driver circuit. By quantifying the coupling relationship between the two types of parameters at different time scales, it is possible to avoid misjudging environmental disturbances as equipment aging or misattributing electrical anomalies to external obstruction factors. Thus, while ensuring the sensitivity of lighting response, unnecessary long-term high-load operation is suppressed, effectively solving the problem of excessively long duration of high-load operation caused by the difficulty in distinguishing between transient environmental disturbances and equipment condition deterioration.

[0044] Furthermore, by triggering the joint analysis of personnel movement speed and dust concentration only when the abrupt change rate of the roadway cross-section exceeds a preset disturbance threshold, this embodiment limits visibility determination to scenarios where structural or construction disturbances may actually exist, reducing the interference of irrelevant working conditions on the determination results from the source. Based on this, a criterion is introduced that personnel movement speed changes little in a short period while dust concentration increases synchronously. This can characterize the state of relatively stable personnel activity but rapid accumulation of suspended particles in the air. This state typically corresponds to increased occlusion and intensified light scattering rather than temporary occlusion caused by instantaneous rapid passage, thus avoiding misidentification of rapid passage or occasional actions as visibility deterioration. Through the above-mentioned layered determination and parameter constraint relationship, the generated suspected visibility deterioration indicator more closely reflects the actual visual environment changes in the roadway, thereby providing a reliable triggering basis for subsequent lighting parameter adjustments and improving the targeting and stability of the lighting response.

[0045] Furthermore, by calculating the sliding change of the operating current ripple coefficient and the temperature of the driver chip within a preset second time window, and further analyzing the correspondence between the directions of change of the two, this embodiment can distinguish between transient current fluctuations caused by a decrease in electrical stability and the heat accumulation process caused by a continuous increase in load: when the change in ripple coefficient continues to increase while the change in temperature does not rise synchronously, it indicates that there is a phenomenon of current fluctuation amplification caused by modulation instability, device parameter drift, etc. in the drive circuit. Its impact is mainly concentrated at the electrical level and has not yet formed a significant thermal effect; when the ripple coefficient and the change in temperature both show an upward trend, it reflects that the drive circuit generates enhanced current fluctuations and continuous heat accumulation under high load or reduced efficiency conditions. The risk evolves from electrical abnormality to thermal stress. It can distinguish and identify different failure evolution paths in the early stage of aging, thereby improving the safety of operation of intrinsically safe LED lighting sensor lamps for mining.

[0046] Furthermore, by introducing short-term changes in personnel movement speed and dust concentration, along with medium-term changes in operating current ripple coefficient and driver chip temperature, under the condition of simultaneous occurrence of suspected visibility deterioration indicators and potential aging modes, this embodiment unifies the characterization of dust disturbances caused by personnel activities in the roadway with abnormal responses to the electrical and thermal states of the lighting fixtures. This provides a distinguishable measurement basis for determining whether abnormal environmental changes truly affect the operating state of the lighting fixtures. Furthermore, by performing deviation analysis between the abnormal coupling index and the distribution of historical abnormal samples, and combining this with a comprehensive judgment based on its fluctuation stability within the evaluation period, the amplified impact of single sudden disturbances or accidental measurement deviations on parameter adjustment decisions can be effectively avoided. This ensures that the adjustment of the benchmark trigger parameters is only triggered when there is a continuous correlation between environmental changes and equipment state changes.

[0047] Furthermore, by applying two key control parameters—the trigger sensitivity threshold and the upper limit of continuous lighting duration—after identifying different potential aging modes, the parameter adjustment direction is aligned with the circuit's operating state and the evolution trend of thermal load. In the circuit disturbance mode, as device aging intensifies, signal stability decreases and the probability of false triggering increases. By appropriately reducing the trigger sensitivity threshold, frequent switching caused by transient fluctuations can be suppressed while ensuring lighting continuity. In the overload risk mode, the device junction temperature accumulates and increases with power-on time. By shortening the upper limit of continuous lighting duration, the continuous accumulation of heat is effectively limited, slowing down the performance degradation of the driving devices and light-emitting units. This ensures that parameter adjustment not only targets the abnormal characteristics themselves but also directly affects the key impact paths of aging evolution, achieving proactive suppression of potential aging risks and an overall improvement in the reliability of the entire lamp operation.

