Mechanical state real-time monitoring system based on Internet of Things

Abstract

The invention is suitable for the technical field of mechanical equipment monitoring and control, and provides a mechanical state real-time monitoring system based on the Internet of Things, and the system comprises a data obtaining module which is used for obtaining a real-time monitoring result when a target feller performs felling operation on a target tree according to a preset cutting pressure parameter; real-time vibration data, real-time pressure feedback data and real-time temperature change data of the target feller saw blade within a preset time range are collected; according to the invention, multi-dimensional real-time monitoring of the state of the feller saw blade and dynamic adjustment of the cutting parameters are realized through the Internet of Things technology, and the stability and the intelligent level of equipment operation are effectively improved. By integrating three key parameters of vibration, pressure and temperature, the system provides more accurate saw blade aging evaluation, and solves the problem that a traditional single-parameter monitoring method is easily influenced by the environment and is not comprehensive enough.

Description

Technical Field

[0001] The present invention belongs to the technical field of mechanical equipment monitoring and control, and particularly relates to a real-time mechanical state monitoring system based on the Internet of Things. Background Technique

[0002] During the operation of logging machinery, the cutting pressure of the saw blade is a key parameter affecting cutting efficiency, operation stability, and tool life. Traditional logging machines usually operate with fixed cutting pressure or preset pressure parameters based on the hardness of tree species, which are obtained through experimental calibration or empirical setting. However, during long-term use, the saw blade will gradually age due to high-intensity friction and heat accumulation, manifested as increased vibration, increased pressure demand, and abnormal temperature rise. Due to the lack of real-time monitoring and evaluation capabilities for the aging state of the saw blade in the existing technology, fixed or preset cutting pressure often fails to adapt to the dynamic changes in the performance of the saw blade, resulting in a decrease in cutting efficiency, increased tool wear, and even equipment failures.

[0003] Some existing technologies attempt to dynamically adjust the cutting pressure through hydraulic feedback control, but these methods usually rely on a single parameter (such as cutting pressure or load data) as the adjustment basis and fail to effectively capture the comprehensive impact of saw blade aging on vibration characteristics, temperature changes, and pressure demand. For example, when the saw blade generates abnormal vibration due to aging, a single hydraulic feedback may not respond in time, resulting in further damage to the saw blade by excessive cutting pressure. In addition, these systems lack multi-dimensional data support in the evaluation of the aging state and cannot accurately judge the degree of saw blade wear, resulting in poor accuracy and adaptability of cutting pressure adjustment. Summary of the Invention

[0004] The purpose of the present invention is to provide a real-time mechanical state monitoring system based on the Internet of Things, aiming to solve the problems raised in the background technique.

[0005] The present invention is implemented as follows. A real-time mechanical state monitoring system based on the Internet of Things, the system includes:

[0006] A data acquisition module, used to collect real-time vibration data, real-time pressure feedback data, and real-time temperature change data of the saw blade of the target logging machine within a preset time range when the target logging machine performs logging operations on the target tree according to the preset cutting pressure parameters;

[0007] A preliminary saw blade aging index determination module, used to obtain the reference vibration data and reference pressure feedback data when the target logging machine uses the saw blade in the initial state to cut the same tree according to the preset cutting pressure parameters, and compare the real-time vibration data with the reference vibration data, and the real-time pressure feedback data with the reference pressure feedback data, and deduce the preliminary saw blade aging index of the saw blade of the target logging machine;

[0008] A deviation percentage calculation module, which is used to fit the real-time temperature change data into a real-time temperature change trend curve, compare it with the ideal temperature change curve when the initial state saw blade cuts the same tree with a preset cutting pressure parameter, and calculate the deviation percentage of the temperature change;

[0009] A supplementary cutting pressure value calculation module, which is used to use the deviation percentage as a correction factor to adjust the preliminary aging index, obtain the optimized saw blade aging index, multiply the optimized saw blade aging index by the preset cutting pressure parameter, and calculate the required supplementary cutting pressure value.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, an aging evaluation model based on dynamic characteristic fitting and multi-parameter correction is set in the preliminary saw blade aging index determination module. This model includes the reference vibration data, reference pressure data, and reference temperature change data when the target logging machine cuts trees with different hardness parameters with different cutting pressure parameters under the condition of using the initial state saw blade.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the preliminary saw blade aging index determination module specifically includes:

