A self-powered stress monitoring system and method for a hoisting bucket based on data inversion

Abstract

The application discloses a self-powered type improved skip stress monitoring system and method based on data inversion, adopts a finite element analysis method to obtain stress distribution of a key region of a skip under different working conditions, determines the key region to be monitored, uses a long short-term memory network to obtain transmission characteristics between different stress measuring points in the key region, and uses the long short-term memory network to predict overall stress data of each key region of the skip under different working conditions by taking stress data collected under different working conditions as input respectively, and according to vibration frequencies of the skip under different working conditions, the array type piezoelectric vibration energy collection module and the energy management module realize collection and management of vibration energy of the skip under different working conditions based on the piezoelectric effect, and the skip running speed sensing strategy based on voltage frequency is used to obtain the current running speed of the skip, and according to stress distribution characteristics of the skip under different working conditions, the periodic working and sleep modes of nodes at different monitoring positions are switched.

Description

Technical Field

[0001] This invention relates to a self-powered lifting skip stress monitoring system and method based on data inversion, belonging to the technical field of stress monitoring node deployment for lifting skips. Background Technology

[0002] As a crucial load-bearing component of mine hoisting systems, the hoisting skip operates under heavy loads in harsh conditions for extended periods, making it susceptible to damage such as support structure failure and deformation, which in turn affects the safety and efficiency of coal mine hoisting. The stress in the hoisting skip directly reflects its deformation and load, making it a key parameter for monitoring its health status. Therefore, it is essential to research methods for stress monitoring of the hoisting skip.

[0003] However, most existing methods for monitoring skip stress employ large-scale wireless sensor network nodes. This method has the following main problems: 1) Due to the large scale of the wireless sensor nodes, long-term additional maintenance by staff is required; 2) The working environment of skips is extremely harsh, making it difficult to use wired power for the nodes, requiring regular battery replacement, which is time-consuming and labor-intensive; 3) When the number of skip stress monitoring points reaches a certain level, the monitoring area will reach an oversaturated monitoring state, at which point the monitoring effect will no longer be significantly improved, and the monitoring efficiency will be reduced. Summary of the Invention

[0004] The technical problem to be solved by this invention is to overcome the defects of the prior art and provide a self-powered skip stress monitoring system and method based on data inversion. This system optimizes the number of skip sensors to achieve real-time acquisition of stress data under different operating conditions. Simultaneously, considering the vibration energy generated during skip operation, this invention utilizes an array-type piezoelectric vibration energy collection module and an energy management module to collect and manage the vibration energy of the skip under different operating conditions, thereby supplementing the energy of the stress monitoring nodes. Furthermore, based on the aforementioned energy management module and energy collection module, this invention discloses a skip operating speed sensing strategy based on voltage frequency, enabling real-time sensing of the skip's current speed. Based on the stress distribution characteristics of the skip under different operating conditions (different loads, speeds), it achieves autonomous switching between periodic working and dormant modes at different monitoring locations.

[0005] Prior to this invention, a self-powered lifting skip stress monitoring system based on data inversion is provided, comprising multiple stress monitoring nodes, an energy harvesting module, and an energy management module. The multiple stress monitoring nodes are installed on the skip, and the energy harvesting module is connected to the energy management module. The energy harvesting module includes a support frame, several piezoelectric vibrators, several piezoelectric cantilever beams, and several mass blocks of different weights. The skip, support frame, piezoelectric cantilever beams, and mass blocks are sequentially and fixedly connected. Piezoelectric vibrators are fixedly installed on each piezoelectric cantilever beam. The piezoelectric vibrators are connected to the energy management module, and the energy management module is connected to the stress monitoring nodes.

[0006] Prior to this, a method for monitoring stress in a self-powered lifting skip based on data inversion is provided, which utilizes the aforementioned system for monitoring stress in a self-powered lifting skip based on data inversion to perform the following:

[0007] Step A: Use the finite element analysis method to obtain the stress distribution of the key areas of the skip under different working conditions, and determine the key areas that need to be monitored. The different working conditions include unloaded descent, full-load acceleration-deceleration ascent, and full-load uniform ascent.

[0008] Step B involves inputting the stress data collected by the stress monitoring nodes under different operating conditions into a long short-term memory neural network for different stress measurement points in different areas of the skip, and predicting and outputting the overall stress data of each key area under different operating conditions of the skip.

[0009] Prioritize adjusting the status of the stress monitoring nodes at the corresponding locations to active or dormant based on the stress data collected from the skip under different operating conditions.

[0010] Preferably, step A includes:

[0011] Step A1: Determine the stress distribution pattern of the key structures in the skip under different connection methods. The key structures include the main hanging plate and the column.

[0012] Step A2: Perform static analysis on the skip under no-load conditions to obtain the stress distribution law caused by the skip's own weight during hoisting;

[0013] Step A3: Under acceleration-deceleration operating conditions, when the skip is fully loaded, analyze and obtain the stress distribution law of the skip under accelerated lifting and emergency braking conditions during normal operation.

[0014] Step A4: Under the condition of full load, generate stress cloud diagram and displacement cloud diagram of the skip working at a constant speed based on finite element simulation, analyze the stress distribution law when the skip is working at a constant speed, and obtain the stress and displacement distribution results when the skip is running at a constant speed under full load.

[0015] Step A5. Based on the stress distribution of each key structure of the skip under different operating conditions, determine the key areas of the skip that need to be monitored.

