A mining lamp LED brightness adjustment method and system based on dynamic fuzzy PID control

Abstract

The present invention discloses a method and system for adjusting the brightness of a mining lamp LED based on dynamic fuzzy PID control. The method comprises: collecting data on various influencing factors in the current environment of an underground worker and calculating an environmental disturbance factor α(t); scaling a formulated standard domain using a corresponding domain scaling rule according to the disturbance level of the current α(t) value; determining a fuzzy set of the current brightness difference E(t) and the brightness difference change rate Ec(t) based on the scaled domain, thereby obtaining fuzzy values ​​of various parameters of PID control; calculating an output value of the PID control based on the fuzzy values ​​of the various parameters, and adjusting a PWM pulse width modulation value; adjusting the brightness of the mining lamp LED based on the PWM pulse width modulation value; and detecting whether the currently adjusted brightness of the mining lamp LED is consistent with a preset brightness value. If not, adjusting the brightness again until the brightness adjustment is completed. The present invention can intelligently adjust the brightness of the mining lamp according to the actual lighting requirements of the underground coal mine environment, thereby reducing operational risks.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine lamps, and in particular to a method and system for adjusting the brightness of a mining lamp LED based on dynamic fuzzy PID control. Background Art

[0002] Coal mine lamps are lighting devices designed specifically for underground coal mine operations, primarily used to provide safe illumination in locations with the risk of gas (methane) and coal dust explosions. The brightness of mining lamps is crucial for ensuring operational safety and improving work efficiency. Traditional mining lamps only have two fixed brightness modes: primary and auxiliary. However, the underground coal mine environment is complex and dynamic, and mining lamp brightness is strongly influenced by multiple factors such as dust concentration and humidity. Single-parameter threshold control cannot meet the real-time balance between light scattering and attenuation and safety and energy efficiency, and cannot dynamically adjust brightness based on the actual underground coal mine environment.

[0003] With the upgrade and optimization of intelligent lighting control technology, fuzzy PID control technology is widely used in the field of LED brightness adjustment. However, the current technology still has many shortcomings when facing the underground working environment of coal mines: due to environmental factors such as fluctuations in dust concentration and humidity changes, the light scattering characteristics show nonlinear changes. The traditional PID control algorithm cannot compensate for environmental disturbances in real time due to fixed parameters, resulting in illumination deviation on the working surface; when personnel move quickly, the adjustment system based on the static fuzzy rule base responds with delays, and the light intensity gradient adjustment lags. This limitation restricts the adaptability of mining lamps under different working conditions, which can easily lead to reduced working efficiency and increased safety risks.

[0004] Therefore, there is an urgent need for an intelligent mining lamp LED brightness adjustment method that integrates environmental perception and dynamic control to improve the adaptability and energy efficiency of underground lighting. Summary of the Invention

[0005] To address the deficiencies in the prior art, the present invention provides a method and system for adjusting the brightness of a mining lamp LED based on dynamic fuzzy PID control. The method and system can adjust the brightness of the mining lamp according to the actual lighting requirements of the underground coal mine environment, thereby realizing intelligent control, improving the lighting experience, and reducing operational risks.

[0006] The present invention adopts the following technical solutions.

[0007] In a first aspect, the present invention provides a method for adjusting the brightness of a mining lamp LED based on dynamic fuzzy PID control, the method comprising:

[0008] Step 1: Collect data on various influencing factors in the current environment of the underground workers and calculate the environmental disturbance factor α(t);

[0009] Step 2: According to the disturbance level of the current α(t) value, the corresponding domain scaling rule is adopted to scale the established standard domain;

[0010] Step 3: Based on the scaled universe, determine the fuzzy sets of the current brightness difference E(t) and the brightness difference change rate Ec(t), thereby obtaining the fuzzy values ​​of each PID control parameter;

[0011] Step 4: Calculate the output value of PID control based on the fuzzy value of each parameter and adjust the PWM pulse width modulation value;

[0012] Step 5: Adjust the brightness of the miner's lamp LED according to the PWM pulse width modulation value;

[0013] Step 6: Check whether the currently adjusted miner's lamp LED brightness is consistent with the preset brightness value. If not, return to step 1 to step 5 and adjust again until the brightness adjustment is completed.

[0014] Optionally, the data of each influencing factor includes personnel movement speed, distance parameter, dust concentration, humidity, temperature and / or air pressure.

[0015] Optionally, the environmental disturbance factor α(t) is calculated as follows:

[0016]

[0017] Where α(t) represents the environmental disturbance factor at the tth adjustment, Indicates the tth detected j The normalized value of the impact factor data; Expressed as The weight of each influencing factor configuration; M represents the number of impact factors, .

[0018] Optionally, in step 2, the domain scaling rule includes:

[0019] When α(t)≥0.7, the disturbance level is high disturbance, and the domain scaling rule is: compress the domain of the current brightness difference E(t) to [max(-0.7E(t),-10), min(+0.7 E(t),10)], and compress the domain of the brightness difference change rate Ec(t) to [max(-0.8Ec(t),-10), min(+0.8 Ec(t),10)];

[0020] When 0.3≤α(t)<0.7, the disturbance level is medium disturbance, and the domain scaling rule is: compress the domain of the current brightness difference E(t) to [max (-0.8E(t),-10), min(+0.8 E(t),10)], and compress the brightness difference change rate to compress the domain of Ec(t) to [max (-0.9E(t),-10), min(+0.9 E(t),10)];

[0021] When α(t)<0.3, the perturbation level is low perturbation, and the domain scaling rule is: E(t) and Ec(t) are both restored to the standard domain [-10, 10].