[0012] A reference data acquisition unit, which is used to determine the specified hardness parameter of the target tree, and obtain the reference vibration data, reference pressure feedback data, and reference temperature change data when the target logging machine uses the initial state saw blade to cut the specified hardness parameter tree with a preset cutting pressure parameter according to the aging evaluation model;

[0013] A deviation value calculation unit, which is used to compare and analyze the collected real-time vibration data with the reference vibration data, and at the same time compare the real-time pressure feedback data with the reference pressure feedback data point by point, and calculate the vibration deviation value and the pressure deviation value respectively;

[0014] A preliminary saw blade aging index determination unit, which is used to fuse the vibration deviation value and the pressure deviation value by using the weighted linear combination method, calculate the comprehensive aging value according to the different influence weights of vibration and pressure on saw blade aging, and set the comprehensive aging value as the preliminary saw blade aging index of the target logging machine saw blade.

[0015] As a further limitation of the technical solution of the embodiment of the present invention, the deviation value calculation unit specifically includes:

[0016] A data preprocessing sub-unit, which is used to preprocess the collected real-time vibration data and real-time pressure feedback data, including denoising, smoothing processing, and time series alignment;

[0017] A deviation sequence generation subunit, configured to perform a point-by-point comparison and analysis of the preprocessed real-time vibration data with the reference vibration data, calculate the vibration difference at each time point, and at the same time perform a point-by-point comparison of the real-time pressure feedback data with the reference pressure feedback data, calculate the corresponding pressure difference, and generate a vibration deviation sequence and a pressure deviation sequence;

[0018] A deviation value calculation subunit, configured to perform an overall quantization process on the vibration deviation sequence and the pressure deviation sequence respectively, and calculate the overall vibration deviation value and pressure deviation value by means of root mean square error or deviation from the mean.

[0019] As a further limitation of the technical solution of the embodiment of the present invention, the deviation percentage calculation module specifically includes:

[0020] A real-time temperature change trend curve generation unit, configured to preprocess the collected real-time temperature change data, including denoising, smoothing, and time series interpolation, and fit the preprocessed real-time temperature change data into a continuous real-time temperature change trend curve;

[0021] A temperature deviation data calculation unit, configured to generate an ideal temperature change curve based on the reference temperature change data obtained when the target logging machine uses the saw blade in the initial state and cuts the specified hardness tree with a preset cutting pressure parameter, and compare it with the fitted real-time temperature change trend curve point by point, and calculate the temperature deviation data at the corresponding time point;

[0022] A deviation percentage calculation unit, configured to perform a quantization process on the temperature deviation data at each time point, and calculate the overall temperature change deviation percentage by using the mean square error formula.

[0023] As a further limitation of the technical solution of the embodiment of the present invention, the supplementary cutting pressure value calculation module specifically includes:

[0024] A preliminary saw blade aging index correction unit, configured to multiply the deviation percentage by the preliminary saw blade aging index to correct the preliminary saw blade aging index in the temperature direction and generate an optimized saw blade aging index;

[0025] A supplementary cutting pressure value calculation unit, configured to multiply the optimized saw blade aging index by the preset cutting pressure parameter to calculate the required supplementary cutting pressure value;

[0026] A supplementary cutting pressure value application unit, configured to feedback the calculated supplementary cutting pressure value to the pressure control system of the target logging machine to dynamically adjust the cutting pressure parameter of the target logging machine.

