Mining area vehicle excavation operation statistical method, system and device and storage medium

Abstract

The invention relates to the technical field of mining area vehicle excavation operation statistics, in particular to a mining area vehicle excavation operation statistics method, system and device and a storage medium. The statistical method comprises the steps that the relative position between the excavator and the mine car is determined through beacon signals, and the distance between the excavator and the mine car is calculated according to position data of the relative position; a high-density vibration sensor is used for detecting periodic vibration signals generated during operation of the excavator, and SD periodic burr signals are extracted through a signal processing algorithm and used for judging whether the excavator is loading or not; when it is judged that the excavator is loading, a loading event is recorded, and a loading record is generated. According to the invention, by fusing various sensor signals, the accuracy of mining area vehicle excavation operation statistics is improved. Specifically, the beacon signal enables the relative position between the excavator and the mine car to be accurately determined, so that misstatistics caused by wrong position judgment is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of mining area vehicle excavation operation statistics, and in particular to a mining area vehicle excavation operation statistics method, system, device and storage medium. Background Art

[0002] In the process of open-pit mining, efficient and accurate mineral transportation statistics are an essential part of ensuring mine production efficiency and safety management. Traditional mineral transportation statistics methods mainly rely on manual records, which is not only inefficient and error-prone, but also difficult to achieve comprehensive monitoring in large mines. In addition, methods based on single sensor signals are prone to misjudgment in scenarios with multiple machines and multiple vehicles, especially when vibration signals and magnetic signals are interfered with, making it difficult to accurately judge loading and unloading events. Therefore, the technical problem raised by the present invention is that the existing automated statistical methods are difficult to accurately judge loading and unloading events, resulting in the problem of low accuracy of statistical results. Summary of the invention

[0003] The present invention provides a mining area vehicle excavation operation statistics method, system, device and storage medium to solve the problem of low mining area vehicle excavation operation statistics efficiency and accuracy.

[0004] The technical solution of the present invention to solve the above technical problems is as follows:

[0005] On the one hand, a method for counting mining operations is provided, the steps of the method comprising:

[0006] The relative position between the excavator and the mining car is determined by the beacon signal, and the distance between the excavator and the mining car is calculated based on the position data of the relative position;

[0007] High-density vibration sensors are used to detect the periodic vibration signals generated by the excavator operation, and the SD periodic burr signals are extracted through signal processing algorithms to determine whether the excavator is loading.

[0008] When it is determined that the excavator is loading, the loading event is recorded and a loading record is generated;

[0009] During the driving process of the mine car, the magnetic sensor detects the change of magnetic force between the bucket and the vehicle body. When the magnetic force change exceeds the preset value, it is judged that the mine car has been unloaded.

[0010] Record unloading events and generate unloading records;

[0011] According to the loading and unloading records, the number of mineral transport trips and the number of excavator loadings are automatically counted, and the statistical results are sent to the management platform.

[0012] On the other hand, a mining area vehicle excavation operation statistics system is provided, the system comprising:

[0013] A beacon signal processing module is used to determine the relative position between the excavator and the mining car, and calculate the distance between the excavator and the mining car;

[0014] The vibration signal processing module is used to detect the periodic vibration signal generated during the excavator operation and extract the SD periodic burr signal through the signal processing algorithm to determine whether the excavator is loading;

[0015] The loading record module is used to record loading events and generate loading records;

[0016] The magnetic change detection module is used to detect the magnetic change between the bucket and the vehicle body during the driving of the mine car, and determine whether the mine car has been unloaded;

[0017] The unloading record module is used to record unloading events and generate unloading records;

[0018] The statistics module is used to automatically count the number of mineral transport trips and the number of excavator loadings based on the loading and unloading records, and send the statistical results to the management platform;

[0019] The image recognition module collects image data of the mine car in real time through the camera installed on the mine car, and determines whether the mine car has reached the unloading point through the image recognition algorithm;

[0020] GPS positioning module, used to collect the location information of excavators and mining vehicles in real time and calculate the driving path;

[0021] Environmental monitoring module, used to monitor working environment parameters in real time and determine whether the working environment is safe;

[0022] Wireless communication module, used to send loading records, unloading records and environmental parameters to the management platform;

[0023] The management platform module is used to receive data and generate visual reports for real-time monitoring and scheduling.

[0024] On the other hand, a mining area vehicle excavation operation statistics device is provided, the statistics device comprising:

[0025] A beacon signal processing device, including a beacon transmitter and receiver, and a processor for processing signal arrival time difference data to achieve accurate calculation of the relative position between the excavator and the mining car;

[0026] The vibration signal processing device includes a high-density vibration sensor and a signal processor, which extracts the SD periodic burr signal through Fourier transform and wavelet transform to determine whether the excavator is loading;

[0027] A loading recording device, including a data storage unit and a communication unit, for recording detailed information of loading events and sending the records to a management platform;

[0028] A magnetic force change detection device, including a magnetic force sensor and a processor, determines whether the mine car has been unloaded by detecting the magnetic force change between the bucket and the vehicle body;

[0029] A cargo unloading recording device, including a data storage unit and a communication unit, for recording detailed information of cargo unloading events and sending the records to a management platform;

[0030] A statistical device, including a data processing unit and a communication unit, is used to count the number of trips of minerals and the number of loadings of the excavator according to the loading and unloading records, and send the statistical results to the management platform;

[0031] The image recognition module, including a camera and an image processor, collects image data of the mine car in real time during its travel, and determines whether the mine car has reached the unloading point through an image recognition algorithm;

[0032] The GPS positioning device, including a GPS receiver and a processor, collects the location information of the excavator and the mining vehicle in real time and calculates the driving path;

[0033] The environmental monitoring device includes an environmental sensor and a processor, which monitors the working environment parameters in real time and generates an environmental abnormality alarm through change rate calculation and threshold judgment;

[0034] A wireless communication device, including a wireless transmitter and a receiver, for sending loading records, unloading records and environmental parameter data to a management platform;

[0035] A management platform device, including a data processing unit and a display unit, for receiving data and generating visual reports for real-time monitoring and scheduling;

[0036] A security management device, including a data processing unit and an alarm unit, for generating security warning information;

[0037] The data verification device includes a data processing unit and a verification unit, and is used to verify the integrity of the collected data.

[0038] On the other hand, a readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a computing device, the computing device executes the above-mentioned mining vehicle excavation operation statistics method.