[0048] Furthermore, by simultaneously characterizing the fluctuation amplitudes of confidence and cross-sectional mutation rate within a unified observation period, and further quantifying the degree of synchronization between their fluctuations, this embodiment can distinguish between the abnormal perception instability driven by structural environment changes and the system's own judgment fluctuations: when the cross-sectional mutation rate fluctuates significantly while the confidence fluctuation is weakly correlated with it, it indicates that the actual impact of environmental structural changes on the judgment results is limited, and continuing to maintain the original disturbance threshold will amplify the risk of misjudgment caused by occasional noise; conversely, by proportionally correcting the disturbance threshold based on the fluctuation correlation, the threshold can adaptively converge with the strength of the environment-judgment coupling, which can suppress frequent state switching caused by invalid structural disturbances and improve the matching degree between the threshold setting and the actual roadway structural stability. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the intrinsically safe LED lighting sensor system for mining in this embodiment;

[0050] Figure 2 This is a front cross-sectional view of the intrinsically safe LED lighting sensor lamp for mining in this embodiment;

[0051] Figure 3 This is a top-view schematic diagram of the intrinsically safe LED lighting sensor lamp for mining in this embodiment;

[0052] Figure 4 This is a logic diagram of the parameter adjustment unit in this embodiment determining the adjustment reference trigger parameter;

[0053] Reference numerals: 1. Light source; 2. Intrinsically safe LED lighting sensor system for mining; 3. Battery. Detailed Implementation

[0054] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0055] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0056] Please see Figure 1-3 As shown, Figure 1 This is a schematic diagram of the intrinsically safe LED lighting sensor system for mining, as described in this embodiment. Figure 2 This is a front cross-sectional view of the intrinsically safe LED lighting sensor lamp for mining in this embodiment. Figure 3 This is a top-view schematic diagram of the intrinsically safe LED lighting sensor lamp for mining in this embodiment. In this embodiment, the intrinsically safe LED lighting sensor lamp for mining is equipped with an intrinsically safe LED lighting sensor lamp system 2, a storage battery 3, and a light source 1.

[0057] This embodiment provides an intrinsically safe LED lighting sensor system for mining applications, including:

[0058] The acquisition module is used to collect multi-dimensional operating data and roadway environment data of the intrinsically safe LED lighting sensor lamps for mining under the control of the reference trigger parameters in real time. The multi-dimensional operating data includes the working current ripple coefficient of the lamp body, the temperature of the driver chip, and the duration of continuous lighting. The roadway environment data includes the movement speed of personnel near the lamp installation point, dust concentration, and the cross-sectional change rate characterizing the roadway structure.

[0059] A marker generation module, connected to the acquisition module, is used to determine whether a structurally sensitive state has been entered based on the cross-sectional abrupt change rate and a preset disturbance threshold. Based on the entry determination result, a suspected visibility deterioration marker is generated by combining the personnel movement speed and the dust concentration change trend within a preset first time window.

[0060] An aging determination module, which is connected to a flag generation module and a data acquisition module respectively, is used to determine whether a potential aging mode has been entered based on the timing combination characteristics of the operating current ripple coefficient and the temperature of the driver chip. The potential aging modes include circuit disturbance mode and overload risk mode.

[0061] The adjustment module is connected to the marker generation module, the acquisition module, and the aging determination module, respectively. It is used to determine the abnormal coupling index and confidence level based on the suspected visibility deterioration marker and the potential aging mode, according to the multi-dimensional operation data and the tunnel environment data, and adjust the benchmark triggering parameters based on the threshold comparison result of the abnormal coupling index and the potential aging mode.

[0062] The correction module, which is connected to the adjustment module, the flag generation module and the acquisition module respectively, is used to correct the preset disturbance threshold based on the fluctuation characteristics of the confidence level and the cross-sectional mutation rate within a preset observation period after adjusting the benchmark trigger parameters.

[0063] In this embodiment, the intrinsically safe LED lighting sensor for mining is a fixed-installation type. The acquisition module is integrated into the explosion-proof housing of the intrinsically safe LED lighting sensor for mining and is electrically connected to the lamp driver circuit and the roadway environment sensing unit. The specific acquisition method is as follows: The operating current ripple coefficient is acquired by a Hall current sensor installed in the output circuit of the LED driver circuit. The Hall current sensor is used to obtain the instantaneous sample value of the driving current. The acquisition module periodically samples the instantaneous sample value, calculates the peak value and average value of the current within a preset power cycle, and calculates the operating current ripple coefficient according to the ratio of peak value to average value. The temperature of the driver chip is acquired by a digital temperature sensor closely attached to the bottom of the LED driver chip package. The digital temperature sensor sends the real-time temperature data to the acquisition module through a single-bus communication method to characterize the thermal operating state of the driver chip under actual load conditions. The duration of continuous lighting is recorded by the timing counting unit inside the acquisition module. Timing starts when the lighting sensor switches from the off state to the on state and stops when the lamp enters the off state again, so as to obtain the duration of continuous lighting corresponding to a single continuous lighting process. Personnel movement speed is collected by a millimeter-wave radar sensor installed in the tunnel area directly below the luminaire. The millimeter-wave radar sensor analyzes the Doppler frequency shift of the reflected echo from the target in the tunnel to obtain real-time movement speed data of personnel within the luminaire's coverage area, and sends the speed data to the acquisition module. Dust concentration is collected by a laser scattering dust sensor installed in the air intake channel on the side of the luminaire housing. The dust sensor measures the scattering intensity of suspended particles in a unit volume of air on the laser and outputs the corresponding mass concentration value to characterize the air visibility near the luminaire installation point. The cross-sectional abrupt change rate is obtained by the acquisition module based on the tunnel cross-sectional contour detection unit. The cross-sectional contour detection unit uses structured light projection to obtain the contour point cloud data of the tunnel cross-section in front of the luminaire. The acquisition module calculates the rate of change of the cross-sectional contour area within adjacent sampling periods to obtain the cross-sectional abrupt change rate, which characterizes the degree of change in the tunnel structure. Through the above acquisition methods, the acquisition module can simultaneously obtain multi-dimensional operational data and environmental data characterizing the electrical, thermal, and usage status of the luminaire, as well as the characteristics of the tunnel environment, under the control of the reference trigger parameters.