[0022] Optionally, in step 3, the step of determining the fuzzy set of the current brightness difference E(t) and the brightness difference change rate Ec(t) includes:

[0023] Step 3.1: Construct and initialize the state-action rule confidence Q table based on the state space and action space;

[0024] Step 3.2: Calculate the state encoding under the current environmental perturbation factor α(t) and the scaled universe;

[0025] Step 3.3: Based on the current probability The corresponding action strategy is selected based on the value, and based on the state-action rule confidence Q table updated last time, the confidence Q values ​​of each state code and its corresponding action are updated;

[0026] Step 3.4: Find the state code to which the maximum value of the updated confidence value Q belongs, and output the fuzzy set of the brightness difference E(t) and the brightness difference change rate Ec(t) corresponding to the state code.

[0027] Optionally, the step of constructing and initializing a state-action rule confidence Q table based on the state space and the action space includes:

[0028] Step A: Create state space S: S=(E, E c ,α);

[0029] Among them, E and E c The state spaces of brightness difference and brightness difference change rate in the standard domain are respectively represented, and both include 7 fuzzy sets; α represents the state space of environmental disturbance factor, which includes 3 fuzzy sets: low disturbance, medium disturbance and high disturbance;

[0030] Step B: E's 7 fuzzy sets, E cd The 7 fuzzy sets of and the 3 fuzzy sets of α are arranged and combined to obtain 147 states, and these 147 states are coded in the order from 0 to 146 to obtain the code ID of each state;

[0031] Step C: Establish action space: action a=0: increase the rule weight; action a=1: keep the current rule;

[0032] Step D: Create a confidence Q table corresponding to the three actions for each state code ID; wherein the initial value of each confidence Q(ID,a) in the Q table is set to 0.

[0033] Optionally, in step 3.2, the formula for calculating each state encoding under the current environmental disturbance factor α(t) and the scaled universe is as follows:

[0034] ID=E(t) id *21+E c (t) id *3+ α id

[0035] Where, α id The value corresponding to the fuzzy set representing the current environmental disturbance factor α(t), where the values ​​of low disturbance, medium disturbance, and high disturbance in the fuzzy set are 0, 1, and 2 respectively; E(t) id and E c (t) id The values ​​corresponding to the fuzzy sets representing the current brightness difference E(t) and the brightness difference change rate Ec(t) are as follows: NB, NM, NS, ZO, PS, PM, PB, which represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large in the fuzzy rules, respectively. The corresponding values ​​are 0, 1, 2, 3, 4, 5, and 6.

[0036] Optionally, in step 3.3, the probability The value is calculated as follows:

[0037]

[0038] Where, represents the probability value at the tth adjustment, represents the initialization value of the probability, and t represents the number of adjustments.

[0039] Optionally, in step 3.3, the probability calculated based on the current time The steps for selecting the corresponding action strategy include:

[0040] like ≤0.5, the actions encoded in each current state are randomly selected;

[0041] like >0.5, then select the action with the largest confidence value Q(ID,a) under each action corresponding to each state code; if the confidence values ​​corresponding to the three actions are equal, the action with a=0 is selected by default.

[0042] Optionally, in step 3.3, the step of updating each confidence Q value of each state code and its corresponding action at the current time includes:

[0043] If the action a=0 is selected in the current state code, the confidence value Q(ID,0) of the state code corresponding to the action a=0 is updated based on the current reward function;

[0044] If the action a=1 is selected in the current state code, the confidence value Q(ID,1) of the state code corresponding to the action a=1 remains unchanged.

[0045] Optionally, the update formula for the confidence value of the state encoding corresponding to the action a=0 based on the current reward function is as follows:

[0046] Q * (ID t ,0)=Q(ID t ,0)+0.1*[ R t +0.9*max Q(ID t +1 t ,a) - Q(ID t ,0)]

[0047] Where, Q(ID t ,0) and Q * (ID t ,0) respectively represent the state code ID of the current t-th adjustment t The current confidence value and the updated confidence value corresponding to the action a=0; max Q(ID t +1 t ,a) indicates the status code ID t The next state encoding corresponds to the maximum of the current confidence values ​​when a=0 and a=1 actions; R t Represents the reward function at the tth adjustment.

[0048] Optionally, the reward function R t The calculation formula is as follows:

[0049]

[0050] Where, E(t) represents the brightness difference at the tth adjustment; y (t ) max Indicates the maximum value of the ambient light brightness monitored in the previous t adjustments; Indicates the mining lamp lighting power consumption during the tth adjustment; Indicates the maximum power consumption of the mining lamp during the previous t adjustments.

[0051] Optionally, in step 4, the calculation formula for the output value of PID control is as follows:

[0052]

[0053] Where, Indicates the PID control output value during the t-th adjustment; 、 and They represent the proportional gain, integral time and differential time of the PID control parameters during the t-th adjustment respectively; Indicates the brightness difference at the tth adjustment; Indicates the cumulative brightness difference of the previous t adjustments.

[0054] Optional, proportional gain , integration time and differential time The calculation formulas are as follows:

[0055]

[0056] Where, represents the probability value at the tth adjustment, and They represent the values ​​corresponding to the fuzzy sets of the brightness difference E(t) and the brightness difference change rate Ec(t) determined at the t-th adjustment respectively; 、 and They represent the numerical values ​​corresponding to the fuzzy sets of proportional gain, integral time and differential time in the PID control obtained at the tth adjustment.

[0057] In a second aspect, the present invention provides a mining lamp LED brightness adjustment system based on dynamic fuzzy PID control, which runs the steps of any method described in the first aspect of the present invention, and the system includes:

[0058] The acquisition module is used to collect data on various influencing factors in the environment where the underground workers are currently working and calculate the environmental disturbance factor α(t);

[0059] The scaling module is used to scale the established standard domain using the corresponding domain scaling rule according to the disturbance level of the current α(t) value;

[0060] A determination module is used to determine the fuzzy sets of the current brightness difference E(t) and the brightness difference change rate Ec(t) based on the scaled universe, thereby obtaining the fuzzy values ​​of each PID control parameter;

[0061] A calculation module is used to calculate the output value of the PID control based on the fuzzy value of each parameter and adjust the PWM pulse width modulation value;

[0062] Adjustment module, used to adjust the brightness of the mining lamp LED according to the PWM pulse width modulation value;

[0063] The repeat module is used to detect whether the currently adjusted mining lamp LED brightness is consistent with the preset brightness value. If not, it will be adjusted again until the brightness adjustment is completed.

