Intelligent mine car operation optimization method and system based on environmental feature

Abstract

The present invention relates to the technical field of intelligent mine car operation. Disclosed is an intelligent mine car operation optimization method based on an environmental feature, comprising: transmitting collected data to a central database via the Internet, and designing an intelligent data processing algorithm in the database to dynamically adjust the collected data; and sampling mine car operation state data on the basis of a prediction model, and determining a sampling result to optimize mine car operation. The present invention realizes real-time monitoring and optimization of a mine car operation state, and by means of data analysis and machine learning prediction, improves the efficiency and safety of mine car operation and reduces the risk of accidents, thereby improving the production efficiency and economic benefits of mines.

Description

A method and system for optimizing the operation of intelligent mining trucks based on environmental characteristics Technical Field

[0001] This invention relates to the field of intelligent mining truck operation technology, and in particular to an intelligent mining truck operation optimization method and system based on environmental characteristics. Background Technology

[0002] Currently, the level of industrialization and informatization in society is at a relatively high stage. At this stage, the equipment and technology involved in industrial production processes are required to be more precise and intelligent. At the same time, the development of information technology has provided better conditions for data collection, processing and analysis.

[0003] Existing technologies for mining truck operation suffer from problems such as frequent accidents, energy waste, and low production efficiency. By adopting intelligent mining truck operation optimization technology based on environmental characteristics, precise monitoring and prediction of the mining truck's operating status can be achieved, thereby promptly identifying potential safety hazards and operational anomalies, and improving the safety and stability of mining truck operation. Simultaneously, by optimizing the mining truck's operating path and speed, unnecessary energy consumption and production delays can be reduced, increasing production efficiency. The application of this technology can effectively solve the problems existing in traditional mining truck operation, realizing intelligent and efficient mining truck production. Summary of the Invention

[0004] In view of the problems existing in the current intelligent mining truck operation optimization methods and systems based on environmental characteristics, this invention is proposed.

[0005] Therefore, the purpose of this invention is to provide an intelligent mining truck operation optimization method and system based on environmental characteristics. This invention addresses the lack of accuracy in traditional mining truck operation status monitoring and adjustment processes by employing intelligent mining truck operation optimization technology.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide an intelligent mining truck operation optimization method based on environmental characteristics, which includes: collecting mining truck operation status data based on sensors installed around the mining truck, transmitting the data to a central database via the Internet, and designing an intelligent data processing algorithm in the database to dynamically adjust the collected data;

[0008] The operating status of the mining truck is analyzed based on the dynamically adjusted data results. A machine learning prediction model is established based on the analysis results. The operating status data of the mining truck is sampled based on the prediction model, and the sampling results are used to optimize the operation of the mining truck.

[0009] As a preferred embodiment of the intelligent mining truck operation optimization method based on environmental characteristics described in this invention, the mining truck operation status data includes obtaining the acceleration, angle and position information of the mining truck from sensors and drawing an image of the surrounding environment, and calculating the acceleration, angle and position information of the mining truck based on wind speed, temperature, humidity and air pressure.

[0010] The formula for calculating the acceleration of the mine car is:

[0011] Among them, a i Represents time t i acceleration, v w The wind speed is represented by m, the mass of the mine car is represented by w. ijk The weight parameters are represented by i, j, and k, which represent the order of the polynomial.

[0012] The formula for calculating the angle of the mine car is:

[0013] Where, θ i+1 Represents time t i+1 The angles are k1, k2, k3, and k4, which represent coefficients, and I represents the moment of inertia of the mine car.

[0014] The formula for calculating the mine car location information is as follows:

[0015] Where, x i+1 Represents time t i+1 Position, v i and v i+1 v represents the velocity at adjacent moments. mid The value represents the median of adjacent velocities, Δt represents the sampling time interval, and v w The wind speed is represented by m, and the mass of the mine car is represented by m.

