Production quality detection and early warning method and device for saw chain gears

Abstract

The present application provides a production quality detection and early warning method and device for saw chain gears, which relates to the technical field of quality detection and early warning, including: parsing the loss parameters of saw chain gears, locating loss values, and constructing a detection parameter hierarchical structure; extracting core detection parameter combinations; using positioning parameters as detection targets and comparison parameters as collaborative follow-up quantities, fitting them into the quality detection module, and performing quality detection on production monitoring data; performing early warning level analysis based on parameter deviations and the hierarchical relationship between the detection target and the detection parameter hierarchical structure; matching early warning paths, and generating early warning feedback information. This application can solve the technical problems in the prior art that the accuracy of quality detection is low and the early warning information is inaccurate due to the difficulty in quickly and accurately locating specific quality problems or structural defects during the production process. By combining positioning parameters and comparison parameters, structural losses can be quickly located, thereby improving the accuracy of quality detection and early warning.

Description

Technical Field

[0001] The present application relates to the technical field of quality detection and early warning, and in particular to a method and device for detecting and early warning the production quality of saw chain gears. Background Art

[0002] Saw chain gears are key transmission components in mechanical equipment such as chain saws. Their main function is to transfer power from the engine to the chain-driven saw blade for cutting through meshing with the chain. They are usually made of high-strength alloy steel, and their geometric parameters include outer diameter, tooth height, tooth pitch, shaft hole size, etc. These parameters need to be precisely controlled within the design requirements. Any structural deviation in the production process may cause the gear to be unable to accurately mesh with the chain, affecting the chain transmission efficiency, increasing vibration, and even causing equipment failure. Although existing quality inspection and early warning methods have improved production efficiency and product quality to a certain extent, they still face some technical challenges in practical applications, especially in the production process, where it is difficult to quickly and accurately locate quality problems and structural defects.

[0003] Due to the complex structure and many complex geometric parameters of saw chain gears, it is often only possible to provide an overall quality assessment, but it is difficult to refine to the specific location of structural defects, resulting in reduced accuracy of quality inspection. Usually, judgments are made based on set thresholds, and an early warning is triggered when the detected parameter deviation exceeds the set value. The complex relationship between the various parameters is not taken into account, so detailed information such as the specific nature, location, and severity of the problem is often not provided, resulting in insufficient accuracy of the early warning information.

[0004] In summary, the prior art has technical problems such as low accuracy of quality inspection and inaccurate early warning information due to the difficulty in quickly and accurately locating specific quality problems or structural defects during the production process. Summary of the invention

[0005] The purpose of this application is to provide a production quality detection and early warning method and device for saw chain gears, so as to solve the technical problems in the prior art that the accuracy of quality detection is low and the early warning information is inaccurate due to the difficulty in quickly and accurately locating specific quality problems or structural defects during the production process.

[0006] In order to achieve the above-mentioned objectives, the present application provides a production quality detection and early warning method and device for saw chain gears.

[0007] In a first aspect, the present application provides a production quality detection and early warning method for saw chain gears, which is implemented by a production quality detection and early warning device for saw chain gears, wherein the production quality detection and early warning method for saw chain gears comprises: parsing the loss parameters of the saw chain gears, locating the loss values ​​of the gear structural parameters according to the loss parameters, and constructing a detection parameter hierarchical structure; extracting a core detection parameter combination according to the detection parameter hierarchical structure, wherein the core detection parameter combination is a quality parameter combination that can quickly locate the structural loss, and the core detection parameter combination has a positioning parameter and a comparison parameter; using the positioning parameter as a detection target and the comparison parameter as a collaborative following quantity, fitting them into a quality detection module, wherein the quality detection module is connected to a data sensing platform, and is used to perform quality detection on production monitoring data using a structural standard quantity of the detection target and the collaborative following quantity to obtain a parameter deviation; performing a warning level analysis according to the parameter deviation and the hierarchical relationship between the detection target and the detection parameter hierarchical structure to obtain a quality risk warning level; matching a warning path according to the quality risk warning level, and generating warning feedback information based on the warning path.