[0027] As a further limitation of the technical solution of the embodiment of the present invention, the deviation percentage reflects the overall deviation degree between the real-time temperature change trend curve and the ideal temperature change curve, and is used to quantify the performance attenuation of the target logging machine saw blade in the temperature dimension.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] Through the Internet of Things technology, multi-dimensional real-time monitoring of the state of the logging machine saw blade and dynamic adjustment of cutting parameters are realized, effectively improving the stability and intelligent level of equipment operation. By comprehensively integrating the three key parameters of vibration, pressure and temperature, the system provides a more accurate evaluation of saw blade aging, solving the problems of being easily affected by the environment and being insufficiently comprehensive in traditional single-parameter monitoring methods. The supplementary cutting pressure value fed back in real time ensures that the logging machine can maintain efficient cutting operations when the tool performance deteriorates, while avoiding saw blade failures caused by excessive wear. The dynamic adjustment mechanism of the system not only extends the service life of the saw blade, but also reduces the equipment maintenance and downtime costs, bringing a double improvement in operation efficiency and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flowchart of the method provided by the embodiment of the present invention;

[0031] Figure 2 is a flowchart of the preliminary saw blade aging index determination module in the method provided by the embodiment of the present invention;

[0032] Figure 3 is a flowchart of the deviation value calculation unit in the method provided by the embodiment of the present invention;

[0033] Figure 4 is a flowchart of the deviation percentage calculation module in the method provided by the embodiment of the present invention;

[0034] Figure 5 is a flowchart of the supplementary cutting pressure value calculation module in the method provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0036] Furthermore, Figure 1 shows the application architecture diagram of the system provided by the embodiment of the present invention.

[0037] Among them, in another preferred embodiment provided by the present invention, an Internet of Things-based mechanical state real-time monitoring system includes:

[0038] The data acquisition module 100 is used to collect the real-time vibration data, real-time pressure feedback data, and real-time temperature change data of the saw blade of the target logging machine within a preset time range when the target logging machine conducts logging operations on the target tree according to the preset cutting pressure parameters.

[0039] In the embodiments of the present invention, the setting of the preset cutting pressure parameters is usually determined by combining multi-dimensional factors such as the hardness characteristics of the target tree, the tree diameter, and the performance of the logging machine tool through experimental determination and data analysis. For example, based on the hardness of a specific tree species and the cutting characteristics of the logging machine, the adaptation range of the initial cutting parameters can be determined through pressure calibration under experimental conditions. At the same time, combined with the data accumulation and model analysis in the historical operations of the logging machine, the selection of this pressure parameter can be further refined. This setting method provides a good basic condition for ensuring the stability and efficiency of the cutting process, enabling the equipment to complete the target tasks within the expected range and has been widely applied in existing practices.

[0040] The preset time range is usually determined according to the dynamic characteristics of the cutting process and the real-time monitoring requirements, generally covering a complete cutting cycle or a specific time period to ensure that the collected data is representative and continuous. The significance of this setting is to provide a fixed reference range for data acquisition, facilitating subsequent analysis and comparison of real-time data.

[0041] The real-time vibration data is obtained through acceleration sensors installed on the saw blade or cutting head of the logging machine. These sensors can accurately capture the vibration amplitude and frequency changes of the saw blade during operation. The real-time pressure feedback data is collected by pressure sensors installed in the hydraulic system of the logging machine or at the pressure contact points of the cutting head. These sensors can record the pressure changes applied to the tool during the cutting process in real time. The real-time temperature change data is monitored through thermocouple sensors or non-contact infrared temperature sensors embedded near the saw blade or cutting tool to accurately reflect the heat accumulation and dissipation of the saw blade during the cutting process. These data provide the basic input for the real-time monitoring system, ensuring that the assessment of the mechanical state has sufficient data support.

[0042] Furthermore, the real-time mechanical state monitoring system based on the Internet of Things further includes:

[0043] The preliminary saw blade aging index determination module 200 is used to obtain the reference vibration data and reference pressure feedback data when the target logging machine uses the saw blade in the initial state to cut the same tree according to the preset cutting pressure parameters, and compare the real-time vibration data with the reference vibration data and the real-time pressure feedback data with the reference pressure feedback data to deduce the preliminary saw blade aging index of the saw blade of the target logging machine.

