Remote monitoring and early warning method for intelligent chain saw equipment operation combined with the Internet of Things

Abstract

The present application provides a remote monitoring and early warning method for the operation of an intelligent chain saw equipment combined with the Internet of Things, which relates to the field of intelligent monitoring technology, including: connecting a first Internet of Things monitoring component to receive a recoil monitoring data set; sending acceleration monitoring data, vibration monitoring data, torque monitoring data, and angle monitoring data to a remote monitoring center for recoil risk identification, and outputting a recoil risk index; when the recoil risk index is greater than a preset recoil risk threshold, issuing a recoil risk early warning signal; activating a second Internet of Things monitoring component to collect grip data from operators of the chain saw equipment; based on real-time grip monitoring data, minimizing the recoil risk index; and sending grip optimization parameters to operators through a remote monitoring center for recoil correction reminders. This application can solve the technical problem in the prior art that the recoil risk of chain saw equipment cannot be accurately identified and analyzed, and achieve the technical effect of comprehensively identifying recoil risks and accurately warning.

Description

Technical Field

[0001] The present application relates to the field of intelligent monitoring technology, and in particular to a remote monitoring and early warning method for the operation of an intelligent chain saw equipment combined with the Internet of Things. Background Art

[0002] During the use of chain saw equipment, the tip of the chain may collide with hard objects, scars or other obstacles, which may easily generate reverse force, causing the chain saw to suddenly rebound. This recoil force makes it difficult for the operator to control the equipment, which in turn causes serious operational safety accidents. In order to address this problem, some current technical means mainly focus on the local monitoring and alarm functions of the equipment, such as installing vibration sensors and acceleration sensors to monitor the operating status of the chain saw or reducing the recoil force through mechanical protection design. However, these technologies have exposed significant deficiencies in practice.

[0003] At present, the monitoring indicators of traditional monitoring equipment are relatively simple. For example, they only focus on vibration and acceleration parameters, but do not fully consider other dynamic characteristic data related to recoil, such as torque and angle changes, which limits the accuracy of risk analysis. This limitation is mainly reflected in the fact that the existing technology fails to systematically capture the multi-dimensional information of the operating status of the chain saw equipment. In actual operation, the recoil of the chain saw is often the result of the combined action of multiple factors, among which vibration and acceleration are only partial characteristics that reflect the status of the equipment. However, if the monitoring system relies only on vibration and acceleration data, other key signals may be missed, resulting in an inability to fully assess the possibility of recoil. In addition, the existing technology fails to provide effective feedback on the operating behavior of the operator. For example, insufficient grip or improper posture of the operator is an important reason for the uncontrolled recoil force, but the existing system generally lacks the function of collecting and optimizing the operator's real-time behavior data, and thus cannot effectively reduce the impact of human factors on operational safety.

[0004] In summary, the existing technology has technical problems such as the inability to accurately identify and analyze the kickback risk of chain saw equipment due to the single monitoring indicator and the lack of comprehensive capture of dynamic feature data, which further affects the operational safety and the effectiveness of risk warning. Summary of the invention

[0005] The purpose of this application is to provide a remote monitoring and early warning method for the operation of an intelligent chainsaw equipment combined with the Internet of Things, so as to solve the technical problem in the prior art that the recoil risk of the chainsaw equipment cannot be accurately identified and analyzed due to the single monitoring indicator and the lack of comprehensive capture of dynamic feature data, which further affects the operational safety and the effectiveness of risk warning.

[0006] In view of the above problems, the present application provides a remote monitoring and early warning method for the operation of an intelligent chainsaw equipment combined with the Internet of Things, including: configuring a first Internet of Things monitoring component for the chainsaw equipment, connecting the first Internet of Things monitoring component to receive a recoil monitoring data set, wherein the recoil monitoring data set includes at least acceleration monitoring data, vibration monitoring data, torque monitoring data and angle monitoring data; sending the acceleration monitoring data, the vibration monitoring data, the torque monitoring data and the angle monitoring data to a remote monitoring center for recoil risk identification, and outputting a recoil risk index; when the recoil risk index is greater than a preset recoil risk threshold, issuing a recoil risk early warning signal; based on the recoil risk early warning signal, activating a second Internet of Things monitoring component to collect grip strength data of an operator of the chainsaw equipment, and generating real-time grip strength monitoring data; based on the real-time grip strength monitoring data, minimizing the recoil risk index through a grip strength optimization module, and generating grip strength optimization parameters; sending the grip strength optimization parameters to the operator through the remote monitoring center for a recoil correction reminder.