[0008] Optionally, a detection sample database is obtained, the detection sample database includes static samples and time-series dynamic samples, the static samples reflect the size detection results of the target saw chain gear and the design drawings, and the time-series dynamic samples reflect the time-series abnormal size detection results of the target saw chain gear during use; according to the detection sample database, abnormal sample cases are extracted for the static samples and the time-series dynamic samples respectively to obtain quality abnormal deviation data; loss parameter clustering is performed on the quality abnormal deviation data of the static samples, and weighted average calculation of the loss amount is performed according to the clustering results to obtain static loss parameters; the quality abnormal deviation data of the time-series dynamic samples are sorted according to time series, and the loss time series curves of each quality deviation parameter are fitted to obtain dynamic loss parameters according to the loss time series curves; the static loss parameters are integrated with the dynamic loss parameters to obtain the loss parameters.

[0009] Optionally, the loss timing curves of the quality deviation parameters are aligned according to the timing relationship, and the nodes of the loss timing curve are marked according to the slope of the curve; the timing chain time window is divided based on the timing alignment relationship, and the timing weight of the time window is configured, wherein the timing weight of the early time is greater than the timing weight of the late time; according to the aligned time window, the timing weight is used to perform continuous loss amount weighted calculation on all nodes of the loss timing curve to obtain the loss value of each quality deviation parameter, and the quality deviation parameter whose loss value meets the basic timing change threshold is used as the dynamic loss parameter.

[0010] Optionally, according to the detection parameter hierarchical structure, the gear structure parameters corresponding to the loss parameters of each hierarchical structure are obtained, and the parameter type of the gear structure parameters is located, and the parameter type includes tooth shape, tooth pitch, and tooth top height; the production process flow of the saw chain gear is analyzed to obtain the processing change state of the parameter type, and the positioning connection position of the corresponding processing process flow is determined; according to the positioning connection position and the processing change state, the positioning parameter is determined; with the positioning parameter as the center, the position relationship analysis of the structural contour of the parameter type is performed to obtain the comparison parameter, and the comparison parameter is a parameter or parameter combination used to locate the structural contour with the relative size parameter of the positioning parameter; according to the positioning parameter and the comparison parameter, the core detection parameter combination is obtained.

[0011] Optionally, a saw chain gear simulation restoration is performed according to the positioning parameters and comparison parameters to obtain a restoration result; when the restoration result meets the requirements, a reduction analysis is performed on the comparison parameters to obtain a reduction restoration result, and a minimum comparison parameter is determined; when the restoration result does not meet the requirements, a deviation position is determined according to the restoration result, and an incremental analysis of the positioning parameters or comparison parameters is performed according to the deviation position to obtain an incremental parameter; and the simulation restoration is re-performed using the incremental parameters until the requirements are met.

[0012] Optionally, based on the number of levels and the hierarchical relationship of the detection parameter hierarchical structure, a module space of the quality detection module is established, the module space corresponds to the detection parameter hierarchical structure and has a spatial identification of the structural parameters; the detection target and the collaborative following amount are used as indexes to perform search and matching with the spatial identification, and the detection target and the collaborative following amount are fitted to the corresponding module space according to the spatial matching relationship; the data sensing platform is connected, and the detection target and the collaborative following amount are used to match the sensor source, and a transmission channel between the sensor source and the module space is established, and the data collected by the matching sensor source is synchronized to the module space through the transmission channel, the detection target and the collaborative following amount obtained by positioning and monitoring are located, and the monitoring amount is compared with the standard amount of the detection target-collaborative following amount to obtain the parameter deviation amount.

[0013] Optionally, according to the parameter type of the detection parameter hierarchy structure, the failure probability of the parameter type is obtained to determine the type risk coefficient; according to the hierarchical relationship, the hierarchy coefficient is set; the type risk coefficient, the hierarchy coefficient and the parameter deviation are superimposed and matched with a preset risk level list to obtain the quality risk warning level.

[0014] Optionally, based on the parameter type and the type risk coefficient, the impact coverage range is determined, and the impact coverage range includes multiple partitions, including a direct range, an adjacent associated range, and a process impact range; based on the quality risk warning level, the warning range level is obtained; the impact coverage range is matched using the warning range level to determine a matching warning range, and the warning communication connection path of the matching warning range is obtained as the warning path.