[0044] In the preliminary saw blade aging index determination module, there is an aging evaluation model based on dynamic characteristic fitting and multi-parameter correction. This model includes the reference vibration data, reference pressure data, and reference temperature change data when the target logging machine cuts trees with different hardness parameters under the condition of the saw blade in the initial state of use at different cutting pressure parameters.

[0045] Specifically, Figure 2 FIG. 5 shows a structural block diagram of the preliminary saw blade aging index determination module 200 in the system provided by the embodiment of the present invention.

[0046] Among them, in the preferred embodiment provided by the present invention, the preliminary saw blade aging index determination module 200 specifically includes:

[0047] A reference data acquisition unit 201, configured to determine the specified hardness parameter of the target tree, and obtain the reference vibration data, reference pressure feedback data, and reference temperature change data when the target logging machine uses the saw blade in the initial state to cut the tree with the specified hardness parameter at the preset cutting pressure parameter according to the aging evaluation model;

[0048] A deviation value calculation unit 202, configured to perform comparative analysis on the collected real-time vibration data and the reference vibration data, and at the same time perform point-by-point comparison on the real-time pressure feedback data and the reference pressure feedback data, and calculate the vibration deviation value and the pressure deviation value respectively;

[0049] A preliminary saw blade aging index determination unit 203, configured to fuse the vibration deviation value and the pressure deviation value by using a weighted linear combination method, calculate the comprehensive aging value according to the different influence weights of vibration and pressure on saw blade aging, and set the comprehensive aging value as the preliminary saw blade aging index of the saw blade of the target logging machine.

[0050] In the embodiment of the present invention, when determining the specified hardness parameter of the target tree, usually a hardness database based on tree species classification or directly obtaining hardness data through a tree hardness measurement sensor is adopted. After the hardness parameter is determined, the reference operation data of the logging machine is traced back or simulated through the aging evaluation model to generate the reference vibration data, reference pressure feedback data, and reference temperature change data when the target logging machine cuts the tree with the specified hardness parameter under the initial state saw blade. This process can be completed through a data calibration experiment, that is, recording the dynamic vibration, pressure, and temperature curves when the saw blade cuts a specific tree in a standard environment and storing them in the model as a reference for real-time data comparison.

[0051] The significance of using the vibration deviation value and the pressure deviation value as preliminary saw blade aging indicators is that these two data dimensions can comprehensively reflect the working performance and health status of the saw blade. The vibration deviation value characterizes the change in the smoothness of the saw blade relative to the reference state during actual operation and can sensitively reflect the wear or structural deformation of the blade. The pressure deviation value directly reflects the impact of the decline in tool performance on the hydraulic system load during the cutting process. This two-dimensional evaluation method can make up for the deficiency that a single parameter cannot comprehensively reflect the aging of the saw blade, provide a more accurate aging evaluation result, and thus support subsequent dynamic pressure adjustment.

[0052] When using the weighted linear combination method to fuse the vibration deviation value and the pressure deviation value, it is first necessary to determine the different weights of the two for saw blade aging, which is usually obtained through experimental data analysis or historical data model training. Subsequently, the vibration deviation value and the pressure deviation value are weighted and calculated according to their weights to generate a comprehensive aging value. The weighted linear combination method can flexibly adjust the influence degree of different parameters, making the comprehensive aging value more in line with the actual working state of the saw blade. After the calculation is completed, the comprehensive aging value is set as the preliminary saw blade aging indicator and fed back to the system for further optimizing the pressure parameter or other key operating conditions.

[0053] Specifically, Figure 3 The structural block diagram of the deviation value calculation unit 202 in the system provided by the embodiment of the present invention is shown.

[0054] Among them, in the preferred embodiment provided by the present invention, the deviation value calculation unit 202 specifically includes:

[0055] The data preprocessing subunit 2021 is used to preprocess the collected real-time vibration data and real-time pressure feedback data, including denoising, smoothing processing, and time series alignment;

[0056] The deviation sequence generation subunit 2022 is used to compare and analyze the preprocessed real-time vibration data point by point with the reference vibration data, calculate the vibration difference at each time point, and at the same time compare the real-time pressure feedback data point by point with the reference pressure feedback data, calculate the corresponding pressure difference, and generate a vibration deviation sequence and a pressure deviation sequence;

[0057] The deviation value calculation subunit 2023 is used to perform overall quantization processing on the vibration deviation sequence and the pressure deviation sequence respectively, and calculate the overall vibration deviation value and pressure deviation value by means of root mean square error or deviation from the mean.