[0064] In this embodiment, the reference triggering parameters are the initial control parameter set used to control the intrinsically safe LED lighting sensor lamp for mine to enter the continuous lighting state. It includes at least a trigger sensitivity threshold for determining the approach of personnel or changes in the environment and an upper limit for continuous lighting duration. The trigger sensitivity threshold is used to limit the determination boundary when the personnel movement speed and environmental disturbance parameters reach the triggering condition, and the upper limit for continuous lighting duration is used to limit the continuous lighting time after a single trigger, so as to control the electrical load and heat accumulation level of the lamp while meeting the roadway lighting needs.

[0065] A preset disturbance threshold is used to characterize the boundary for determining whether the roadway cross-sectional structure changes from a stable state to a significantly changed state. It depends on the historical fluctuation range of the roadway cross-sectional profile under normal operating conditions and is usually set to 1.2 to 1.5 times the average historical cross-sectional mutation rate. In this embodiment, it is set to 1.3 times, which can effectively distinguish abnormal changes caused by construction disturbances or structural mutations. A preset first time window is used to characterize the short-term changes in personnel activity and dust diffusion. It depends on the personnel passage speed and dust suspension attenuation characteristics in the roadway and is usually set between 5 and 15 seconds. In this embodiment, it is set to 10 seconds, which can cover the visibility change process caused by a single person passing through the lighting installation area. A preset second time window is used to characterize the evolution of the electrical and thermal characteristics of the lighting fixtures. It depends on the thermal inertia of the LED driving circuit and the load change response time and is usually set between 60 and 300 seconds. In this embodiment, it is set to 120 seconds, which can reflect the cumulative change trend of potential aging characteristics in the stable operation stage.

[0066] By simultaneously introducing the operating current ripple coefficient, which characterizes the electrical stability of the luminaire, the driver chip temperature, which reflects the thermal load state of the driver, and the continuous lighting duration, which characterizes the intensity of use, and performing time-scale differentiation and joint analysis with environmental parameters such as personnel movement speed, dust concentration, and abrupt change rate of roadway cross-section, it is possible to distinguish between short-term visibility fluctuations caused by changes in the roadway environment and operational anomalies caused by changes in the internal state of the luminaire. Among them, the coordinated changes of personnel movement speed and dust concentration within a short time window are used to characterize the direct impact of obstruction and dust on visibility, while the evolution trend of the operating current ripple coefficient and driver chip temperature within a longer time window is used to reflect the cumulative effect of the load state and thermal stress of the driver circuit. By quantifying the coupling relationship between the two types of parameters at different time scales, it is possible to avoid misjudging environmental disturbances as equipment aging or misattributing electrical anomalies to external obstruction factors. Thus, while ensuring the sensitivity of lighting response, unnecessary long-term high-load operation is suppressed, effectively solving the problem of excessively long high-load operation caused by the inability to distinguish between transient environmental disturbances and equipment condition deterioration.

[0067] In this embodiment, the battery is used to continuously provide stable power output to the lighting system under conditions of instantaneous fluctuations in the underground power supply line, partial power outages, or emergency situations, avoiding lighting interruptions due to abnormal external power supply, thereby ensuring the continuity of roadway operations and the safety of personnel passage; at the same time, the battery's output characteristics are relatively stable, which can effectively reduce the transient impact of the complex underground electromagnetic environment and load shocks on the drive circuit, reduce operating current ripple and device thermal stress fluctuations, which is conducive to improving the operational stability and service life of the drive chip and light source components, and further enhancing the reliability and safety margin of the entire lighting system in complex mining environments.

[0068] Specifically, the flag generation module includes:

[0069] A sensitive state determination unit is used to determine that the structure has entered a sensitive state when the cross-sectional abrupt change rate is greater than the preset disturbance threshold.

[0070] The trend feature calculation unit is used to calculate the difference in the movement speed of the personnel corresponding to the first and last moments of the preset first time window when it is determined that the structure is in a sensitive state, so as to obtain the speed change amount; and to calculate the difference in the dust concentration corresponding to the first and last moments of the preset first time window, so as to obtain the concentration change amount.

[0071] A flag generation unit is used to generate the suspected visibility deterioration flag based on the change in velocity and the change in concentration.