[0039] The beneficial effects of the present invention are:

[0040] The present invention improves the accuracy of mining operation statistics by fusing multiple sensor signals. Specifically, the beacon signal enables the relative position between the excavator and the mine car to be accurately determined, thereby avoiding erroneous statistics caused by incorrect position judgment. Furthermore, the high-density vibration signal processing algorithm extracts the SD periodic burr signal in the vibration signal through Fourier transform and wavelet transform, which can accurately determine whether the excavator is in the loading state, and effectively reduces the misjudgment caused by vibration signal interference. The magnetic change detection module can accurately determine the unloading event of the mine car by detecting the magnetic change between the bucket and the vehicle body, further improving the accuracy of statistics. In addition, the present invention also introduces image recognition and GPS positioning technology to monitor the driving path and environmental parameters of the mine car in real time, ensuring that when multiple excavators and mine cars are operating at the same time, accurate monitoring and statistics of each mine car and excavator can still be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of a mining area vehicle excavation operation statistics method in one embodiment of the present invention;

[0042] Figure 2 A schematic diagram of a vibration signal and a magnetic signal in one embodiment of the present invention;

[0043] Figure 3 A schematic diagram of vibration signal processing in one embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of magnetic signal processing in one embodiment of the present invention;

[0045] Figure 5 A schematic diagram of the relative positions of an excavator and a mining vehicle in one embodiment of the present invention;

[0046] Figure 6 A schematic diagram of a spectrum analysis of a vibration signal in one embodiment of the present invention;

[0047] Figure 7 FIG. 4 is a schematic diagram of magnetic signal distribution in one embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention provides the following preferred embodiments:

[0049] Embodiment 1

[0050] In order to solve the problem in the prior art that in a complex environment where multiple excavators and multiple mining vehicles are operating simultaneously, the automated statistical method is difficult to accurately determine loading and unloading events, resulting in low reliability of statistical results, this embodiment proposes a mining vehicle excavation operation statistics method to improve the accuracy of mining vehicle excavation operation statistics.

[0051] like Figure 1As shown, the steps of the statistical method include:

[0052] S100, determining the relative position between the excavator and the mining car through the beacon signal, and calculating the distance between the excavator and the mining car according to the position data of the relative position.

[0053] S200, using a high-density vibration sensor to detect the periodic vibration signal generated by the excavator operation, and extracting the SD periodic burr signal through a signal processing algorithm to determine whether the excavator is loading.

[0054] S300: When it is determined that the excavator is loading, the loading event is recorded and a loading record is generated.

[0055] S400: During the driving of the mine car, the magnetic sensor is used to detect the change in magnetic force between the bucket and the vehicle body. When the magnetic force change exceeds a preset value, it is determined that the mine car has been unloaded.

[0056] S500: Record the unloading event and generate an unloading record.

[0057] S600, according to the loading and unloading records, automatically count the number of mineral transportation trips and the number of excavator loadings, and send the statistical results to the management platform.

[0058] Specifically, in open-pit mines, the relative position between the excavator and the mining vehicle is determined by beacon signals to achieve accurate statistics, such as Figure 5 As shown. In this embodiment, a beacon transmitter and a beacon receiver are installed on the excavator and the mine car, respectively. The beacon transmitter sends a signal once a second. The signal is a low-power Bluetooth (BLE) signal with a transmission distance of less than 100 meters, which is suitable for mining environments. After the beacon receiver receives the signal, the relative position between the excavator and the mine car can be determined by calculating the signal arrival time difference (TDOA) data. Specifically, the beacon receiver collects TDOA data of multiple beacon signals and uses the least squares method to calculate the position. The least squares method can effectively eliminate noise interference in the signal transmission process by minimizing the sum of squares of errors, thereby improving the accuracy of position calculation.

[0059] Furthermore, this embodiment uses a high-density vibration sensor to detect the periodic vibration signal generated by the excavator during operation to determine whether the excavator is loading a vehicle. The high-density vibration sensor is installed on the working arm of the excavator, and its sampling frequency is 1000Hz, which can capture the high-frequency vibration signal of the excavator during operation, such as Figure 2 After the vibration signal is collected, it is analyzed by signal processing algorithm, such as Figure 3First, the vibration signal is Fourier transformed to convert the time domain signal into a frequency domain signal and extract the frequency domain features of the vibration signal, as shown in Figure 6 As shown. Next, wavelet transform is applied to perform multi-scale analysis on the frequency domain features to identify SD periodic burr signals. It should be understood that SD periodic burr signals are unique vibration signals during excavator operation, and their frequency and amplitude have obvious periodic changes. By setting a threshold, when the amplitude change of the vibration signal exceeds the preset threshold, it can be judged that the excavator is in the loading state. The setting of the specific threshold can be determined by statistical analysis of historical data to determine a reasonable threshold range.

[0060] Furthermore, when it is determined that the excavator is loading, the present embodiment records the loading event and generates a loading record. The loading record includes detailed information such as loading time, excavator number, mine car number, loading location, etc. This information is sent to the management platform through the wireless communication module installed on the excavator and the mine car. The wireless communication module uses 4G or 5G network to ensure the real-time and reliability of data transmission. After receiving the loading record, the management platform updates the loading data in real time and generates a visual report to facilitate real-time monitoring and scheduling by management personnel.

[0061] Furthermore, during the driving process of the mine car, the present embodiment detects the magnetic force change between the bucket and the vehicle body through the magnetic sensor to determine whether the mine car has been unloaded. The magnetic sensor is installed at the connection between the bucket and the vehicle body, and can monitor the magnetic force change in real time, such as Figure 2 Shown and Figure 4 When the magnetic force change exceeds the preset value, it is determined that the mine car has been unloaded. The preset value can be set by measuring and statistically analyzing the magnetic force signal of the mine car at the unloading point multiple times to determine a reasonable threshold range, such as Figure 7 It should be understood that when the bucket is separated from the vehicle body, the reading of the magnetic sensor will change significantly, so the unloading event can be effectively judged by detecting the magnetic change.

[0062] Furthermore, when it is determined that the mine car has been unloaded, this embodiment records the unloading event and generates an unloading record. The unloading record includes detailed information such as unloading time, mine car number, unloading location, etc. This information is also sent to the management platform through the wireless communication module. After receiving the unloading record, the management platform updates the unloading data in real time and generates a visual report to facilitate real-time monitoring and scheduling by management personnel.

[0063] Furthermore, based on the loading and unloading records, this embodiment automatically counts the number of trips of the mineral and the number of loadings of the excavator, and sends the statistical results to the management platform. The statistical module calculates the number of trips of the mine car and the number of loadings of the excavator by analyzing the time and location information of the loading and unloading records. For example, when the mine car is loaded from the same excavator and drives to the unloading point to complete the unloading, and returns to the original excavator position, the statistical module regards it as a complete trip. Similarly, when the excavator is loaded multiple times and the mine car can successfully unload after each loading, the statistical module records the number of loadings of the excavator. It should be understood that this statistical method can effectively avoid statistical errors caused by the mine car stopping or multiple loadings, and ensure the accuracy and reliability of the statistical results.

[0064] Furthermore, the wireless communication module is not only used to send loading and unloading records to the management platform, but also to receive instructions from the management platform to achieve two-way data transmission. For example, when the management platform needs to adjust the operation plan, it can send instructions to the excavator and mining car through the wireless communication module to guide them to adjust the operation status. This not only improves the accuracy of statistics, but also enhances the real-time response capability of the system.

[0065] The benefit of this embodiment is that by utilizing beacon signals, high-density vibration signals, and magnetic change detection, the statistical accuracy of the number of mine car trips and the number of excavator loadings in complex operating environments can be effectively improved. The precise position calculation of the beacon signal avoids position judgment errors, the multi-scale analysis and threshold judgment of the high-density vibration signal effectively identify the loading status of the excavator, and the magnetic change detection ensures the accurate judgment of the unloading event. The combination of these technical features enables the present invention to achieve efficient and accurate automated statistics when multiple excavators and multiple mine cars are operating simultaneously.