[0064] In a third aspect, the present invention provides a terminal including a processor and a storage medium;

[0065] The storage medium is used to store instructions;

[0066] The processor is configured to operate according to the instructions to execute the steps of any one of the methods described in the first aspect of the present invention.

[0067] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect of the present invention.

[0068] The beneficial effect of the present invention is that, compared with the prior art,

[0069] 1. In order to adapt to the complex dynamic environment of underground coal mines, the present invention generates a nonlinear dynamic environment disturbance factor α by integrating multi-source data such as dust concentration, humidity, personnel movement speed, and personnel distance, and divides the disturbance levels into high, medium, and low according to the α value. For different disturbance levels, the formulated standard domain is scaled using an adaptive domain scaling rule. The brightness of the mining lamp can be adjusted according to the lighting requirements of the actual environment in the coal mine, realizing adaptive adjustment of the light as the environment changes, improving the lighting experience, and reducing operational risks.

[0070] 2. Based on the integration of environmental perception, the present invention constructs a state space based on the environmental disturbance factor α, brightness difference E, and brightness difference change rate E. CThe proposed method uses a state-action rule confidence Q table with 147 discrete combinations and action space A (rule weight increase / maintenance), takes lighting brightness and energy consumption as reward functions, and drives dynamic optimization of the fuzzy rule base through action selection strategy. It solves the problems of traditional fuzzy rules relying on expert experience, being unable to evolve autonomously according to actual changes in coal mine environment, and having rigid rule base, and breaks through the limitations of traditional mining lamp dimming systems in dynamic adaptability, realizes intelligent control, improves the adaptability and energy efficiency of underground lighting, and provides an effective solution for safe and efficient lighting in coal mines, which is suitable for promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 This is a flow chart of a method for adjusting the brightness of a mining lamp LED based on dynamic fuzzy PID control in the present invention;

[0072] Figure 2 This is a logic block diagram of the mining lamp LED brightness adjustment system based on dynamic fuzzy PID control in the present invention;

[0073] Figure 3 It is a structural schematic diagram of the mining lamp LED brightness adjustment system based on dynamic fuzzy PID control in the present invention. DETAILED DESCRIPTION

[0074] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. The embodiments described in the present invention are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making any creative efforts are all within the scope of protection of the present invention.

[0075] Example 1:

[0076] like Figure 1 As shown, the embodiment of the present invention provides a mining lamp LED brightness adjustment method based on dynamic fuzzy PID control, which specifically includes the following steps:

[0077] Step 1: Collect data on various influencing factors in the current environment of the underground workers and calculate the environmental disturbance factor α(t);

[0078] Specifically, the data of each influencing factor includes personnel movement speed, distance parameters, dust concentration, humidity, temperature, air pressure, etc. The calculation formula of the environmental disturbance factor α(t) is as follows:

[0079]

[0080] Where α(t) represents the environmental disturbance factor at the tth adjustment, Indicates the tth detectedj The normalized value of the impact factor data; Expressed as The weight of each influencing factor configuration; M represents the number of impact factors, .

[0081] In this embodiment, the relevant parameters are obtained through the coal mine safety monitoring system sensor: the environmental parameter dust concentration C (range 0~1000mg / m 3 ), humidity H (range 0-100% RH), obtain the personnel movement speed v (range 0-5 m / s), and personnel distance d (range 0-50 m) through the UWB positioning module, and monitor the ambient light illuminance value y(t) of the LED light source in real time through the brightness detection module; the parameters dust concentration C, humidity H, personnel movement speed v, and personnel distance d are normalized and the environmental disturbance factor is calculated; the normalization process is as follows:

[0082] Dust concentration C:

[0083] Humidity H:

[0084] Personnel movement speed v:

[0085] Personnel distance d:

[0086] Calculate the environmental disturbance factor α:

[0087]

[0088] Where w_C is the dust concentration weight, with an initial value of 0.4; w_H is the humidity weight, with an initial value of 0.1; w_v is the personnel speed weight, with an initial value of 0.2; w_d is the personnel distance weight, with an initial value of 0.3; and the weights satisfy: .

[0089] The weights w_C, w_H, w_d, and w_v are automatically adjusted based on real-time data and are not specifically limited here. For example:

[0090] During high dust period: increase w_c (e.g. from 0.4 to 0.5) and reduce w_v;

[0091] Crowded areas: Increase the weights of w_d and w_v.

[0092] Step 2: According to the disturbance level of the current α(t) value, the corresponding domain scaling rule is adopted to scale the established standard domain;

[0093] In this embodiment, the fuzzy rules of the environmental disturbance factor α(t) are as follows:

[0094] Table 1. Fuzzy set table of environmental disturbance factor α

[0095]

[0096] Furthermore, the measured brightness value is compared with the preset brightness value to calculate the brightness difference and brightness difference change rate ;The actual brightness value of the last adjustment is ; The current actual brightness value is y(t); The preset brightness value is r(t); The brightness difference : ; Brightness difference change rate : .

[0097] In this embodiment, seven fuzzy subsets are set according to the distribution interval of brightness difference and brightness difference change rate. The standard domain is -10~10. The seven fuzzy subsets are: NB (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium), and PB (positive large). The value ranges and values ​​corresponding to each fuzzy set are shown in Table 2 below.

[0098] Table 2. Standard domain table corresponding to the fuzzy set of brightness difference and brightness difference change rate

[0099]

[0100] As an embodiment of the present invention, the universe scaling rule based on α(t) includes:

[0101] When α(t) ≥ 0.7, the disturbance level is high, and the domain scaling rule is as follows: compress the domain of the current brightness difference E(t) to [max(-0.7E(t),-10), min(+0.7 E(t),10)], and compress the domain of the brightness difference change rate Ec(t) to [max(-0.8Ec(t),-10), min(+0.8 Ec(t),10)]; ensure that it does not exceed the original standard domain [-10, 10] to improve the control response speed.