[0016] Preferably, in step A1, it includes:

[0017] A1.1. The main suspension plate bears the total weight of the skip and the coal it carries. Conduct a simulation analysis on the main suspension plate to determine the stress distribution law of the main suspension plate.

[0018] A1.2. Analyze the columns in the skip. Based on the characteristics of various different connection methods of the columns, determine the stress distribution law of the columns.

[0019] A1.3. Analyze the bolts in the skip to determine the stress distribution law at each bolt connection.

[0020] Preferably, in step A2, conduct a static analysis on the skip in the no-load state, calculate and obtain the stress distribution laws caused by the self-weight of the skip during hoisting, and obtain the stress and displacement distribution results of each key structure when the skip is no-load.

[0021] Preferably, in step A3, it includes:

[0022] A3.1. Determine the static load, dynamic load, impact load of coal on the skip, and impact load of acceleration during the acceleration and deceleration of the skip that the skip bears during operation.

[0023] A3.2. Based on the acceleration of the skip during hoisting and transportation not exceeding a1 and the acceleration during braking not exceeding a2, where a1 < a2, select various accelerations of different magnitudes between [a1, a2] for modeling analysis during the acceleration and deceleration of the fully-loaded skip, and obtain stress nephograms and displacement nephograms under multiple accelerations.

[0024] A3.3. Based on the stress nephograms and displacement nephograms under multiple accelerations, analyze the stress distribution law during the acceleration or deceleration operation of the fully-loaded skip under different accelerations, and obtain the stress and displacement distribution results during the acceleration and deceleration of the fully-loaded skip.

[0025] Preferably, in step A4, it includes:

[0026] A4.1. Based on the speed range of the skip during uniform motion under actual working conditions, determine the speed range v1 - v2 of the fully-loaded skip during uniform motion in the simulation software.

[0027] A4.2. Based on the speed range v1 - v2 of the skip during uniform motion, generate stress nephograms and displacement nephograms of the skip during uniform motion at different speeds, analyze the stress distribution law of the fully-loaded skip during uniform motion at different speeds, and obtain the stress and displacement distribution results during the fully-loaded uniform motion of the skip.

[0028] Preferred, step B includes:

[0029] Step B1: For different stress measurement points within the region, a relational model based on a data inversion algorithm is established using a long short-term memory network to obtain the transmission characteristics between different stress measurement points within the key region; stress monitoring nodes are deployed at some important locations within the key region of the skip, and stress monitoring nodes are evenly distributed within the region, resulting in various node deployment strategies for skip stress monitoring; important locations include the connection points of the key regions, the midpoints of the key regions, and the apexes of the key regions.

[0030] Step B2: Input the stress data acquired by the stress monitoring nodes under the node deployment strategy into the long short-term memory network to obtain the regional stress prediction results under each node deployment strategy; compare the stress prediction results under different node deployment strategies to determine the optimal node deployment strategy and determine the stress monitoring network of the trapezoid in the initial state.

[0031] Preferably, step B2 includes:

[0032] B2.1 Obtain the horizontal and vertical normal stresses of the node groups to be tested in all regions based on the stress monitoring network;

[0033] B2.2 Under various operating conditions of the skip, the horizontal and vertical normal stresses of the node groups to be measured in all regions are used as inputs to the long short-term memory neural network. The long short-term memory neural network outputs the predicted regional stress results under each node deployment strategy.

[0034] Preferably, the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described in any of the first aspects.

[0035] Preferably, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0036] The beneficial effects achieved by this invention are as follows:

[0037] 1) For different stress measurement points in the region, a relational model of the data inversion algorithm was established based on the Long Short-Term Memory (LSTM) network to obtain the transmission characteristics between different stress measurement points in the key region. Thus, the overall stress change in the key area of ​​the trapezoid was inverted by deploying a few key sensor nodes.

[0038] 2) To address the challenges of wired charging or frequent battery replacement in harsh underground working environments, this invention designs an array-type piezoelectric energy harvesting module and an energy management module, which can collect and manage vibration energy of the skip under different operating conditions to meet the energy consumption requirements of various loads on the skip.

[0039] 3) Based on the energy harvesting module and energy management module, a skip operation speed sensing strategy based on voltage frequency was designed to obtain the current operation speed of the skip in real time. On this basis, a node adaptive working mode switching strategy was proposed. According to the stress distribution characteristics of the skip under different operating conditions, the node at different monitoring positions can autonomously switch between periodic working and dormant modes, further reducing the energy consumption of the node. Attached Figure Description

[0040] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a schematic diagram of the steps of the present invention;

[0042] Figure 2 This is a schematic diagram of the energy harvesting module in this invention;

[0043] Figure 3 This is a schematic diagram of the energy management module structure in this invention;

[0044] Figure 4 This is a flowchart of the skip-bucket running speed sensing strategy based on voltage frequency in this invention;

[0045] Figure 5 This is a schematic diagram of the stress monitoring node deployment for the skip under conventional conditions in this invention;

[0046] Figure 6 This is a schematic diagram of the node deployment during the entire process of the skip operation in this invention;

[0047] Figure 7 This is a schematic diagram of the prediction model based on Long Short-Term Memory Network in this invention;