[0072] Specifically, the flag generation unit includes:

[0073] The change comparison subunit is used to compare the change in velocity with a preset velocity change threshold, and to compare the change in concentration with a preset concentration change threshold, so as to obtain the change comparison result;

[0074] A flag generation subunit is used to determine a suspected visibility deterioration when the change comparison result is that the change in speed is less than the preset speed change threshold and the change in concentration is greater than the preset concentration change threshold, so as to generate the suspected visibility deterioration flag.

[0075] The preset speed change threshold is used to characterize the judgment boundary when the movement state of personnel changes from stable passage to rapid passage or accumulation disturbance within a preset first time window. It depends on the statistical fluctuation range of normal walking speed of personnel in the roadway, and is usually set between 0.2m / s and 0.5m / s. In this embodiment, it is set to 0.3m / s, which can effectively distinguish between stable passage conditions and obvious speed fluctuation conditions, and avoid misjudging small speed changes caused by normal walking as abnormal activities. The preset concentration change threshold is used to characterize the judgment boundary when the dust accumulation level in the preset first time window reaches the level that has a substantial impact on visibility. It depends on the fluctuation range of background dust concentration in the roadway air under normal ventilation conditions, and is usually set between 5mg / m³ and 15mg / m³. In this embodiment, it is set to 10mg / m³, which can identify the significant dust rise process caused by disturbance, and provide a reliable basis for the judgment of suspected visibility deterioration.

[0076] By triggering a joint analysis of personnel movement speed and dust concentration only when the abrupt change rate of the roadway cross-section exceeds a preset disturbance threshold, this embodiment limits visibility determination to scenarios where structural or construction disturbances may actually exist, reducing the interference of irrelevant working conditions on the judgment results from the source. Based on this, a criterion is introduced where personnel movement speed changes little in a short period while dust concentration increases synchronously. This can characterize the state of relatively stable personnel activity but rapid accumulation of suspended particles in the air. This state typically corresponds to increased occlusion and intensified light scattering rather than temporary occlusion caused by instantaneous rapid passage, thus avoiding misidentification of rapid passage or occasional actions as visibility deterioration. Through the above-mentioned layered determination and parameter constraint relationship, the generated suspected visibility deterioration indicator more closely reflects the actual visual environment changes in the roadway, thereby providing a reliable triggering basis for subsequent lighting parameter adjustments and improving the targeting and stability of the lighting response.

[0077] Specifically, the aging determination module includes:

[0078] An aging characteristic calculation unit is used to calculate the sliding beginning and end difference of the working current ripple coefficient and the sliding beginning and end difference of the driving chip temperature respectively within a preset second time window based on a sliding sub-window, so as to obtain the sliding change of the ripple coefficient and the sliding change of the temperature respectively.

[0079] The trend correlation determination unit is used to determine whether the intrinsically safe LED lighting sensor lamp for mining has entered the potential aging mode based on the relationship between the sliding change of the ripple coefficient and the sliding change of the temperature.

[0080] Specifically, the trend correlation determination unit includes:

[0081] The correlation comparison subunit is used to compare the change direction of the ripple coefficient sliding change with the change direction of the temperature sliding change in order to generate the change direction relationship;

[0082] The associated determination subunit is used to determine that the intrinsically safe LED lighting sensor for mining has entered the circuit disturbance mode when the ripple coefficient sliding change increases and the temperature sliding change does not increase synchronously, and to determine that the intrinsically safe LED lighting sensor for mining has entered the overload risk mode when both the ripple coefficient sliding change and the temperature sliding change show an increasing trend.

[0083] In this embodiment, an increase in the ripple coefficient sliding change means that within the same sliding sub-window, the difference between the beginning and end values ​​of the ripple coefficient is positive, and this difference continuously increases relative to the difference between the beginning and end values ​​of the previous sliding sub-window. A failure to synchronously increase the temperature sliding change means that within the same sliding sub-window corresponding to the ripple coefficient sliding change, the difference between the beginning and end values ​​of the driver chip temperature is less than a preset temperature change judgment threshold. By directionally limiting the ripple and temperature changes under a unified sliding time scale, the circuit disturbance mode is entered only when electrical parameter fluctuations precede heat accumulation, while the overload risk mode is entered when both ripple and temperature changes show a continuous increase within the same time window.