[0102] When 0.3≤α(t)<0.7, the disturbance level is medium disturbance, and the domain scaling rule is: compress the domain of the current brightness difference E(t) to [max (-0.8E(t),-10), min(+0.8 E(t),10)], and compress the domain of the brightness difference change rate Ec(t) to [max (-0.9E(t),-10), min(+0.9 E(t),10)]; ensure that it does not exceed the original standard domain [-10,10] to improve the control response speed.

[0103] When α(t)<0.3, the disturbance level is low disturbance, and the domain scaling rule is: E(t) and Ec(t) are both restored to the standard domain [-10, 10] to ensure control accuracy.

[0104] The specific optimized domain scaling rules are shown in Table 3:

[0105] Table 3. Domain scaling rules

[0106]

[0107] Step 3: Based on the scaled universe, determine the fuzzy sets of the current brightness difference E(t) and the brightness difference change rate Ec(t), thereby obtaining the fuzzy values ​​of each PID control parameter;

[0108] A preferred but non-limiting embodiment, referring to Figure 2 This paper combines 147 state spaces (E(t), Ec(t), α(t)) with two action spaces A (rule weight increase / maintenance)) to construct a state-action rule confidence table Q. This system uses reward functions (brightness deviation, energy consumption) to drive dynamic optimization of the fuzzy rule base and adjust rule weights in real time to address the problems of traditional fuzzy rules that rely on expert experience, cannot evolve autonomously according to actual changes in the coal mine environment, and have a rigid rule base. The specific process includes:

[0109] Step 3.1: Construct and initialize the state-action rule confidence Q table based on the state space and action space;

[0110] Step A: Create state space S: S=(E, E c ,α);

[0111] Among them, E and E c The state spaces of brightness difference and brightness difference change rate in the standard domain are respectively composed of 7 fuzzy sets; α represents the state space of environmental disturbance factor, which includes 3 fuzzy sets: low disturbance, medium disturbance and high disturbance; that is:

[0112] E(t): ambient illumination deviation (7 fuzzy sets: NB, NM, NS, ZO, PS, PM, PB);

[0113] E c (t): deviation change rate (7 fuzzy sets: NB, NM, NS, ZO, PS, PM, PB);

[0114] α: Environmental disturbance factor (3 fuzzy sets: low, medium, high).

[0115] The total number of state spaces is: 7 (fuzzy sets of E(t))×7 (E c (fuzzy set of t) × 3 (fuzzy set of α) = 147 states.

[0116] Step B: E's 7 fuzzy sets, E cd The 7 fuzzy sets of and the 3 fuzzy sets of α are arranged and combined to obtain 147 states, and these 147 states are coded in the order from 0 to 146 to obtain the code ID of each state;

[0117] Specifically, the state combination encoding is to convert E(t), E c (t), α The fuzzy sets are mapped to integer numbers and combined into a unique state identifier:

[0118] E(t)∈{0(NB),1(NM),...,6(PB)}

[0119] E c (t)∈{0(NB),1(NM),...,6(PB)}

[0120] α∈{0(low),1(medium),2(high)}

[0121] Status code ID calculation:

[0122] ID=E(t) id *21+E c (t) id *3+ α id

[0123] Where, α id The value corresponding to the fuzzy set representing the current environmental disturbance factor α(t), where the values ​​of low disturbance, medium disturbance, and high disturbance in the fuzzy set are 0, 1, and 2 respectively; E(t) id and E c (t) idThe values ​​corresponding to the fuzzy sets representing the current brightness difference E(t) and the brightness difference change rate Ec(t) are as follows: NB, NM, NS, ZO, PS, PM, PB, which represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large in the fuzzy rules, respectively. The corresponding values ​​are 0, 1, 2, 3, 4, 5, and 6.

[0124] ID calculation example: E(t)=PM(5), E c (t)=PS(4), α=high(2);

[0125] ID=5*21+4*3+2=119

[0126] For example, see 4 for some state combinations:

[0127] Table 4

[0128]

[0129] Step C: Establish action space A

[0130] Action a=0: Increase the rule weight and improve the confidence of the current rule;

[0131] Action a=1: Keep the current rule and do not adjust the weight.

[0132] Step D: Create a confidence Q table corresponding to the three actions for each state code ID; wherein the initial value of each confidence Q(ID,a) in the Q table is set to 0.

[0133] Specifically, the Q-table structure dimensions are |S| × |A|, 147 states × 2 actions (increase / maintain rule weights). Some examples of rule confidence Q-tables are shown in Table 5:

[0134] Table 5

[0135]

[0136] The initial value of the rule confidence Q(ID,a) is set to 0.

[0137] Step 3.2: Calculate the state encoding under the current environmental perturbation factor α(t) and the scaled universe;

[0138] For the specific calculation process, please refer to the calculation formula in step B above.

[0139] Step 3.3: Based on the current probability The corresponding action strategy is selected based on the value, and based on the state-action rule confidence Q table updated last time, the confidence Q values ​​of each state code and its corresponding action are updated;

[0140] Specifically, probability The value is calculated as follows:

[0141]

[0142] Where, represents the probability value at the tth adjustment, represents the initialization value of the probability, and t represents the number of adjustments.

[0143] As an embodiment of the present invention, according to the probability calculated at the current time The steps for selecting the corresponding action strategy include:

[0144] (1) If ≤0.5, the actions encoded in each current state are randomly selected;

[0145] (2) If >0.5, then select the action with the largest confidence value Q(ID,a) under each action corresponding to each state code; if the confidence values ​​corresponding to the three actions are equal, the action with a=0 is selected by default.