[0007] The technical solution provided in the present application has at least the following technical effects or advantages: by configuring a first Internet of Things monitoring component for a chain saw device, connecting the first Internet of Things monitoring component to receive a recoil monitoring data set, wherein the recoil monitoring data set includes at least acceleration monitoring data, vibration monitoring data, torque monitoring data and angle monitoring data; sending the acceleration monitoring data, the vibration monitoring data, the torque monitoring data and the angle monitoring data to a remote monitoring center for recoil risk identification, and outputting a recoil risk index; when the recoil risk index is greater than a preset recoil risk threshold, issuing a recoil risk warning signal; based on the recoil risk warning signal, activating a second Internet of Things monitoring component to collect grip strength data of an operator of the chain saw device, and generating real-time grip strength monitoring data; based on the real-time grip strength monitoring data, minimizing the recoil risk index through a grip strength optimization module, and generating grip strength optimization parameters; sending the grip strength optimization parameters to the operator through the remote monitoring center for a recoil correction reminder, that is, by achieving the technical goal of multi-dimensional dynamic feature monitoring and analysis of the operating status of the chain saw device, the technical effects of comprehensively identifying recoil risks, accurately warning and effectively optimizing operator behavior are achieved.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0010] Figure 1 This is a flow chart of a remote monitoring and early warning method for operation of an intelligent chain saw equipment combined with the Internet of Things in this application;

[0011] Figure 2 This is a flow chart of outputting a kickback risk indicator in a remote monitoring and early warning method for operation of an intelligent chain saw equipment combined with the Internet of Things in this application. DETAILED DESCRIPTION

[0012] This application provides a remote monitoring and early warning method for intelligent chain saw equipment combined with the Internet of Things, which solves the technical problem in the prior art that the recoil risk of chain saw equipment cannot be accurately identified and analyzed due to the single monitoring indicator and the lack of comprehensive capture of dynamic feature data, further affecting the safety of operation and the effectiveness of risk early warning. The technical goal of multi-dimensional dynamic feature monitoring and analysis of the operating status of chain saw equipment is achieved, and the technical effect of fully identifying recoil risks, accurately warning and effectively optimizing operator behavior is achieved.

[0013] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0014] For example, please refer to the attached Figure 1 The present application provides a remote monitoring and early warning method for operation of an intelligent chain saw equipment combined with the Internet of Things, which specifically includes the following steps:

[0015] Step 1: Configure a first Internet of Things monitoring component for the chain saw device, and connect the first Internet of Things monitoring component to receive a recoil monitoring data set, wherein the recoil monitoring data set includes at least acceleration monitoring data, vibration monitoring data, torque monitoring data, and angle monitoring data.

[0016] Specifically, a first IoT monitoring component is configured for the chain saw equipment. The first IoT monitoring component is a device used to monitor the recoil in real time during the operation of the chain saw. Through the IoT monitoring technology, the first IoT monitoring component can receive a recoil monitoring data set. The recoil monitoring data set includes acceleration monitoring data, vibration monitoring data, torque monitoring data, and angle monitoring data. The acceleration monitoring data is used to reflect whether the chain saw is affected by sudden force during operation. For example, when the tip of the chain collides with a hard object, an acceleration change will occur. The vibration monitoring data is used to capture the vibration fluctuations caused by the collision. The vibration will propagate in the chain saw body and can help determine the severity of the recoil. The torque monitoring data is used to record whether the torque fluctuation caused by the collision occurs in the chain saw motor during operation. If the torque changes too much, it may mean that the chain recoil force is too strong and the operating environment is dangerous. The angle monitoring data is used to monitor the movement angle of the chain saw. If a recoil occurs, the angle of the chain saw will shift instantly, which may affect the safety of the operator. Through the joint monitoring of the recoil monitoring data set, it is possible to evaluate in real time whether the chain saw is in an abnormal working state, thereby effectively warning of the recoil risk and ensuring the safety of the operator.

[0017] Step 2: Send the acceleration monitoring data, the vibration monitoring data, the torque monitoring data and the angle monitoring data to a remote monitoring center for recoil risk identification, and output a recoil risk index.

[0018] Specifically, the acceleration monitoring data, vibration monitoring data, torque monitoring data, and angle monitoring data are sent to the remote monitoring center, which can conduct a comprehensive analysis to evaluate the current operating status of the chain saw, identify the potential risk of kickback, and output a kickback risk indicator. For example, if the acceleration monitoring data shows that the chain saw has an abnormal acceleration change during operation, and the torque monitoring data also shows a sudden increase in torque, it may indicate that the chain saw is at risk of kickback. By analyzing all relevant data, the remote monitoring center can calculate the kickback risk indicator to reflect whether the equipment currently has a kickback risk, and can provide real-time warnings based on the indicator to help operators take timely measures.

[0019] Step 3: When the recoil risk indicator is greater than the preset recoil risk threshold, a recoil risk warning signal is issued.