[0066] Embodiment 2

[0067] In order to solve the problem in the prior art that in a complex environment where multiple excavators and multiple mining vehicles are operating simultaneously, the automated statistical method is difficult to accurately determine the relative position between the excavator and the mining vehicle, resulting in inaccurate position data and low reliability of statistical results, this embodiment further optimizes the method of determining the relative position between the excavator and the mining vehicle through beacon signals. Specifically, this embodiment realizes the accurate distance calculation between the excavator and the mining vehicle by installing a beacon transmitter and a receiver on the excavator and the mining vehicle respectively, using the Time Difference of Arrival (TDOA) data of the beacon signal, and combining the least squares method.

[0068] Specifically, in the working environment of the open-pit mine, beacon transmitters and beacon receivers are installed on the excavator and the mine car respectively. The beacon transmitter uses a low-power Bluetooth (BLE) transmitter with a transmission distance of less than 100 meters, which is suitable for short-distance communication needs in mines. The BLE transmitter sends a signal once a second, and the signal contains unique identification information to distinguish different excavators and mine cars. The beacon receiver is installed on the mine car and can receive signals from the excavator beacon transmitter. The receiver uses a high-performance BLE receiver with high sensitivity and low power consumption to ensure long-term stable operation in the mining environment.

[0069] Furthermore, after the beacon receiver receives the signal, it first records the arrival time of the signal. It should be understood that since the transmission speed of the beacon signal is close to the speed of light, the transmission time of the signal from the excavator to the mining car is very short, but the arrival time of each signal can be accurately recorded through high-precision clock synchronization. Specifically, high-precision clock modules are installed on the excavator and the mining car, and wireless synchronization technology is used to ensure that the clock synchronization error is at the microsecond level. After the receiver receives the beacon signal, it immediately records and stores the signal arrival time for subsequent calculations.

[0070] Furthermore, the arrival time difference data received by multiple beacon receivers is used to calculate the precise distance between the excavator and the mine car using the least squares method. Specifically, multiple beacon receivers are installed on each mine car, located at different positions such as the front, rear and bucket of the car. These receivers receive the signal from the same beacon transmitter at the same time and record the arrival time of each signal. The beacon receiver sends the recorded arrival time data to the central processing unit through the wireless communication module. After the central processing unit receives the arrival time data from multiple receivers, it uses the least squares method to calculate the position. The least squares method can effectively eliminate noise interference in the signal transmission process and improve the accuracy of position calculation by minimizing the sum of squares of errors.

[0071] Furthermore, the specific calculation steps are as follows:

[0072] 1. Record the signal arrival time t at each receiver ij , where i represents the i-th receiver on the minecart and j represents the j-th signal transmission.

[0073] 2. Calculate the average signal arrival time at each receiver

[0074]

[0075] Where n is the total number of times the signal is sent.

[0076] 3. Calculate the relative distance d between the excavator and the mining car using the average signal arrival time i:

[0077]

[0078] Where c is the speed of light (about 3×10^8 m / s), t 0 The base time for the excavator to send signals.

[0079] 4. Use the least squares method to calculate multiple relative distances d i Fitting is performed to calculate the exact distance D between the excavator and the mine cart:

[0080]

[0081] Where m is the number of receivers on the minecart, D i is the theoretical distance calculated through geometric relationship.

[0082] Furthermore, in order to improve the accuracy of position calculation, this embodiment also takes into account the multipath effect and interference problems during the transmission of beacon signals. The multipath effect may cause errors in the signal arrival time, thereby affecting the accuracy of distance calculation. To this end, this embodiment introduces a filter-based signal processing technology, such as a Kalman filter, to perform real-time filtering on the arrival time data of the beacon signal to further improve the reliability and accuracy of the data. It should be understood that the Kalman filter can effectively reduce the impact of multipath effect and noise interference by recursively estimating the arrival time of the signal.

[0083] Furthermore, in order to ensure stable communication between the excavator and the mining car, the present embodiment also optimizes the signal transmission protocol of the wireless communication module. Specifically, the wireless communication module adopts adaptive frequency hopping technology to dynamically adjust the transmission frequency according to the signal interference in the environment to reduce the impact of interference on signal transmission. In addition, the wireless communication module also supports multi-channel transmission, that is, sending signals simultaneously through multiple frequency channels at the same time, further improving the reliability of data transmission. It should be understood that the combination of adaptive frequency hopping technology and multi-channel transmission enables the present embodiment to still achieve stable data transmission in a complex mining environment, ensuring the accuracy and real-time performance of the statistical method.

[0084] Furthermore, in order to facilitate real-time monitoring and scheduling by management personnel, this embodiment also introduces a visual monitoring system. Specifically, the central processing unit sends the calculated precise location data between the excavator and the mine car to the management platform through the wireless communication module. After receiving the data, the management platform generates a real-time mine operation map, showing the precise location, operation status and operation trajectory of each excavator and mine car. Through this map, managers can monitor the operation of the mine in real time and adjust the operation plan in time. It should be understood that the introduction of the visual monitoring system not only improves the convenience of management, but also enhances the transparency and operability of the system.

[0085] The benefit of this embodiment is that by installing beacon transmitters and receivers on the excavator and the mine car respectively, and using the arrival time difference data of multiple receivers and the least squares method, the accurate distance between the excavator and the mine car is calculated. This not only solves the problem of inaccurate position judgment in the prior art, but also improves the reliability of the mining operation statistics method. Accurate distance calculation provides reliable basic data for subsequent vibration signal processing and magnetic change detection, ensures accurate judgment of loading and unloading events, and further improves the accuracy of statistical results.

[0086] Embodiment 3

[0087] In order to solve the problem that the high-density vibration sensor in the prior art is susceptible to interference and misjudgment when detecting the periodic vibration signal generated by the excavator operation in a complex mining environment, this embodiment further optimizes the method of using the high-density vibration sensor to detect the periodic vibration signal generated by the excavator operation and extracting the SD periodic burr signal through the signal processing algorithm. This embodiment specifically refines the steps of vibration data collection, Fourier transform, wavelet transform analysis and threshold setting to ensure that the loading status of the excavator can be accurately judged in a complex operating environment.

[0088] Specifically, an obvious periodic vibration signal is generated when the excavator is operating. In this embodiment, a high-density vibration sensor is installed on the working arm of the excavator, which can capture the high-frequency vibration signal when the excavator is operating. The vibration sensor is selected to have a high sampling frequency and high sensitivity, such as a vibration sensor of model MEMS-3000, which has a sampling frequency of 1000Hz and can effectively capture the vibration signal generated when the excavator is operating. This type of vibration sensor has the characteristics of low power consumption and high precision, and is suitable for the needs of long-term continuous monitoring.

[0089] Furthermore, when the excavator is operating, the vibration sensor collects high-density vibration data. It should be understood that high-density vibration data refers to a large amount of vibration signal data collected in a short period of time, which can more accurately reflect the vibration characteristics of the excavator during operation. The collected vibration data includes information such as timestamp, vibration amplitude and vibration frequency, which are transmitted to the central processing unit in real time through the wireless communication module. The central processing unit stores and pre-processes the received vibration data for subsequent analysis.