[0146] Correspondingly, the steps of updating the confidence values ​​Q of the current state codes and their corresponding actions include:

[0147] (1) If the action a=0 is selected in the current state code, the confidence value Q(ID,0) of the state code corresponding to the action a=0 is updated based on the current reward function; the update formula is as follows:

[0148] Q * (ID t ,0)=Q(ID t ,0)+0.1*[ R t +0.9*max Q(ID t +1 t ,a) - Q(ID t ,0)]

[0149]

[0150] Where, Q(ID t ,0) and Q * (ID t ,0) respectively represent the state code ID of the current t-th adjustment t The current confidence value and the updated confidence value corresponding to the action a=0; max Q(ID t +1 t ,a) indicates the status code ID tThe next state code corresponds to the maximum value of the current confidence value when a=0 and a=1 actions (the next state code with state code ID 167 defaults to 0). R t represents the reward function at the t-th adjustment; E(t) represents the brightness difference at the t-th adjustment; y ( t ) max Indicates the maximum value of the ambient light brightness monitored in the previous t adjustments; Indicates the mining lamp lighting power consumption during the tth adjustment; Indicates the maximum power consumption of the mining lamp during the previous t adjustments.

[0151] (2) If the action a=1 is selected in the current state code, the confidence value Q(ID,1) of the state code corresponding to the action a=1 remains unchanged.

[0152] Some examples of status code updates are shown in the following table:

[0153] Table 6

[0154]

[0155] ①The first round of update process:

[0156] The initial value of probability ϵ is 0.8. If a=0 is randomly selected to increase the rule weight, the probability of the next update is:

[0157] =0.792

[0158] The current real-time monitoring ambient light illuminance value y(t) is 6;

[0159] The preset brightness value r(t) is 8;

[0160] The maximum value of the real-time monitoring ambient light illuminance value y(t) max is 10

[0161] Then the current brightness difference E(t) is r(t) -y(tk)=2

[0162] The current mining lamp lighting power consumption P(t) is 0.9W

[0163] The maximum power consumption of the mining lamp Pmax(t) is 1W

[0164] Then, the performance index Rt based on the dimming system is 0.52;

[0165] Q(0,0) value update, select the largest Q value

[0166] Calculation formula: maxQ(1,a)=max{Q(1,0),Q(1,1)}

[0167] From the table, we know that: Q(1,0)=0, Q(1,1)=0, then maxQ(1,a)=0;

[0168] Original Q(0,0)=0

[0169] New Q * (0,0)=0 +0.1*[0.52+0.9*0-0]=0.052

[0170] The Q(1,0) value is updated based on maxQ(2,a)=max{Q(2,0),Q(2,1)};

[0171] Assume that after the update, Q(1,0)=0.2;

[0172] According to Table 1, the state code ID corresponding to the maximum Q(ID,a) value after the first round of updates is 1.

[0173] ②The second round of update process

[0174] The probability ϵ value is 0.792. If a=0 is randomly selected to increase the rule weight, the next probability will be updated.

[0175] =0.784

[0176] If Rt is 0.52

[0177] Q(0,0) value update

[0178] Select the largest Q value

[0179] Calculation formula: maxQ(1,a)=max{Q(S1,0),Q(S1,1)}

[0180] From the table, we know that Q(1,0)=0, Q(1,1)=0.2, so maxQ(S0,a)=0.2;

[0181] Original Q(0,0)=0.052

[0182] New Q(0,0) = 0.052 + 0.1*[0.52+0.9*0.2-0.052]=0.1168

[0183] The Q(1,0) value is updated. Assume that after the update, Q(1,0)=0.3;

[0184] According to Table 1, the state code ID corresponding to the maximum Q(ID,a) value after the second round of update is 1.

[0185] Step 3.4: Find the state code to which the maximum value of the updated confidence value Q belongs, and output the fuzzy set of the brightness difference E(t) and the brightness difference change rate Ec(t) corresponding to the state code.

[0186] Example: Based on the update rule in step 3.3, assume that the state code ID corresponding to the maximum value of the rule confidence Q table at the current t-th update is 49; according to the state combination table, when ID = 49, the fuzzy sets of each state are as follows:

[0187] Table 7

[0188]

[0189] Step 4: Calculate the output value of PID control based on the fuzzy value of each parameter and adjust the PWM pulse width modulation value;

[0190] The process of calculating the output value of the PID controller in this embodiment is as follows:

[0191] A brightness fuzzy rule is established for the proportional gain Kp, integral time Ki, and differential time Kd in the PID controller parameters with respect to the fuzzy set of the brightness difference and the brightness difference change rate, as shown in Table 8 below. This rule is an existing general PID control rule. The fuzzy values ​​of Kp, Ki, and Kd are obtained according to the fuzzy set of the brightness difference and the brightness difference change rate.

[0192] Table 8 Brightness fuzzy rule table

[0193]

[0194] The rows in Table 8 represent different fuzzy sets for the luminance difference E(t), and the columns represent different fuzzy sets for the luminance difference change rate Ec(t). Each cell in Table 8 contains a triple, and the value in each cell represents the corresponding adjustment of KP, Ki, and KD for the corresponding luminance difference and luminance difference change rate. For example, if the luminance difference is negative (NB) and the luminance difference change rate is also negative (NB), then KP (PB) should be increased significantly, Ki (PM) should be increased moderately, and KD (PM) should be increased moderately. Combining the luminance difference E(t) with the luminance difference change rate Ec(t) and looking up Table 8, the corresponding fuzzy values ​​for the proportional gain (Kp), integral gain (Ki), and differential gain (Kd) are obtained.

[0195] Furthermore, the proportional gain , integration time and differential time The calculation formulas are as follows:

[0196]

[0197] Where, represents the probability value at the tth adjustment, and They represent the values ​​corresponding to the fuzzy sets of the brightness difference E(t) and the brightness difference change rate Ec(t) determined at the t-th adjustment respectively; 、 and They represent the values ​​corresponding to the fuzzy sets of proportional gain, integral time and differential time in the PID control obtained at the tth adjustment, that is, the values ​​corresponding to the fuzzy sets in Table 2.