[0015] In a second aspect, the present application further provides a production quality detection and early warning device for saw chain gears, which is used to execute the production quality detection and early warning method for saw chain gears as described in the first aspect, wherein the production quality detection and early warning device for saw chain gears comprises: a loss positioning module, which is used to parse the loss parameters of the saw chain gears, locate the loss values ​​of the gear structural parameters according to the loss parameters, and construct a detection parameter hierarchy structure; a detection parameter extraction module, which is used to extract a core detection parameter combination according to the detection parameter hierarchy structure, wherein the core detection parameter combination is a quality parameter combination that can quickly locate the structural loss, and the core detection parameter combination has a positioning parameter and a comparison parameter; a quality detection parameter extraction module, which is used to extract a core detection parameter combination according to the detection parameter hierarchy structure, wherein the core detection parameter combination is a quality parameter combination that can quickly locate the structural loss, and the core detection parameter combination has a positioning parameter and a comparison parameter; a quality detection parameter extraction module, which is used to extract a core detection parameter combination according to the detection parameter hierarchy structure, and wherein ... A measurement module, the quality detection module is used to use the positioning parameter as the detection target, and the comparison parameter as the collaborative following quantity, which is fitted into the quality detection module. The quality detection module is connected to the data sensing platform, and is used to use the structural standard quantity of the detection target and the collaborative following quantity to perform quality detection on the production monitoring data to obtain the parameter deviation; an early warning level determination module, the early warning level determination module is used to perform early warning level analysis according to the parameter deviation and the hierarchical relationship between the detection target and the detection parameter hierarchical structure, and obtain the quality risk early warning level; an early warning feedback module, the early warning feedback module is used to match the early warning path according to the quality risk early warning level, and generate early warning feedback information based on the early warning path.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0017] By analyzing the loss parameters of the saw chain gear, the loss values ​​of the gear structural parameters are located according to the loss parameters, and a detection parameter hierarchical structure is constructed; according to the detection parameter hierarchical structure, a core detection parameter combination is extracted, and the core detection parameter combination is a quality parameter combination that can quickly locate the structural loss, and the core detection parameter combination has a positioning parameter and a comparison parameter; the positioning parameter is used as a detection target, and the comparison parameter is used as a collaborative following quantity, which is fitted into a quality detection module, and the quality detection module is connected to a data sensing platform, and is used to perform quality detection on production monitoring data using the structural standard quantity of the detection target and the collaborative following quantity to obtain a parameter deviation; according to the parameter deviation and the hierarchical relationship between the detection target and the detection parameter hierarchical structure, a warning level analysis is performed to obtain a quality risk warning level; according to the quality risk warning level, a warning path is matched, and warning feedback information is generated based on the warning path. In other words, by constructing a hierarchical structure of detection parameters, the production process is evaluated in layers according to the detection standards of different levels. The positioning parameters and comparison parameters are combined to accurately identify and locate the specific structural parameters where quality problems occur. The quality risk warning level is derived based on the hierarchical structure analysis, and the warning path is automatically generated, which improves the accuracy of quality detection and thus the accuracy of warning information.

[0018] 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

[0019] 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.

[0020] Figure 1 A schematic diagram of the process of the production quality detection and early warning method of the saw chain gear of the present application;

[0021] Figure 2 This is a schematic diagram of the structure of the production quality detection and early warning device of the saw chain gear in this application.

[0022] Explanation of the accompanying drawings: loss location module 11, detection parameter extraction module 12, deviation determination module 13, warning level determination module 14, warning feedback module 15. DETAILED DESCRIPTION

[0023] This application solves the technical problems in the prior art that the quality inspection accuracy is low and the warning information is inaccurate due to the difficulty in quickly and accurately locating specific quality problems or structural defects in the production process by providing a production quality inspection and early warning method and device for saw chain gears. By constructing a hierarchical structure of inspection parameters, the production process is evaluated in layers according to the inspection standards of different levels, and the specific structural parameters where quality problems occur are accurately identified and located by combining positioning parameters and comparison parameters. The quality risk early warning level is obtained based on the hierarchical structure analysis, and the early warning path is automatically generated, thereby improving the accuracy of quality inspection and thus improving the accuracy of early warning information.

[0024] 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.

[0025] For example, please refer to the attached Figure 1 The present application provides a production quality detection and early warning method for saw chain gears, wherein the production quality detection and early warning method for saw chain gears is applied to a production quality detection and early warning device for saw chain gears, and the production quality detection and early warning method for saw chain gears specifically comprises the following steps:

[0026] Step 1: Analyze the loss parameters of the saw chain gear, locate the loss values ​​of the gear structure parameters according to the loss parameters, and build a hierarchical structure of detection parameters.