[0058] In the embodiment of the present invention, in the data preprocessing stage, vibration data and pressure feedback data of the saw blade during the cutting process of the logging machine are collected in real time through sensors. Since the collected original data may contain interferences such as environmental noise and abnormal fluctuations, the system preprocesses this data, including removing noise signals, smoothing the data waveform, and aligning the time series of vibration data and pressure data so that they can accurately correspond on the same time axis. This step of processing ensures the accuracy of subsequent analysis. For example, when sharp peaks appear in the vibration data due to short-term equipment vibration during the saw blade cutting process, the preprocessing subunit will identify these abnormal points and smooth them to a reasonable range that conforms to the actual cutting trend.

[0059] In the deviation sequence generation stage, the system compares the preprocessed vibration data point by point with the reference vibration data to calculate the vibration difference at each time point. Similarly, the preprocessed pressure feedback data is compared point by point with the reference pressure feedback data to calculate the pressure difference at each time point. Through this comparison process, a vibration deviation sequence and a pressure deviation sequence can be generated. For example, during a certain period, the real-time vibration data may be higher than the reference vibration data by a certain amplitude, which may indicate additional vibration of the saw blade due to aging during the cutting process. Similarly, the continuous increase in the pressure feedback data compared to the reference data may reflect a decrease in cutting efficiency, and the hydraulic system needs to apply greater pressure to maintain operation.

[0060] In the deviation value calculation stage, the system conducts an overall quantitative analysis of the entire vibration deviation sequence and pressure deviation sequence to evaluate the overall deviation degree of the real-time data relative to the reference data. Through the analysis of the vibration deviation sequence, the overall change range and degree of vibration can be calculated, thereby evaluating the change in the smoothness of the saw blade during the cutting process. Similarly, through the quantification of the pressure deviation sequence, the load change situation of the hydraulic system can be evaluated. For example, the deviation value calculation subunit may identify that during a long period, the pressure deviation value shows a significant upward trend, indicating that the saw blade may have entered a relatively serious aging stage and it is necessary to adjust the cutting pressure in a timely manner or replace the cutting tool.

[0061] The above process provides accurate evaluation results on the performance of the saw blade through a comprehensive analysis of vibration and pressure data, providing solid technical support for subsequent dynamic adjustment and maintenance decisions.

[0062] Furthermore, the real-time mechanical state monitoring system based on the Internet of Things further includes:

[0063] A deviation percentage calculation module 300, configured to fit the real-time temperature change data into a real-time temperature change trend curve, and compare it with the ideal temperature change curve when the saw blade in the initial state cuts the same tree with a preset cutting pressure parameter, and calculate the deviation percentage of the temperature change.

[0064] Specifically, Figure 4 A structural block diagram of the deviation percentage calculation module 300 in the system provided by the embodiment of the present invention is shown.

[0065] Among them, in the preferred embodiment provided by the present invention, the deviation percentage calculation module 300 specifically includes:

[0066] A real-time temperature change trend curve generation unit 301, configured to preprocess the collected real-time temperature change data, including denoising, smoothing, and time series interpolation, and fit the preprocessed real-time temperature change data into a continuous real-time temperature change trend curve;

[0067] A temperature deviation data calculation unit 302, configured to generate an ideal temperature change curve based on the reference temperature change data obtained when the target logging machine uses the saw blade in the initial state and cuts a tree with a specified hardness at a preset cutting pressure parameter, and compare it point by point with the fitted real-time temperature change trend curve to calculate the temperature deviation data at the corresponding time point;

[0068] A deviation percentage calculation unit 303, configured to perform quantization processing on the temperature deviation data at each time point, and calculate the overall temperature change deviation percentage using the mean square error formula.