[0198] Example:

[0199] According to ID=49 determined in step 3.4, E(t) =ZO, Ec(t)=PS

[0200] Look up Table 8 to get the relationship table with Kp, Ki, and Kd:

[0201] Table 9

[0202]

[0203] The calculation formulas for the three control parameters are as follows:

[0204]

[0205] According to Table 2, the corresponding value of the ZO fuzzy set is 0, the corresponding value of the PS fuzzy set is 3, and the corresponding value of the PM fuzzy set is 7. Substituting them into the above formula together, the current gain, integral time and differential time values ​​can be calculated.

[0206] Furthermore, the calculation formula of the output value of PID control is as follows:

[0207]

[0208] Where, Indicates the PID control output value during the t-th adjustment; 、 and They represent the proportional gain, integral time and differential time of the PID control parameters during the t-th adjustment respectively; Indicates the brightness difference at the tth adjustment; Indicates the cumulative brightness difference of the previous t adjustments.

[0209] Step 5: Adjust the brightness of the miner's lamp LED according to the PWM pulse width modulation value;

[0210] Step 6: Check whether the currently adjusted miner's lamp LED brightness is consistent with the preset brightness value. If not, return to step 1 to step 5 and adjust again until the brightness adjustment is completed.

[0211] The beneficial effect of the present invention is that, compared with the prior art,

[0212] 1. In order to adapt to the complex dynamic environment of underground coal mines, the present invention generates a nonlinear dynamic environment disturbance factor α by integrating multi-source data such as dust concentration, humidity, personnel movement speed, and personnel distance, and divides the disturbance levels into high, medium, and low according to the α value. For different disturbance levels, the formulated standard domain is scaled using an adaptive domain scaling rule. The brightness of the mining lamp can be adjusted according to the lighting requirements of the actual environment in the coal mine, realizing adaptive adjustment of the light as the environment changes, improving the lighting experience, and reducing operational risks.

[0213] 2. Based on the integration of environmental perception, the present invention constructs a state space based on the environmental disturbance factor α, brightness difference E, and brightness difference change rate E. C The proposed method uses a state-action rule confidence Q table with 147 discrete combinations and action space A (rule weight increase / maintenance), takes lighting brightness and energy consumption as reward functions, and drives dynamic optimization of the fuzzy rule base through action selection strategy. It solves the problems of traditional fuzzy rules relying on expert experience, being unable to evolve autonomously according to actual changes in coal mine environment, and having rigid rule base, and breaks through the limitations of traditional mining lamp dimming systems in dynamic adaptability, realizes intelligent control, improves the adaptability and energy efficiency of underground lighting, and provides an effective solution for safe and efficient lighting in coal mines, which is suitable for promotion and application.

[0214] Example 2:

[0215] like Figure 2 As shown, the present invention provides a mining lamp LED brightness adjustment system based on dynamic fuzzy PID control, the system is used to implement the steps of the method in the above embodiment 1, and the system specifically includes:

[0216] The acquisition module is used to collect data on various influencing factors in the environment where the underground workers are currently working and calculate the environmental disturbance factor α(t);

[0217] The scaling module is used to scale the established standard domain using the corresponding domain scaling rule according to the disturbance level of the current α(t) value;

[0218] A determination module is used to determine the fuzzy sets of the current brightness difference E(t) and the brightness difference change rate Ec(t) based on the scaled universe, thereby obtaining the fuzzy values ​​of each PID control parameter;

[0219] A calculation module is used to calculate the output value of the PID control based on the fuzzy value of each parameter and adjust the PWM pulse width modulation value;

[0220] Adjustment module, used to adjust the brightness of the mining lamp LED according to the PWM pulse width modulation value;

[0221] The repeat module is used to detect whether the currently adjusted mining lamp LED brightness is consistent with the preset brightness value. If not, it will be adjusted again until the brightness adjustment is completed.

[0222] As an embodiment of the present invention, refer to Figure 3 The mining lamp LED brightness control system provided by the present invention also includes: a lamp head USB camera, a brightness detection module, a fuzzy PID control module, a PWM brightness control module, a brightness adjustment module and a control center.

[0223] Among them, the lamp head USB camera is set on the lamp head of the mining lamp for taking pictures and recording videos of the environment; the brightness detection module is connected to the lamp head USB camera for receiving camera data and analyzing the ambient brightness.

[0224] In underground coal mine environments, images are captured by a USB camera on a smart lamp head, converted to the HSV color space through a brightness detection module, and then the value of the V channel is directly read to determine the brightness of the environment. The higher the V value, the brighter the environment, and vice versa.

[0225] The fuzzy PID control module is connected to the brightness detection module and is used to generate a PWM control signal through fuzzy rules based on the brightness detection results; the PWM output module is connected to the fuzzy PID control module and is used to receive the PID control signal and adjust the brightness of the LED light source; the brightness adjustment module is connected to the PWM output module and is used to adjust the brightness of the LED light source according to the PID control signal.

[0226] Furthermore, the control center of this embodiment is preferably a single-chip microcomputer, which integrates the functions of the above-mentioned acquisition module, scaling module, determination module, and calculation module, and is connected to the fuzzy PID control module, for receiving manual, voice and automatic adjustment instructions, and controlling the brightness adjustment module and the range adjustment module; the preset brightness value input module is used to input the preset brightness value of the mining lamp through manual, voice or automatic adjustment instructions.

[0227] Among them, manual brightness adjustment: allows miners to manually set the brightness according to personal preferences;

[0228] Voice brightness adjustment: Brightness can be adjusted through voice commands, which is convenient for miners to use when their hands are busy;

[0229] Automatic brightness adjustment: The system automatically adjusts the brightness according to the ambient light intensity.

[0230] When performing automatic brightness adjustment, the system uses the lamp's USB camera to take a photo. The brightness detection module analyzes the current ambient light intensity based on the photo. If the environment is dark, a higher brightness value is preset. The microcontroller generates a PID control output value and sends it to the fuzzy PID control module. The fuzzy PID control module generates a PWM brightness control signal based on the received output value. The PWM output module controls the brightness adjustment module based on this signal, adjusting the LED light source brightness. This process repeats until the brightness reaches the preset value. The microcontroller receives various adjustment commands and controls the entire brightness adjustment process.