[0090] Furthermore, the central processing unit performs Fourier transform on the collected vibration data to extract frequency domain features. Fourier transform is a mathematical method that converts time domain signals into frequency domain signals, and can effectively extract frequency components in vibration signals. Specifically, the central processing unit processes the collected vibration data through a fast Fourier transform (FFT) algorithm, converts the time domain signal into a frequency domain signal, and obtains vibration amplitudes at different frequencies. It should be understood that the extraction of frequency domain features can help identify specific frequency components in vibration signals, thereby providing basic data for subsequent wavelet transform analysis.

[0091] Furthermore, wavelet transform is applied to perform multi-scale analysis on frequency domain features to identify SD periodic glitch signals. Wavelet transform is a multi-resolution analysis method that can effectively capture local features in the signal. Specifically, the central processing unit uses multi-scale wavelet transform to analyze frequency domain features and identify SD periodic glitch signals. SD periodic glitch signals are unique vibration signals during excavator operation, and their frequency and amplitude have obvious periodic changes. Wavelet transform decomposes frequency domain signals through wavelet basis functions of different scales, and can effectively extract these periodic glitch signals. It should be understood that multi-scale analysis can capture both high-frequency and low-frequency features in the signal, thereby improving the accuracy of signal recognition.

[0092] Furthermore, a first threshold is set to determine whether the excavator is in a loading state. The calculation formula of the first threshold is:

[0093]

[0094] Among them, X i is the amplitude of the ith vibration signal, μ is the mean of the vibration signal, σ is the standard deviation of the vibration signal, and N is the number of signal samples. Specifically, the central processing unit first calculates the mean and standard deviation of the vibration signal, and then calculates the first threshold value according to the above formula. It should be understood that the setting of the first threshold value takes into account the relative change of the signal amplitude, which can effectively distinguish the normal vibration generated during the operation of the excavator from the specific vibration generated during loading. When the amplitude change of the vibration signal exceeds the preset first threshold value, it is judged that the excavator is in the loading state.

[0095] Furthermore, in order to improve the accuracy of threshold setting, this embodiment also adopts a method of dynamic threshold adjustment. Specifically, the central processing unit dynamically adjusts the range of the first threshold by analyzing historical vibration data in real time. For example, when an increase in interference signals is detected in the mining environment, the upper limit of the first threshold is appropriately increased to reduce misjudgment. It should be understood that dynamic threshold adjustment can adapt to changes in interference in different time periods in the mining environment.

[0096] Furthermore, in order to further improve the processing accuracy of vibration data, this embodiment introduces a data preprocessing technology based on a Kalman filter. Specifically, before performing Fourier transform and wavelet transform, the central processing unit preprocesses the vibration data through a Kalman filter to remove noise and interference signals. The Kalman filter can effectively reduce noise interference in high-density vibration data and improve the accuracy of subsequent signal processing by recursively estimating the true value of the signal. It should be understood that the use of the Kalman filter further enhances the reliability and accuracy of the system.

[0097] Furthermore, in order to ensure the real-time and continuity of vibration data, the present embodiment optimizes the timing of data collection and transmission. Specifically, the high-density vibration sensor starts the collection mode when the excavator is operating, and automatically turns off when the excavator stops operating or enters low-power mode. This not only reduces the collection of invalid data, but also saves energy. At the same time, after receiving the vibration data, the wireless communication module immediately sends the data to the central processing unit to ensure the real-time nature of the data. It should be understood that this optimized design of collection and transmission enables the system to reduce energy consumption and improve the overall performance of the system while maintaining the real-time nature of the data.

[0098] The benefit of this embodiment is that by optimizing the installation position, sampling frequency and model selection of high-density vibration sensors, combined with the multi-scale analysis of Fourier transform and wavelet transform, as well as the data preprocessing technology of dynamic threshold adjustment and Kalman filter, efficient and accurate detection and processing of periodic vibration signals generated by excavator operations are achieved. This method not only solves the problem of susceptibility to interference and misjudgment in the prior art, but also improves the reliability of statistical methods in complex mining environments. Through this embodiment, mine managers can more accurately judge the loading status of the excavator, thereby achieving accurate statistics on the number of mine car trips and the number of excavator loadings, further improving production efficiency and management level.

[0099] Embodiment 4

[0100] In order to solve the problem that the recognition of SD periodic glitch signals in the prior art is not accurate enough and is easily affected by environmental interference and noise, this embodiment further optimizes the steps of identifying SD periodic glitch signals. Specifically, this embodiment extracts the high-frequency component of the vibration signal through wavelet transform, performs sliding window analysis on the high-frequency component, and calculates the difference between the maximum and minimum values ​​in each window to determine whether it is an SD periodic glitch signal. In addition, this embodiment further determines whether the excavator is in a continuous loading state by the difference in multiple consecutive windows.

[0101] Furthermore, the central processing unit performs wavelet transform on the collected vibration data to extract high-frequency components. Wavelet transform is a multi-resolution analysis method that can effectively capture local features in the signal. Specifically, the central processing unit processes the vibration signal using continuous wavelet transform (CWT) or discrete wavelet transform (DWT) to extract high-frequency components. It should be understood that the high-frequency components represent the rapidly changing parts of the vibration signal. These changing parts are usually periodic vibrations unique to the excavator operation, which are used to determine the loading status of the excavator.

[0102] Furthermore, a sliding window analysis is performed on the extracted high-frequency components. The window size is W and the window step size is S. Specifically, the central processing unit divides the extracted high-frequency components into multiple sliding windows, each window size is W, and the step size between windows is S. It should be understood that the sliding window analysis can process the signal in segments, thereby capturing the local changes in the signal in more detail. By analyzing the high-frequency components in each window, the SD periodic burr signal can be effectively identified.

[0103] Furthermore, the difference between the maximum value and the minimum value is calculated in each window, and when the difference exceeds a preset threshold, it is determined to be an SD periodic glitch signal.

[0104] Furthermore, the difference in multiple consecutive windows is used to determine whether the excavator is in a continuous loading state. The specific judgment formula is:

[0105]

[0106] Among them, X j is the vibration signal in the jth window, M is the number of windows, and when Δ exceeds the preset threshold, it is judged as a continuous loading state. The preset threshold can be set according to the operating characteristics of the excavator and the specific conditions of the mining environment. It should be understood that comprehensive judgment through the difference in multiple windows can more accurately identify the continuous loading state of the excavator and avoid misjudgment of a single window.

[0107] Furthermore, in order to improve the processing accuracy of vibration signals, this embodiment introduces a data preprocessing technology based on a Kalman filter. Specifically, before performing wavelet transform and sliding window analysis, the central processing unit preprocesses the vibration data through a Kalman filter to remove noise and interference signals. The Kalman filter can effectively reduce noise interference in high-density vibration data and improve the accuracy of subsequent signal processing by recursively estimating the true value of the signal. It should be understood that the use of the Kalman filter further enhances the reliability of the system.

[0108] Furthermore, in order to ensure the real-time and continuity of vibration data, the present embodiment optimizes the timing of data collection and transmission. Specifically, the high-density vibration sensor starts the collection mode when the excavator is operating, and automatically turns off when the excavator stops operating or enters low-power mode. This not only reduces the collection of invalid data, but also saves energy. At the same time, after receiving the vibration data, the wireless communication module immediately sends the data to the central processing unit to ensure the real-time nature of the data. It should be understood that this optimized design of collection and transmission enables the system to reduce energy consumption and improve the overall performance of the system while maintaining the real-time nature of the data.