[0016] As a preferred embodiment of the intelligent mining truck operation optimization method based on environmental characteristics described in this invention, the intelligent data processing algorithm includes: dynamically adjusting the calculation results of mining truck operation status data based on a deep learning neural network model; establishing an intelligent data algorithm model of a multilayer perceptron; and using mining truck acceleration, mining truck angle, and mining truck position information as inputs to the intelligent data algorithm model. The output result is the dynamically adjusted result.

[0017] The specific calculation formula for the intelligent data algorithm model is: h (1) =g (1) (W (1) ·X+b (1) h (2 ) = g (2) (W (2) ·h(1) +b (2) ... h (n) =g (n) (W (2) ·h (n-1) +b (n) Y = W (n+1) ·h (n) +b (n+1)

[0018] Where X represents the calculated results of the input acceleration, angle, and position, and h (i) W represents the output of the i-th hidden layer. (i) and b (i) The weights and biases of the i-th layer, g (i) Y represents the activation function of the i-th layer, and Y represents the output of the model, i.e., the adjusted result.

[0019] As a preferred embodiment of the intelligent mining truck operation optimization method based on environmental characteristics described in this invention, the prediction model includes labeling the adjusted result as a target variable. The labeling includes standardizing the target variable based on time series data and calculating the target variable using a nearest neighbor interpolation algorithm. The interpolation algorithm includes polynomial interpolation and linear interpolation.

[0020] If the target variable feature X = {x i x2, ..., x n When dealing with missing values ​​x in the target variable, i When a marker is detected around the target variable, a linear interpolation algorithm is used to sample the target variable.

[0021] When no markers are detected around the target variable, a polynomial interpolation algorithm is used to sample the target variable, and the missing values ​​are estimated based on the sampling results.

[0022] The specific formula for the linear interpolation algorithm is as follows:

[0023] in, Indicates missing value x i The estimated value, x nearest x represents i The known value before, x next x represents i The known value after that, d represents x i With x nearest The distance between them, Δ represents x next and x nearest The distance between them;

[0024] The specific formula for the polynomial interpolation algorithm is as follows:

[0025] Where n represents the number of known data points, x represents the x-coordinate to be interpolated, and y i This represents the ordinate of a known data point.

[0026] As a preferred embodiment of the intelligent mining truck operation optimization method based on environmental characteristics described in this invention, the sampling includes outlier detection of the calculation results of the target variable using a quantile method;

[0027] Calculate the median, first quartile, and third quartile of feature X. Identify outliers based on the interquartile range calculation results. Set thresholds for outliers as σ and τ. Replace outliers exceeding the threshold range with the median. The specific calculation formula for outlier detection is as follows:

[0028] Where, x i Q represents the observed value of feature X. 0.5 represents the median, Q 0.25 Q represents the first quartile. 0.75 The third quartile is represented by IQR, which represents the interquartile range.

[0029] As a preferred embodiment of the intelligent mining truck operation optimization method based on environmental characteristics described in this invention, the replacement includes comparing the observed values ​​and the interquartile range to determine the sampling stage, and the specific steps are as follows:

[0030] If x i Greater than Q 0.75 When +1.5×IQR, then x i It is at the upper limit outlier, and x is also... i Replace with median Q 0.5 At this point, we are in the first type of sampling phase;

[0031] If x i Less than Q 0.25 When x = -1.5 × IQR, then i It is at the lower limit outlier, and x is also... i Replace with median Q 0.5 This is currently in the second sampling phase.

[0032] As a preferred embodiment of the intelligent mining truck operation optimization method based on environmental characteristics described in this invention, the optimization includes processing the operating efficiency of the mining truck according to two stages.

[0033] The first type of sampling stage includes analyzing the sampling data of the mine car's running speed to determine if the mine car has deviated from the predetermined path;

[0034] The second type of sampling stage includes identifying fault warning signals of mining trucks based on a predetermined path and monitoring the operation of mining trucks in real time.

[0035] Secondly, embodiments of the present invention provide an intelligent mining truck operation optimization system based on environmental characteristics, which includes: an adjustment module that transmits data to a central database via the Internet, and designs an intelligent data processing algorithm in the database to dynamically adjust the collected data;

[0036] The optimization module samples the mining truck's operating status data based on the prediction model and uses the sampling results to optimize the mining truck's operation.

