A dynamic self-checking method and system for a mine overhead passenger conveyance

Abstract

The application discloses a kind of dynamic self-checking method and system for mine overhead passenger device, it is related to the field of mine machinery, it is difficult to predict the future state of switch contact according to the use of mine overhead passenger device and the environment where it is located in the maintenance of mine overhead passenger device, and the technical problem of ignoring the superposition effect of the two physical processes that vibration of mine overhead passenger device prevents new dust from adhering and promotes old dust to fall off;The application determines the standard oxidation factor and the standard adhesion factor of the switch contact corresponding to the monitoring point, inputs the standard oxidation factor and the standard adhesion factor into the state analysis model to obtain the state factor of the monitoring point, determines the future vibration frequency and the future target data by the historical vibration frequency and the historical target data, and determines the maintenance time based on the state factor, the future vibration frequency and the future target data;The application can improve the prediction of the future state of equipment, improve the availability and reliability of equipment.

Description

Technical Field

[0001] This invention relates to the field of mining machinery, specifically a dynamic self-inspection method and system for aerial personnel transport devices in mines. Background Technology

[0002] Mining aerial personnel transport systems are auxiliary personnel transport devices used in inclined, horizontal, or undulating mine roadways. They transport personnel via drive wheels, wire ropes, and tensioning devices. In mining aerial personnel transport systems, because the switch contacts are usually located inside the device, disassembly and inspection are cumbersome. Therefore, inspections are typically performed by manual pressing tests or by measuring contact resistance with a multimeter. However, these methods only provide a binary judgment of whether the contacts are usable and cannot accurately assess potential contact breakage due to oxidation and dust accumulation. This limitation necessitates periodic manual inspections, increasing labor costs and the difficulty of maintenance.

[0003] Currently, most dynamic self-inspection methods for mine aerial personnel carriers struggle to analyze the damage level and lifespan of switch contacts based on the usage and environment of the equipment during maintenance. This makes it impossible to predict the future condition of the equipment, schedule maintenance in advance, reduce unexpected downtime and accident risks, and improve equipment availability and reliability. Furthermore, most dynamic self-inspection methods for mine aerial personnel carriers neglect the combined effect of vibration on switch contacts—a process that prevents new dust adhesion and promotes the shedding of old dust—when analyzing dust contamination, further compromising the accuracy of equipment condition predictions.

[0004] Therefore, this invention discloses a dynamic self-inspection method and system for aerial passenger transport devices in mines, which solves the above-mentioned technical problems. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art. To this end, this invention proposes a dynamic self-inspection method and system for aerial personnel carriers in mines. This method addresses the technical problems encountered during the maintenance of aerial personnel carriers in mines, such as the difficulty in predicting the future state of switch contacts based on the usage and environment of the aerial personnel carrier, and the neglect of the superimposed effect of vibration on the aerial personnel carrier, which prevents new dust adhesion and promotes the shedding of old dust. This invention solves these problems by determining the standard oxidation factor and standard adhesion factor of the switch contacts corresponding to the monitoring point, inputting these factors into a state analysis model to obtain the state factor of the monitoring point, determining the future vibration frequency and future target data using historical vibration frequencies and historical target data, determining the maintenance time based on the state factor, future vibration frequency, and future target data, and scheduling personnel for maintenance based on the maintenance time.

[0006] To achieve the above objectives, a first aspect of the present invention provides a dynamic self-testing method for aerial passenger transport devices in mines, comprising:

[0007] S1: Collect vibration frequency and target data at monitoring points; the monitoring points are manually set and include the contact locations of the head overrun switch, tail overrun switch, rope drop protection switch, and emergency stop switch along the line in the mine's overhead personnel carrier; the target data includes equipment temperature, ambient temperature, ambient humidity, and dust concentration.

[0008] S2: Analyze the target data to obtain characteristic target data, determine the standard oxidation factor based on the characteristic equipment temperature, characteristic ambient temperature and characteristic ambient humidity, and determine the standard adhesion factor based on the vibration frequency and characteristic dust concentration; establish a state analysis model for the monitoring point, and input the standard oxidation factor and standard adhesion factor into the state analysis model to obtain the state factors of the monitoring point; wherein, the characteristic target data includes the characteristic equipment temperature, characteristic ambient temperature, characteristic ambient humidity and characteristic dust concentration;

[0009] S3: Determine future vibration frequency and future target data based on historical vibration frequency and historical target data; determine maintenance time based on state factor, future vibration frequency and future target data; and arrange personnel to carry out maintenance based on maintenance time.

[0010] Preferably, the collection of vibration frequency and target data at the monitoring points includes:

[0011] The monitoring time is set according to the monitoring interval. The equipment temperature at each monitoring point is obtained by several temperature sensors installed on the mine's aerial personnel carrier, and the ambient temperature at each monitoring point is obtained by several temperature sensors installed inside the mine, and the ambient humidity at each monitoring point is obtained by several humidity sensors installed inside the mine, and the dust concentration at each monitoring point is obtained by several dust concentration sensors installed inside the mine, all based on the monitoring time. The monitoring interval is set according to the operating status of the mine's aerial personnel carrier. When the aerial personnel carrier is running, a monitoring interval is generally 5 seconds; when the aerial personnel carrier is not running, a monitoring interval is generally 1 minute.

[0012] Preferably, the step of analyzing the target data to obtain feature target data includes:

[0013] The equipment temperature at each monitoring point is extracted sequentially, and the equipment temperature corresponding to the temperature sensor that is less than n meters away from the monitoring point is marked as the influence temperature of the current monitoring point. And based on the influence of temperature The characteristic equipment temperature TW at the current monitoring point is determined by calculation formula (1); where n is obtained through experience and is generally taken as 0.5 meters; i is the temperature influencing factor. The corresponding temperature sensor number, where i ranges from [1, m], and m is the maximum value of the temperature sensor number;

[0014] The system acquires the location and monitoring time of each monitoring point in the aerial personnel carrier in the mine in real time. The ambient temperature obtained by the temperature sensor closest to the location of the monitoring point at the monitoring time is used as the characteristic ambient temperature of the current monitoring point at the monitoring time; the ambient humidity obtained by the humidity sensor closest to the location of the monitoring point at the monitoring time is used as the characteristic ambient humidity of the current monitoring point at the monitoring time; and the dust concentration obtained by the dust concentration sensor closest to the location of the monitoring point at the monitoring time is used as the characteristic dust concentration of the current monitoring point at the monitoring time.

[0015] The calculation formula (1) is:

[0016] ;

[0017] In the formula, To affect temperature The straight-line distance between the corresponding temperature sensor and the current monitoring point.