[0027] Specifically, in order to locate and analyze the quality problems of saw chain gears, it is first necessary to determine the loss parameters, including but not limited to the dimensional deviation, surface wear, structural deformation, etc. of the gears. Obtain a test sample database, which contains a database of various types of data, including static samples and time-series dynamic samples, that is, an unused sample database and a used sample database. According to the design requirements, determine the abnormal quality deviation data in the test sample database, that is, the parameters that have been lost. Cluster the static parts of these abnormal quality deviation data and divide them into multiple clusters according to the loss type. Perform weighted average calculation according to the weight of each quality deviation parameter to obtain the static loss parameters. For the time-series dynamic samples, sort them in chronological order, fit a loss time-series curve for each quality deviation parameter, and obtain the dynamic loss parameters based on the loss time-series curve of each quality deviation parameter.

[0028] Static loss parameters are integrated with dynamic loss parameters. If some parameters appear in both static and dynamic samples, they will be weighted according to the preset weights. Usually, static loss has a larger weight because it reflects initial quality problems in the production process, and quality problems have occurred before use, while dynamic loss reflects degradation during use.

[0029] According to the determined loss parameters, the loss values ​​of the gear structural parameters are located, and each loss parameter is compared with its design standard to determine whether the parameter meets the design requirements. The size of the loss value determines the severity of the gear quality problem. The structural parameters of the gear refer to the key parameters that describe the geometry and size of the gear, such as outer diameter, tooth height, tooth pitch, tooth thickness, shaft hole size, etc. The loss value indicates the severity of a certain quality deviation and is calculated based on the difference between the actual measured value and the design value. Generally, the larger the loss value, the more serious the deviation and the more obvious the quality problem.

[0030] According to the size of the loss value and the impact on the gear function, the various detection parameters are organized into a hierarchical structure and divided according to the severity of the loss. For example, it is divided into primary parameters (highest level), secondary parameters (middle level), and tertiary parameters (lowest level). Among them, primary parameters are usually the most critical parameters for gear performance, such as outer diameter, tooth height and other dimensional parameters; secondary parameters have a certain impact on gear performance, but the impact is small, such as tooth pitch, shaft hole size, etc.; tertiary parameters are parameters with the least impact, such as surface roughness, etc. The detection parameter hierarchy is divided according to actual conditions, and it is not necessarily an example of hierarchical division. After constructing the detection parameter hierarchy, the key quality problems can be quickly located and screened out according to the comparison between the loss value of each parameter and the design requirements. By analyzing the loss parameters and constructing the detection parameter hierarchy, it is possible to quickly locate which parameters have the greatest impact on the function of the gear, and give priority to solving these problems to avoid waste of resources.

[0031] Step 2: extracting a core detection parameter combination according to the detection parameter hierarchical structure, wherein the core detection parameter combination is a quality parameter combination capable of quickly locating structural losses, and the core detection parameter combination has a positioning parameter and a comparison parameter.

[0032] Specifically, the most critical parameters are extracted from the detection parameter hierarchy, that is, the core detection parameter combination is constructed. The corresponding gear structure parameters are obtained according to the loss parameters of each hierarchical structure, and the parameter type is located, including tooth shape, tooth pitch, tooth top height, etc. According to the production process of the saw chain gear, the positioning parameters are determined. The positioning parameters are the most basic and critical parameters, which are used to determine the basic geometry and position of the gear. The accuracy of the positioning parameters directly determines whether the gear can effectively cooperate with components such as chains and shafts. According to the positioning parameters, the position relationship of the structural profile of the saw chain gear is analyzed to obtain the comparison parameters. The comparison parameters are auxiliary parameters used to further verify the accuracy of the gear structure. By comparing the positioning parameters and the comparison parameters, it is possible to check whether the accuracy of the gear meets the design requirements.

[0033] In order to quickly locate structural losses, it is necessary to screen and optimize the core detection parameters in combination with the gear's usage requirements, manufacturing process and actual processing data. According to the positioning parameters and comparison parameters, the saw chain gear is simulated and restored, and the results of the simulation and restoration are analyzed to see whether they meet the design requirements. The comparison parameters or positioning parameters are adjusted accordingly until the design requirements are met. At this time, the positioning parameters and comparison parameters constitute a quality parameter combination. The core detection parameter combination can find possible quality problems or structural defects in the gear through detection in a relatively short period of time. By extracting the most critical core detection parameters, the detection time can be shortened, unnecessary redundant detection can be avoided, and production efficiency can be improved.