[0109] The benefit of this embodiment is that by optimizing the extraction method of high-frequency components, sliding window analysis, and the method for determining the continuous loading state, efficient and accurate recognition of the periodic vibration signal generated by the excavator operation is achieved.

[0110] Embodiment 5

[0111] In order to solve the problem that the separation state between the bucket and the vehicle body is easily affected by environmental interference and misjudgment when the magnetic sensor is used to detect the separation state between the bucket and the vehicle body in the prior art, this embodiment further optimizes the method of detecting the magnetic change between the bucket and the vehicle body by using the magnetic sensor. Specifically, this embodiment installs a magnetic sensor between the bucket of the mine car and the vehicle body, collects magnetic data during the driving of the mine car, and determines whether the bucket is separated from the vehicle body by setting a second threshold.

[0112] Specifically, in the working environment of an open-pit mine, the mine car will encounter various complex situations during driving, including road bumps, changes in the environmental magnetic field, etc. These factors may cause the reading of the magnetic sensor to fluctuate, thereby affecting the judgment of the separation state. In this embodiment, multiple magnetic sensors are installed between the bucket of the mine car and the vehicle body. The magnetic sensor uses a high-sensitivity magnetic sensor model HMC5883L, which has the characteristics of low power consumption and high precision, and can work stably for a long time in a mining environment.

[0113] Furthermore, during the driving process of the mining car, the magnetic sensor collects magnetic data in real time. Specifically, the magnetic sensor records the magnetic changes between the bucket and the vehicle body at a sampling frequency of 100 Hz. The collected magnetic data includes information such as timestamp, magnetic strength and direction, which are transmitted to the central processing unit in real time through the wireless communication module. The central processing unit stores and pre-processes the received magnetic data for subsequent analysis.

[0114] Furthermore, a second threshold is set to determine whether the bucket is separated from the vehicle body. The calculation formula of the second threshold is:

[0115]

[0116] Among them, M j is the reading of the jth magnetic sensor, μ M is the mean of the magnetic sensor, σM is the standard deviation of the magnetic sensor, and K is the number of sensors. Specifically, the central processing unit first calculates the mean and standard deviation of all magnetic sensor readings, and then calculates the second threshold value according to the above formula. When the magnetic force change exceeds the preset second threshold value, it is judged that the bucket is separated from the vehicle body. It should be understood that this threshold setting method based on statistical analysis can effectively eliminate the influence of environmental interference and noise, and improve the accuracy of separation state judgment.

[0117] The benefit of this embodiment is that by optimizing the installation position, sampling frequency and model selection of the magnetic sensor, combined with the second threshold setting method and the data preprocessing technology of the Kalman filter, efficient and accurate detection of the separation state of the mine car bucket and the vehicle body is achieved.

[0118] Embodiment 6

[0119] In order to solve the problem of being easily disturbed by the environment and misjudged when judging the unloading status of the mine car by magnetic sensors in the prior art, this embodiment further optimizes the method of installing a camera on the mine car, collecting image data of the mine car in real time during its driving, and judging whether the mine car has reached the unloading point by image recognition algorithm. Specifically, this embodiment collects a reference image of the unloading point, compares the real-time image with the reference image, calculates the image similarity, and judges that the mine car has reached the unloading point when the similarity exceeds a preset threshold.

[0120] Specifically, in the operating environment of an open-pit mine, the mine car will encounter various complex situations during driving, including road bumps, environmental changes, etc. These factors may cause the readings of the magnetic sensor to fluctuate, thereby affecting the judgment of the unloading status. In this embodiment, a camera is installed on the mine car to collect image data in real time during the driving process of the mine car. The camera uses a high-resolution camera model OV5647, which has a resolution of 1080p and a frame rate of 30Hz, and can clearly capture image data during the driving process of the mine car in a mining environment.

[0121] Furthermore, when the excavator is operating, the camera collects image data in real time during the driving of the mine car. Specifically, the camera is installed at the front and rear of the mine car to ensure that the field of view information during the driving of the mine car can be fully captured. The collected image data includes information such as timestamp, image resolution and image content, which is transmitted to the central processing unit in real time through the wireless communication module. The central processing unit stores and pre-processes the received image data for subsequent analysis.

[0122] Furthermore, the central processing unit determines whether the mine car has reached the unloading point through an image recognition algorithm.

[0123] The specific steps include:

[0124] 1. Collect reference images of the unloading point. It should be understood that the reference image is a static image taken when the mine car is at the unloading point, which is used for comparison with the real-time image. The reference image can be taken at the unloading point by a pre-set camera and stored in the database of the central processing unit.

[0125] 2. Compare the real-time image with the reference image and calculate the image similarity. The specific similarity calculation formula is:

[0126]

[0127] Where I(x, y) is the pixel value of the real-time image, R(x, y) is the pixel value of the reference image, W is the image width, and H is the image height. The central processing unit calculates the pixel difference between the real-time image and the reference image through the above formula to obtain the similarity S. It should be understood that the similarity S represents the degree of match between the real-time image and the reference image. The smaller the value, the higher the degree of match.

[0128] 3. When the similarity exceeds the preset threshold, it is judged that the mine car has arrived at the unloading point. The preset threshold can be set according to the specific environment of the mine and the characteristics of the unloading point. For example, a reasonable threshold range can be determined by analyzing multiple historical data. When S is lower than the preset threshold, it is judged that the mine car has arrived at the unloading point. It should be understood that the image similarity judgment method can effectively eliminate the influence of environmental interference and noise, and improve the accuracy of unloading point judgment.

[0129] Furthermore, in order to improve the processing accuracy of image data, this embodiment introduces an image recognition algorithm based on machine learning. Specifically, the central processing unit uses a convolutional neural network (CNN) to extract and classify features of real-time images to determine whether the mine car has reached the unloading point. It should be understood that the convolutional neural network has a powerful image feature extraction capability and can more accurately identify the features of the unloading point. By training and optimizing the neural network model, the accuracy and reliability of image recognition are further improved.

[0130] Furthermore, in order to improve the adaptability of the system, this embodiment also introduces an environmental adaptive algorithm. Specifically, the central processing unit dynamically adjusts the parameters of the image recognition algorithm by analyzing environmental changes in real time. For example, when influencing factors such as changes in illumination and weather in the mining environment are detected, the parameters of the convolutional neural network are appropriately adjusted to improve the accuracy of recognition. It should be understood that the introduction of the environmental adaptive algorithm enables the system to maintain high-precision image recognition capabilities in a complex and changeable mining environment, further improving the stability and reliability of the system.

[0131] The benefit of this embodiment is that by optimizing the installation position, model selection and image recognition algorithm of the camera, combined with the calculation method of image similarity and environmental adaptation technology, the arrival status of the mine car unloading point can be judged efficiently and accurately. This method not only solves the problem of being susceptible to environmental interference and misjudgment in the prior art, but also improves the accuracy of statistical methods in complex mining environments.

[0132] Embodiment 7

[0133] In order to solve the problem in the prior art that the recording of loading and unloading events is not timely and accurate, resulting in low production efficiency and management difficulties, this embodiment further optimizes the steps of recording loading and unloading events, and sends the data to the management platform through the wireless communication module to generate visual reports for real-time monitoring and scheduling.