[0069] In the embodiment of the present invention, the real-time temperature change trend curve generation unit 301 first collects the temperature change data of the saw blade of the logging machine during the cutting process through a sensor. These data may contain noise or discontinuous points, such as measurement errors or discontinuous changes caused by external environmental influences. The unit preprocesses these data, including a denoising operation to filter out abnormal signals that do not conform to the actual temperature change, and at the same time uses a smoothing algorithm to reduce the random fluctuations of the data. In addition, if the time intervals of the collected temperature data are uneven or there are missing points, the data is complemented through time series interpolation technology to ensure its continuity and time consistency. After these processes, the unit fits the preprocessed data into a continuous temperature change trend curve to reflect the real-time temperature dynamics of the saw blade during the cutting process.

[0070] The temperature deviation data calculation unit 302 generates an ideal temperature change curve based on the reference temperature change data obtained when the target logging machine cuts trees with a specified hardness at a preset cutting pressure parameter in the initial state of use. This curve is usually generated through laboratory calibration or ideal working condition data in historical records, and can truly reflect the temperature characteristics of a new saw blade under the same cutting conditions. Subsequently, this unit compares the fitted real-time temperature change trend curve point by point with the ideal temperature change curve, and calculates the temperature deviation data at each time point. For example, in certain time periods, the real-time temperature change may be higher than the ideal temperature change by a specific amplitude, which may indicate additional heat generation during the cutting process of the saw blade due to wear or reduced efficiency.

[0071] The deviation percentage calculation unit 303 performs an overall quantification process on the calculated temperature deviation data at each time point to evaluate the overall deviation degree between the real-time temperature change and the ideal temperature change. By analyzing the deviation data at all time points, this unit normalizes them to a comprehensive index, quantifies these data using the mean square error formula, and generates the temperature change deviation percentage. The deviation percentage, as the final output, reflects the overall change degree of the current temperature characteristics of the saw blade relative to the reference state. This result can be directly used for subsequent saw blade aging analysis and cutting parameter optimization, providing data support for dynamic adjustment of pressure or saw blade replacement decisions.

[0072] Furthermore, the real-time mechanical state monitoring system based on the Internet of Things further includes:

[0073] The supplementary cutting pressure value calculation module 400 is used to adjust the preliminary aging index with the deviation percentage as a correction factor to obtain an optimized saw blade aging index, and multiply the optimized saw blade aging index by the preset cutting pressure parameter to calculate the required supplementary cutting pressure value.

[0074] Specifically, Figure 5 The structure block diagram of the supplementary cutting pressure value calculation module 400 in the system provided by the embodiment of the present invention is shown.

[0075] Among them, in the preferred embodiment provided by the present invention, the supplementary cutting pressure value calculation module 400 specifically includes:

[0076] The preliminary saw blade aging index correction unit 401 is used to multiply the deviation percentage by the preliminary saw blade aging index to correct the preliminary saw blade aging index in the temperature direction and generate an optimized saw blade aging index;

[0077] The supplementary cutting pressure value calculation unit 402 is used to multiply the optimized saw blade aging index by the preset cutting pressure parameter to calculate the required supplementary cutting pressure value;

[0078] The supplementary cutting pressure value application unit 403 is used to feedback the calculated supplementary cutting pressure value to the target logger pressure control system, and dynamically adjust the cutting pressure parameters of the target logger.

[0079] The deviation percentage reflects the overall deviation degree between the real-time temperature change trend curve and the ideal temperature change curve, and is used to quantify the performance attenuation of the target logger saw blade in the temperature dimension.

[0080] In the embodiment of the present invention, the preliminary saw blade aging index correction unit 401 first receives the deviation percentage and the preliminary saw blade aging index as inputs, and corrects the preliminary aging index by using the real-time temperature change reflected by the deviation percentage. The correction process incorporates the temperature dimension change into the aging index through a multiplication operation, making it more comprehensively reflect the current comprehensive aging state of the saw blade. For example, if the deviation percentage is relatively high, indicating that the temperature change significantly deviates from the ideal state, the system will adjust the preliminary aging index to amplify the impact of temperature on saw blade aging, generating an optimized saw blade aging index.