[0034] Step three: Use the positioning parameter as the detection target and the comparison parameter as the collaborative following quantity, and fit them into the quality detection module. The quality detection module is connected to the data sensing platform to perform quality detection on the production monitoring data using the structural standard quantity of the detection target and the collaborative following quantity to obtain the parameter deviation.

[0035] Specifically, positioning parameters refer to the key parameters that best reflect product quality in quality inspection. Taking positioning parameters as the inspection target means that these parameters will be the main reference standard for evaluating whether the gear meets the design requirements. Comparison parameters are auxiliary means closely related to positioning parameters, which are used to assist in the analysis of inspection targets. Co-following quantities refer to those parameters that change with the inspection target and can help judge the quality of the product. The quality inspection module is established based on the hierarchical structure of inspection parameters. It is a tool for receiving and analyzing data. It can be a hardware platform, a software program, or a combination of the two. It is specifically used to collect sensor data and compare it with standard data to determine quality problems in the production process. The quality inspection module can identify deviations between target parameters and standards, and provide early warnings or adjustment suggestions.

[0036] The positioning parameters and comparison parameters are fitted into the quality inspection module. Through software configuration and data mapping, the inspection targets (positioning parameters) and collaborative following quantities (comparison parameters) are matched with the corresponding functions in the quality inspection module. The quality inspection module is connected to the data sensing platform. The sensor will collect the positioning parameters and collaborative following quantities of the gears in real time and transmit the data to the quality inspection module. The quality inspection module compares the received real-time data with the pre-set structural standard quantities. The structural standard quantities can be obtained directly from the design drawings or verified through experiments. By comparing the difference between the actual measurement data and the standard quantities, the quality inspection module can calculate the deviation of the parameters and evaluate whether the gears meet the quality standards.

[0037] The deviation is obtained by calculating the difference between the actual measurement value and the standard value. By calculating these deviations, the difference between the actual size of the gear and the design requirements can be clearly understood. Parameters with large deviations usually indicate that the gear may have quality problems and require further analysis and adjustment. By fitting the positioning parameters and comparison parameters to the quality inspection module and comparing the actual measurement data with the structural standard, the parameter deviation of the gear is accurately calculated, and automated quality inspection is achieved. It provides accurate and real-time quality assessment and timely feedback of deviation information, thereby improving production efficiency, reducing quality problems, optimizing the production process, and realizing automated early warning.

[0038] Step 4: Perform warning level analysis based on the parameter deviation and the hierarchical relationship between the detection target and the detection parameter hierarchical structure to obtain the quality risk warning level.

[0039] Specifically, the detection target is the positioning parameter, that is, the specific parameter that needs to be monitored. The detection parameter hierarchy structure represents the importance, dependency and impact level of each parameter in the detection process. According to the hierarchical relationship between the detection target and the detection parameter hierarchy structure, combined with the parameter deviation, the risk is assessed through the preset threshold. For each detection target, the corresponding risk coefficient is calculated according to its position in the hierarchy. When the deviation is greater than a certain threshold, the risk coefficient will increase, thereby increasing the risk level of the parameter. According to the comprehensive deviation and weight of multiple detection targets, a final quality risk warning level is obtained. The quality risk warning level includes low risk level, medium risk level and high risk level. The low risk level means that the parameter deviation in the test result is small and the impact is relatively limited. At this time, the production process can continue, but it still needs attention; the medium risk level means that the parameter deviation in the test result is large, which may have a certain impact on the product's performance, and the production process needs to be adjusted or partially corrected; the high risk level means that the parameter deviation in the test result exceeds the predetermined maximum tolerance range, directly affecting the product's safety or functionality, and immediate measures must be taken, such as stopping production, rework or adjusting equipment. Through early warning level analysis, quality risks in the production process can be accurately and quickly identified, and resources and time can be precisely allocated. For example, high-risk issues can be immediately stopped and repaired, while low-risk issues can be prevented by adjusting parameters or strengthening quality inspections.

[0040] Step 5: Match the warning path according to the quality risk warning level, and generate warning feedback information based on the warning path.