[0134] Specifically, in the working environment of an open-pit mine, the excavator and the mine car work closely together. In this embodiment, multiple high-density vibration sensors, magnetic sensors and cameras are installed on the excavator and the mine car to monitor the status of the excavator and the mine car in real time. The vibration sensor uses a high-precision sensor of model MEMS-3000, the magnetic sensor uses a high-sensitivity sensor of model HMC5883L, and the camera uses a high-resolution camera of model OV5647. The selection of these sensors and cameras is intended to ensure the high precision and real-time nature of the data, thereby improving the accuracy of event recording.

[0135] Furthermore, the step of recording the loading event includes recording the first data information of the loading time, the excavator number, the mine car number and the loading location. Specifically, when the excavator is in the loading state, the central processing unit will record the specific time of loading, the excavator number, the mine car number and the geographical coordinates of the loading location. It should be understood that the recording of this information not only helps production statistics, but also provides data support for subsequent management analysis. The central processing unit detects the loading state of the excavator through the vibration signal processing module, and once it is determined that the excavator is in the loading state, the corresponding data information is immediately recorded.

[0136] Furthermore, the first data information is sent to the management platform through the wireless communication module. Specifically, the wireless communication module uses a high-performance wireless communication module of model ESP32, which has the characteristics of low power consumption and high transmission rate. After receiving the data, the management platform updates the data of the loading record in real time. It should be understood that the use of the wireless communication module ensures the real-time and integrity of the data, so that the management personnel can obtain the loading information in time and carry out effective production scheduling.

[0137] Furthermore, a visual report is generated on the management platform for real-time monitoring and scheduling of loading events. Specifically, after receiving the loading record data, the management platform generates a visual report containing information such as the excavator number, mine car number, loading time, and loading location. The report displays the real-time location of the excavator and mine car in the form of a map, marking the time and location of the loading event. Managers can use this report to monitor the loading status of the mine car in real time and adjust the production plan in a timely manner. It should be understood that the generation of visual reports improves the convenience and transparency of management, allowing managers to make decisions quickly.

[0138] Furthermore, the step of recording the unloading event includes recording the second data information of the unloading time, the mine car number and the unloading location. When the mine car arrives at the unloading point and completes the unloading, the central processing unit detects the magnetic change between the bucket and the vehicle body through the magnetic change detection module to determine whether the mine car has unloaded. If the magnetic change exceeds the preset second threshold, the central processing unit will record the specific time of unloading, the mine car number and the geographical coordinates of the unloading location. It should be understood that the introduction of the magnetic change detection module improves the judgment accuracy of the unloading event and reduces the possibility of misjudgment.

[0139] Furthermore, the second data information is sent to the management platform through the wireless communication module. Specifically, the wireless communication module transmits the data of the unloading record to the management platform in real time, and after the management platform receives the data, it updates the data of the unloading record in real time. It should be understood that the use of the wireless communication module ensures the real-time and integrity of the data, so that the management personnel can obtain the unloading information in time and carry out effective production scheduling.

[0140] Furthermore, a visual report is generated on the management platform for real-time monitoring and scheduling of unloading events. Specifically, after receiving the unloading record data, the management platform generates a visual report containing information such as the mine car number, unloading time, and unloading location. The report displays the real-time location of the mine car in the form of a map, marking the time and location of the unloading event. Managers can use this report to monitor the unloading status of the mine car in real time and adjust the production plan in a timely manner. It should be understood that the generation of visual reports not only improves the convenience and transparency of management, but also enables managers to make decisions quickly.

[0141] The benefit of this embodiment is that by optimizing the recording method of loading and unloading events, combined with the real-time data transmission of the wireless communication module and the visual report generation of the management platform, efficient and accurate monitoring and recording of the status of the excavator and the mining car are achieved. The monitoring and scheduling efficiency of the management personnel is improved, and the production management of the mine is further optimized.

[0142] Embodiment 8

[0143] In order to solve the problem of insufficient coordination between various modules of the mining vehicle excavation operation statistics system in the prior art, resulting in low data processing and transmission efficiency, this embodiment proposes a mining vehicle excavation operation statistics system.

[0144] The statistical system in this embodiment includes the following functional modules.

[0145] Specifically, the beacon signal processing module is used to determine the relative position between the excavator and the mining car, and calculate the distance between the excavator and the mining car. Specifically, the beacon signal processing module includes a beacon transmitter and a receiver, and a processor for processing signal arrival time difference (TDOA) data. The beacon transmitter is installed on the excavator and the receiver is installed on the mining car. By calculating the signal arrival time difference, the processor can accurately calculate the distance between the excavator and the mining car. It should be understood that this beacon-based positioning method can provide real-time relative position information, which helps to accurately judge the coordination status of the excavator and the mining car.

[0146] Furthermore, the vibration signal processing module is used to detect the periodic vibration signal generated during the operation of the excavator, and extract the SD periodic burr signal through the signal processing algorithm to determine whether the excavator is loading. Specifically, the vibration signal processing module includes a high-density vibration sensor and a signal processor. The vibration sensor is installed on the working arm of the excavator and records vibration data at a sampling frequency of 1000 Hz. The signal processor processes the vibration data through Fourier transform and wavelet transform to extract the SD periodic burr signal. When the amplitude change of the vibration signal exceeds the preset first threshold, it is judged that the excavator is in the loading state. It should be understood that this detection method based on vibration signals can effectively identify the operating status of the excavator and improve the accuracy of event recording.

[0147] Furthermore, the loading record module is used to record loading events and generate loading records. Specifically, the loading record module includes a data storage unit and a communication unit. When the excavator is in the loading state, the data storage unit will record the specific time of loading, the excavator number, the mine car number and the geographical coordinates of the loading location. The communication unit transmits these data to the management platform in real time through the wireless communication module. After the management platform receives the data, it updates the data of the loading record in real time. It should be understood that the use of the loading record module ensures the timely recording of loading events and the integrity of the data, which is convenient for subsequent production statistics and management analysis.

[0148] Furthermore, the magnetic change detection module is used to detect the change in magnetic force between the bucket and the vehicle body during the travel of the mine car, and to determine whether the mine car has been unloaded. Specifically, the magnetic change detection module includes multiple magnetic sensors and a processor. The magnetic sensor is installed between the bucket and the vehicle body of the mine car, and records magnetic data at a sampling frequency of 100 Hz. The processor determines whether the bucket is separated from the vehicle body by calculating the mean and standard deviation of the magnetic change and setting a second threshold. When the magnetic change exceeds the preset second threshold, it is determined that the mine car has been unloaded. It should be understood that this detection method based on magnetic changes can effectively identify the unloading status of the mine car and improve the accuracy of event recording.

[0149] Furthermore, the unloading record module is used to record unloading events and generate unloading records. Specifically, the unloading record module includes a data storage unit and a communication unit. When the mine car completes unloading, the data storage unit will record the specific time of unloading, the number of the mine car and the geographical coordinates of the unloading location. The communication unit transmits these data to the management platform in real time through the wireless communication module. After the management platform receives the data, it updates the data of the unloading record in real time. It should be understood that the use of the unloading record module ensures the timely recording of unloading events and the integrity of the data, which is convenient for subsequent production statistics and management analysis.