[0037] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the above-described intelligent mining truck operation optimization method based on environmental characteristics.

[0038] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the above-described intelligent mining truck operation optimization method based on environmental characteristics.

[0039] The beneficial effects of this invention are as follows: This invention utilizes intelligent mining truck operation optimization technology, employs sensors to collect data and dynamically adjusts it through intelligent algorithms, thereby achieving real-time monitoring and optimization of the mining truck's operating status. Through data analysis and machine learning prediction, it improves the efficiency and safety of mining truck operation, reduces the risk of accidents, and thus enhances mine production efficiency and economic benefits. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments 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. Wherein:

[0041] Figure 1 is a flowchart of a method and system for optimizing the operation of intelligent mining trucks based on environmental characteristics, provided by an embodiment of the present invention.

[0042] Figure 2 is an internal structure diagram of a computer device for an intelligent mining truck operation optimization method and system based on environmental characteristics, provided in an embodiment of the present invention. Detailed Implementation

[0043] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0044] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0046] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0047] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0048] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0049] Example 1

[0050] Referring to Figures 1 and 2, the first embodiment of the present invention provides a method for optimizing the operation of intelligent mining trucks based on environmental characteristics, including:

[0051] S1: Collect mining truck operating status data based on sensors installed around the mining truck, transmit the data to a central database via the Internet, and design intelligent data processing algorithms in the database to dynamically adjust the collected data.

[0052] Among them, the mining truck operation status data includes the acceleration, angle and position information of the mining truck obtained by the sensor and the surrounding environment image drawn. The acceleration, angle and position information of the mining truck are calculated based on wind speed, temperature, humidity and air pressure.

[0053] The formula for calculating the acceleration of a mine car is:

[0054] Among them, a i Represents time t i acceleration, v w The wind speed is represented by m, the mass of the mine car is represented by w. ijk The weight parameters are represented by i, j, and k, which represent the order of the polynomial.

[0055] The formula for calculating the angle of the mine car is:

[0056] Where, θ i+1 Represents time t i+1 The angles are k1, k2, k3, and k4, which represent coefficients, and I represents the moment of inertia of the mine car.

[0057] The formula for calculating the location information of the mining truck is:

[0058] Where, x i+1 Represents time t i+1 Position, v i and v i+1 v represents the velocity at adjacent moments. mid The value represents the median of adjacent velocities, Δt represents the sampling time interval, and v w The wind speed is represented by m, and the mass of the mine car is represented by m.

[0059] S1.1: The design of intelligent data processing algorithms includes the calculation results of the mine car running status data dynamically adjusted by a deep learning-based neural network model, the establishment of a multilayer perceptron intelligent data algorithm model, and the input of the mine car acceleration, mine car angle and mine car position information as the input of the intelligent data algorithm model. At this time, the output result is the dynamically adjusted result.

[0060] The specific calculation formula for the intelligent data algorithm model is: h(1) =g (1) (W (1) ·X+b (1) h (2) =g (2) (W (2) ·h (1) +b (2) ... h (n) =g (n) (W (2) ·h (n-1) +b (n) Y = W (n+1) ·h (n) +b (n+1)

[0061] Where X represents the calculated results of the input acceleration, angle, and position, and h (i) W represents the output of the i-th hidden layer. (i) and b (i) The weights and biases of the i-th layer, g (i) Y represents the activation function of the i-th layer, and Y represents the output of the model, i.e., the adjusted result.

[0062] Furthermore, the design of intelligent data processing algorithms includes dynamically adjusting the calculation results of mining truck operating status data based on a deep learning-based neural network model, and establishing an intelligent data algorithm model of a multilayer perceptron. When the input acceleration, angle, and position calculation results are a... i =2.5, θ i =30°, x i =100.