[0084] The preset second time window is used to characterize the co-evolution process of electrical and thermal parameters of intrinsically safe LED lighting induction lamps in continuous operation. It depends on the thermal inertia characteristics of the LED driving circuit and the response time lag of chip temperature rise to load changes. It is usually set between 60 seconds and 300 seconds. In this embodiment, it is set to 120 seconds, which can fully reflect the cumulative change trend of potential aging characteristics in the stable working stage while eliminating the influence of instantaneous disturbances. The sliding sub-window refers to the time segment unit used for continuous difference calculation within the preset second time window. It depends on the minimum effective observation scale of the fluctuation frequency of electrical parameters of the driving circuit and temperature response. It is usually set between 5 seconds and 20 seconds. In this embodiment, it is set to 10 seconds, which can stably extract the evolution trend of ripple coefficient and driving chip temperature without amplifying instantaneous noise. The preset temperature change judgment threshold is the difference limit used to determine whether the temperature change of the driving chip constitutes a significant thermal response. It depends on the normal temperature rise fluctuation range of the driving chip under steady-state load conditions. It is usually set between 1℃ and 3℃. In this embodiment, it is set to 2℃, which can effectively distinguish the non-thermal changes caused by short-term disturbances of electrical parameters from the real heat accumulation process under continuous load.

[0085] By calculating the sliding changes of the operating current ripple coefficient and the driver chip temperature within a preset second time window, and further analyzing the correspondence between their changing directions, this embodiment can distinguish between transient current fluctuations caused by decreased electrical stability and heat accumulation caused by continuous load increase. When the ripple coefficient changes continuously while the temperature change does not increase synchronously, it indicates that there is a phenomenon of current fluctuation amplification caused by modulation instability, device parameter drift, etc. in the drive circuit. Its impact is mainly concentrated at the electrical level and has not yet formed a significant thermal effect. When the ripple coefficient and temperature change both show an upward trend, it reflects that the drive circuit generates enhanced current fluctuations and continuous heat accumulation under high load or reduced efficiency conditions. The risk evolves from electrical abnormality to thermal stress. It can distinguish and identify different failure evolution paths in the early stage of aging, thereby improving the safety of intrinsically safe LED lighting sensor lamps in mines.

[0086] Please see Figure 4 As shown, this is a logic diagram for determining the adjustment reference trigger parameter by the parameter adjustment unit in this embodiment. In this embodiment, the adjustment module includes:

[0087] The coupling index calculation unit is used to calculate the abnormal coupling index, which characterizes the degree of correlation between environmental changes and equipment status changes, based on the changes in the personnel movement speed and dust concentration within a preset first time window, and the changes in the operating current ripple coefficient and the driver chip temperature within a preset second time window, when the suspected visibility deterioration indicator is generated and the potential aging mode is determined.

[0088] A confidence level determination unit is used to determine the corresponding confidence level based on the degree of matching between the abnormal coupling index and the distribution of historical abnormal samples.

[0089] The parameter adjustment unit is used to adjust the benchmark trigger parameters according to the potential aging mode when the abnormal coupling index is greater than a preset coupling threshold and the confidence level is greater than a preset confidence threshold.

[0090] Specifically, the confidence determination unit includes:

[0091] The deviation calculation subunit is used to calculate the degree of deviation of the abnormal coupling index from the center value of the historical abnormal sample distribution to obtain the index deviation.

[0092] The stability assessment subunit is used to determine the index stability index based on the fluctuation range of the abnormal coupling index within a preset assessment period.

[0093] A confidence generation subunit is used to comprehensively determine the confidence level corresponding to the abnormal coupling index based on the index deviation and the index stability index.

[0094] The preset coupling threshold is used to define whether a significant correlation exists between environmental changes and equipment status changes. It depends on the distribution difference of the abnormal coupling index under historical normal and abnormal operating conditions, and is usually set between 1.2 and 1.6 times the average of the historical abnormal coupling index. In this embodiment, it is set to 1.4 times, which can effectively distinguish between weakly correlated changes caused by random environmental disturbances and strongly correlated anomalies that have a substantial impact on the operating status of the lamps. The preset reliability threshold is used to determine the credibility of the abnormal coupling index discrimination results. It depends on the joint distribution characteristics of the index deviation and stability index in historical samples, and is usually set between 0.6 and 0.8. In this embodiment, it is set to 0.7, which can avoid triggering parameter adjustments when the abnormal features are unstable or the sample matching degree is insufficient, thereby improving the reliability of decision-making. The preset evaluation time period is used to evaluate the temporal stability of the abnormal coupling index. It depends on the duration of the roadway environmental changes and the response delay characteristics of the lamp operating status, and is usually set between 30 and 120 seconds. In this embodiment, it is set to 60 seconds, which can fully reflect the fluctuation characteristics of the abnormal coupling index during continuous operation.

[0095] In this embodiment, the abnormal coupling index is calculated using a normalized weighted combination method. The data acquisition module first obtains the speed change of personnel movement within a preset first time window, the concentration change of dust within a preset first time window, the ripple coefficient change of the operating current within a preset second time window, and the temperature change of the drive chip within a preset second time window. Then, it normalizes the speed change, concentration change, ripple coefficient change, and temperature change according to the maximum variation amplitude of the corresponding parameters under historical normal operating conditions, obtaining dimensionless standardized change values. Based on this, the acquisition module performs a weighted summation of the standardized speed change and concentration change values ​​according to preset weighting coefficients to obtain an environmental change intensity term, and then performs a weighted summation of the standardized ripple coefficient change and temperature change values ​​according to preset weighting coefficients to obtain an equipment status change intensity term. Finally, the environmental change intensity term and the equipment status change intensity term are multiplied to obtain an abnormal coupling index, which characterizes the correlation between environmental changes and equipment status changes. The multiplication operation is used to amplify the synchronous amplification of environmental changes and equipment status changes, thus causing the abnormal coupling index to exhibit a non-linear growth characteristic when both change significantly at the same time.