[0048] The attached diagram shows the following reference numerals: 1. Skip; 2. Support frame; 3. Piezoelectric vibrator; 4. Piezoelectric cantilever beam; 5. Mass block m1; 6. Mass block m2; 7. Mass block m3; 8. Winch house control box; 9. Wireless receiver box; 10. Head sheave; 11. Wireless transmitter box; 12. End reinforcing rib; 13. Column; 14. Main hanging plate; 15. First longitudinal beam; 16. Second longitudinal beam; 17. Bolt. Detailed Implementation

[0049] Example 1

[0050] See Figure 1 This application discloses a self-powered lifting skip stress monitoring system based on data inversion, including multiple stress monitoring nodes, an energy harvesting module, and an energy management module. The multiple stress monitoring nodes are installed on the skip 1, and the energy harvesting module is connected to the energy management module. The energy harvesting module includes a support 2, several piezoelectric vibrators 3, several piezoelectric cantilever beams 4, and several mass blocks of different weights. The skip 1, support 2, piezoelectric cantilever beams 4, and mass blocks are fixedly connected in sequence. Piezoelectric vibrators 3 are fixedly installed on each piezoelectric cantilever beam 4. The piezoelectric vibrators 3 are connected to the energy management module, and the energy management module is connected to the stress monitoring nodes.

[0051] Prior to this, a method for monitoring stress in a self-powered lifting skip based on data inversion is provided, which utilizes the aforementioned system for monitoring stress in a self-powered lifting skip based on data inversion to perform the following:

[0052] Step A: Use the finite element analysis method to obtain the stress distribution of the key area of ​​the skip 1 under different working conditions, and determine the key area that needs to be monitored. The different working conditions include unloaded descent, full-load acceleration-deceleration ascent, and full-load uniform ascent.

[0053] Step B involves inputting the stress data collected by the stress monitoring nodes under different operating conditions into a long short-term memory neural network for different stress test points in different areas of skip 1, and predicting and outputting the overall stress data of each key area under different operating conditions of skip 1.

[0054] Prioritize adjusting the status of the stress monitoring nodes at the corresponding locations to either active or dormant based on the stress data collected from the stress monitoring nodes under different operating conditions of the skip 1 and the predicted overall stress data for each key area of ​​the skip 1.

[0055] Preferably, step A includes:

[0056] Step A1: Determine the stress distribution law of the key structure in the skip 1 under different connection methods. The key structure includes the main hanging plate and the column.

[0057] Step A2: Perform static analysis on skip 1 under no-load conditions to obtain the stress distribution law caused by the weight of skip 1 itself during hoisting;

[0058] Step A3: When skip 1 is fully loaded under acceleration-deceleration operation conditions, analyze the stress distribution law of skip 1 under acceleration lifting and emergency braking conditions during normal operation.

[0059] Step A4: Under the condition that the skip 1 is fully loaded, generate the stress nephogram and displacement nephogram of the skip 1 under uniform motion based on finite element simulation, analyze the stress distribution law of the skip 1 during uniform motion, and obtain the stress and displacement distribution results of the skip 1 during full-load uniform operation;

[0060] Step A5: Based on the stress distribution of each key structure of the skip 1 under different operating conditions, determine the key areas of the skip 1 that need to be monitored.

[0061] Preferably, in step A1, it includes:

[0062] A1.1: The main hanging plate bears the total weight of the skip 1 and the coal carried. Conduct a simulation analysis on the main hanging plate to determine the stress distribution law of the main hanging plate;

[0063] A1.2: Analyze the columns in the skip 1. Based on the characteristics of various different connection methods of the columns, determine the stress distribution law of the columns;

[0064] A1.3: Analyze the bolts in the skip 1 to determine the stress distribution law at each bolt connection.

[0065] Preferably, in step A2, conduct a static analysis on the skip 1 under the no-load state, calculate and obtain the stress distribution laws of each part caused by the self-weight of the skip 1 during hoisting, and obtain the stress and displacement distribution results of each key structure of the skip 1 under no-load.

[0066] Preferably, in step A3, it includes:

[0067] A3.1: Determine the static load, dynamic load, impact load of coal on the skip 1, and impact load of acceleration during acceleration and deceleration of the skip 1 that the skip 1 bears during operation;

[0068] A3.2: Based on the condition that the acceleration of the skip 1 during hoisting and transportation shall not exceed a1 and the acceleration of the skip 1 during braking shall not exceed a2, where a1 < a2, select various accelerations of different magnitudes within [a1, a2] for modeling analysis during full-load acceleration of the skip 1, and obtain the stress nephogram and displacement nephogram under multiple accelerations;

[0069] A3.3: Based on the stress nephogram and displacement nephogram under multiple accelerations, analyze the stress distribution law of the full-load skip 1 during acceleration or deceleration operation under different accelerations, and obtain the stress and displacement distribution results of the full-load skip 1 during acceleration and deceleration.

[0070] Preferably, in step A4, it includes:

[0071] A4.1: Based on the speed range of the skip 1 during uniform motion under actual working conditions, determine the speed range v1 - v2 of the skip 1 during full-load uniform motion in the simulation software;

[0072] A4.2 Based on the speed range v1-v2 of the skip 1 during uniform motion, generate multiple sets of stress cloud diagrams and displacement cloud diagrams of the skip 1 during uniform motion at different speeds, analyze the stress distribution law of the skip 1 under full load during uniform motion at different speeds, and obtain the stress and displacement distribution results of the skip 1 during uniform motion under full load.