[0020] Specifically, when the kickback risk index is greater than the preset kickback risk threshold, it indicates that the operation of the chain saw equipment may cause a kickback accident. The corresponding kickback risk index is calculated. If the index exceeds the preset kickback risk threshold, it means that the equipment has a high kickback risk and may be dangerous. A kickback risk warning signal is then issued to ensure that the operator can be warned in time. The kickback risk threshold is a pre-set standard used to indicate the safe range for the operation of the chain saw equipment. The kickback risk warning signal is transmitted to the operator in the form of sound or prompt information on the display screen, so that the operator can take preventive measures, adjust the operation method or stop the operation before the kickback occurs to avoid accidents.

[0021] Step 4: Based on the recoil risk warning signal, activate the second Internet of Things monitoring component to collect grip strength data of the operator of the chain saw equipment to generate real-time grip strength monitoring data.

[0022] Specifically, when the kickback risk warning signal is activated, the second IoT monitoring component is triggered to collect grip strength data of the operator of the chainsaw equipment, and the operator's grip strength is monitored in real time through sensors installed on the chainsaw equipment. Grip strength data collection refers to the continuous measurement of the force applied by the operator's hand. For example, when the operator's grip strength weakens, the chainsaw equipment may be more prone to kickback. Conversely, a stronger grip helps control the stability of the chainsaw. By generating real-time grip strength monitoring data, it reflects the changes in the operator's grip strength at each moment, and can promptly detect whether there is a situation where the risk of kickback is increased due to insufficient grip strength, providing an important basis for subsequent kickback risk analysis and early warning decisions.

[0023] Step 5: Based on the real-time grip strength monitoring data, the recoil risk index is minimized through a grip strength optimization module to generate grip strength optimization parameters.

[0024] Specifically, the grip force optimization module analyzes real-time data such as the equipment's working status and adjusts the grip force to minimize the recoil risk index and reduce the possibility of recoil. Finally, it generates grip force optimization parameters, which are recommendations for adjusting the operator's grip force, to help the operator change the grip force distribution and keep it in the optimal state to reduce the risk of recoil and thus improve operation safety.

[0025] Step 6: Send the grip optimization parameter to the operator through the remote monitoring center to remind the operator to correct the recoil.

[0026] Specifically, the grip optimization parameters are sent to the operator through the remote monitoring center to remind the operator to correct the recoil. That is, the remote monitoring center acts as an information transfer station, sends these optimization parameters to the operator in real time through the wireless network, and displays or plays them on the display terminal or voice prompt device to help the operator to correct the recoil in time, thereby reducing operational risks and improving safety. The feedback mechanism can effectively achieve collaboration with the operator, and even in a remote environment, it can ensure the timeliness and accuracy of information transmission.

[0027] The remote monitoring and early warning method for the operation of intelligent chainsaw equipment combined with the Internet of Things can achieve the technical goal of multi-dimensional dynamic feature monitoring and analysis of the operating status of the chainsaw equipment, and achieve the technical effects of comprehensively identifying the risk of recoil, accurately warning and effectively optimizing the operator's behavior.

[0028] Furthermore, the present application also includes: the first Internet of Things monitoring component includes at least an acceleration sensor, a vibration sensor, a torque sensor and an angle sensor, the acceleration sensor and the vibration sensor are located on the handle of the chain saw device, the torque sensor is located between the drive shaft and the chain of the chain saw device, and the angle sensor is located on the guide plate of the chain saw device; the second Internet of Things monitoring component includes a strain gauge sensor located on the handle of the chain saw device.

[0029] Specifically, the first IoT monitoring component includes at least an accelerometer, a vibration sensor, a torque sensor, and an angle sensor. These sensors are responsible for collecting different physical changes of the chain saw during operation. The accelerometer is used to detect the acceleration change of the chain saw equipment during operation. When the chain collides with a hard object, the recoil force may cause a sharp change in acceleration. The vibration sensor is used to detect the vibration of the chain saw equipment due to the friction or impact between the chain and the wood during operation. The vibration can reflect whether the working state of the equipment is normal. The torque sensor monitors the torque change between the chain saw drive shaft and the chain. During recoil, the torque value may fluctuate violently, indicating that the equipment may be in danger. The angle sensor is used to monitor the guide bar angle of the chain saw equipment. When recoil occurs, the angle of the guide bar may change instantly, which helps to determine whether the equipment is out of control.

[0030] Next, the acceleration sensor and vibration sensor are located on the handle of the chain saw equipment. The acceleration change and vibration of the chain saw are obtained through the handle. If a kickback occurs, the sensor on the handle can sense this change in time, obtain the position of the chain saw, and transmit data for early warning. The sensor on the handle obtains the danger signal caused by the kickback to ensure timely processing.

[0031] Similarly, the torque sensor is located between the drive shaft and the chain of the chain saw equipment. Because the movement of the chain is driven by the drive shaft, the torque sensor can accurately detect the torque change between the drive shaft and the chain. If the chain is hit by an obstacle, the torque will change instantly, reflecting the intensity and direction of the recoil. Through monitoring, the working status of the chain can be understood in real time, and whether recoil will occur can be predicted.