[0063] S2: Analyze the operating status of the mining truck based on the results of dynamic adjustment data, establish a machine learning prediction model based on the analysis results, sample the operating status data of the mining truck based on the prediction model, and optimize the operation of the mining truck based on the sampling results.

[0064] The prediction model includes labeling the adjusted results as target variables. Labeling includes standardizing the target variables based on time series data and calculating the target variables using a nearest neighbor interpolation algorithm. The interpolation algorithm includes polynomial interpolation and linear interpolation.

[0065] If the target variable feature X = {x i x2, ..., x n When dealing with missing values ​​x in the target variable, i When a marker is detected around the target variable, a linear interpolation algorithm is used to sample the target variable.

[0066] When no markers are detected around the target variable, a polynomial interpolation algorithm is used to sample the target variable, and the missing values ​​are estimated based on the sampling results.

[0067] The specific formula for the linear interpolation algorithm is as follows:

[0068] in, Indicates missing value x i The estimated value, x nearest x represents i The known value before, x next x represents i The known value after that, d represents x i With x nearest The distance between them, Δ represents x next and x nearest The distance between them;

[0069] The specific formula for polynomial interpolation is:

[0070] Where n represents the number of known data points, x represents the x-coordinate to be interpolated, and y i This represents the ordinate of a known data point.

[0071] Furthermore, the distances mentioned above include two: the distance between the missing value and the previous known value, and the distance between the missing value and the next known value. Assuming the distance between the missing value and the previous known value is d1 = 10, and the distance between the missing value and the next known value is d2 = 15, this means the missing value is 10 units of time away from the previous known value and 15 units of time away from the next known value. The distances between the missing and next values ​​are shown in Table 1 below.

[0072] Table 1: Distance data between preceding and following values

[0073] The table above shows the distance between each missing value and the previous and next known values ​​during interpolation calculations. This distance information is crucial for determining which interpolation algorithm to use and how to estimate the missing values.

[0074] S2.1: Sampling includes outlier detection of the calculation results of the target variable using quantile methods;

[0075] Calculate the median, first quartile, and third quartile of feature X. Identify outliers based on the interquartile range. Set thresholds for outliers as σ and τ. Replace outliers exceeding the threshold range with the median. The specific calculation formula for outlier detection is as follows:

[0076] Where, x i Q represents the observed value of feature X. 0.5 represents the median, Q 0.25 Q represents the first quartile. 0.75 The third quartile is represented by IQR, which represents the interquartile range.

[0077] S2.2: The replacement involves comparing the observations with the interquartile range to determine the sampling stage. The specific steps are as follows:

[0078] If x i Greater than Q 0.75 When +1.5×IQR, then x i It is at the upper limit outlier, and x is also... i Replace with median Q 0.5 At this point, we are in the first type of sampling phase;

[0079] If x i Less than Q 0.25 When x = -1.5 × IQR, then i It is at the lower limit outlier, and x is also... i Replace with median Q 0.5 This is currently in the second sampling phase.

[0080] Furthermore, the upper limit mentioned above is 1.5 times the interquartile range plus the third quartile, i.e., Q. 0.75 +1.5×IQR, the upper limit is obtained by using the third quartile Q 0.75 The result is obtained by adding 1.5 times the interquartile range (IQR).

[0081] S2.3: The first sampling stage includes analyzing the sampling data of the mine car's running speed to determine the predetermined path where the mine car has deviated;

[0082] The second sampling phase includes identifying fault warning signals of mining trucks based on a predetermined path and monitoring the operation of mining trucks in real time.

[0083] In a preferred embodiment, an intelligent mining truck operation optimization system based on environmental characteristics includes an adjustment module that transmits data to a central database via the Internet, and designs an intelligent data processing algorithm in the database to dynamically adjust the collected data.

[0084] The optimization module samples the mining truck's operating status data based on the prediction model and uses the sampling results to optimize the mining truck's operation.