[0073] Preferred, step B includes:

[0074] Step B1: For different stress test points within the region, a relational model based on a data inversion algorithm is established using a long short-term memory network to obtain the transmission characteristics between different stress test points within the key region; stress monitoring nodes are deployed at some important locations within the key region of the skip 1, and stress monitoring nodes are evenly distributed within the region, resulting in various node deployment strategies for stress monitoring of the skip 1; important locations include the connection points of the key regions, the midpoints of the key regions, and the apexes of the key regions.

[0075] Step B2: Input the stress data acquired by the stress monitoring nodes under the node deployment strategy into the long short-term memory network to obtain the regional stress prediction results under each node deployment strategy; compare the stress prediction results under different node deployment strategies to determine the optimal node deployment strategy and determine the stress monitoring network of the bucket 1 in the initial state.

[0076] Preferably, step B2 includes:

[0077] B2.1 Obtain the horizontal and vertical normal stresses of the node groups to be tested in all regions based on the stress monitoring network;

[0078] B2.2 Under various operating conditions of the skip 1, the horizontal and vertical normal stresses of the node groups to be measured in all regions are used as inputs to the long short-term memory neural network. The long short-term memory neural network outputs the predicted regional stress results under each node deployment strategy.

[0079] In this embodiment of the application, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the methods described above.

[0080] In this application embodiment, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0081] Example 2

[0082] The first aspect of this invention discloses a self-powered lifting skip stress monitoring system based on data inversion, which includes an energy harvesting module, an energy management module, and a node module.

[0083] like Figure 2 As shown, the energy harvesting module triggers energy harvesting by vibrating the skip 1 to obtain periodic piezoelectric signals. It is characterized by comprising a support 2, a piezoelectric vibrator 3, and a suspended piezoelectric assembly. The suspended piezoelectric assembly includes a piezoelectric cantilever beam 4, one end of which is fixed to the support 2, while the other end is suspended. The suspended end of the piezoelectric cantilever beam 4 is equipped with mass blocks of different weights, forming an array of piezoelectric energy harvesting devices. When the skip 1 is lifted, the skip 1 vibrates at different frequencies. Based on the piezoelectric effect, the array of piezoelectric energy harvesting devices can convert vibration energy into electrical energy to supplement the energy of the stress monitoring nodes. The support 2 is arranged on the skip 1, and the piezoelectric cantilever beam 4 remains suspended throughout the lifting process of the skip 1.

[0084] As a further optimization of the present invention, such as Figure 3-4 As shown, the energy management module uses the LTC3331 chip to rectify, filter, boost / buck, and process the electrical signals collected from the array piezoelectric energy harvesting device. It stores excess energy in the node battery and can sense the current operating speed of the boosting bucket 1 by periodically monitoring the frequency of the generator voltage of the array piezoelectric energy harvesting device.

[0085] Specifically, when the collected energy is at a high voltage, it is stepped down to power the node, while excess energy is stored in the node battery; when the collected energy is at a low voltage, it automatically switches to lithium battery to power the load node.

[0086] Specifically, the simple sinusoidal voltage generated by the piezoelectric vibrator is first converted into a square wave voltage using a comparator of a microprocessor; then the high and low pulse periods of the square wave voltage per unit time are detected; finally, the frequency of the skip subjected to the piezoelectric vibrator is calculated; since the excitation frequency of the skip vibration is related to the running speed of the skip, the frequency of the voltage generated by the piezoelectric vibrator is related to the running speed of the skip, thereby obtaining the current running speed of the skip.

[0087] Specifically, part of the AC signal generated by the energy harvesting device enters the energy management module, mainly used to power the wireless sensor nodes, microprocessors, and signal preprocessing; the other part is converted into a square wave signal that can be detected by the microprocessor through signal preprocessing. That is, the frequency value f of the current AC signal is obtained by detecting the period of high and low pulses per unit time. Then, according to the relationship between the power generation frequency and the running speed of the skip, the running speed v of the skip is obtained, which is expressed as: v = k × f, where k is the conversion coefficient of the device.

[0088] Using the LTC3331 chip, the collected electrical signals are rectified, filtered, boosted / buckled, and other processed to store excess energy in the node battery. The current operating speed of the lifting skip can be sensed by periodically monitoring the frequency of the piezoelectric vibrator's generated voltage.

[0089] As a further optimization of the present invention, the node module includes: Step A, using finite element analysis to obtain the stress distribution of the key area of ​​the skip 1 under different working conditions, and determining the key area that needs to be monitored; the different working conditions include unloaded descent, full-load acceleration-deceleration ascent and full-load uniform speed ascent;

[0090] Step B involves establishing a relational model based on a data inversion algorithm using a long short-term memory network for different stress measurement points within the region, in order to obtain the transmission characteristics between different stress measurement points within the key region; determining the deployment strategy of sensors within the key region based on the transmission characteristics; inputting the dataset including stress data collected from stress monitoring nodes under different operating conditions into the long short-term memory neural network, and using the long short-term memory neural network to predict the overall stress data of each key region under different operating conditions of the skip 1, so as to achieve full-process stress data prediction within the key region.

[0091] The node adaptive working mode switching strategy includes: based on the dataset of stress data of stress monitoring nodes under different operating conditions of the skip 1, adjusting the state of the stress monitoring nodes at the corresponding positions to working or dormant, realizing the autonomous switching of periodic working and dormant modes of stress monitoring nodes at different monitoring positions, and further reducing the energy consumption of stress monitoring nodes.

[0092] A specific model of 50t skip with unbalanced loading was selected, and a stress monitoring method for self-powered lifting skip based on data inversion was used to deploy detection nodes in the key areas of the skip.