[0018] Preferably, the determination of the standard oxidation factor based on characteristic device temperature, characteristic ambient temperature, and characteristic ambient humidity includes:

[0019] A1: Extract each monitoring point sequentially. When the monitoring point meets one of the following three conditions: the corresponding characteristic equipment temperature TW is greater than the oxidation initiation temperature, the corresponding characteristic ambient temperature TJ is greater than the oxidation initiation temperature, or the corresponding characteristic ambient humidity TH is greater than the oxidation initiation humidity, proceed to A2; otherwise, proceed to A3. The oxidation initiation temperature and oxidation initiation humidity are set based on experience.

[0020] A2: Extract the standard oxidation factor ABY corresponding to the previous monitoring at the current monitoring time, as well as the characteristic equipment temperature TW, characteristic ambient temperature TJ, and characteristic ambient humidity TH, and determine the standard oxidation factor BY at the current monitoring time through formula (2);

[0021] A3: Extract the standard oxidation factor ABY corresponding to the previous monitoring at the current monitoring time, as well as the characteristic equipment temperature TW, characteristic ambient temperature TJ, and characteristic ambient humidity TH, and determine the standard oxidation factor BY at the current monitoring time through formula (3);

[0022] The calculation formula (2) is:

[0023] ;

[0024] In the formula, BH is the standard humidity set according to the contact material, r is the humidity acceleration constant, with a value of [2,3]; D is the activation energy, which represents the energy barrier that molecules must overcome in a chemical reaction so that the reaction can proceed; RC is the molar volume constant, which represents the heat absorbed or released by each mole of gas when the temperature changes by 1 Kelvin; BW is the standard temperature set according to the contact material, and CG is the duration between the current monitoring time and the previous monitoring time. The oxidation fluctuation coefficient is set according to the contact sealing performance, and its value range is [0,1].

[0025] The calculation formula (3) is:

[0026] .

[0027] Preferably, the determination of the standard adhesion factor based on vibration frequency and characteristic dust concentration includes:

[0028] Each monitoring point is extracted sequentially, and the vibration frequency DP of the monitoring point at the current monitoring time is extracted. The attenuation coefficient of the vibration frequency on the adhesion effect is determined by formula (4). The calculation formula (4) is:

[0029] ;

[0030] In the formula, It is the influence coefficient of vibration frequency set according to the average size of dust particles in the mine tunnel;

[0031] The dust concentration FN at the monitoring point at the current monitoring time is extracted, and the enhancement coefficient of dust concentration on adhesion effect is determined by formula (5). The calculation formula (5) is:

[0032] ;

[0033] In the formula, BFN is the standard dust concentration set according to the amount of dust particles in the mine tunnel;

[0034] The vibration frequency DP is used to obtain the probability coefficient of dust detachment from the attached dust by formula (6). The calculation formula (6) is:

[0035] ;

[0036] In the formula, It is an influence coefficient on the probability of detachment, set based on the contact material and the tightness of the current dust structure in the mine tunnel.

[0037] attenuation coefficient Enhancement coefficient and shedding probability coefficient The standard adhesion factor FY is integrated through calculation formula (7), which is:

[0038] ;

[0039] In the formula, AFY is the standard adhesion factor corresponding to the previous monitoring at the current monitoring time.

[0040] Preferably, the establishment of the status analysis model for the monitoring points includes:

[0041] Extract the standard oxidation factor BY, standard adhesion factor FY, and corresponding state factors from the historical reference data; the historical reference data includes several instances of standard oxidation factor BY and standard adhesion factor FY, as well as the state factors set by experts based on the standard oxidation factor BY and standard adhesion factor FY.

[0042] The standard oxidation factor BY, standard adhesion factor FY, and corresponding state factors are integrated into several sets of training and testing data. The training data is used to train the artificial intelligence model, and the testing data is used to test the trained artificial intelligence model. The artificial intelligence model is adjusted according to the testing results. Finally, a state analysis model is obtained with the standard oxidation factor BY and standard adhesion factor FY as inputs and the state factors as outputs. The artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0043] Preferably, the step of determining the future vibration frequency and future target data based on historical vibration frequencies and historical target data includes:

[0044] Acquire vibration frequency, equipment temperature, ambient temperature, ambient humidity, and dust concentration at various time points over several historical days; integrate vibration frequency into several frequency data groups, equipment temperature into several equipment temperature data groups, ambient temperature into several ambient temperature data groups, ambient humidity into several ambient humidity data groups, and dust concentration into several concentration data groups.

[0045] Each data group is extracted sequentially, and the variance of the extracted data group is obtained. It is determined whether the variance is less than the corresponding threshold. If yes, the average value of the data group is calculated to obtain the feature value. If no, the data with the largest absolute value of the difference from the average value of the data group is removed, and the variance is judged again until the variance of the data group is less than the corresponding threshold. Then, the average value of the remaining data in the data group is calculated to obtain the feature value. The threshold value is determined empirically.

[0046] The feature values ​​obtained from the frequency data set are marked as the future vibration frequency at the corresponding time point; the feature values ​​obtained from the equipment temperature data set are marked as the future equipment temperature at the corresponding time point; the feature values ​​obtained from the ambient temperature data set are marked as the future ambient temperature at the corresponding time point; the feature values ​​obtained from the ambient humidity data set are marked as the future ambient humidity at the corresponding time point; and the feature values ​​obtained from the concentration data set are marked as the future dust concentration at the corresponding time point. Among these, the future target data includes the future equipment temperature, future ambient temperature, future ambient humidity, and future dust concentration.

[0047] Preferably, determining the maintenance time based on the state factor, future vibration frequency, and future target data includes:

[0048] B1: Extract the status factor and determine whether the status factor is greater than the status factor prediction threshold; if yes, proceed to B2; if no, do nothing; wherein, the status factor prediction threshold is set according to the material used by the contact and is used to determine whether the service time of the contact corresponding to the monitoring point is close to the damage threshold.

[0049] B2: After replacing the vibration frequency in step S2 with the future vibration frequency and the target data in step S2 with the future target data, repeat step S2 to obtain the state factors of the contact points corresponding to the monitoring points at various future monitoring times; compare the state factors of the monitoring points at various future monitoring times with the state threshold values ​​in chronological order from front to back; when the state factor is less than and closest to the state threshold value, mark the monitoring time corresponding to the current state factor as the target time point; mark the time corresponding to the target time point as the maintenance time; wherein, the state threshold value is obtained experimentally in the laboratory.

[0050] Preferably, the step of scheduling personnel for maintenance based on maintenance time includes:

[0051] When there is a marked maintenance time on the corresponding contact of each monitoring point in the mine's aerial passenger transport device, the maintenance time is sent to the management office to remind maintenance personnel to go to the corresponding monitoring point to perform maintenance on the contact at the specified maintenance time.

[0052] If, during the maintenance of the aforementioned contacts, the maintenance personnel perform oxidation cleaning and dust cleaning on other contacts without marked maintenance times, then the standard oxidation factor BY and standard adhesion factor FY for the first monitoring time after the corresponding contact cleaning will be set to 0.