[0032] In addition, an angle sensor is located on the guide plate of the chain saw device. The function of the angle sensor is to monitor the inclination angle of the guide plate. When the chain encounters an obstacle and rebounds, the guide plate of the chain saw may deviate momentarily, which is captured by the angle sensor to help determine whether the chain saw is out of control.

[0033] Then, the second IoT monitoring component includes a strain gauge sensor located on the handle of the chain saw equipment. The strain gauge sensor is used to detect the strain changes on the handle of the chain saw. When the chain saw kicks back, the handle of the chain saw will be subjected to different stresses, which are sensed by the strain gauge sensor. If the chain saw gets out of control, there will be obvious strain changes on the handle, and the sensor will transmit the data to the monitoring system to help take timely measures to prevent accidents.

[0034] The status of the chain saw equipment is fully monitored through sensors in different locations and types. The acceleration sensor and vibration sensor are located on the handle, which can timely sense the acceleration and vibration caused by recoil; the torque sensor is located between the drive shaft and the chain to monitor the torque change; the angle sensor is located on the guide plate to capture the change of the chain saw angle; and the strain gauge sensor is specifically used to sense the strain change on the handle. Through the coordinated work of sensors, the operating status of the chain saw equipment can be monitored in real time, providing timely warnings to the operator to ensure safe operation.

[0035] For further information, please see the attached Figure 2 , the present application also includes: determining the equipment model of the chain saw equipment; collecting a historical recoil monitoring data set based on the equipment model; performing a dynamic feature analysis of acceleration, vibration, torque and angle in the pre-recoil accident time zone based on the historical recoil monitoring data set, and establishing multiple recoil accident pre-monitoring dynamic features, wherein the multiple recoil accident pre-monitoring dynamic features have corresponding recoil risk degree identifiers; constructing a recoil risk prediction model based on the multiple recoil accident pre-monitoring dynamic features and the recoil risk degree identifier, and embedding it into the remote monitoring center; inputting the acceleration monitoring data, the vibration monitoring data, the torque monitoring data and the angle monitoring data into the recoil risk prediction model for risk analysis to generate the recoil risk index.

[0036] Specifically, determine the equipment model of the chainsaw equipment. The equipment model refers to the specific specifications or categories of the chainsaw equipment. By identifying the equipment model, the applicability of subsequent monitoring and data collection can be ensured. Different models of chainsaws may have different operating parameters and risk characteristics. Therefore, clarifying the equipment model provides a basic constraint for subsequent operations and ensures the accuracy and relevance of data analysis.

[0037] Next, we collected historical recoil monitoring data sets based on the equipment model. The historical data sets include information such as acceleration, vibration, torque, and angle recorded during the equipment's previous use, which helps analyze the occurrence patterns of recoil events. By using the equipment model as a constraint, we ensure that the collected data matches the equipment of a specific model, making the data more valid and representative.

[0038] By monitoring acceleration, vibration, torque and angle in the data, potential recoil risks can be identified in advance. For example, before a recoil occurs, torque fluctuations may become abnormal, and acceleration and vibration may show certain early warning signals. Establishing multiple dynamic features can help identify early signs of accidents. Among them, multiple recoil accident pre-monitoring dynamic features have corresponding recoil risk level indicators, such as the relationship between transfer time and recoil risk.

[0039] Then, a recoil risk prediction model is constructed based on multiple recoil accident pre-monitoring dynamic features and recoil risk level indicators, and embedded in the remote monitoring center. That is, by modeling the dynamic features and their corresponding risk indicators, a recoil risk prediction model is constructed to analyze the current status of the chain saw equipment and predict whether there is a potential recoil risk. The data and model are integrated into the remote monitoring center, which can monitor the working status of the chain saw in real time, detect potential dangers in a timely manner, and ensure the safety of operators.

[0040] Finally, the acceleration monitoring data, vibration monitoring data, torque monitoring data and angle monitoring data are input into the recoil risk prediction model for risk analysis. The recoil risk prediction model analyzes the current data and generates a recoil risk index, which is used to reflect the risk level of recoil accidents that may occur in the current chainsaw equipment, and help the remote monitoring center evaluate the safety of the chainsaw equipment.

[0041] By determining the equipment model, the accuracy and pertinence of data collection are ensured; the collection and analysis of historical data provides a basis for risk prediction; the analysis based on dynamic characteristics can effectively identify the early signs of kickback; the constructed kickback risk prediction model provides technical support for real-time monitoring; by inputting real-time data, the generated kickback risk indicators help the monitoring system to respond in time and avoid potential safety hazards, thereby forming a comprehensive monitoring and early warning system to effectively improve the safety of chain saw equipment.