[0093] like Figure 1 As shown, a stress monitoring method for self-powered hoisting skips based on data inversion is presented. This method includes:

[0094] A. Use the finite element analysis method to obtain the stress distribution in the key areas of skip 1 under different working conditions, and determine the key areas that need to be monitored; different working conditions include unloaded descent, full-load acceleration-deceleration ascent, and full-load uniform ascent.

[0095] B. For different stress measurement points in the region, a relational model based on the data inversion algorithm is established using a long short-term memory network to obtain the transmission characteristics between different stress measurement points in the key region; the deployment strategy of sensors in the key region is determined based on the transmission characteristics; the dataset including stress data of stress monitoring nodes under different operating conditions is input into the long short-term memory neural network, and the long short-term memory neural network is used to predict the overall stress data of each key region under different operating conditions of the skip 1, so as to realize the prediction of stress data throughout the entire process in the key region.

[0096] C. Based on the vibration frequency f of skip 1 under different operating conditions i (f) i (∈f1, f2 and f3, where f1, f2 and f3 represent the vibration frequency under no-load, the vibration frequency under full-load acceleration-deceleration and the vibration frequency under full-load uniform speed, respectively), an array-type piezoelectric vibration energy harvesting module and an energy management module were designed to collect and manage the vibration energy of the skip 1 under different working conditions based on the piezoelectric effect.

[0097] D. Based on the energy harvesting module and energy management module, the current operating speed of the skip 1 is obtained using a voltage frequency-based speed sensing strategy. The node adaptive operating mode switching strategy includes: adjusting the state of the stress monitoring nodes at corresponding locations to either working or dormant based on the dataset of stress data collected from the stress monitoring nodes under different operating conditions of the skip 1, thereby achieving autonomous switching between periodic working and dormant modes of the stress monitoring nodes at different monitoring locations, further reducing the energy consumption of the stress monitoring nodes. Based on the stress distribution characteristics of the stress data collected from the stress monitoring nodes under different operating conditions of the skip 1, the autonomous switching between periodic working and dormant modes of the stress monitoring nodes at different monitoring locations is achieved, further reducing the energy consumption of the stress monitoring nodes.

[0098] Step A includes:

[0099] A1. Determine the stress distribution law of the key structure in skip 1 under different connection methods. The key structure includes the main hanging plate and the column.

[0100] A2. Perform static analysis on skip 1 under no-load conditions, that is, use simulation software to simulate and calculate the stress distribution law caused by the weight of skip 1 itself during hoisting.

[0101] A3. When skip 1 is actually working, it goes through three stages: acceleration, constant speed and deceleration. First, when skip 1 is fully loaded under acceleration-deceleration operation, the stress distribution law of skip 1 under acceleration lifting and emergency braking conditions during normal operation is analyzed.

[0102] A4. Under the condition of full load of skip 1, generate stress cloud diagram and displacement cloud diagram of it under uniform speed operation based on finite element simulation, analyze the stress distribution law of skip 1 under uniform speed operation, and obtain the stress and displacement distribution results of skip 1 under full load and uniform speed operation.

[0103] A5. Based on the stress distribution of each key structure under different operating conditions of skip 1, determine the key areas of skip 1 that need to be monitored.

[0104] Furthermore, in step A, taking a certain model of 50-ton skip with opposite-side loading as an example, stress cloud diagrams of its key structures are obtained based on finite element modeling analysis. The stress distribution law of key structures in large skips under various connection methods is analyzed. The key structures mainly include the main lifting plate 14, the column 13, etc., as detailed below:

[0105] A1.1 The main lifting plate 14 bears the entire weight of the skip body and the coal it carries. First, a simulation analysis is performed on the main lifting plate 14 to determine the stress distribution law of the main lifting plate.

[0106] A1.2 Analyze the column 13 in the bucket, and determine the stress distribution law of each structure based on the characteristics of the various connection methods of the column 13;

[0107] A1.3 Finally, the commonly used bolts 17 in the skip are analyzed to determine the stress distribution pattern at each bolt connection in the skip.

[0108] Analysis shows that the maximum stress concentration of the skip is located on the upper sides of the main suspension plate 14 in the upper plate suspension device and on the column 13 in the skip chassis. The stress distribution of the bolt group 17 is relatively large, and it mostly occurs at the root of the bolt 17.

[0109] Furthermore, in step A2, a static analysis of the skip under no-load conditions is performed, that is, the stress distribution law caused by the skip's own weight during hoisting is simulated and calculated using simulation software, and the stress and displacement distribution results of each key structure of the skip under no-load conditions are obtained.

[0110] The stress range of the skip under no-load conditions is 3.924 × 10⁻⁶. -4 The maximum stress is ~141.99 MPa, and the maximum stress occurs in the upper suspension device.