[0053] The second invention provides a dynamic self-testing system for aerial personnel transport devices in mines, comprising: a predictive analysis module, and a data collection module and a self-testing execution module connected to the predictive analysis module;

[0054] The data collection module is used to collect vibration frequency and target data at monitoring points. The monitoring points include the locations of the contacts of the head overrun switch, tail overrun switch, rope drop protection switch, and emergency stop switch along the line in the mine's aerial personnel carrier device. The target data includes equipment temperature, ambient temperature, ambient humidity, and dust concentration.

[0055] The predictive analysis module is used to analyze target data to obtain characteristic target data, determine standard oxidation factors based on characteristic equipment temperature, characteristic ambient temperature, and characteristic ambient humidity, and determine standard adhesion factors based on vibration frequency and characteristic dust concentration; establish a state analysis model for monitoring points, and input the standard oxidation factors and standard adhesion factors into the state analysis model to obtain the state factors of the monitoring points; wherein, the characteristic target data includes characteristic equipment temperature, characteristic ambient temperature, characteristic ambient humidity, and characteristic dust concentration;

[0056] The self-test execution module is used to determine the future vibration frequency and future target data based on historical vibration frequency and historical target data; determine the maintenance time based on the state factor, future vibration frequency and future target data; and arrange personnel to carry out maintenance based on the maintenance time.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] 1. This invention obtains characteristic target data by analyzing target data, determines standard oxidation factors based on characteristic equipment temperature, characteristic ambient temperature, and characteristic ambient humidity, and determines standard adhesion factors based on vibration frequency and characteristic dust concentration; establishes a state analysis model for monitoring points, inputs the standard oxidation factors and standard adhesion factors into the state analysis model to obtain the state factors of the monitoring points; determines future vibration frequencies and future target data based on historical vibration frequencies and historical target data; determines maintenance time based on state factors, future vibration frequencies, and future target data, and schedules personnel for maintenance based on the maintenance time. This solves the technical problem of difficulty in predicting the future state of switch contacts based on the usage and environment of overhead personnel carriers in mines during maintenance. This invention can improve the prediction of future equipment state, arrange maintenance in advance, reduce unexpected downtime and accident risks, and improve equipment availability and reliability.

[0059] 2. This invention introduces both vibration frequency and dust concentration as influencing factors to construct a standard adhesion factor calculation model that can dynamically reflect changes in mine working conditions, achieving a more accurate prediction of dust adhesion status at switch contacts. Vibration frequency not only reflects its negative impact on current dust adhesion efficiency through the attenuation coefficient—that is, as the vibration frequency increases, the stable adhesion tendency of dust particles on the contact weakens—but also characterizes the redistribution or peeling effect on already adhered dust through the detachment probability coefficient. This allows the model to simultaneously capture the superimposed effect of the two physical processes of "preventing new dust adhesion" and "promoting the detachment of old dust." The enhancing effect of dust concentration on adhesion is quantified by the enhancement coefficient; the higher the dust concentration, the more particulate matter in the air, and the higher the probability that the contact surface will be covered by dust. This positive correlation is clearly reflected in the model. Because this model effectively integrates the attenuation coefficient, enhancement coefficient, detachment probability coefficient, and the standard adhesion factor from the previous monitoring, it can achieve temporal continuity and gradual cumulative effect analysis of the state. This reflects both the direct impact of the instantaneous environment on adhesion and the continuation effect of historical conditions on the current adhesion level. This comprehensive algorithm avoids the prediction bias caused by considering only one factor, making the results closer to the contact wear and failure patterns under actual mine tunnel operating conditions. Especially in complex environments with drastic vibration and dust changes, it can significantly improve the scientific rigor and effectiveness of predicting contact maintenance cycles and formulating cleaning or replacement plans. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a schematic diagram of the operation steps of the present invention;

[0062] Figure 2 A schematic diagram illustrating the operational steps for determining the standard adhesion factor in this invention;

[0063] Figure 3 This is a schematic diagram of the system modules of the present invention. Detailed Implementation

[0064] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] Please see Figure 1 The first aspect of the present invention provides a dynamic self-testing method and system for aerial passenger transport devices in mines, comprising:

[0066] S1: Collect vibration frequency and target data at monitoring points; the monitoring points are manually set and include the contact locations of the head overrun switch, tail overrun switch, rope drop protection switch, and emergency stop switch along the line in the mine's overhead personnel carrier; the target data includes equipment temperature, ambient temperature, ambient humidity, and dust concentration.

[0067] S2: Analyze the target data to obtain characteristic target data, determine the standard oxidation factor based on the characteristic equipment temperature, characteristic ambient temperature and characteristic ambient humidity, and determine the standard adhesion factor based on the vibration frequency and characteristic dust concentration; establish a state analysis model for the monitoring point, and input the standard oxidation factor and standard adhesion factor into the state analysis model to obtain the state factors of the monitoring point; wherein, the characteristic target data includes the characteristic equipment temperature, characteristic ambient temperature, characteristic ambient humidity and characteristic dust concentration;

[0068] S3: Determine future vibration frequency and future target data based on historical vibration frequency and historical target data; determine maintenance time based on state factor, future vibration frequency and future target data; and arrange personnel to carry out maintenance based on maintenance time.

[0069] This application collects vibration frequency and target data from monitoring points, including:

[0070] The monitoring time is set according to the monitoring interval. The equipment temperature at each monitoring point is obtained by several temperature sensors installed on the mine's aerial personnel carrier, and the ambient temperature at each monitoring point is obtained by several temperature sensors installed inside the mine, and the ambient humidity at each monitoring point is obtained by several humidity sensors installed inside the mine, and the dust concentration at each monitoring point is obtained by several dust concentration sensors installed inside the mine, all based on the monitoring time. The monitoring interval is set according to the operating status of the mine's aerial personnel carrier. When the aerial personnel carrier is running, a monitoring interval is generally 5 seconds; when the aerial personnel carrier is not running, a monitoring interval is generally 1 minute.

[0071] In this application, the target data is analyzed to obtain characteristic target data, including:

[0072] The equipment temperature at each monitoring point is extracted sequentially, and the equipment temperature corresponding to the temperature sensor that is less than n meters away from the monitoring point is marked as the influence temperature of the current monitoring point. And based on the influence of temperature The characteristic equipment temperature TW at the current monitoring point is determined by calculation formula (1); where n is obtained through experience and is generally taken as 0.5 meters; i is the temperature influencing factor. The corresponding temperature sensor number, where i ranges from [1, m], and m is the maximum value of the temperature sensor number;

[0073] The system acquires the location and monitoring time of each monitoring point in the aerial personnel carrier in the mine in real time. The ambient temperature obtained by the temperature sensor closest to the location of the monitoring point at the monitoring time is used as the characteristic ambient temperature of the current monitoring point at the monitoring time; the ambient humidity obtained by the humidity sensor closest to the location of the monitoring point at the monitoring time is used as the characteristic ambient humidity of the current monitoring point at the monitoring time; and the dust concentration obtained by the dust concentration sensor closest to the location of the monitoring point at the monitoring time is used as the characteristic dust concentration of the current monitoring point at the monitoring time.