[0042] Furthermore, the present application also includes: based on the historical recoil monitoring data set, a statistical recoil transfer time data set is generated, wherein the recoil transfer time data set includes multiple groups of recoil transfer time data, and any group of recoil transfer time data includes characteristic values ​​of acceleration, vibration, torque and angle corresponding to the monitoring moment, and the transfer duration from the current monitoring moment state to the recoil state; based on the transfer duration, the multiple groups of recoil transfer time data are configured with recoil risk data to generate the recoil risk level identification, wherein the recoil risk is inversely proportional to the transfer duration; the multiple recoil accident pre-monitoring dynamic characteristics are generated using the characteristic values ​​of acceleration, vibration, torque and angle corresponding to the monitoring moment in the multiple groups of recoil transfer time data, and the corresponding recoil risk level identification.

[0043] Specifically, based on the historical recoil monitoring data set, the recoil transfer time data set is statistically analyzed. Recoil has a process, and data such as acceleration and vibration will have a change process. When the change reaches a certain level, recoil will occur. For each data, the corresponding recoil event of any level of data is statistically analyzed. The recoil transfer time data set is the change from the normal working state of the chain saw equipment to the recoil state. It contains multiple sets of recoil transfer data. Each set of data includes the characteristic values ​​of acceleration, vibration, torque and angle corresponding to the monitoring time, reflecting the physical state of the chain saw at different working times. In addition, each set of data also includes the transfer time from the current monitoring time to the occurrence of the recoil state, that is, the equipment transitions from one state to another before the recoil occurs, which is then used to analyze the occurrence trend of recoil.

[0044] Next, recoil risk data is configured for multiple sets of recoil transfer time data based on the transfer duration, and a recoil risk level indicator is generated. At this stage, the risk level of recoil occurrence is evaluated based on the transfer duration in each set of recoil transfer time data. The shorter the transfer duration, the faster the chain saw changes from a normal working state to a recoil state, and the higher the risk of recoil. The recoil risk is inversely proportional to the transfer duration. When the transfer duration is shorter, the recoil risk is higher, and when the transfer duration is longer, the recoil risk is lower. A recoil risk level indicator is assigned to each set of data through the evaluation, and the high or low level of the indicator indicates the potential risk in the chain saw operation.

[0045] Finally, through the acceleration, vibration, torque and angle characteristic values ​​corresponding to the monitoring time in multiple sets of recoil transfer time data, and combined with the recoil risk level identification corresponding to each set of data, multiple recoil accident pre-monitoring dynamic features are generated. These are the change patterns of the chain saw before the recoil occurs, which are used to predict the occurrence of recoil events. For example, before the recoil occurs, the acceleration value of the chain saw may increase rapidly, and the vibration amplitude may also increase significantly. The dynamic features can warn the operator in advance and improve safety.

[0046] By collecting historical recoil monitoring data, we statistically calculated the recoil transfer time data set, which includes the characteristic values ​​of acceleration, vibration, torque and angle, as well as the transfer duration. By analyzing the relationship between the transfer duration and the recoil risk, we assigned a recoil risk level identifier to each set of data. Combining the characteristic values ​​and risk identifiers, we eventually generated multiple dynamic features for the pre-monitoring of recoil accidents, which can effectively warn of the occurrence of recoil events and enhance the accuracy and response speed of the monitoring system.

[0047] Furthermore, the present application also includes: identifying grip force distribution characteristics based on the real-time grip force monitoring data to generate real-time grip force distribution characteristics; performing recoil risk reduction analysis for grip force equalization based on the real-time grip force distribution characteristics, and generating a grip force posture adjustment decision when the reduction index meets a preset reduction requirement, wherein the preset reduction requirement is determined based on the preset recoil risk threshold and the recoil risk index; and generating the grip force optimization parameters based on the grip force posture adjustment decision.

[0048] Specifically, the grip force distribution characteristics are identified based on the real-time grip force monitoring data, and the real-time grip force distribution characteristics are generated to find out the distribution pattern of grip force in different time periods or operation processes. By analyzing the generated real-time grip force distribution characteristics, it is possible to understand the changes in the operator's grip force during the entire operation process.

[0049] Next, based on the real-time grip force distribution characteristics, a recoil risk reduction analysis of grip force equalization is performed to analyze whether the risk of recoil can be reduced by adjusting the grip force distribution. When the reduction index meets the preset reduction requirements, a grip force posture adjustment decision is generated. For example, if the grip force is uneven at a certain moment, it may cause unstable operation of the chain saw and increase the risk of recoil. By analyzing the real-time grip force characteristics, check whether the preset reduction requirements are met. The preset reduction requirements are set based on the recoil risk threshold and the recoil risk index. If the grip force adjustment can effectively reduce the recoil risk and meet the preset reduction requirements, a grip force posture adjustment decision is generated to guide the operator to adjust the grip method and reduce the possibility of recoil.