[0085] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or independent of it, or they can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0086] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0087] In summary, this invention utilizes intelligent mining truck operation optimization technology, employs sensors to collect data and dynamically adjusts it using intelligent algorithms, thereby achieving real-time monitoring and optimization of the mining truck's operating status. Through data analysis and machine learning prediction, it improves the efficiency and safety of mining truck operation, reduces accident risks, and ultimately enhances mine production efficiency and economic benefits.

[0088] Example 2

[0089] Referring to Figures 1 and 2, the second embodiment of the present invention is provided, which offers an intelligent mining truck operation optimization method based on environmental characteristics. To verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0090] Data on the operating status of the mining trucks was collected and preprocessed. Sensors were installed to transmit the data to a database, and missing value handling, outlier detection, and data standardization were performed. The sampling time interval was set to 0.1 seconds, the wind speed to 3 m / s, and the weight of the mining truck to 1000 kg. The data was analyzed, and a deep learning-based neural network model was established to predict the status of the mining trucks. In the modeling process, two hidden layers were set, with 50 and 30 nodes in each layer, respectively.

[0091] After model training, dynamically adjusted data is input into the model for prediction. Data sampling is performed based on the prediction results, and the data is processed using a nearest neighbor interpolation algorithm. The sampling stage is determined based on outliers. The operation of the mining truck is optimized based on the sampling results, including analyzing speed data and identifying potential fault warning signals to monitor the operating status of the mining truck in real time. The entire experiment aims to improve the safety and efficiency of mining truck operation by comprehensively utilizing data processing and analysis techniques. The mining truck operating status data parameters are shown in Table 2 below:

[0092] Table 2. Data Parameters for Mine Car Operation Status

[0093] The table above shows the changes of different parameters in the mine car's operating status data. Among them, the values ​​of acceleration, angle, and position at different time points show diverse trends. By comparing the values ​​of these parameters, we can better understand the operating status of the mine car and provide a reference for subsequent optimization measures. The comparison with existing technologies is shown in Table 3 below:

[0094] Table 3 Comparison with Existing Technologies

[0095] The table above illustrates the advantages of the present invention over existing technologies, including more comprehensive data acquisition, more intelligent data processing algorithms, more accurate prediction models, and more flexible data sampling and outlier handling strategies.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing the operation of intelligent mining trucks based on environmental characteristics, characterized in that: include, The system collects data on the operating status of the mining trucks using sensors installed around them, and transmits this data to a central database via the internet. Intelligent data processing algorithms are then designed in the database to dynamically adjust the collected data. The operating status of the mining truck is analyzed based on the dynamically adjusted data results. A machine learning prediction model is established based on the analysis results. The operating status data of the mining truck is sampled based on the prediction model, and the sampling results are used to optimize the operation of the mining truck.

2. The intelligent mining truck operation optimization method based on environmental characteristics as described in claim 1, characterized in that: The mining truck operating status data includes the acceleration, angle and position information of the mining truck obtained by sensors and the surrounding environment image drawn. The acceleration, angle and position information of the mining truck are calculated based on wind speed, temperature, humidity and air pressure. The formula for calculating the acceleration of the mine car is: Among them, a i Represents time t i acceleration, v w The wind speed is represented by m, the mass of the mine car is represented by w. ijk The weight parameters are represented by i, j, and k, which represent the order of the polynomial. The formula for calculating the angle of the mine car is: Where, θ i+1 Represents time t i+1 The angles are k1, k2, k3, and k4, which represent coefficients, and I represents the moment of inertia of the mine car. The formula for calculating the mine car location information is as follows: Where, x i+1 Represents time t i+1 Position, v i and v i+1 v represents the velocity at adjacent moments. mid The value represents the median of adjacent velocities, Δt represents the sampling time interval, and v w The wind speed is represented by m, and the mass of the mine car is represented by m.