[0096] The preset weighting coefficients are a first weighting coefficient and a second weighting coefficient. The first weighting coefficient corresponds to the environmental change factor formed by the change in personnel movement speed and dust concentration, and the second weighting coefficient corresponds to the equipment state change factor formed by the change in operating current ripple coefficient and the change in driver chip temperature. The value of the weighting coefficient depends on the degree to which environmental disturbances in the work scenario dominate the impact on equipment operation and the sensitivity of the equipment's own thermal-electric stability to anomaly identification. Under normal circumstances, both the first weighting coefficient and the second weighting coefficient are set between 0.3 and 0.7, and the first weighting coefficient + the second weighting coefficient = 1. In this embodiment, the first weighting coefficient is set to 0.6 and the second weighting coefficient is set to 0.4, which can improve the response sensitivity of the anomaly coupling index to environmentally induced anomalies under working conditions with frequent personnel activity and significant dust fluctuations, while avoiding excessive interference from the short-term thermal and electrical fluctuations of the equipment itself on the anomaly judgment results.

[0097] By introducing short-term changes in personnel movement speed and dust concentration, along with medium-term changes in operating current ripple coefficient and driver chip temperature, under the condition of simultaneous appearance of suspected visibility deterioration signs and potential aging modes, this embodiment unifies the characterization of dust disturbances caused by personnel activities in the roadway and abnormal responses of the electrical and thermal states of the lighting fixtures. This provides a distinguishable measurement basis for whether abnormal environmental changes truly affect the working state of the lighting fixtures. Furthermore, by analyzing the deviation between the abnormal coupling index and the distribution of historical abnormal samples, and combining this with a comprehensive judgment based on its fluctuation stability within the evaluation period, the amplified impact of single sudden disturbances or accidental measurement deviations on parameter adjustment decisions can be effectively avoided. This ensures that the adjustment of the benchmark trigger parameters is only triggered when there is a continuous correlation between environmental changes and equipment state changes.

[0098] Specifically, the parameter adjustment unit includes:

[0099] A first adjustment subunit is used to reduce a preset sensitivity threshold for triggering continuous illumination in the reference trigger parameters according to a preset first adjustment ratio based on the potential aging mode of the circuit disturbance mode.

[0100] The second adjustment subunit is used to reduce the preset continuous lighting duration upper limit in the reference triggering parameters according to a preset second adjustment ratio based on the potential aging mode of the overload risk mode.

[0101] The preset first adjustment ratio is a proportional coefficient used to characterize the magnitude of the reduction in the trigger sensitivity threshold. It depends on the combined level of ripple fluctuation intensity and false trigger risk under circuit disturbance mode, and is usually set between 5% and 20%. In this embodiment, it is set to 10%, which can suppress false triggering caused by transient disturbances while maintaining normal response capability when people approach. The preset second adjustment ratio is a proportional coefficient used to characterize the magnitude of the reduction in the upper limit of continuous lighting duration. It depends on the operating current load level and the temperature rise rate of the driver chip under overload risk mode, and is usually set between 10% and 30%. In this embodiment, it is set to 20%, which can effectively limit the continuous accumulation of heat and reduce the risk of accelerated device aging.

[0102] By identifying different potential aging modes and applying two key control parameters—the trigger sensitivity threshold and the upper limit of continuous lighting duration—the adjustment direction is aligned with the circuit's operating state and the evolution trend of thermal load. In the circuit disturbance mode, as device aging intensifies, signal stability decreases and the probability of false triggering increases. Appropriately reducing the trigger sensitivity threshold can suppress frequent switching caused by transient fluctuations while ensuring lighting continuity. In the overload risk mode, the junction temperature of devices accumulates and increases with power-on time. Shortening the upper limit of continuous lighting duration effectively limits the continuous accumulation of heat, slowing down the performance degradation of the driving devices and light-emitting units. This ensures that parameter adjustment not only targets the abnormal characteristics themselves but also directly affects the key impact paths of aging evolution, achieving proactive suppression of potential aging risks and an overall improvement in the reliability of the entire lamp operation.

[0103] Specifically, the correction module includes:

[0104] The confidence fluctuation calculation unit is used to calculate the standard deviation of the confidence level within the preset observation period to obtain the fluctuation characteristics of the confidence level.

[0105] The correlation calculation unit is used to calculate the standard deviation of the cross-sectional mutation rate within the same preset observation period to obtain the fluctuation characteristics of the cross-sectional mutation rate, and to calculate the fluctuation correlation based on the fluctuation characteristics of the cross-sectional mutation rate and the fluctuation characteristics of the confidence level.