[0050] Finally, the operator's grip is optimized through grip posture adjustment decisions, including increasing or decreasing the force distribution of a certain finger, or changing the grip angle, etc. The operator can adjust his grip to make the operation of the chain saw more stable, reduce the risk of kickback, and generate grip optimization parameters as the basis for the operator to adjust the grip posture. The safety of chain saw equipment is improved through a timely warning mechanism, reducing the possibility of accidents.

[0051] Furthermore, the present application also includes: when the reduction index does not meet the preset reduction requirement, receiving the grip posture adjustment decision corresponding to the maximum reduction index; through the historical grip strength optimization database in the grip strength optimization module, continuing to optimize the grip force change trend in the direction of recoil risk reduction based on the maximum reduction index, and generating a recommended grip force change direction; generating the grip optimization parameters based on the grip posture adjustment decision corresponding to the maximum reduction index and the recommended grip force change direction.

[0052] Specifically, when the reduction index does not meet the preset reduction requirement, the grip posture adjustment decision corresponding to the maximum reduction index is received. The reduction index indicates the degree to which the risk of recoil can be reduced through grip adjustment, and the preset reduction requirement refers to the minimum risk reduction requirement that the grip adjustment should achieve within the set safety range. When the reduction index does not meet this preset requirement, further optimization is performed to receive the grip posture adjustment decision corresponding to the maximum reduction index. The maximum reduction index refers to the grip posture of the operator adjusted according to the maximum risk reduction range in order to reduce the risk of recoil.

[0053] Next, the grip force optimization module uses the historical grip force optimization database to optimize the grip force change trend in the direction of reducing the recoil risk based on the maximum reduction index. The grip force optimization module is responsible for optimizing the grip force adjustment based on real-time monitoring data and historical data. The historical grip force optimization database contains records and data of past grip force optimizations, helping the system identify the recoil risks that have been effectively reduced in past operations. Based on the maximum reduction index, the system continues to optimize in the direction of reducing the recoil risk, further analyzing the grip force change trend that can more effectively reduce the risk, thereby determining the best grip force change strategy.

[0054] Finally, the grip posture adjustment decision corresponding to the maximum reduction index and the recommended grip change direction are combined to form specific operating parameters and generate grip optimization parameters, including the size of the grip, the angle of the grip, and the distribution of force applied by various parts of the hand. The operator adjusts his grip according to the optimization parameters to improve safety during the operation.

[0055] Grip optimization parameters help operators adjust their grip posture to minimize the risk of kickback and improve operational safety.

[0056] Furthermore, the present application also includes: collecting historical grip force distribution data sets and corresponding recoil risk indicator sample sets; analyzing the mapping relationship between recoil risk and grip force distribution changes based on the historical grip force distribution data sets and the recoil risk indicator sample sets; performing a recoil risk reduction analysis of grip force equalization on the real-time grip force distribution characteristics based on the mapping relationship to generate the reduction index.

[0057] Specifically, the historical grip force distribution data set and the corresponding recoil risk indicator sample set are collected. The historical grip force distribution data set refers to the distribution of the operator's grip force during past operations, including the force applied by the operator's hand at different time points and under different operating conditions. The recoil risk indicator sample set is the recoil risk assessment result corresponding to the grip force data, reflecting the risk level of recoil under different grip force distribution conditions. By combining these two data sets, important reference data is provided for subsequent analysis.

[0058] Next, the mapping relationship between the recoil risk and the change in grip force distribution is analyzed based on the historical grip force distribution data set and the recoil risk indicator sample set. Analyzing the mapping relationship between the recoil risk and the change in grip force distribution refers to finding the regularity between the recoil risk and the grip force distribution. For example, if the operator's grip force is too weak, the risk of recoil is high, while if the grip force is too strong, the risk of recoil may be relatively low. By analyzing and identifying the corresponding relationship between a specific grip force distribution and recoil risk, we can understand which grip force distribution will lead to a higher or lower recoil risk, and build a mathematical model that can predict the recoil risk based on grip force adjustment.

[0059] Finally, based on the mapping relationship, the real-time grip force distribution characteristics are analyzed to reduce the recoil risk by grip force balance, that is, to analyze whether the current operator's grip force meets the standard of minimum recoil risk, compare the current grip force distribution with the optimal grip force distribution in the historical data, generate reduction indicators, and obtain the recoil risk that can be reduced by the current grip force adjustment. For example, the grip force distribution monitored in real time is relatively uneven, and identifying the imbalance may lead to a higher recoil risk, so adjustment suggestions are given. By generating reduction indicators, operators are guided to adjust their grip force to reduce recoil risks and ensure operation safety.