[0041] Specifically, according to the quality risk warning level, the corresponding warning path is automatically selected or matched. The warning path defines the notification and response process from the production process to the relevant personnel or system when a quality abnormality occurs. The warning path is usually set according to the risk level, the scope of the problem and the corresponding response process. The low-risk warning path simply notifies the production line personnel and requires continued monitoring and confirmation in the next inspection cycle; the medium-risk warning path notifies the production line manager or quality control department to conduct product inspections and make adjustments if necessary; the high-risk warning path immediately notifies the management, maintenance personnel or quality control team, which may include production line shutdown, equipment adjustment, recalibration or replacement of parts. Warning feedback information refers to the information content delivered to relevant personnel through appropriate channels (such as email, SMS, APP notification, management system, etc.) according to the warning path, including problem description, warning level, affected products or process links, recommended treatment measures, time requirements, etc. Through path matching and feedback information generation based on the warning level, relevant personnel can obtain problem information in a timely manner, so as to respond quickly and avoid further expansion of quality problems.

[0042] Furthermore, step one of this application includes:

[0043] A detection sample database is obtained, wherein the detection sample database includes static samples and time-series dynamic samples, wherein the static samples reflect the size detection results of the target saw chain gear and the design drawings, and the time-series dynamic samples reflect the time-series abnormal size detection results of the target saw chain gear during use; according to the detection sample database, abnormal sample cases are extracted for the static samples and the time-series dynamic samples respectively to obtain quality abnormal deviation data; loss parameter clustering is performed on the quality abnormal deviation data of the static samples, and weighted average calculation of the loss amount is performed according to the clustering results to obtain static loss parameters; the quality abnormal deviation data of the time-series dynamic samples are sorted according to time series, the loss time series curves of each quality deviation parameter are fitted, and dynamic loss parameters are obtained according to the loss time series curves; the static loss parameters are integrated with the dynamic loss parameters to obtain the loss parameters.

[0044] Specifically, various sensors and detection equipment are used to collect quality inspection data of saw chain gears, including static samples and time-series dynamic samples, to obtain an inspection sample database. The inspection sample database contains a database of various types of data, which is usually used for quality inspection and analysis. Static samples reflect whether the geometric dimensions of the saw chain gears (such as outer diameter, tooth pitch, tooth height, shaft hole size, etc.) meet the requirements of the design drawings. They are usually carried out in a stationary state, that is, when the saw chain gears are not working or loaded during use. Static samples are usually obtained by using a 3D scanner or laser measuring instrument to measure the geometric dimensions of each gear to ensure that these dimensions meet the requirements of the design drawings.

[0045] The time series dynamic sample is the usage data of the saw chain gear in actual work, including its dimensional changes and deviations during use. It is usually collected in real time through real-time monitoring equipment, reflecting the performance of the saw chain gear under dynamic use conditions, such as the impact of factors such as vibration, temperature changes, and load changes on its size.

[0046] Extract abnormal sample cases from the test sample database, that is, identify samples with abnormal quality from static samples and time-series dynamic samples. When the size deviation of a test sample exceeds the design requirements or the predetermined tolerance range, the sample is regarded as an abnormal sample. According to the abnormal sample cases, determine the quality abnormal deviation data, that is, the value of the size deviation of the target saw chain gear from the design standard, which is calculated based on the difference between the size test results and the design requirements. The quality abnormal deviation data includes static quality abnormal deviation data and dynamic time-series quality abnormal deviation data.

[0047] The loss parameters of the abnormal quality deviation data of the static samples are clustered. Similar quality deviations are grouped through clustering algorithms (such as K-means algorithm), and similar loss (i.e. deviation) values ​​are classified into the same category. The static loss parameters are calculated by weighted average based on these data. The weighted average is calculated based on the importance (or weight) of each type of loss parameter. Data with larger weights will have a greater impact on the final calculation results.

[0048] The quality deviation data of the time series dynamic samples are sorted in chronological order, and the loss time series curve of each quality deviation parameter is fitted to show the change curve of the quality deviation over time during the use of the saw chain gear, revealing the dynamic change and quality degradation process of the gear at different time points. The dynamic loss parameters are obtained from the loss time series curve. According to the weights of different time periods in the loss time series curve, the loss values ​​of each quality deviation parameter are calculated, and the quality deviation parameters that meet the basic time series change threshold are used as dynamic loss parameters. The static loss parameters are integrated with the dynamic loss parameters to form the final loss parameters. If some parameters appear in both static and dynamic samples, they will be weighted according to the preset weights. Usually, the weight of static loss is larger because it reflects the initial quality problems in the production process, while the dynamic loss reflects the degradation during use. By combining static loss and dynamic loss, a comprehensive loss parameter is obtained to fully understand all quality problems that may occur in the production and use of saw chain gears, accurately identify the key factors affecting gear quality, and timely adjust process parameters or provide a basis for equipment maintenance and replacement to reduce the occurrence of quality problems.