[0074] The calculation formula (1) is:

[0075] ;

[0076] In the formula, To affect temperature The straight-line distance between the corresponding temperature sensor and the current monitoring point.

[0077] It is worth noting that the vibration and high temperature generated during the operation of the aerial passenger transport device in the mine can easily damage the temperature sensor installed on the device. Therefore, in the analysis, this invention uses a combination of distance and temperature analysis to obtain a characteristic device temperature TW that can represent the temperature of the current monitoring point, thus avoiding the impact of temperature anomalies caused by the failure of a single temperature sensor on subsequent analysis.

[0078] It should be noted that the monitoring times can be illustrated as follows: if the current time is 15:00, the aerial work platform in the mine is operating at the same time, and the monitoring interval is 5 seconds, then the monitoring times during the operation of the aerial work platform in this instance are: 15:00:00, 15:00:05, 15:00:10, 15:00:15, 15:00:20, ...

[0079] This application determines the standard oxidation factor based on characteristic equipment temperature, characteristic ambient temperature, and characteristic ambient humidity, including:

[0080] A1: Extract each monitoring point sequentially. When the monitoring point meets one of the following three conditions: the corresponding characteristic equipment temperature TW is greater than the oxidation initiation temperature, the corresponding characteristic ambient temperature TJ is greater than the oxidation initiation temperature, or the corresponding characteristic ambient humidity TH is greater than the oxidation initiation humidity, proceed to A2; otherwise, proceed to A3. The oxidation initiation temperature and oxidation initiation humidity are set based on experience.

[0081] A2: Extract the standard oxidation factor ABY corresponding to the previous monitoring at the current monitoring time, as well as the characteristic equipment temperature TW, characteristic ambient temperature TJ, and characteristic ambient humidity TH, and determine the standard oxidation factor BY at the current monitoring time through formula (2);

[0082] A3: Extract the standard oxidation factor ABY corresponding to the previous monitoring at the current monitoring time, as well as the characteristic equipment temperature TW, characteristic ambient temperature TJ, and characteristic ambient humidity TH, and determine the standard oxidation factor BY at the current monitoring time through formula (3);

[0083] The calculation formula (2) is:

[0084] ;

[0085] In the formula, BH is the standard humidity set according to the contact material, r is the humidity acceleration constant, with a value of [2,3]; D is the activation energy, which represents the energy barrier that molecules must overcome in a chemical reaction so that the reaction can proceed; RC is the molar volume constant, which represents the heat absorbed or released by each mole of gas when the temperature changes by 1 Kelvin; BW is the standard temperature set according to the contact material, and CG is the duration between the current monitoring time and the previous monitoring time. The oxidation fluctuation coefficient is set according to the contact sealing performance, and its value range is [0,1].

[0086] The calculation formula (3) is:

[0087] .

[0088] It is worth noting that this invention establishes a standard oxidation factor calculation mechanism based on real-time monitoring data dynamic updates by comprehensively considering the influence of characteristic equipment temperature, characteristic ambient temperature, and characteristic ambient humidity on the contact oxidation process. This allows for a more accurate and timely reflection of the oxidation state and deterioration trend of contacts in the actual operating environment. Specifically, this method first sets thresholds for oxidation initiation temperature and oxidation initiation humidity, and uses empirical parameters to quickly determine whether the current environment and equipment state have entered a condition range that significantly accelerates oxidation. This branch judgment design makes the calculation process adaptive and targeted, avoiding complex exponential calculations when temperature and humidity changes are not significant, thus improving calculation efficiency. When the accelerated oxidation conditions are met, formula (2) is used to introduce multiple key influencing factors such as humidity, time, and temperature into the calculation through a combination of exponential and power functions, simulating the accelerated reaction characteristics in real physicochemical processes. In particular, the temperature part uses an exponential term to represent the influence of temperature on the chemical reaction rate, which can more scientifically and accurately characterize the actual influence of temperature and humidity changes on the oxidation rate. Meanwhile, parameters such as the humidity acceleration constant r, standard humidity BH, standard temperature BW, and activation energy D are all set in conjunction with the characteristics of the contact material to ensure the applicability and reliability of the method. When significant oxidation conditions are not met, linear cumulative calculations are performed using formula (3) to reduce unnecessary complex calculations and to retain the time factor and oxidation fluctuation coefficient. The method allows for fine-tuning of the results, achieving a balance between computational complexity and prediction accuracy under different operating conditions. Overall, this method, through a dual-path computational model, organically combines physicochemical mechanisms with engineering empirical parameters. This ensures that changes in the standard oxidation factor reflect both the influence of macroscopic temperature and humidity environments and the differences in material properties and sealing performance. It boasts advantages such as strong real-time performance, wide applicability, and high accuracy of calculation results, and can be used for contact life prediction, preventative maintenance decisions, and reliability assessment.

[0089] Please see Figure 2 The standard adhesion factor determined in this application based on vibration frequency and characteristic dust concentration includes:

[0090] Each monitoring point is extracted sequentially, and the vibration frequency DP of the monitoring point at the current monitoring time is extracted. The attenuation coefficient of the vibration frequency on the adhesion effect is determined by formula (4). The calculation formula (4) is:

[0091] ;

[0092] In the formula, It is the influence coefficient of vibration frequency set according to the average size of dust particles in the mine tunnel;

[0093] The dust concentration FN at the monitoring point at the current monitoring time is extracted, and the enhancement coefficient of dust concentration on adhesion effect is determined by formula (5). The calculation formula (5) is:

[0094] ;

[0095] In the formula, BFN is the standard dust concentration set according to the amount of dust particles in the mine tunnel;

[0096] The vibration frequency DP is used to obtain the probability coefficient of dust detachment from the attached dust by formula (6). The calculation formula (6) is:

[0097] ;

[0098] In the formula, It is an influence coefficient on the probability of detachment, set based on the contact material and the tightness of the current dust structure in the mine tunnel.

[0099] attenuation coefficient Enhancement coefficient and shedding probability coefficient The standard adhesion factor FY is integrated through calculation formula (7), which is:

[0100] ;

[0101] In the formula, AFY is the standard adhesion factor corresponding to the previous monitoring at the current monitoring time.