[0231] The mining lamp LED brightness adjustment system based on dynamic fuzzy PID control provided in the embodiment of the present invention and the mining lamp LED brightness adjustment method based on dynamic fuzzy PID control provided in Example 1 are based on the same technical concept and can produce the beneficial effects described in Example 1. For the contents not fully described in this embodiment, please refer to Example 1.

[0232] Example 3:

[0233] An embodiment of the present invention provides a terminal including a processor and a storage medium;

[0234] The storage medium is used to store instructions;

[0235] The processor is configured to operate according to the instruction to execute the steps of the method according to any one of the first embodiments.

[0236] Example 4:

[0237] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the method described in any one of the first embodiments are implemented.

[0238] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0239] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punched card or raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse passing through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0240] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0241] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, the state information of the computer-readable program instructions is used to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), so that the electronic circuit can execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0242] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A mining lamp LED brightness adjustment method based on dynamic fuzzy PID control, characterized in that: Methods include: Step 1: Collect data on various influencing factors in the current environment of the underground workers and calculate the environmental disturbance factor α(t); The calculation formula of the environmental disturbance factor α(t) is as follows: Where α(t) represents the environmental disturbance factor at the tth adjustment, Indicates the tth detected j The normalized value of the impact factor data; The weight of the configuration; M represents the number of impact factors, ; Step 2: According to the disturbance level of the current α(t) value, the corresponding domain scaling rule is adopted to scale the established standard domain; The domain scaling rules include: When α(t)≥0.7, the disturbance level is high disturbance, and the domain scaling rule is: compress the domain of the current brightness difference E(t) to [max(-0.7E(t),-10), min(+0.7E(t),10)], and compress the domain of the brightness difference change rate Ec(t) to [max(-0.8Ec(t),-10), min(+0.8 Ec(t),10) ]; When 0.3≤α(t)<0.7, the disturbance level is medium disturbance, and the domain scaling rule is: compress the domain of the current brightness difference E(t) to [max (-0.8E(t),-10), min(+0.8E(t),10)], and compress the brightness difference change rate to compress the domain of Ec(t) to [max (-0.9E(t),-10), min(+0.9E(t),10)]; When α(t)<0.3, the perturbation level is low perturbation, and the domain scaling rule is: E(t) and Ec(t) are both restored to the standard domain [-10, 10]; Step 3: Compare the measured brightness value with the preset brightness value to calculate the brightness difference E(t) and the brightness difference change rate Ec(t). Based on the scaled universe, determine the fuzzy set of the current brightness difference E(t) and the brightness difference change rate Ec(t), thereby obtaining the fuzzy values ​​of each PID control parameter. Step 4: Calculate the output value of PID control based on the fuzzy value of each parameter, and adjust the PWM pulse width modulation value according to the output value of PID control; Step 5: Adjust the brightness of the miner's lamp LED according to the PWM pulse width modulation value; Step 6: Check whether the currently adjusted miner's lamp LED brightness is consistent with the preset brightness value. If not, return to step 1 to step 5 and adjust again until the brightness adjustment is completed.

2. The method for adjusting the brightness of a miner's lamp LED based on dynamic fuzzy PID control according to claim 1, characterized in that: The data of each influencing factor includes personnel movement speed, distance parameter, dust concentration, humidity, temperature and / or air pressure.

3. The method for adjusting the brightness of a mining lamp LED based on dynamic fuzzy PID control according to claim 1, characterized in that: In step 3, the step of determining the fuzzy set of the current brightness difference E(t) and the brightness difference change rate Ec(t) includes: Step 3.1: Construct and initialize the state-action rule confidence Q table based on the state space and action space; Step 3.2: Calculate the state encoding under the current environmental perturbation factor α(t) and the scaled universe; Step 3.3: Based on the current probability The corresponding action strategy is selected based on the value, and based on the state-action rule confidence Q table updated last time, the confidence Q values ​​of each state code and its corresponding action are updated; Step 3.4: Find the state code to which the maximum value of the updated confidence value Q belongs, and output the fuzzy set of the brightness difference E(t) and the brightness difference change rate Ec(t) corresponding to the state code.

4. The method for adjusting the brightness of a mining lamp LED based on dynamic fuzzy PID control according to claim 3, characterized in that: The steps of constructing and initializing a state-action rule confidence Q table based on the state space and the action space include: Step A: Create state space S: S=(E, E c ,α); Among them, E and E c The state spaces of brightness difference and brightness difference change rate in the standard domain are respectively represented, and both include 7 fuzzy sets; α represents the state space of environmental disturbance factor, which includes 3 fuzzy sets: low disturbance, medium disturbance and high disturbance; Step B: E's 7 fuzzy sets, E c The 7 fuzzy sets of and the 3 fuzzy sets of α are arranged and combined to obtain 147 states, and these 147 states are coded in the order from 0 to 146 to obtain the code ID of each state; Step C: Establish an action space: Action a=0: Increase the rule weight and improve the confidence of the current rule; Action a=1: Maintain the current rule, that is, maintain the confidence of the current rule; Step D: Create a confidence Q table corresponding to the three actions for each state code ID; wherein the initial value of each confidence Q(ID,a) in the Q table is set to 0.

5. The method for adjusting the brightness of a miner's lamp LED based on dynamic fuzzy PID control according to claim 3 or 4, characterized in that: In step 3.2, the formula for calculating each state encoding under the current environmental disturbance factor α(t) and the scaled universe is as follows: ID=E(t) id *21+E c (t) id *3+ α id Where, α id The value corresponding to the fuzzy set representing the current environmental disturbance factor α(t), where the values ​​of low disturbance, medium disturbance, and high disturbance in the fuzzy set are 0, 1, and 2 respectively; E(t) id and E c (t) id The values ​​corresponding to the fuzzy sets representing the current brightness difference E(t) and the brightness difference change rate Ec(t) are as follows: NB, NM, NS, ZO, PS, PM, PB, which represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large in the fuzzy rules, respectively. The corresponding values ​​are 0, 1, 2, 3, 4, 5, and 6.