[0049] Furthermore, the present application also includes the following steps:

[0050] The loss timing curves of the quality deviation parameters are aligned according to the timing relationship, and the nodes of the loss timing curve are marked according to the slope of the curve; the timing chain time window is divided based on the timing alignment relationship, and the timing weight of the time window is configured, wherein the timing weight of the early time is greater than the timing weight of the late time; according to the aligned time window, all nodes of the loss timing curve are continuously weightedly calculated using the timing weight to obtain the loss value of each quality deviation parameter, and the quality deviation parameter whose loss value meets the basic timing change threshold is used as the dynamic loss parameter.

[0051] Specifically, the loss time series curve describes the trend curve of the quality deviation over time. During the production or use process, the quality deviation of the saw chain gear may change over time, and the loss time series curve is a tool used to represent this change. For each quality deviation parameter (such as outer diameter, tooth height, tooth pitch, etc.), a loss time series curve is obtained, which represents the change of the parameter over time. If the sample data in different time periods have different time lengths (such as some data may have a longer collection period and some data are shorter), time alignment is required, that is, the data are adjusted to the same time period for comparison. In other words, the different parameter curves are also aligned in chronological order to ensure that the data in different time periods can be analyzed synchronously to avoid inaccurate analysis results due to time deviation.

[0052] On the aligned loss time series curve, the mass deviation data at each time point is a node, representing the specific value of the mass change. The slope of the curve indicates the rate of change of the curve at a certain point, that is, the acceleration of the loss. In the loss time series curve, the slope reflects the speed at which the mass deviation changes over time. For example, if the slope of the curve at a certain moment is large, it means that the mass loss at that moment is more drastic. On the loss time series curve, each node represents the mass deviation value at a point in time. Node labeling refers to marking the slope of the curve of each node to distinguish and analyze the quality changes at different time points.

[0053] According to the timing alignment relationship, the time series data is divided into different time periods (i.e., time windows) so that the quality loss can be analyzed within each time window. Each time window represents a specific time period and is usually divided in chronological order. Time window segmentation of a time series chain refers to dividing the entire time series chain into multiple time windows, each of which contains a certain time period. Within each time window, the change in quality loss will have a specific weight, and the timing weight is used to reflect the degree of influence of the time period on the overall quality loss. Usually, the timing weight of the early time is larger because the early stage of the production process often determines the basic state of quality. The timing weight of the later time period is smaller because the rate of change of product quality often slows down during use.

[0054] According to the aligned time windows, all nodes of the loss time series curve are weightedly calculated using the timing weights. That is to say, the loss time series curve of one of the quality deviation parameters is weightedly calculated according to the multiple time windows of the loss time series curve and the corresponding timing weights to obtain the loss value of the loss time series curve. Similarly, the above calculations are performed on each quality deviation parameter to obtain the loss value of each quality deviation parameter. If some samples have different time lengths, the change relationship within the same time period is considered.

[0055] The basic timing change threshold is a standard value used to screen out deviation parameters that meet the quality loss requirements. This threshold takes into account the wear and tear of normal use, that is, some wear and tear is normal and allowed within the design range. If the change of a quality deviation parameter exceeds this threshold, it is considered a significant dynamic loss. The loss value calculated from the loss timing curve of each quality deviation parameter is compared with the basic timing change threshold. If the weighted loss exceeds the threshold, then the quality deviation is considered abnormal and belongs to a dynamic loss parameter. If it does not exceed the threshold, it means normal wear and tear. Through timing alignment and timing chain time window segmentation, the changing trend of quality deviation over time is captured in detail to ensure that the quality loss in each time period is accurately analyzed and the key time points of quality loss are accurately identified, especially the impact of quality loss in the early time window on the overall quality.

[0056] Further, step 2 of this application includes:

[0057] According to the detection parameter hierarchical structure, the gear structural parameters corresponding to the structural loss parameters of each hierarchical level are obtained, and the parameter type of the gear structural parameters is located, and the parameter type includes tooth shape, tooth pitch, and tooth top height; the production process of the saw chain gear is analyzed to obtain the processing change state of the parameter type, and the positioning connection position of the corresponding processing process is determined; according to the positioning connection position and the processing change state, the positioning parameter is determined; with the positioning parameter as the center, the position relationship of the structural contour of the parameter type is analyzed to obtain the comparison parameter, and the comparison parameter is a parameter or parameter combination used to locate the structural contour with the relative size parameter of the positioning parameter; according to the positioning parameter and the comparison parameter, the core detection parameter combination is obtained.