3. The intelligent mining truck operation optimization method based on environmental characteristics as described in claim 2, characterized in that: The intelligent data processing algorithm is designed to dynamically adjust the calculation results of the mine car operating status data based on a deep learning neural network model, establish an intelligent data algorithm model of a multilayer perceptron, and take the mine car acceleration, mine car angle and mine car position information as inputs to the intelligent data algorithm model. At this time, the output result is the dynamically adjusted result. The specific calculation formula for the intelligent data algorithm model is as follows: h (1) =g (1) (W (1) ·X+b (1) ) h (2) =g (2) (W (2) ·h (1) +b (2) ) ... h (n) =g (n) (W (2) ·h (n-1) +b (n) ) Y=W (n+1) ·h (n) +b (n+1) Where X represents the calculated results of the input acceleration, angle, and position, and h (i) W represents the output of the i-th hidden layer. (i) and b (i) The weights and biases of the i-th layer, g (i) Y represents the activation function of the i-th layer, and Y represents the output of the model, i.e., the adjusted result.

4. The intelligent mining truck operation optimization method based on environmental characteristics as described in claim 3, characterized in that: The prediction model includes labeling the adjusted result as the target variable. The labeling includes standardizing the target variable based on time series data and calculating the target variable using a nearest neighbor interpolation algorithm. The interpolation algorithm includes polynomial interpolation and linear interpolation. If the target variable feature X = {x i x2, ..., x n When dealing with missing values ​​x in the target variable, i When a marker is detected around the target variable, a linear interpolation algorithm is used to sample the target variable. When no markers are detected around the target variable, a polynomial interpolation algorithm is used to sample the target variable, and the missing values ​​are estimated based on the sampling results. The specific formula for the linear interpolation algorithm is as follows: in, Indicates missing value x i The estimated value, x nearest x represents i The known value before, x next x represents i The known value after that, d represents x i With x nearest The distance between them, Δ represents x next and x nearest The distance between them; The specific formula for the polynomial interpolation is as follows: Where n represents the number of known data points, x represents the x-coordinate to be interpolated, and y i This represents the ordinate of a known data point.

5. The intelligent mining truck operation optimization method based on environmental characteristics as described in claim 4, characterized in that: The sampling includes outlier detection of the calculation results of the target variable using a quantile method; Calculate the median, first quartile, and third quartile of feature X. Identify outliers based on the interquartile range calculation results. Set thresholds for outliers as σ and τ. Replace outliers exceeding the threshold range with the median. The specific calculation formula for outlier detection is as follows: Where, x i Let Q represent the observed value of feature X. 0.5 represents the median, Q 0.25 Q represents the first quartile. 0.75 The third quartile is represented by IQR, which represents the interquartile range.

6. The intelligent mining truck operation optimization method based on environmental characteristics as described in claim 5, characterized in that: The replacement involves comparing the observed values ​​with the interquartile range to determine the sampling stage, and the specific steps are as follows: If x i Greater than Q 0.75 When +1.5×IQR, then x i It is at the upper limit outlier, and x is also... i Replace with median Q 0.5 At this point, we are in the first type of sampling phase; If x i Less than Q 0.25 When x = -1.5 × IQR, then i It is at the lower limit outlier, and x is also... i Replace with median Q 0.5 This is currently in the second sampling phase.

7. The intelligent mining truck operation optimization method based on environmental characteristics as described in claim 6, characterized in that: The optimization includes processing the operating efficiency of the mining trucks in two phases; The first type of sampling stage includes analyzing the sampling data of the mine car's running speed to determine if the mine car has deviated from the predetermined path; The second type of sampling stage includes identifying fault warning signals of mining trucks based on a predetermined path and monitoring the operation of mining trucks in real time.

8. An intelligent mining truck operation optimization system based on environmental characteristics, based on the intelligent mining truck operation optimization method based on environmental characteristics according to any one of claims 1 to 7, characterized in that: include, The adjustment module transmits data to a central database via the internet, where intelligent data processing algorithms are designed to dynamically adjust the collected data. The optimization module samples the mining truck's operating status data based on the prediction model and uses the sampling results to optimize the mining truck's operation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the intelligent mining truck operation optimization method based on environmental characteristics as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the intelligent mining truck operation optimization method based on environmental characteristics as described in any one of claims 1 to 7.

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