[0106] The correction unit is used to correct the preset disturbance threshold according to the fluctuation correlation.

[0107] In this embodiment, the correlation of fluctuations is calculated as follows: within the preset observation period, the confidence sequence and the cross-sectional mutation rate sequence are standardized to obtain the corresponding dimensionless fluctuation sequence; based on this, the mean of the product of the two at the same sampling time is calculated according to time alignment, and normalized by combining their respective standard deviations to obtain the correlation index characterizing the synchronization degree of the two fluctuations. The larger the correlation value, the more significant the impact of the cross-sectional mutation rate fluctuation on the confidence fluctuation; the smaller the correlation value, the weaker the correlation between the two.

[0108] Specifically, the correction unit includes:

[0109] A threshold comparison subunit is used to compare the fluctuation correlation with a preset correlation threshold to obtain a threshold comparison result;

[0110] The correction subunit is used to correct the preset perturbation threshold according to the deviation ratio between the fluctuation correlation and the preset correlation threshold when the threshold comparison result is that the fluctuation correlation is less than the preset correlation threshold. Y'=Y×[1-k×(R-R0) / R0], where Y' is the corrected preset perturbation threshold, Y is the original preset perturbation threshold, k is the preset correction coefficient, R is the fluctuation correlation, and R0 is the preset correlation threshold.

[0111] The preset correlation threshold is a criterion used to measure whether there is a significant correlation between the fluctuation of the cross-sectional abrupt change rate and the fluctuation of the confidence level. It depends on the system's sensitivity to whether "changes in the environmental structure should affect the disturbance judgment result." It is usually set based on the statistical correlation level between structural changes and judgment stability in historical operating data, and is generally set between 0.2 and 0.6. In this embodiment, it is set to 0.35, which can effectively distinguish the judgment fluctuation caused by the actual change in the roadway structure from the invalid fluctuation caused by random noise, providing a stable and reliable triggering basis for the adaptive correction of the disturbance threshold. The preset correction coefficient is used to limit the adjustment intensity of the preset disturbance threshold as the fluctuation correlation deviates. It depends on the system's stability requirements for the adaptive correction of the threshold and the allowable threshold change rate. It is usually set in combination with the impact of the disturbance threshold correction on the false trigger rate and the missed judgment rate under historical operating conditions, and is generally set between 0.1 and 0.5. In this embodiment, it is set to 0.25, which can make the preset disturbance threshold gradually decrease as the correlation weakens when the fluctuation correlation is lower than the preset correlation threshold, avoiding judgment oscillation caused by threshold abrupt changes, while maintaining the gradual adjustment effect of the structural change on the disturbance judgment.

[0112] By simultaneously characterizing the fluctuation amplitudes of confidence level and cross-sectional mutation rate within a unified observation period, and further quantifying the degree of synchronization between the two fluctuations, this embodiment can distinguish between the abnormal perception instability driven by structural environment changes and the system's own judgment fluctuations: when the cross-sectional mutation rate fluctuates significantly while the confidence level fluctuation is weakly correlated with it, it indicates that the actual impact of environmental structural changes on the judgment results is limited, and continuing to maintain the original disturbance threshold will amplify the risk of misjudgment caused by occasional noise; conversely, by proportionally correcting the disturbance threshold based on the fluctuation correlation, the threshold can adaptively converge with the strength of the environment-judgment coupling, which can suppress frequent state switching caused by invalid structural disturbances and improve the matching degree between the threshold setting and the actual roadway structural stability.