[0060] Furthermore, the present application also includes: testing the optimized response time of the grip optimization module, adding it to the preset action response time of the staff, and then performing tolerance amplification according to the preset step size to generate a target response time; configuring an emergency recoil risk indicator threshold based on the target response time; when the recoil risk indicator is greater than the emergency recoil risk indicator threshold, issuing an emergency shutdown warning signal; and performing emergency shutdown control on the chainsaw equipment with the emergency shutdown warning signal.

[0061] Specifically, the optimization response time of the grip optimization module refers to the time required from identifying the need to optimize the grip state to actually making adjustments. The shorter the optimization response time, the faster adjustments can be made to ensure safety. The staff's preset action response time refers to the time it takes for the staff to react and take corresponding actions during operation, such as adjusting the grip when feeling the risk of recoil. Test the optimization response time of the grip optimization module and add it to the staff's preset action response time to obtain the total response time for optimizing the recoil risk. The total response time is tolerantly amplified according to the preset step size to generate a target response time, which represents the uncertainty and delay in actual operation, ensuring that sufficient adjustments can be made within a given time frame.

[0062] Next, the target response time is used as the transfer time to configure the emergency backlash risk indicator threshold. The emergency backlash risk indicator threshold is the limit value at which the backlash risk reaches a critical state. If the backlash risk cannot be effectively reduced within the target response time, it is considered that the backlash risk has reached a dangerous level and emergency measures should be taken.

[0063] When the kickback risk index is greater than the emergency kickback risk index threshold, an emergency shutdown warning signal is issued, which means that when the kickback risk index is monitored in real time to exceed the preset emergency threshold, a warning signal is immediately issued to remind the staff that the chain saw equipment is in a dangerous state so that measures can be taken.

[0064] Finally, the chain saw equipment is controlled by the emergency shutdown warning signal, and the chain saw equipment is automatically shut down through the emergency shutdown control to stop the equipment from continuing to operate to prevent injury or damage. For example, if the risk of kickback is extremely high, the equipment will be automatically shut down to protect the safety of the operator. Through real-time monitoring and timely response, the danger of kickback is effectively reduced.

[0065] In summary, the remote monitoring and early warning method for the operation of an intelligent chainsaw equipment combined with the Internet of Things provided in the present application has the following technical effects: by configuring a first Internet of Things monitoring component for the chainsaw equipment, connecting the first Internet of Things monitoring component to receive a recoil monitoring data set, wherein the recoil monitoring data set includes at least acceleration monitoring data, vibration monitoring data, torque monitoring data and angle monitoring data; sending the acceleration monitoring data, the vibration monitoring data, the torque monitoring data and the angle monitoring data to a remote monitoring center for recoil risk identification, and outputting a recoil risk index; when the recoil risk index is greater than a preset recoil risk threshold value, and send out a kickback risk warning signal; based on the kickback risk warning signal, activate the second Internet of Things monitoring component to collect the grip strength data of the operator of the chain saw equipment, and generate real-time grip strength monitoring data; based on the real-time grip strength monitoring data, minimize the kickback risk index through the grip strength optimization module, and generate grip strength optimization parameters; send the grip strength optimization parameters to the operator through the remote monitoring center to remind the operator to correct the kickback. In other words, by realizing the technical goal of multi-dimensional dynamic feature monitoring and analysis of the operating status of the chain saw equipment, the technical effect of comprehensively identifying the kickback risk, accurately warning and effectively optimizing the operator's behavior is achieved.

[0066] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0067] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A remote monitoring and early warning method for operation of intelligent chain saw equipment combined with the Internet of Things, characterized in that: include: A first Internet of Things monitoring component is configured for the chain saw device, and the first Internet of Things monitoring component is connected to receive a recoil monitoring data set, wherein the recoil monitoring data set includes at least acceleration monitoring data, vibration monitoring data, torque monitoring data, and angle monitoring data; Sending the acceleration monitoring data, the vibration monitoring data, the torque monitoring data and the angle monitoring data to a remote monitoring center for recoil risk identification, and outputting a recoil risk index; When the backlash risk indicator is greater than the preset backlash risk threshold, a backlash risk warning signal is issued; Based on the kickback risk warning signal, a second Internet of Things monitoring component is activated to collect grip strength data of operators of the chain saw equipment to generate real-time grip strength monitoring data; Based on the real-time grip strength monitoring data, the recoil risk index is minimized by a grip strength optimization module to generate grip strength optimization parameters; The grip optimization parameter is sent to the operator through the remote monitoring center to remind the operator to correct the recoil.

2. The remote monitoring and early warning method for operation of an intelligent chain saw equipment combined with the Internet of Things as claimed in claim 1, characterized in that: The first Internet of Things monitoring component includes at least an acceleration sensor, a vibration sensor, a torque sensor and an angle sensor, wherein the acceleration sensor and the vibration sensor are located on the handle of the chain saw device, the torque sensor is located between the drive shaft and the chain of the chain saw device, and the angle sensor is located on the guide plate of the chain saw device; The second IoT monitoring component includes a strain gauge sensor located on a handle of the chainsaw device.