[0150] Furthermore, the statistical module is used to automatically count the number of mineral transports and the number of excavators loaded according to the loading and unloading records, and send the statistical results to the management platform. Specifically, the statistical module includes a data processing unit and a communication unit. The data processing unit processes the received loading and unloading records to calculate the number of mineral transports and the number of excavators loaded. The statistical results are transmitted to the management platform in real time through the communication unit, and the management platform generates detailed statistical reports for production management and scheduling. It should be understood that the introduction of the statistical module makes the statistics of production data more automated and accurate, and improves the efficiency of management.

[0151] Furthermore, the image recognition module collects image data of the mine car in real time during its travel through a camera installed on the mine car, and determines whether the mine car has reached the unloading point through an image recognition algorithm. Specifically, the image recognition module includes a high-resolution camera and an image processor. The cameras are installed at the front and rear of the mine car and record image data at a frame rate of 30Hz. The image processor processes the image data through a convolutional neural network (CNN), extracts the features of the unloading point, and calculates the image similarity. When the similarity exceeds a preset threshold, it is determined that the mine car has reached the unloading point. It should be understood that the use of the image recognition module can further improve the accuracy of unloading point identification and reduce the possibility of misjudgment.

[0152] Furthermore, the GPS positioning module is used to collect the location information of the excavator and the mining car in real time and calculate the driving path. Specifically, the GPS positioning module includes a high-precision GPS receiver and a processor. The GPS receiver is installed on the excavator and the mining car to record their geographic coordinates in real time. The processor generates a driving trajectory map by calculating the driving path of the excavator and the mining car. It should be understood that the introduction of the GPS positioning module makes the location information of the excavator and the mining car more accurate, which is convenient for production scheduling and path optimization.

[0153] Furthermore, the environmental monitoring module is used to monitor the working environment parameters in real time and determine whether the working environment is safe. Specifically, the environmental monitoring module includes a variety of environmental sensors and processors. The environmental sensors can monitor parameters such as temperature, humidity, and air quality. The processor generates an environmental abnormality alarm through rate of change calculation and threshold judgment. It should be understood that the use of the environmental monitoring module improves the safety of the working environment and ensures the smooth progress of mining operations.

[0154] Through this embodiment, the various modules of the mining area vehicle excavation operation statistics system achieve efficient and coordinated work, ensuring the real-time and accuracy of data. This optimized system design not only improves the efficiency of production statistics, but also enhances the monitoring and scheduling capabilities of managers.

[0155] Embodiment 9

[0156] Based on the mining area vehicle excavation operation statistics system of the aforementioned embodiment, this embodiment further proposes a mining area vehicle excavation operation statistics device to ensure the accuracy of data analysis.

[0157] The mining area vehicle excavation operation statistics device in this embodiment includes a beacon signal processing device, a vibration signal processing device, a loading and unloading recording device, a magnetic change detection device, an unloading recording device, a statistics device, an image recognition device, a GPS positioning device, an environmental monitoring device, a wireless communication device, and a management platform device. These components transmit data and coordinate work through a wireless communication network.

[0158] It should be noted that the implementation method that is the same as the mining area vehicle excavation operation statistics system will not be described in detail here.

[0159] The difference between the mining area vehicle excavation operation statistics system and the mining area vehicle excavation operation statistics system is that the present embodiment also includes:

[0160] The wireless communication device is used to send loading records, unloading records and environmental parameter data to the management platform. Specifically, the wireless communication device includes a wireless transmitter and a receiver, which has the characteristics of low power consumption and high transmission rate. The central processing unit transmits the collected data to the management platform in real time through the wireless communication device to ensure the timeliness of the data. It should be understood that the use of the wireless communication device makes the data transmission between the components more efficient.

[0161] Furthermore, the management platform device is used to receive data and generate visual reports for real-time monitoring and scheduling. Specifically, the management platform device includes a data processing unit and a display unit. The data processing unit processes the received loading records, unloading records and environmental parameter data, and generates a visual report containing information such as the excavator number, mine car number, loading time, loading location, unloading time, and unloading location. The report displays the real-time location of the excavator and mine car in the form of a map, marking the time and location of the loading and unloading events. Managers can use the report to monitor the loading and unloading status of the mine car in real time and adjust the production plan in time. It should be understood that the generation of visual reports not only improves the convenience of management, but also enables managers to make decisions quickly.

[0162] Furthermore, the safety management device is used to generate safety warning information to improve the safety of the working environment. Specifically, the safety management device includes a data processing unit and an alarm unit. When the environmental monitoring device detects that the working environment parameters are abnormal, the safety management device will generate safety warning information and send it to the management personnel through the alarm unit. It should be understood that the introduction of the safety management device can timely discover potential risks in the working environment and improve the safety of mining operations.

[0163] Furthermore, the data verification device is used to verify the integrity of the collected data to ensure the accuracy and reliability of the data. Specifically, the data verification device includes a data processing unit and a verification unit. The verification unit performs integrity verification on the transmitted data by verifying redundant data and other methods. When incomplete or erroneous data is detected, the verification unit triggers a data retransmission mechanism to ensure the reliability of the data. Through this embodiment, efficient and coordinated work is achieved between the various components of the mining vehicle excavation operation statistics device, ensuring the real-time and accuracy of the data.

[0164] Embodiment 10

[0165] In order to solve the problem in the prior art that the implementation of the mining area vehicle excavation operation statistics method depends on hardware equipment and lacks flexibility and scalability, this embodiment further optimizes the implementation method of the mining area vehicle excavation operation statistics method, and stores computer programs in a readable storage medium so that the computing device can execute the method, thereby improving the flexibility and scalability of the system.

[0166] The readable storage medium in this embodiment is a non-volatile storage device capable of storing computer programs. Specifically, the readable storage medium may be a USB flash drive, an SD card, a solid-state hard drive, or other storage medium that complies with industrial standards. The computer program stored on the storage medium includes all logic and algorithms for implementing the mining vehicle excavation operation statistics method, and can be run on a computing device and perform related functions.

[0167] Furthermore, the computer program on the storage medium is designed as a modular structure, which is convenient for maintenance and expansion. Specifically, the computer program includes multiple submodules, each of which is responsible for processing a specific task, such as beacon signal processing, vibration signal processing, loading record generation, magnetic change detection, unloading record generation, data statistics, image recognition, GPS positioning, environmental monitoring, wireless communication and management platform data processing. The modular design makes the maintenance of the program more convenient, and it is also possible to add new functional modules when necessary to improve the scalability of the system.

[0168] Through this embodiment, the implementation method of the mining area vehicle excavation operation statistics method is not only more flexible and scalable, but also improves the efficiency of data processing and transmission, ensuring the security of the system. The benefit of this embodiment is that through modular design and multiple safety measures, the maintenance of the system is more convenient, while improving the accuracy of production data and the decision-making efficiency of managers.