[0111] Further, in step A3, when the skip actually works, it experiences three stages, namely acceleration, constant speed, and deceleration. First, considering the acceleration-deceleration operating conditions, when the skip is fully loaded, the stress distribution laws during normal acceleration hoisting and emergency braking are analyzed respectively to obtain the stress and displacement distribution results of the fully loaded skip during acceleration-deceleration, as follows:

[0112] A3.1. Determine the static load, dynamic load, impact load of coal on the skip 1, and impact load of the acceleration of the skip 1 during acceleration-deceleration

[0113] A3.2. Based on the fact that the acceleration of the skip 1 during hoisting and transportation shall not exceed a1 and the acceleration of the skip 1 during braking shall not exceed a2, where a1 < a2, between the above two acceleration thresholds, multiple accelerations of different magnitudes are selected for modeling analysis of the fully loaded skip 1 during acceleration to obtain stress nephograms and displacement nephograms under multiple accelerations

[0114] A3.3. Based on the stress nephograms and displacement nephograms under multiple accelerations, analyze the stress distribution laws of the fully loaded skip 1 during acceleration or deceleration under different accelerations to obtain the stress and displacement distribution results of the fully loaded skip 1 during acceleration-deceleration

[0115] Among them, based on different accelerations, the statistical results of the stress analysis of multiple finite element analyses of the skip are as follows:

[0116] The overall stress distribution of the skip mainly concentrates on the upper suspension device (main hanging plate 14, first longitudinal beam 15, and second longitudinal beam 16), the middle and lower parts of the skip box, especially the lower half of the lower box of the skip is particularly prominent; the maximum stress position appears at the right end of the stiffener 12 at the front end of the box body; the displacement of the upper half of the skip body is small, and the displacement of the lower box of the skip is significant; the maximum displacement appears below the stiffener 12 at the lower end of the skip box, about one-third of the distance from the unloading opening of the box body

[0117] Further, in step A4, under the condition that the skip 1 is fully loaded, based on finite element simulation, stress nephograms and displacement nephograms of its operation at a constant speed are generated, and the stress distribution law of the skip during constant-speed operation is analyzed to obtain the stress and displacement distribution results of the fully loaded skip during constant-speed operation, as follows:

[0118] A4.1. Based on the speed range of the skip during constant-speed operation under actual working conditions, determine the speed range of 5 m / s - 15 m / s for the fully loaded skip during constant-speed movement in the simulation software

[0119] A4.2. Based on the speed range of 5 m / s - 15 m / s for the skip during constant-speed movement, generate stress nephograms and displacement nephograms of the skip during constant-speed operation at different speeds, analyze the stress distribution law of the fully loaded skip during constant-speed movement under different speeds, and obtain the stress and displacement distribution results of the fully loaded skip during constant-speed operation

[0120] Among them, the finite element stress analysis results of multiple skips under full load and uniform speed show that the maximum stress of the skip is concentrated on the main hanging plate 14 in the upper plate suspension device, and the maximum deformation is mostly distributed in the middle and lower middle area of ​​the lower box of the skip.

[0121] Furthermore, in step A5, based on the stress distribution of each key part under different working conditions of the skip, the key areas that need to be monitored are determined in order to achieve real-time monitoring of the stress in the key areas.

[0122] In the unloaded state, the stress is concentrated on the upper suspension device of the skip (main suspension plate 14 and first longitudinal beam 15 and second longitudinal beam 16). Therefore, the upper suspension device is the key monitoring area S1 under this working condition.

[0123] When the skip is fully loaded and moving at a constant speed, the maximum stress of the skip is concentrated in the upper middle part of the upper plate suspension device, the first longitudinal beam 15 and the second longitudinal beam 16 on both sides connected to the main hanging plate 14, and the column 13 in the skip chassis, namely regions S1, S2 and S3. The maximum deformation is mostly distributed in the middle of the lower middle part of the skip body, namely region S4.

[0124] When the skip is fully loaded and accelerates and decelerates, the maximum stress is concentrated at the right end of the reinforcing rib 12 at the front end of the box, i.e., region S5; the maximum deformation mostly occurs below the reinforcing rib 12 at the lower end of the skip, about one-third of the distance from the unloading port of the box, i.e., region S6.

[0125] Furthermore, in step B, the specifics are as follows:

[0126] B1. For different stress measurement points within the region, a relational model based on a data inversion algorithm is established using a long short-term memory network to obtain the transmission characteristics between different stress measurement points within the key region. Stress monitoring nodes are deployed at some important locations within the key region of the skip 1, with the nodes evenly arranged at intervals of d, 2d, 3d, 4d, ..., to determine various node deployment strategies for stress monitoring of skip 1. Important locations include the connection points of the key region, the midpoint of the key region, and the apex of the key region.

[0127] B2. Input the stress data obtained by the stress monitoring nodes under the node deployment strategy into the long short-term memory network to obtain the regional stress prediction results under each node deployment strategy; compare the stress prediction results under different node deployment strategies to determine the optimal node deployment strategy and determine the stress monitoring network of the bucket 1 in the initial state.

[0128] The skip is simplified into a cuboid spatial deployment model. Stress monitoring node one and stress monitoring node two are arranged on the central axis of the lower end face of the cuboid, with an average straight-line distance d1 between them. These serve as two monitoring node groups on the first longitudinal beam 15 and the second longitudinal beam 16 connected to the main hanging plate 14, enabling stress monitoring of the key monitoring area S2. Stress monitoring node three is arranged on the central axis of the left side face of the cuboid at a distance d2 from the lower end face. Stress monitoring node four is arranged on the central axis of the right side face of the cuboid at a corresponding position to stress monitoring node three, serving as two monitoring node groups on the symmetrical columns 13 in the skip, enabling stress monitoring of the key monitoring area S3. Stress monitoring node five is arranged on the central axis of the front view face of the cuboid at a distance d4 from the upper end face, enabling stress monitoring of the key monitoring area S4. Stress monitoring node six is ​​arranged on the central axis of the front view face of the cuboid at a distance d3 from the lower end face. Stress monitoring is implemented in key monitoring areas S5 and S6. Stress monitoring node seven is located at the intersection of the central axis and the top face of the cuboid's front view to monitor stress in area S1. It should be noted that, for ease of representation, only one node is placed in the key monitoring area; this node can represent the node group within that area.