[0081] The supplementary cutting pressure value calculation unit 402 calculates the required supplementary pressure value during the cutting process by using the optimized saw blade aging index and the preset cutting pressure parameters. The optimized saw blade aging index directly reflects the comprehensive state of the saw blade in the vibration, pressure, and temperature dimensions. Therefore, combined with the preset cutting pressure parameters, the accurate pressure supplementary value can be calculated. For example, when the aging degree of the saw blade increases, the system improves the cutting efficiency through the supplementary pressure value to ensure that the logger can adapt to the change of tool performance decline.

[0082] The supplementary cutting pressure value application unit 403 receives the supplementary pressure value and feedbacks it to the pressure control system of the logger, dynamically adjusting the cutting pressure parameters of the logger. This process realizes closed-loop adjustment through the pressure control system, optimizes the cutting performance of the logger in real time, and at the same time protects the saw blade from excessive wear. For example, when the calculated supplementary pressure value indicates that the current cutting pressure is insufficient, the system immediately increases the cutting pressure to ensure the continuity and stability of the cutting process.

[0083] In summary, the real-time mechanical status monitoring system based on the Internet of Things realizes multi-dimensional real-time monitoring of the saw blade status of a logging machine and dynamic adjustment of cutting parameters through Internet of Things technology, effectively improving the stability and intelligent level of equipment operation. By comprehensively integrating the three key parameters of vibration, pressure, and temperature, the system provides a more accurate evaluation of saw blade aging, solving the problems of the traditional single-parameter monitoring method being easily affected by the environment and being insufficiently comprehensive. The supplementary cutting pressure value fed back in real time ensures that the logging machine can maintain efficient cutting operations when the tool performance deteriorates, while avoiding saw blade failures caused by excessive wear. The dynamic adjustment mechanism of the system not only extends the service life of the saw blade but also reduces equipment maintenance and downtime costs, bringing a double improvement in operation efficiency and economic benefits.

[0084] The system has extremely high adaptability and can adjust cutting parameters according to different tree hardnesses and operating conditions to meet the diverse working condition requirements. At the same time, this technical solution conforms to the trends of Industry 4.0 and the intelligent development of the Internet of Things. In the future, it can not only be applied to the field of logging machines but also be extended to various industrial scenarios such as construction machinery and intelligent manufacturing equipment, providing broad development space and application value for the intelligent monitoring and predictive maintenance of industrial equipment.

[0085] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown sequentially in the direction of the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless clearly stated in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages do not necessarily have to be executed at the same moment but can be executed at different moments. The execution order of these sub-steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0086] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0087] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0088] The above embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

[0089] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A real-time monitoring system for mechanical status based on the Internet of Things, characterized in that: The system comprises: A data acquisition module, used to collect real-time vibration data, real-time pressure feedback data and real-time temperature change data of a saw blade of a target felling machine within a preset time range when the target felling machine performs felling operations on target trees according to preset cutting pressure parameters; A preliminary saw blade aging index determination module is used to obtain the baseline vibration data and baseline pressure feedback data when the target felling machine uses the initial state saw blade to cut the same tree with the preset cutting pressure parameters, and compare the real-time vibration data with the baseline vibration data, and the real-time pressure feedback data with the baseline pressure feedback data, to derive the preliminary saw blade aging index of the target felling machine saw blade; A deviation percentage calculation module is used to fit the real-time temperature change data into a real-time temperature change trend curve, and compare it with the ideal temperature change curve when the saw blade in the initial state cuts the same tree with the preset cutting pressure parameters, and calculate the deviation percentage of the temperature change; The supplementary cutting pressure value calculation module is used to adjust the preliminary aging index with the deviation percentage as the correction factor to obtain the optimized saw blade aging index, and multiply the optimized saw blade aging index by the preset cutting pressure parameter to calculate the required supplementary cutting pressure value.