[0102] It is worth noting that this method, by introducing the dual influencing factors of vibration frequency and dust concentration, constructs a standard adhesion factor calculation model that can dynamically reflect changes in mine tunnel conditions, achieving a more accurate prediction of dust adhesion status at switch contacts. Vibration frequency is not only determined by the attenuation coefficient... This reflects its negative impact on the current dust adhesion efficiency; that is, as the vibration frequency increases, the stable adhesion tendency of dust particles on the contact point weakens. Simultaneously, the vibration frequency also affects the detachment probability coefficient. The model depicts the redistribution or stripping of already adhered dust, enabling it to simultaneously capture the combined effect of two physical processes: "preventing new dust adhesion" and "promoting the shedding of old dust." The enhancing effect of dust concentration on adhesion is determined by an enhancement coefficient. Quantitatively, the higher the dust concentration, the more particulate matter in the air, and the higher the probability that the contact surface will be covered by dust. This positive correlation is clearly reflected in the model. Because the model uses the attenuation coefficient... Enhancement coefficient Shedding probability coefficient By effectively integrating the standard adhesion factor AFY from the previous monitoring, this method enables analysis of the continuous effect over time and the gradual accumulation effect of the state. This reflects both the direct impact of the instantaneous environment on adhesion and the continuation of historical conditions on the current adhesion level. This comprehensive algorithm avoids the prediction bias caused by considering only one factor, making the results closer to the wear and failure patterns of contacts under actual mine operating conditions. Especially in complex environments with drastic vibration and dust changes, it significantly improves the scientific rigor and effectiveness of predicting contact maintenance cycles and developing cleaning or replacement plans. Furthermore, this method does not rely on a large number of empirical parameters but directly calculates using measurable real-time monitoring data such as vibration frequency DP and dust concentration FN. It has good field applicability and automation integration potential, and can be embedded in mine monitoring systems for online monitoring and early warning. In addition, model parameters such as influence coefficients... Influence coefficients of standard dust concentration (BFN) and shedding probability. All settings can be customized according to the specific dust particle size distribution, dust concentration level, and contact material characteristics in the mine tunnel, thereby improving adaptability and robustness under different mines and equipment operating conditions.

[0103] Overall, this invention achieves quantitative analysis of the impact of environmental vibration and dust on the dust adhesion process of contact points through scientific modeling. It can more accurately assess the dust adhesion trend and supplement and correct the dust stripping process, thus forming a more reliable basis for predicting contact life. This provides important technical support for reducing mine equipment failures, extending switch life, reducing maintenance costs, and improving production safety.

[0104] It is worth noting that the vibration frequency DP affects the motion state of dust particles in the air; when the vibration frequency is high, the dust particles move faster, leading to an increase in their suspension time in the air, thereby reducing the probability of collision between the dust particles and the contact surface, and thus reducing the adhesion effect. Therefore, the effect of vibration frequency on the adhesion effect can be expressed as:

[0105] ;

[0106] In the formula, It is the attenuation coefficient of the vibration frequency on the adhesion effect. It is the influence coefficient of vibration frequency set according to the average size of dust particles in the mine tunnel;

[0107] Dust concentration (FN) affects the number of dust particles in the air, thus influencing their adhesion. Higher dust concentration means a greater number of dust particles in the air, resulting in better adhesion. Therefore, the effect of dust concentration on adhesion can be expressed as:

[0108] ;

[0109] In the formula, It is the enhancement factor of dust concentration on adhesion effect. BFN is the standard dust concentration set according to the amount of dust particles in the mine tunnel.

[0110] Taking into account the effects of vibration frequency and dust concentration on the adhesion of airborne dust, a preliminary expression for the adhesion factor CFY can be obtained:

[0111] ;

[0112] Besides affecting the adhesion of currently floating dust, the vibration frequency DP also affects the stability of already adhered dust. When the vibration frequency DP is high, the adhered dust may detach due to vibration, thereby reducing dust resistance at the contact points. Therefore, the effect of vibration frequency DP on the detachment of already adhered dust is as follows:

[0113] ;

[0114] In the formula, This is the shedding probability coefficient. It is an influence coefficient on the probability of detachment, set based on the contact material and the tightness of the current dust structure in the mine tunnel.

[0115] Taking into account the influence of vibration frequency DP on the current dust adhesion effect and the shedding of already adhered dust, a formula for determining the standard adhesion factor FY can be obtained:

[0116] ;

[0117] The standard adhesion factor formula FY can more comprehensively describe the influence of vibration frequency and dust concentration on the adhesion effect, thus providing a more accurate basis for predicting the failure time of switch contacts.

[0118] This application establishes a state analysis model for monitoring points, including:

[0119] Extract the standard oxidation factor BY, standard adhesion factor FY, and corresponding state factors from the historical reference data; the historical reference data includes several instances of standard oxidation factor BY and standard adhesion factor FY, as well as the state factors set by experts based on the standard oxidation factor BY and standard adhesion factor FY.

[0120] The standard oxidation factor BY, standard adhesion factor FY, and corresponding state factors are integrated into several sets of training and testing data. The training data is used to train the artificial intelligence model, and the testing data is used to test the trained artificial intelligence model. The artificial intelligence model is adjusted according to the testing results. Finally, a state analysis model is obtained with the standard oxidation factor BY and standard adhesion factor FY as inputs and the state factors as outputs. The artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0121] It should be noted that the state factor obtained by the state analysis model in this invention is a state status of the corresponding contact point of each monitoring point, and a time value representing the service duration of the contact point. This time value is not simply a statistical analysis of the service duration, but also includes the superimposed value of the effects caused by different vibration frequencies, equipment temperature, ambient temperature, ambient humidity and dust concentration.

[0122] The status of each monitoring point's corresponding contact is obtained by comparing it with the contact in the standard mode. For example, if a contact serves for one hour in a higher standard oxidation factor BY and a higher standard adhesion factor FY, it is equivalent to serving for 3 hours in the standard mode. In this case, the service duration is recorded as 3 hours. Among them, the vibration frequency, equipment temperature, ambient temperature, ambient humidity, and dust concentration in the standard mode are set based on experience.

[0123] The state factor is proportional to the standard oxidation factor BY and the standard adhesion factor FY. The larger the state factor, the longer the service time under the current standard oxidation factor BY and standard adhesion factor FY, which indirectly indicates that the remaining service life of the contact is smaller. When the service time exceeds the state critical time in the standard mode, it indicates that the current contact needs to be repaired.

[0124] Specifically, the steps for testing the trained AI model using validation data and adjusting the AI ​​model based on the validation results are as follows:

[0125] The standard oxidation factor BY and standard adhesion factor FY from the test data are input into the trained artificial intelligence model to obtain the corresponding state factors. The corresponding state factors are compared with the corresponding state factors in the test data. If the difference between the two is within a threshold (the threshold is obtained empirically), no parameter adjustment is required, and the next set of test data is tested. If it is not within the threshold, the corresponding parameters are adjusted until the output state factors of the corresponding test data are within the threshold, and then the next set of test data is tested. When the number of test data in which the difference in state factors obtained from all test data is within the threshold accounts for 90% or more of the total number of test data, a state analysis model is obtained with the input of standard oxidation factor BY and standard adhesion factor FY and the output of state factors.