Claims

1. A mining area vehicle excavation operation statistics method, characterized in that: The steps of the statistical method include: Determine the relative position between the excavator and the mining car through the beacon signal, and calculate the distance between the excavator and the mining car according to the position data of the relative position; A high-density vibration sensor is used to detect the periodic vibration signal generated by the excavator operation, and a SD periodic burr signal is extracted through a signal processing algorithm to determine whether the excavator is loading a vehicle; When it is determined that the excavator is loading, recording the loading event and generating a loading record; During the driving of the mine car, the magnetic sensor detects the change of magnetic force between the bucket and the vehicle body, and when the magnetic force change exceeds a preset value, it is determined that the mine car has been unloaded; Record unloading events and generate unloading records; According to the loading record and the unloading record, the number of mineral transportation trips and the number of loadings of the excavator are automatically counted, and the statistical results are sent to the management platform.

2. The mining area vehicle excavation operation statistics method according to claim 1, characterized in that: The step of determining the relative position between the excavator and the mining vehicle by means of a beacon signal comprises: Installing a beacon transmitter and a receiver on the excavator and the mining car respectively; The beacon transmitter sends a signal once per second, and the receiver calculates the signal arrival time difference after receiving the signal; The accurate distance between the excavator and the mining vehicle is calculated by using the least square method through the arrival time difference data of multiple receivers.

3. The mining area vehicle excavation operation statistics method according to claim 2, characterized in that: The steps of using a high-density vibration sensor to detect the periodic vibration signal generated by the excavator operation and extracting the SD periodic burr signal through a signal processing algorithm include: Collecting high-density vibration data of the excavator during operation; Performing Fourier transform on the vibration data to extract frequency domain features; Applying wavelet transform to perform multi-scale analysis on the frequency domain features to identify SD periodic burr signals; A first threshold is set to determine whether the excavator is in a loading state. The calculation formula of the first threshold is: Among them, X i is the amplitude of the i-th vibration signal, μ is the mean of the vibration signal, σ is the standard deviation of the vibration signal, and N is the number of signal samples.

4. The mining area vehicle excavation operation statistics method according to claim 3, characterized in that: The step of identifying the SD periodic glitch signal comprises: Extract high-frequency components of vibration signals through wavelet transform; Performing sliding window analysis on the high frequency component; The difference between the maximum value and the minimum value is calculated in each window. When the difference exceeds the preset threshold, it is judged as an SD periodic glitch signal. The difference in multiple consecutive windows is used to determine whether the excavator is in a continuous loading state. The judgment formula is: Among them, X j is the vibration signal in the jth window, M is the number of windows, and when Δ exceeds the preset threshold, it is judged as a continuous loading state.

5. The mining area vehicle excavation operation statistics method according to claim 1, characterized in that: The step of detecting the magnetic force change between the bucket and the vehicle body by means of a magnetic sensor comprises: Installing a magnetic sensor between the bucket and the vehicle body of the mining vehicle; Collecting magnetic data during the travel of the mine car; Set the second threshold to determine whether the bucket is separated from the vehicle body. The threshold calculation formula is: Among them, M j is the reading of the jth magnetic sensor, μ M is the mean of the magnetic sensor, σM is the standard deviation of the magnetic sensor, and K is the number of sensors.

6. The mining area vehicle excavation operation statistics method according to claim 1, characterized in that: The statistical method also includes the following steps: A camera is installed on the mine car to collect image data of the mine car in real time during its travel; The image recognition algorithm is used to determine whether the mine car has reached the unloading point. The image recognition algorithm includes: Collect reference images of the unloading point; Compare the real-time image with the reference image and calculate the image similarity. The similarity calculation formula is: Where I(x,y) is the pixel value of the real-time image, R(x,y) is the pixel value of the reference image, W is the image width, and H is the image height; When the similarity exceeds a preset threshold, it is determined that the mine car has arrived at the unloading point.

7. The mining area vehicle excavation operation statistics method according to claim 1, characterized in that: The steps of recording the loading event and generating the loading record include: Record the first data information of loading time, excavator number, mining car number and loading location; The first data information is sent to a management platform through a wireless communication module, and the management platform updates the data of the loading record in real time; Generate a visual report on the management platform for real-time monitoring and scheduling of the loading events; The steps of recording the unloading event and generating the unloading record include: Second data information recording unloading time, mine car number, and unloading location; The second data information is sent to the management platform through the wireless communication module, and the management platform updates the data of the unloading record in real time; A visual report is generated on the management platform for real-time monitoring and scheduling of the unloading events.

8. A mining area vehicle excavation operation statistics system, characterized in that: The system comprises: A beacon signal processing module is used to determine the relative position between the excavator and the mining car, and calculate the distance between the excavator and the mining car; The vibration signal processing module is used to detect the periodic vibration signal generated during the excavator operation and extract the SD periodic burr signal through the signal processing algorithm to determine whether the excavator is loading; The loading record module is used to record loading events and generate loading records; The magnetic change detection module is used to detect the magnetic change between the bucket and the vehicle body during the driving of the mine car, and determine whether the mine car has been unloaded; The unloading record module is used to record unloading events and generate unloading records; The statistics module is used to automatically count the number of mineral transport trips and the number of excavator loadings based on the loading and unloading records, and send the statistical results to the management platform; The image recognition module collects image data of the mine car in real time through the camera installed on the mine car, and determines whether the mine car has reached the unloading point through the image recognition algorithm; GPS positioning module, used to collect the location information of excavators and mining vehicles in real time and calculate the driving path; Environmental monitoring module, used to monitor working environment parameters in real time and determine whether the working environment is safe; Wireless communication module, used to send loading records, unloading records and environmental parameters to the management platform; The management platform module is used to receive data and generate visual reports for real-time monitoring and scheduling.

9. A mining area vehicle excavation operation statistics device, characterized in that: The statistical device comprises: A beacon signal processing device, including a beacon transmitter and receiver, and a processor for processing signal arrival time difference data to achieve accurate calculation of the relative position between the excavator and the mining car; The vibration signal processing device includes a high-density vibration sensor and a signal processor, which extracts the SD periodic burr signal through Fourier transform and wavelet transform to determine whether the excavator is loading; A loading recording device, including a data storage unit and a communication unit, for recording detailed information of loading events and sending the records to a management platform; A magnetic force change detection device, including a magnetic force sensor and a processor, determines whether the mine car has been unloaded by detecting the magnetic force change between the bucket and the vehicle body; A cargo unloading recording device, including a data storage unit and a communication unit, for recording detailed information of cargo unloading events and sending the records to a management platform; A statistical device, including a data processing unit and a communication unit, is used to count the number of trips of minerals and the number of loadings of the excavator according to the loading and unloading records, and send the statistical results to the management platform; The image recognition module, including a camera and an image processor, collects image data of the mine car in real time during its travel, and determines whether the mine car has reached the unloading point through an image recognition algorithm; The GPS positioning device, including a GPS receiver and a processor, collects the location information of the excavator and the mining vehicle in real time and calculates the driving path; The environmental monitoring device includes an environmental sensor and a processor, which monitors the working environment parameters in real time and generates an environmental abnormality alarm through change rate calculation and threshold judgment; A wireless communication device, including a wireless transmitter and a receiver, for sending loading records, unloading records and environmental parameter data to a management platform; A management platform device, including a data processing unit and a display unit, for receiving data and generating visual reports for real-time monitoring and scheduling; A security management device, including a data processing unit and an alarm unit, for generating security warning information; The data verification device includes a data processing unit and a verification unit, and is used to verify the integrity of the collected data.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a computing device, the computing device executes the mining vehicle excavation operation statistics method according to any one of claims 1 to 7.

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