2. The real-time monitoring system for mechanical status based on the Internet of Things according to claim 1 is characterized in that: The preliminary saw blade aging index determination module is provided with an aging assessment model based on dynamic characteristic fitting and multi-parameter correction, which includes baseline vibration data, baseline pressure data and baseline temperature change data when the target feller cuts trees with different hardness parameters with different cutting pressure parameters under the condition of using the initial state saw blade.

3. The real-time monitoring system for mechanical status based on the Internet of Things according to claim 2 is characterized in that: The preliminary saw blade aging index determination module specifically includes: A reference data acquisition unit, for determining a specified hardness parameter of a target tree, and acquiring reference vibration data, reference pressure feedback data, and reference temperature change data when a target felling machine uses an initial state saw blade to cut a tree with a specified hardness parameter at a preset cutting pressure parameter according to an aging assessment model; A deviation value calculation unit is used to compare and analyze the collected real-time vibration data with the reference vibration data, and to compare the real-time pressure feedback data with the reference pressure feedback data point by point, and calculate the vibration deviation value and the pressure deviation value respectively; The preliminary saw blade aging index determination unit is used to fuse the vibration deviation value and the pressure deviation value using a weighted linear combination method, calculate a comprehensive aging value based on the different influence weights of vibration and pressure on saw blade aging, and set the comprehensive aging value as the preliminary saw blade aging index of the target felling machine saw blade.

4. The real-time monitoring system for mechanical status based on the Internet of Things according to claim 3 is characterized in that: The deviation value calculation unit specifically includes: The data preprocessing subunit is used to preprocess the collected real-time vibration data and real-time pressure feedback data, including denoising, smoothing and time series alignment; The deviation sequence generation subunit is used to compare and analyze the pre-processed real-time vibration data with the reference vibration data point by point, calculate the vibration difference at each time point, and compare the real-time pressure feedback data with the reference pressure feedback data point by point, calculate the corresponding pressure difference, and generate a vibration deviation sequence and a pressure deviation sequence; The deviation value calculation subunit is used to perform overall quantization processing on the vibration deviation sequence and the pressure deviation sequence respectively, and calculate the overall vibration deviation value and pressure deviation value by the root mean square error or deviation from the mean method.

5. The real-time monitoring system for mechanical status based on the Internet of Things according to claim 3 is characterized in that: The deviation percentage calculation module specifically includes: A real-time temperature change trend curve generating unit is used to pre-process the collected real-time temperature change data, including denoising, smoothing and time series interpolation, and fit the pre-processed real-time temperature change data into a continuous real-time temperature change trend curve; The temperature deviation data calculation unit is used to generate an ideal temperature change curve based on the reference temperature change data obtained when the target feller uses the initial state saw blade and the preset cutting pressure parameters to cut the tree with the specified hardness, and compare it with the fitted real-time temperature change trend curve point by point to calculate the temperature deviation data at the corresponding time point; The deviation percentage calculation unit is used to quantify the temperature deviation data at each time point and calculate the overall temperature change deviation percentage using the mean square error formula.

6. The real-time monitoring system for mechanical status based on the Internet of Things according to claim 3 is characterized in that: The supplementary cutting pressure value calculation module specifically includes: A preliminary saw blade aging index correction unit, used for multiplying the deviation percentage with the preliminary saw blade aging index, correcting the preliminary saw blade aging index in the temperature direction, and generating an optimized saw blade aging index; A supplementary cutting pressure value calculation unit, used for multiplying the optimized saw blade aging index with the preset cutting pressure parameter to calculate the required supplementary cutting pressure value; The supplementary cutting pressure value application unit is used to feed back the calculated supplementary cutting pressure value to the target feller pressure control system to dynamically adjust the cutting pressure parameters of the target feller.

7. The real-time monitoring system for mechanical status based on the Internet of Things according to claim 6 is characterized in that: The deviation percentage reflects the overall deviation between the real-time temperature change trend curve and the ideal temperature change curve, and is used to quantify the performance degradation of the target felling machine saw blade in the temperature dimension.

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