[0126] This application determines future vibration frequencies and future target data based on historical vibration frequencies and historical target data, including:

[0127] Acquire vibration frequency, equipment temperature, ambient temperature, ambient humidity, and dust concentration at various time points over several historical days; integrate vibration frequency into several frequency data groups, equipment temperature into several equipment temperature data groups, ambient temperature into several ambient temperature data groups, ambient humidity into several ambient humidity data groups, and dust concentration into several concentration data groups.

[0128] Each data group is extracted sequentially, and the variance of the extracted data group is obtained. It is then determined whether the variance is less than the corresponding threshold. If yes, the average value of the data group is calculated to obtain the feature value. If no, the data with the largest absolute value of the difference from the average value of the data group is removed, and the variance is re-determined until the variance of the data group is less than the corresponding threshold. Then, the average value of the remaining data in the data group is calculated to obtain the feature value. The threshold value is determined empirically.

[0129] The feature values ​​obtained from the frequency data set are marked as the future vibration frequency at the corresponding time point; the feature values ​​obtained from the equipment temperature data set are marked as the future equipment temperature at the corresponding time point; the feature values ​​obtained from the ambient temperature data set are marked as the future ambient temperature at the corresponding time point; the feature values ​​obtained from the ambient humidity data set are marked as the future ambient humidity at the corresponding time point; and the feature values ​​obtained from the concentration data set are marked as the future dust concentration at the corresponding time point. Among these, the future target data includes the future equipment temperature, future ambient temperature, future ambient humidity, and future dust concentration.

[0130] It should be noted that the threshold values ​​for frequency data groups, temperature data groups, and concentration data groups are all the same.

[0131] It should be noted that in the process of removing the data with the largest absolute difference from the average value in the data set and re-performing the variance assessment, the average value is the average value of the data retained in the current variance assessment step.

[0132] It should be noted that, when removing the data with the largest absolute difference from the average value in the data set, if the data set with the largest absolute difference from the average value has both a maximum and a minimum value, the minimum value will be removed first.

[0133] It should be noted that if, after removing 90% of the data, the variance of the remaining data is still not less than the defined threshold, then the average value of the original data in that data set is used as the feature value.

[0134] This application determines maintenance time based on state factors, future vibration frequencies, and future target data, including:

[0135] B1: Extract the status factor and determine whether the status factor is greater than the status factor prediction threshold; if yes, jump to B2; if no, do nothing; the status factor prediction threshold is set according to the material used by the contact and is used to determine whether the service time of the contact corresponding to the monitoring point is close to the damage threshold.

[0136] B2: After replacing the vibration frequency in step S2 with the future vibration frequency and the target data in step S2 with the future target data, repeat step S2 to obtain the state factor of the contact corresponding to the monitoring point at each future monitoring time; compare the state factor of the monitoring point with the state threshold value at each future monitoring time in chronological order from front to back. When the state factor is less than and closest to the state threshold value, mark the monitoring time corresponding to the current state factor as the target time point; mark the time corresponding to the target time point as the maintenance time; wherein, the state threshold value is obtained experimentally in the laboratory.

[0137] It should be noted that in step B2, the monitoring interval is the detection interval value set when the mine aerial passenger transport device is in operation.

[0138] It should be noted that after replacing the vibration frequency in step S2 with the future vibration frequency and the target data in step S2 with the future target data, and then repeating step S2 to obtain the state factors of the contact points corresponding to the monitoring points at various future monitoring times, this can be explained in detail as follows:

[0139] The future monitoring times are extracted sequentially from front to back. At each monitoring time, the corresponding future target data is analyzed to obtain characteristic future target data for each future monitoring time. Based on the characteristic future equipment temperature, characteristic future ambient temperature, and characteristic future ambient humidity, the future standard oxidation factor for each future monitoring time is determined. Based on the future vibration frequency and characteristic future dust concentration, the future standard adhesion factor for each future monitoring time is determined. A state analysis model for the monitoring points is established, and the future standard oxidation factor and future standard adhesion factor are input into the state analysis model to obtain the state factors of the monitoring points for each future monitoring time. The characteristic target data includes characteristic future equipment temperature, characteristic future ambient temperature, characteristic future ambient humidity, and characteristic future dust concentration.

[0140] This application's scheduling of personnel for maintenance based on maintenance time includes:

[0141] When there is a marked maintenance time on the corresponding contact of each monitoring point in the mine's overhead personnel carrier device, the maintenance time will be sent to the management office to remind and arrange maintenance personnel to go to the corresponding monitoring point to perform maintenance on the contact during the maintenance time.

[0142] If, during the maintenance of the aforementioned contacts, the maintenance personnel perform oxidation cleaning and dust cleaning on other contacts without marked maintenance times, then the standard oxidation factor BY and standard adhesion factor FY for the first monitoring time after the corresponding contact cleaning will be set to 0.

[0143] Please see Figure 3 A second aspect of the present invention provides a dynamic self-inspection system for a mine aerial passenger transport device, comprising: a predictive analysis module, and a data collection module and a self-inspection execution module connected to the predictive analysis module;

[0144] The data collection module is used to collect vibration frequency and target data at monitoring points. The monitoring points include the locations of the contacts of the head overrun switch, tail overrun switch, rope drop protection switch, and emergency stop switch along the line in the mine's aerial personnel carrier device. The target data includes equipment temperature, ambient temperature, ambient humidity, and dust concentration.

[0145] The predictive analysis module is used to analyze target data to obtain characteristic target data, determine standard oxidation factors based on characteristic equipment temperature, characteristic ambient temperature, and characteristic ambient humidity, and determine standard adhesion factors based on vibration frequency and characteristic dust concentration; establish a state analysis model for monitoring points, and input the standard oxidation factors and standard adhesion factors into the state analysis model to obtain the state factors of the monitoring points; wherein, the characteristic target data includes characteristic equipment temperature, characteristic ambient temperature, characteristic ambient humidity, and characteristic dust concentration;

[0146] The self-test execution module is used to determine the future vibration frequency and future target data based on historical vibration frequency and historical target data; determine the maintenance time based on the state factor, future vibration frequency and future target data; and arrange personnel to carry out maintenance based on the maintenance time.

[0147] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0148] Working principle of the invention:

[0149] This invention first collects vibration frequency and target data at monitoring points; then, it analyzes the target data to obtain characteristic target data, determines the standard oxidation factor based on characteristic equipment temperature, characteristic ambient temperature, and characteristic ambient humidity, and determines the standard adhesion factor based on vibration frequency and characteristic dust concentration; it establishes a state analysis model for the monitoring points, inputs the standard oxidation factor and standard adhesion factor into the state analysis model to obtain the state factors of the monitoring points; finally, it determines the future vibration frequency and future target data based on historical vibration frequency and historical target data; and it determines the maintenance time based on the state factors, future vibration frequency, and future target data, and arranges personnel to carry out maintenance based on the maintenance time.