[0129] As a further optimization of the present invention, the specific implementation steps in step B2 are as follows:

[0130] B2.1 Obtain the horizontal and vertical normal stresses of the node groups to be tested in all regions based on the stress monitoring network;

[0131] B2.2 Under various operating conditions of the skip 1, the horizontal and vertical normal stresses of the node groups to be measured in all regions are used as inputs to a Long Short-Term Memory (LSTM) neural network. The network sequentially passes through an LSTM layer, an attention mechanism, and a fully connected layer to obtain the regional stress prediction results for each node deployment strategy. Specifically, first, an LSTM layer is used to fully utilize the temporal characteristics of each region. Then, an attention mechanism is used to extract the input features that play a key role in stress data prediction. Finally, a fully connected layer is used to output the prediction results. This iterative process is repeated to achieve real-time prediction of complete stress data within key monitoring regions 1, 2, ..., m.

[0132] B2.3 During the fully loaded and accelerated (decelerated) ascent of the skip, the normal stress datasets in the horizontal and vertical directions of the stress monitoring node group acquired in regions S1, S2, S3, S5, S6, and S7 are used as input to a Long Short-Term Memory (LSTM) neural network. The network sequentially passes through an LSTM, an attention mechanism, and a fully connected layer. First, an LSTM layer is used to fully utilize the temporal characteristics of each region. Then, an attention mechanism is used to extract the input features that play a crucial role in stress data prediction. Finally, a fully connected layer outputs the prediction results. This iterative process is repeated to achieve real-time prediction of complete stress data within the key monitoring regions S1, S2, S3, S5, S6, and S7.

[0133] B2.4. When the bucket rises at a constant speed, the normal stress datasets of the node groups to be measured in the horizontal and vertical directions, obtained from regions S1, S2, S3, S4, and S7, are used as input to a Long Short-Term Memory (LSTM) neural network. The network sequentially passes through an LSTM, an attention mechanism, and a fully connected layer. First, an LSTM layer is used to fully utilize the temporal characteristics of each region. Then, an attention mechanism is used to extract input features that play a crucial role in stress data prediction. Finally, a fully connected layer outputs the prediction result. This iterative process is repeated to achieve real-time prediction of complete stress data within the key monitoring regions S1, S2, S3, S4, and S7.

[0134] B2.5 Based on the above steps B2.2-B2.4, output the stress distribution results of the entire process in the key monitoring area of ​​the skip.

[0135] Furthermore, in step C, based on the vibration frequency f of the skip under different working conditions... i (f1, f2 and f3 represent the vibration frequencies under no-load, full-load acceleration-deceleration and full-load constant speed conditions, respectively). An array-type piezoelectric vibration energy harvesting module and an energy management module were designed to collect and manage the vibration energy of the skip under different working conditions based on the piezoelectric effect.

[0136] Furthermore, in step D, based on the energy harvesting module and the energy management module, a skip running speed sensing strategy based on voltage frequency is used to obtain the current running speed of the skip. In this way, according to the stress distribution characteristics of the skip under different working conditions, the node at different monitoring positions can autonomously switch between periodic working and dormant modes, thereby further reducing the energy consumption of the node.

[0137] Figure 5 This is a schematic diagram of the node deployment for monitoring the stress state of a skip under normal conditions, in which each stress sensor is uniformly arranged at a spacing d on the outer wall of skip 1.

[0138] Figure 6The diagram shown illustrates the node deployment during the entire skip operation of this invention, including:

[0139] The diagram shows the node deployment of the skip during idle operation. When the skip is idle, nodes 1, 2, and 7 operate normally, while the remaining nodes are in a dormant state. (Yellow nodes indicate that they are operating normally, and white nodes indicate that they are in a dormant state.)

[0140] A schematic diagram of node deployment when the skip is running at a constant speed under full load. When the skip is running at a constant speed under full load, nodes 1, 2, 3, 4, 5, and 7 are working normally, while node 6 is in a dormant state.

[0141] A schematic diagram of node deployment during full-load acceleration and deceleration of the skip. During full-load acceleration and deceleration, nodes 1, 2, 3, 4, 6, and 7 operate normally, while node 5 is in a dormant state.

[0142] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0143] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention described herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not invented herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0144] The above specific embodiments further illustrate the purpose, technical solution and beneficial effects of this application. It should be understood that the above are only specific embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of this application should be included within the scope of protection of this application.