6. The method for adjusting the brightness of a mining lamp LED based on dynamic fuzzy PID control according to claim 3, characterized in that: In step 3.3, the probability The value is calculated as follows: Where, represents the probability value at the tth adjustment, represents the initialization value of the probability, and t represents the number of adjustments.

7. The method for adjusting the brightness of a mining lamp LED based on dynamic fuzzy PID control according to claim 4, characterized in that: In step 3.3, the probability calculated based on the current The steps for selecting the corresponding action strategy include: like ≤0.5, the actions encoded in each current state are randomly selected; like >0.5, then select the action with the largest confidence value Q(ID,a) under each action corresponding to each state code; if the confidence values ​​corresponding to the three actions are equal, the action with a=0 is selected by default.

8. The method for adjusting the brightness of a miner's lamp LED based on dynamic fuzzy PID control according to claim 4 or 7, characterized in that: In step 3.3, the step of updating the confidence values ​​Q of the current state codes and their corresponding actions includes: If the action a=0 is selected in the current state code, the confidence value Q(ID,0) of the state code corresponding to the action a=0 is updated based on the current reward function; If the action a=1 is selected in the current state code, the confidence value Q(ID,1) of the state code corresponding to the action a=1 remains unchanged.

9. The method for adjusting the brightness of a mining lamp LED based on dynamic fuzzy PID control according to claim 8, characterized in that: The update formula for the confidence value of the state encoding corresponding to the action a=0 based on the current reward function is as follows: Q * (ID t ,0)=Q(ID t ,0)+0.1*[ R t +0.9*max Q(ID t +1,a) - Q(ID t ,0)] Where, Q(ID t ,0) and Q * (ID t ,0) respectively represent the state code ID of the current t-th adjustment t The current confidence value and the updated confidence value corresponding to the action a=0; max Q(ID t +1,a) indicates the status code ID t The next state encoding corresponds to the maximum of the current confidence values ​​when a=0 and a=1 actions; R t Represents the reward function at the tth adjustment.

10. The method for adjusting the brightness of a mining lamp LED based on dynamic fuzzy PID control according to claim 9, characterized in that: The reward function R t The calculation formula is as follows: Where, E(t) represents the brightness difference at the tth adjustment; y ( t ) max Indicates the maximum value of the ambient light brightness monitored in the previous t adjustments; Indicates the mining lamp lighting power consumption during the tth adjustment; Indicates the maximum power consumption of the mining lamp during the previous t adjustments.

11. The method for adjusting the brightness of a mining lamp LED based on dynamic fuzzy PID control according to claim 1, characterized in that: In step 4, the calculation formula of the output value of PID control is as follows: Where, Indicates the PID control output value during the t-th adjustment; 、 and They represent the proportional gain, integral time and differential time of the PID control parameters during the t-th adjustment respectively; Indicates the brightness difference at the tth adjustment; Indicates the cumulative brightness difference of the previous t adjustments.

12. The method for adjusting the brightness of a mining lamp LED based on dynamic fuzzy PID control according to claim 11, characterized in that: Proportional gain , integration time and differential time The calculation formulas are as follows: Where, represents the probability value at the tth adjustment, and They represent the values ​​corresponding to the fuzzy sets of the brightness difference E(t) and the brightness difference change rate Ec(t) determined at the t-th adjustment respectively; 、 and They represent the numerical values ​​corresponding to the fuzzy sets of proportional gain, integral time and differential time in the PID control obtained at the tth adjustment.

13. A mining lamp LED brightness adjustment system based on dynamic fuzzy PID control using the method according to any one of claims 1 to 12, characterized in that: The system includes: The acquisition module is used to collect data on various influencing factors in the environment where the underground workers are currently working and calculate the environmental disturbance factor α(t); The calculation formula of the environmental disturbance factor α(t) is as follows: Where α(t) represents the environmental disturbance factor at the tth adjustment, Indicates the tth detected j The normalized value of the impact factor data; The weight of the configuration; M represents the number of impact factors, ; The scaling module is used to scale the established standard domain using the corresponding domain scaling rule according to the disturbance level of the current α(t) value; The domain scaling rules include: When α(t)≥0.7, the disturbance level is high disturbance, and the domain scaling rule is: compress the domain of the current brightness difference E(t) to [max(-0.7E(t),-10), min(+0.7E(t),10)], and compress the domain of the brightness difference change rate Ec(t) to [max(-0.8Ec(t),-10), min(+0.8 Ec(t),10) ]; When 0.3≤α(t)<0.7, the disturbance level is medium disturbance, and the domain scaling rule is: compress the domain of the current brightness difference E(t) to [max (-0.8E(t),-10), min(+0.8E(t),10)], and compress the brightness difference change rate to compress the domain of Ec(t) to [max (-0.9E(t),-10), min(+0.9E(t),10)]; When α(t)<0.3, the perturbation level is low perturbation, and the domain scaling rule is: E(t) and Ec(t) are both restored to the standard domain [-10, 10]; The determination module is used to compare the measured brightness value with the preset brightness value, calculate the brightness difference E(t) and the brightness difference change rate Ec(t); and based on the scaled universe, determine the fuzzy set of the current brightness difference E(t) and the brightness difference change rate Ec(t), thereby obtaining the fuzzy values ​​of each PID control parameter; A calculation module is used to calculate the output value of the PID control based on the fuzzy value of each parameter, and adjust the PWM pulse width modulation value according to the output value of the PID control; Adjustment module, used to adjust the brightness of the mining lamp LED according to the PWM pulse width modulation value; The repeat module is used to detect whether the currently adjusted mining lamp LED brightness is consistent with the preset brightness value. If not, it will be adjusted again until the brightness adjustment is completed.

14. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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