[0150] The above embodiments are only used to illustrate the technical methods 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 methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A dynamic self-testing method for aerial passenger transport devices in mines, characterized in that, include: S1: Collect vibration frequency and target data at monitoring points; among which, the monitoring points include the locations of the contacts of the head overrun switch, tail overrun switch, rope drop protection switch and emergency stop switch along the line in the mine's overhead personnel carrier; the target data includes equipment temperature, ambient temperature, ambient humidity and dust concentration. S2: Analyze the target data to obtain characteristic target data, determine the standard oxidation factor based on the characteristic equipment temperature, characteristic ambient temperature and characteristic ambient humidity, and determine the standard adhesion factor based on the vibration frequency and characteristic dust concentration; establish a state analysis model for the monitoring point, and input the standard oxidation factor and standard adhesion factor into the state analysis model to obtain the state factors of the monitoring point; wherein, the characteristic target data includes the characteristic equipment temperature, characteristic ambient temperature, characteristic ambient humidity and characteristic dust concentration; S3: Determine future vibration frequency and future target data based on historical vibration frequency and historical target data; determine maintenance time based on state factor, future vibration frequency and future target data; and arrange personnel to carry out maintenance based on the maintenance time. The determination of the standard oxidation factor based on characteristic device temperature, characteristic ambient temperature, and characteristic ambient humidity includes: A1: Extract each monitoring point in sequence. When the monitoring point meets one of the following three conditions, namely, the corresponding characteristic equipment temperature TW is greater than the oxidation initiation temperature, the corresponding characteristic ambient temperature TJ is greater than the oxidation initiation temperature, or the corresponding characteristic ambient humidity TH is greater than the oxidation initiation humidity, jump to A2; otherwise, jump to A3. A2: Extract the standard oxidation factor ABY corresponding to the previous monitoring at the current monitoring time, as well as the characteristic equipment temperature TW, characteristic ambient temperature TJ, and characteristic ambient humidity TH, and determine the standard oxidation factor BY at the current monitoring time through formula (2); A3: Extract the standard oxidation factor ABY corresponding to the previous monitoring at the current monitoring time, as well as the characteristic equipment temperature TW, characteristic ambient temperature TJ, and characteristic ambient humidity TH, and determine the standard oxidation factor BY at the current monitoring time through formula (3); The calculation formula (2) is: ； In the formula, BH is the standard humidity set according to the contact material, r is the humidity acceleration constant, with a value of [2,3]; D is the activation energy, RC is the molar volume constant; BW is the standard temperature set according to the contact material, and CG is the duration between the current monitoring time and the previous monitoring time; The oxidation fluctuation coefficient is set according to the contact sealing performance, and its value range is [0,1]. The calculation formula (3) is: ； The determination of the standard adhesion factor based on vibration frequency and characteristic dust concentration includes: Each monitoring point is extracted sequentially, and the vibration frequency DP of the monitoring point at the current monitoring time is extracted. The attenuation coefficient of the vibration frequency on the adhesion effect is determined by formula (4). The calculation formula (4) is: ； In the formula, It is the influence coefficient of vibration frequency set according to the average size of dust particles in the mine tunnel; The dust concentration FN at the monitoring point at the current monitoring time is extracted, and the enhancement coefficient of dust concentration on adhesion effect is determined by formula (5). The calculation formula (5) is: ； In the formula, BFN is the standard dust concentration set according to the amount of dust particles in the mine tunnel; The vibration frequency DP is used to obtain the probability coefficient of dust detachment from the attached dust by formula (6). The calculation formula (6) is: ； In the formula, It is an influence coefficient on the probability of detachment, set based on the contact material and the tightness of the current dust structure in the mine tunnel. attenuation coefficient Enhancement coefficient and shedding probability coefficient The standard adhesion factor FY is integrated through calculation formula (7), which is: ； In the formula, AFY is the standard adhesion factor corresponding to the previous monitoring at the current monitoring time.

2. The dynamic self-testing method for an aerial passenger transport device in a mine according to claim 1, characterized in that, The vibration frequency and target data collected at the monitoring points include: The monitoring time is set according to the monitoring interval. The equipment temperature at each monitoring point is obtained by several temperature sensors installed on the mine's aerial personnel carrier device according to the monitoring time. The ambient temperature at each monitoring point is obtained by several temperature sensors installed inside the mine tunnel according to the monitoring time. The ambient humidity at each monitoring point is obtained by several humidity sensors installed inside the mine tunnel according to the monitoring time. The dust concentration at each monitoring point is obtained by several dust concentration sensors installed inside the mine tunnel according to the monitoring time. The monitoring interval is set according to the operating status of the mine's aerial personnel carrier device.

3. The dynamic self-testing method for an aerial passenger transport device in a mine according to claim 1, characterized in that, The process of analyzing the target data to obtain feature target data includes: The equipment temperature at each monitoring point is extracted sequentially, and the equipment temperature corresponding to the temperature sensor that is less than n meters away from the monitoring point is marked as the influence temperature of the current monitoring point. And based on the influence of temperature The characteristic equipment temperature TW at the current monitoring point is determined by formula (1); where i is the temperature influencing factor. The corresponding temperature sensor number, where i ranges from [1, m], and m is the maximum value of the temperature sensor number; The system acquires the location and monitoring time of each monitoring point in the aerial personnel carrier in the mine in real time. The ambient temperature obtained by the temperature sensor closest to the location of the monitoring point at the monitoring time is used as the characteristic ambient temperature of the current monitoring point at the monitoring time; the ambient humidity obtained by the humidity sensor closest to the location of the monitoring point at the monitoring time is used as the characteristic ambient humidity of the current monitoring point at the monitoring time; and the dust concentration obtained by the dust concentration sensor closest to the location of the monitoring point at the monitoring time is used as the characteristic dust concentration of the current monitoring point at the monitoring time. The calculation formula (1) is: ； In the formula, To affect temperature The straight-line distance between the corresponding temperature sensor and the current monitoring point.

4. The dynamic self-testing method for an aerial passenger transport device in a mine according to claim 1, characterized in that, The establishment of the status analysis model for the monitoring points includes: Extract the standard oxidation factor BY, standard adhesion factor FY, and corresponding state factors from the historical reference data; the historical reference data includes several instances of standard oxidation factor BY and standard adhesion factor FY, as well as the state factors set by experts based on the standard oxidation factor BY and standard adhesion factor FY. The standard oxidation factor BY, standard adhesion factor FY, and corresponding state factors are integrated into several sets of training and testing data. The training data is used to train the artificial intelligence model, and the testing data is used to test the trained artificial intelligence model. The artificial intelligence model is adjusted according to the testing results. Finally, a state analysis model is obtained with the standard oxidation factor BY and standard adhesion factor FY as inputs and the state factors as outputs. The artificial intelligence model includes a BP neural network model and an RBF neural network model.