3. The remote monitoring and early warning method for operation of an intelligent chain saw equipment combined with the Internet of Things as claimed in claim 1, characterized in that: The acceleration monitoring data, the vibration monitoring data, the torque monitoring data and the angle monitoring data are sent to a remote monitoring center for recoil risk identification, and a recoil risk index is output, including: Determining the device model of the chain saw device; Taking the equipment model as a constraint, collecting a historical recoil monitoring data set; Based on the historical recoil monitoring data set, dynamic characteristic analysis of acceleration, vibration, torque and angle in the pre-recoil accident time zone is performed to establish multiple recoil accident pre-monitoring dynamic characteristics, wherein the multiple recoil accident pre-monitoring dynamic characteristics have corresponding recoil risk degree identifiers; A recoil risk prediction model is constructed based on the multiple recoil accident pre-monitoring dynamic characteristics and the recoil risk degree identifier, and is embedded in the remote monitoring center; The acceleration monitoring data, the vibration monitoring data, the torque monitoring data and the angle monitoring data are input into the recoil risk prediction model for risk analysis to generate the recoil risk index.

4. The remote monitoring and early warning method for operation of an intelligent chain saw equipment combined with the Internet of Things as claimed in claim 3 is characterized in that: Based on the historical recoil monitoring data set, the dynamic characteristics of acceleration, vibration, torque and angle in the pre-recoil accident time zone are analyzed, and multiple pre-recoil accident monitoring dynamic characteristics are established, including: Based on the historical recoil monitoring data set, a recoil transfer time data set is counted, wherein the recoil transfer time data set includes multiple sets of recoil transfer time data, and any set of recoil transfer time data includes characteristic values ​​of acceleration, vibration, torque and angle corresponding to the monitoring moment, and a transfer time length from a current monitoring moment state to a recoil state; Based on the transfer duration, recoil risk data is configured for the multiple sets of recoil transfer time data to generate the recoil risk degree identifier, wherein the recoil risk is inversely proportional to the transfer duration; The plurality of recoil accident pre-monitoring dynamic features are generated based on the characteristic values ​​of acceleration, vibration, torque and angle corresponding to the monitoring moments in the plurality of sets of recoil transfer time data, and the corresponding recoil risk level identifiers.

5. The remote monitoring and early warning method for operation of intelligent chain saw equipment combined with the Internet of Things as claimed in claim 1, characterized in that: Based on the real-time grip strength monitoring data, the recoil risk index is minimized by a grip strength optimization module to generate grip strength optimization parameters, including: Performing grip force distribution feature recognition based on the real-time grip force monitoring data to generate real-time grip force distribution features; Performing a recoil risk reduction analysis of grip force balancing based on the real-time grip force distribution characteristics, and generating a grip force posture adjustment decision when the reduction index meets a preset reduction requirement, wherein the preset reduction requirement is determined based on the preset recoil risk threshold and the recoil risk index; The grip force optimization parameter is generated based on the grip force posture adjustment decision.

6. The remote monitoring and early warning method for operation of an intelligent chain saw equipment combined with the Internet of Things as claimed in claim 5, characterized in that: Also includes: When the reduction index does not meet the preset reduction requirement, receiving a grip posture adjustment decision corresponding to the maximum reduction index; Through the historical grip force optimization database in the grip force optimization module, based on the maximum reduction index, continue to optimize the grip force change trend in the direction of reducing the recoil risk, and generate a recommended grip force change direction; The grip force optimization parameter is generated based on the grip force posture adjustment decision corresponding to the maximum reduction index and the recommended grip force change direction.

7. The remote monitoring and early warning method for operation of an intelligent chain saw equipment combined with the Internet of Things as claimed in claim 5, characterized in that: The recoil risk reduction analysis of grip force balancing based on the real-time grip force distribution characteristics includes: Collect historical grip force distribution data sets and corresponding recoil risk indicator sample sets; Analyzing the mapping relationship between the recoil risk and the change in grip force distribution based on the historical grip force distribution data set and the recoil risk indicator sample set; A recoil risk reduction analysis of grip force balancing is performed on the real-time grip force distribution characteristics based on the mapping relationship to generate the reduction index.

8. The remote monitoring and early warning method for operation of an intelligent chain saw equipment combined with the Internet of Things as claimed in claim 1, characterized in that: Also includes: Testing the optimized response time of the grip optimization module, adding it to the preset action response time of the staff, and then performing tolerance amplification according to the preset step length to generate a target response time; Configure an emergency backlash risk indicator threshold based on the target response time; When the recoil risk index is greater than the emergency recoil risk index threshold, an emergency shutdown warning signal is issued; The chain saw equipment is subjected to emergency shutdown control using the emergency shutdown warning signal.

Images