[0113] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A mine intrinsically safe LED lighting induction lamp system, characterized in that, include: The acquisition module is used to collect multi-dimensional operating data and roadway environment data of the intrinsically safe LED lighting sensor lamps for mining under the control of the reference trigger parameters in real time. The multi-dimensional operating data includes the working current ripple coefficient of the lamp body, the temperature of the driver chip, and the duration of continuous lighting. The roadway environment data includes the movement speed of personnel near the lamp installation point, dust concentration, and the cross-sectional change rate characterizing the roadway structure. The marker generation module is used to determine whether a structurally sensitive state has been entered based on the cross-sectional abrupt change rate and a preset disturbance threshold. Based on the entry determination result, and combined with the change trend of the personnel movement speed and the dust concentration within a preset first time window, a suspected visibility deterioration marker is generated. An aging determination module is used to determine whether a potential aging mode has been entered based on the timing combination characteristics of the operating current ripple coefficient and the temperature of the driver chip. The potential aging modes include circuit disturbance mode and overload risk mode. The adjustment module is used to determine the abnormal coupling index and confidence level based on the suspected visibility deterioration indicator and the potential aging mode, according to the multi-dimensional operating data and the tunnel environment data, and adjust the benchmark triggering parameters based on the threshold comparison result of the abnormal coupling index and the potential aging mode. The correction module is used to correct the preset disturbance threshold based on the fluctuation characteristics of the confidence level and the cross-sectional mutation rate within a preset observation period after adjusting the benchmark trigger parameters. The aging determination module includes: An aging characteristic calculation unit is used to calculate the sliding beginning and end difference of the working current ripple coefficient and the sliding beginning and end difference of the driving chip temperature respectively within a preset second time window based on a sliding sub-window, so as to obtain the sliding change of the ripple coefficient and the sliding change of the temperature respectively. The trend correlation determination unit is used to determine whether the intrinsically safe LED lighting sensor lamp for mining has entered the potential aging mode based on the relationship between the ripple coefficient sliding change and the temperature sliding change. The trend correlation determination unit includes: The correlation comparison subunit is used to compare the change direction of the ripple coefficient sliding change with the change direction of the temperature sliding change in order to generate the change direction relationship; The associated determination subunit is used to determine that the intrinsically safe LED lighting sensor for mining has entered the circuit disturbance mode when the ripple coefficient sliding change increases and the temperature sliding change does not increase synchronously; and to determine that the intrinsically safe LED lighting sensor for mining has entered the overload risk mode when both the ripple coefficient sliding change and the temperature sliding change show an increasing trend. The adjustment module includes: The coupling index calculation unit is used to calculate the abnormal coupling index, which characterizes the degree of correlation between environmental changes and equipment status changes, based on the changes in the personnel movement speed and dust concentration within a preset first time window, and the changes in the operating current ripple coefficient and the driver chip temperature within a preset second time window, when the suspected visibility deterioration indicator is generated and the potential aging mode is determined. A confidence level determination unit is used to determine the corresponding confidence level based on the degree of matching between the abnormal coupling index and the distribution of historical abnormal samples. A parameter adjustment unit is used to adjust the baseline triggering parameters according to the potential aging mode when the abnormal coupling index is greater than a preset coupling threshold and the confidence level is greater than a preset confidence threshold. The confidence level determination unit includes: The deviation calculation subunit is used to calculate the degree of deviation of the abnormal coupling index from the center value of the historical abnormal sample distribution to obtain the index deviation. The stability assessment subunit is used to determine the index stability index based on the fluctuation range of the abnormal coupling index within a preset assessment period. A confidence generation subunit is used to comprehensively determine the confidence level corresponding to the abnormal coupling index based on the index deviation and the index stability index. The parameter adjustment unit includes: The first adjustment subunit is used to reduce the preset sensitivity threshold for triggering continuous illumination in the reference triggering parameters according to a preset first adjustment ratio based on the potential aging mode of the circuit disturbance mode. The second adjustment subunit is used to reduce the preset continuous lighting duration upper limit in the reference triggering parameters according to a preset second adjustment ratio based on the potential aging mode of the overload risk mode. The correction module includes: The confidence fluctuation calculation unit is used to calculate the standard deviation of the confidence level within the preset observation period to obtain the fluctuation characteristics of the confidence level. The correlation calculation unit is used to calculate the standard deviation of the cross-sectional mutation rate within the same preset observation period to obtain the fluctuation characteristics of the cross-sectional mutation rate, and to calculate the fluctuation correlation based on the fluctuation characteristics of the cross-sectional mutation rate and the fluctuation characteristics of the confidence level. A correction unit is used to correct the preset disturbance threshold according to the fluctuation correlation. The correction unit includes: A threshold comparison subunit is used to compare the fluctuation correlation with a preset correlation threshold to obtain a threshold comparison result; A correction subunit is used to correct the preset disturbance threshold according to the deviation ratio between the fluctuation correlation and the preset correlation threshold when the threshold comparison result is that the fluctuation correlation is less than the preset correlation threshold.

2. The intrinsically safe LED lighting sensor system for mining applications according to claim 1, characterized in that, The flag generation module includes: A sensitive state determination unit is used to determine that the structure has entered a sensitive state when the cross-sectional abrupt change rate is greater than the preset disturbance threshold. The trend feature calculation unit is used to calculate the difference in the movement speed of the personnel corresponding to the first and last moments of the preset first time window when it is determined that the structure is in a sensitive state, so as to obtain the speed change amount; and to calculate the difference in the dust concentration corresponding to the first and last moments of the preset first time window, so as to obtain the concentration change amount. A flag generation unit is used to generate the suspected visibility deterioration flag based on the change in velocity and the change in concentration.

3. The intrinsically safe LED lighting sensor system for mining according to claim 2, characterized in that, The flag generation unit includes: The change comparison subunit is used to compare the change in velocity with a preset velocity change threshold, and to compare the change in concentration with a preset concentration change threshold, so as to obtain the change comparison result; A flag generation subunit is used to determine a suspected visibility deterioration when the change comparison result is that the change in speed is less than the preset speed change threshold and the change in concentration is greater than the preset concentration change threshold, so as to generate the suspected visibility deterioration flag.

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