5. A dynamic self-testing method for an aerial passenger transport device in a mine, as described in claim 1, is characterized in that... The determination of future vibration frequencies and future target data based on historical vibration frequencies and historical target data includes: Acquire vibration frequency, equipment temperature, ambient temperature, ambient humidity, and dust concentration at various time points over several historical days; integrate vibration frequency into several frequency data groups, equipment temperature into several equipment temperature data groups, ambient temperature into several ambient temperature data groups, ambient humidity into several ambient humidity data groups, and dust concentration into several concentration data groups. Extract each data group sequentially, obtain the variance of the extracted data group, and determine whether the variance is less than the corresponding threshold. If yes, calculate the average value of the data group to obtain the feature value. If no, remove the data with the largest absolute value of the difference from the average value of the data in the data group, and re-determine the variance until the variance of the data group is less than the corresponding threshold. Then, calculate the average value of the remaining data in the data group to obtain the feature value. The feature values ​​obtained from the frequency data set are marked as the future vibration frequency at the corresponding time point; the feature values ​​obtained from the equipment temperature data set are marked as the future equipment temperature at the corresponding time point; the feature values ​​obtained from the ambient temperature data set are marked as the future ambient temperature at the corresponding time point; the feature values ​​obtained from the ambient humidity data set are marked as the future ambient humidity at the corresponding time point; and the feature values ​​obtained from the concentration data set are marked as the future dust concentration at the corresponding time point. Among these, the future target data includes the future equipment temperature, future ambient temperature, future ambient humidity, and future dust concentration.

6. A dynamic self-testing method for an aerial passenger transport device in a mine, as described in claim 1, is characterized in that... The method of determining maintenance time based on state factors, future vibration frequencies, and future target data includes: B1: Extract the state factor and determine whether the state factor is greater than the state factor prediction threshold; if yes, proceed to B2; if no, do nothing; wherein, the state factor prediction threshold is set according to the material used for the contact. B2: After replacing the vibration frequency in step S2 with the future vibration frequency and the target data in step S2 with the future target data, repeat step S2 to obtain the state factors of the contact points corresponding to the monitoring points at various future monitoring times; compare the state factors of the monitoring points at various future monitoring times with the state threshold values ​​in order from front to back; when the state factor is less than and closest to the state threshold value, mark the monitoring time corresponding to the current state factor as the target time point; mark the time corresponding to the target time point as the maintenance time.

7. A dynamic self-testing method for an aerial passenger transport device in a mine, as described in claim 1, is characterized in that... The method of scheduling personnel for maintenance based on maintenance schedule includes: When there is a marked maintenance time on the corresponding contact of each monitoring point in the mine's aerial passenger transport device, the maintenance time is sent to the management office to remind maintenance personnel to go to the corresponding monitoring point to perform maintenance on the contact at the specified maintenance time. If, during the maintenance of the aforementioned contacts, the maintenance personnel perform oxidation cleaning and dust cleaning on other contacts without marked maintenance times, then the standard oxidation factor BY and standard adhesion factor FY for the first monitoring time after the corresponding contact cleaning will be set to 0.

8. A dynamic self-testing system for aerial passenger transport in mines, used to operate the dynamic self-testing method for aerial passenger transport in mines as described in any one of claims 1 to 7, characterized in that, include: The predictive analytics module, as well as the data collection module and self-test execution module connected to the predictive analytics module; The data collection module is used to collect vibration frequency and target data at monitoring points. The monitoring points include the locations of the contacts of the head overrun switch, tail overrun switch, rope drop protection switch, and emergency stop switch along the line in the mine's aerial personnel carrier device. The target data includes equipment temperature, ambient temperature, ambient humidity, and dust concentration. The predictive analysis module is used to analyze target data to obtain characteristic target data, determine standard oxidation factors based on characteristic equipment temperature, characteristic ambient temperature, and characteristic ambient humidity, and determine standard adhesion factors based on vibration frequency and characteristic dust concentration; establish a state analysis model for monitoring points, and input the standard oxidation factors and standard adhesion factors into the state analysis model to obtain the state factors of the monitoring points; wherein, the characteristic target data includes characteristic equipment temperature, characteristic ambient temperature, characteristic ambient humidity, and characteristic dust concentration; The self-test execution module is used to determine the future vibration frequency and future target data based on historical vibration frequency and historical target data; determine the maintenance time based on state factors, future vibration frequency, and future target data; and arrange personnel to carry out maintenance based on the maintenance time. The determination of the standard oxidation factor based on characteristic device temperature, characteristic ambient temperature, and characteristic ambient humidity includes: A1: Extract each monitoring point in sequence. When the monitoring point meets one of the following three conditions, namely, the corresponding characteristic equipment temperature TW is greater than the oxidation initiation temperature, the corresponding characteristic ambient temperature TJ is greater than the oxidation initiation temperature, or the corresponding characteristic ambient humidity TH is greater than the oxidation initiation humidity, jump to A2; otherwise, jump to A3. A2: Extract the standard oxidation factor ABY corresponding to the previous monitoring at the current monitoring time, as well as the characteristic equipment temperature TW, characteristic ambient temperature TJ, and characteristic ambient humidity TH, and determine the standard oxidation factor BY at the current monitoring time through formula (2); A3: Extract the standard oxidation factor ABY corresponding to the previous monitoring at the current monitoring time, as well as the characteristic equipment temperature TW, characteristic ambient temperature TJ, and characteristic ambient humidity TH, and determine the standard oxidation factor BY at the current monitoring time through formula (3); The calculation formula (2) is: ； In the formula, BH is the standard humidity set according to the contact material, r is the humidity acceleration constant, with a value of [2,3]; D is the activation energy, RC is the molar volume constant; BW is the standard temperature set according to the contact material, and CG is the duration between the current monitoring time and the previous monitoring time; The oxidation fluctuation coefficient is set according to the contact sealing performance, and its value range is [0,1]. The calculation formula (3) is: ； The determination of the standard adhesion factor based on vibration frequency and characteristic dust concentration includes: Each monitoring point is extracted sequentially, and the vibration frequency DP of the monitoring point at the current monitoring time is extracted. The attenuation coefficient of the vibration frequency on the adhesion effect is determined by formula (4). The calculation formula (4) is: ； In the formula, It is the influence coefficient of vibration frequency set according to the average size of dust particles in the mine tunnel; The dust concentration FN at the monitoring point at the current monitoring time is extracted, and the enhancement coefficient of dust concentration on adhesion effect is determined by formula (5). The calculation formula (5) is: ； In the formula, BFN is the standard dust concentration set according to the amount of dust particles in the mine tunnel; The vibration frequency DP is used to obtain the probability coefficient of dust detachment from the attached dust by formula (6). The calculation formula (6) is: ； In the formula, It is an influence coefficient on the probability of detachment, set based on the contact material and the tightness of the current dust structure in the mine tunnel. attenuation coefficient Enhancement coefficient and shedding probability coefficient The standard adhesion factor FY is integrated through calculation formula (7), which is: ； In the formula, AFY is the standard adhesion factor corresponding to the previous monitoring at the current monitoring time.

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