Claims

1. A method for monitoring stress in a self-powered hoisting skip based on data inversion, characterized in that, The system utilizes a data inversion-based self-powered lifting skip stress monitoring system. The system includes multiple stress monitoring nodes, an energy harvesting module, and an energy management module. Multiple stress monitoring nodes are installed on the skip (1), and the energy harvesting module is connected to the energy management module. The energy harvesting module includes a support (2), several piezoelectric vibrators (3), several piezoelectric cantilever beams (4), and several mass blocks of different weights. The skip (1), support (2), piezoelectric cantilever beams (4), and mass blocks are sequentially fixedly connected. Piezoelectric vibrators (3) are fixedly installed on each piezoelectric cantilever beam (4). The piezoelectric vibrators (3) are connected to the energy management module, and the energy management module is connected to the stress monitoring nodes. Perform the following steps: Step A: Use the finite element analysis method to obtain the stress distribution of the key area of ​​the skip (1) under different working conditions, and determine the key area that needs to be monitored. The different working conditions include unloaded descent, full-load acceleration-deceleration ascent and full-load uniform ascent. Step A includes: Step A1: Determine the stress distribution law of the key structure in the skip (1) under different connection methods. The key structure includes the main hanging plate and the column. Step A2: Perform static analysis on the skip (1) under no-load conditions to obtain the stress distribution law caused by the weight of the skip (1) itself during hoisting; Step A3: When the skip (1) is fully loaded under acceleration-deceleration operation conditions, analyze the stress distribution law of the skip (1) under acceleration lifting and emergency braking conditions during normal operation. Step A4: Under the condition of full load of skip (1), generate stress cloud diagram and displacement cloud diagram of skip (1) under uniform speed operation based on finite element simulation, analyze the stress distribution law of skip (1) under uniform speed operation, and obtain the stress and displacement distribution results of skip (1) under full load and uniform speed operation. Step A5: Based on the stress distribution of each key structure of the skip (1) under different operating conditions, determine the key areas of the skip (1) that need to be monitored; Step B: For different stress test points in different areas of the skip (1), input the stress data of the stress monitoring node under different operating conditions collected by the stress monitoring node into the long short-term memory neural network, and predict and output the overall stress data of each key area of ​​the skip (1) under different operating conditions. Step B includes: Step B1: For different stress test points in the region, a relational model based on the data inversion algorithm is established using a long short-term memory network to obtain the transmission characteristics between different stress test points in the key region; stress monitoring nodes are deployed at some important locations in the key region of the skip (1), and stress monitoring nodes are evenly distributed in the region to obtain a variety of node deployment strategies for stress monitoring of the skip (1); important locations include the connection point of the key region, the midpoint of the key region and the vertex of the key region; Step B2: Input the stress data obtained by the stress monitoring nodes under the node deployment strategy into the long short-term memory network to obtain the regional stress prediction results under each node deployment strategy; compare the stress prediction results under different node deployment strategies, determine the optimal node deployment strategy, and determine the stress monitoring network of the skip (1) in the initial state. In step B2, it includes: B2.1: Obtain the horizontal normal stress and vertical normal stress of the待测 node groups in all regions based on the stress monitoring network. B2.2: Under various operating conditions of the skip (1), take the horizontal normal stress and vertical normal stress of the待测 node groups obtained in all regions as the input of the long short-term memory neural network, and the long short-term memory neural network outputs the predicted regional stress prediction results under each node deployment strategy.

2. The method for monitoring stress in a self-powered hoisting skip based on data inversion according to claim 1, characterized in that, Based on the stress data of the stress monitoring nodes collected under different operating conditions of the skip (1), adjust the states of the stress monitoring nodes at the corresponding positions to working or dormant.

3. The method for monitoring stress in a self-powered hoisting skip based on data inversion according to claim 1, characterized in that, In step A1, it includes: A1.1: The main suspension plate bears the total weight of the skip (1) and the coal carried. Conduct a simulation analysis on the main suspension plate to determine the stress distribution law of the main suspension plate. A1.2: Analyze the columns in the skip (1), and based on the characteristics of various different connection methods of the columns, determine the stress distribution law of the columns. A1.3: Analyze the bolts in the skip (1) to determine the stress distribution law at each bolt connection.

4. The method for monitoring stress in a self-powered hoisting skip based on data inversion according to claim 1, characterized in that, In step A2, conduct a static analysis of the skip (1) in the no-load state, calculate and obtain the stress distribution laws of each part of the skip (1) caused by its own gravity during hoisting, and obtain the stress and displacement distribution results of each key structure of the skip (1) in the no-load state.

5. The method for monitoring stress in a self-powered hoisting skip based on data inversion according to claim 1, characterized in that, In step A3, it includes: A3.1: Determine the static load, dynamic load, impact load of coal on the skip (1), and impact load of acceleration during the acceleration and deceleration of the skip (1) that the skip (1) bears during operation. A3.2: Based on the acceleration of the skip (1) during hoisting and transportation not exceeding a1 and the acceleration of the skip (1) during braking not exceeding a2, where a1 < a2, select various different accelerations within [a1, a2] for modeling analysis during the full-load acceleration of the skip (1) to obtain stress nephograms and displacement nephograms under multiple accelerations. A3.3: Based on the stress nephograms and displacement nephograms under multiple accelerations, analyze the stress distribution laws of the full-load skip (1) during acceleration or deceleration under different accelerations, and obtain the stress and displacement distribution results of the full-load skip (1) during acceleration and deceleration.

6. The method for monitoring stress in a self-powered hoisting skip based on data inversion according to claim 1, characterized in that, In step A4, it includes: A4.1: Based on the speed range of the skip (1) during uniform motion under actual working conditions, determine the speed range v1 - v2 of the skip (1) during full-load uniform motion in the simulation software. A4.2: Based on the speed range v1 - v2 of the skip (1) during uniform motion, generate stress nephograms and displacement nephograms of the skip (1) during uniform motion at different speeds, analyze the stress distribution laws of the skip (1) during full-load uniform motion under different speeds, and obtain the stress and displacement distribution results of the skip (1) during full-load uniform motion.

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