[0058] Specifically, according to the hierarchical structure of the detection parameters, the loss parameters in each level are determined, and the corresponding gear structural parameters are found. The various structural parameters of the gear are classified, mainly into tooth shape, tooth pitch, tooth top height, etc. Tooth shape refers to the shape of the gear tooth surface, usually refers to the geometric shape and profile of the gear tooth surface, such as involute tooth shape, curved tooth shape, etc. The tooth shape directly affects the meshing performance and transmission efficiency of the gear; the tooth pitch refers to the distance between the centers of two adjacent teeth, and the accuracy of the tooth pitch has an important influence on the meshing force and noise of the gear; the tooth top height refers to the distance from the base circle of the gear to the top of the gear tooth. The correctness of the tooth top height directly affects the meshing quality and load-bearing capacity of the gear. For example, during the inspection, if a deviation in the outer diameter is found, check the gear structural parameters corresponding to the outer diameter, such as tooth shape and tooth top height, to determine the source of the deviation.

[0059] Analyze the production process of saw chain gears, identify the key processing steps that affect various structural parameters, analyze the processing change state of the gears, and determine which factors may cause parameter changes. The production process includes raw material preparation, heat treatment, gear cutting, and finishing processes. Each step may have an impact on the final quality of the gear, especially in terms of gear size, tooth shape, surface finish, etc. For each production process link, analyze its impact on the gear structural parameters (such as tooth shape, tooth pitch, tooth top height, etc.). The processing change state refers to the parameter changes caused by factors such as equipment accuracy, tool wear, and environmental changes during the gear production process.

[0060] During the production of saw chain gears, certain positions are critical to the final quality of the gears. These positions are defined as the locating and connecting positions, which are key points in determining the gear size and tooth shape. Depending on the processing technology, the locating and connecting positions may vary. By precisely controlling the processing of the locating and connecting positions, it can be ensured that the various parameters of the gear meet the design requirements.

[0061] According to the positioning connection position and processing change state, determine the positioning parameters that need to be focused on. Positioning parameters refer to the key parameters determined by positioning the connection position and processing change state, which help to locate the geometric shape and structure of the gear. Centered on the positioning parameters, the position relationship analysis of the structural profile of the parameter type is carried out to obtain the relative size parameters used to compare and evaluate the gear structural parameters, that is, the comparison parameters. Comparison parameters refer to other parameters or parameter combinations associated with positioning parameters, which are used to further verify and locate the quality problems of the gear through relative size relationships. The structural profile of the parameter type refers to the geometric structural characteristics of the gear, such as tooth shape, tooth pitch, tooth top height, outer diameter, shaft hole, etc., which jointly determine the function and performance of the gear. Analyze the geometric relationship between the positioning parameters and other related parameters, and derive the comparison parameters related to the positioning parameters.

[0062] According to the positioning parameters and comparison parameters, the saw chain gear is simulated and restored. The design parameters of the saw chain gear (including positioning parameters and comparison parameters) are input into the model through numerical simulation or physical model to simulate the processing process. Determine whether the simulation restoration results meet the design requirements, perform reduction analysis and increment analysis on the comparison parameters, and gradually adjust the gear detection parameters to ensure that the final gear design fully matches the production requirements. The core detection parameter combination refers to the combination of positioning parameters and comparison parameters to determine the key detection parameters of the gear structure, which can help better locate quality problems and ensure that the gear structure meets the design requirements. Through the analysis of positioning parameters and comparison parameters, the quality problems of the gear can be accurately located and potential defects in the processing process can be discovered in time.

[0063] Furthermore, the present application also includes the following steps:

[0064] The saw chain gear is simulated and restored according to the positioning parameters and the comparison parameters to obtain a restoration result; when the restoration result meets the requirements, the comparison parameters are reduced and analyzed to obtain a reduced restoration result, and the minimum comparison parameters are determined; when the restoration result does not meet the requirements, the deviation position is determined according to the restoration result, and the positioning parameters or the comparison parameters are incrementally analyzed according to the deviation position to obtain incremental parameters; and the simulation restoration is re-performed using the incremental parameters until the requirements are met.

[0065] Specifically, according to the positioning parameters and comparison parameters, the saw chain gear is simulated and restored. The design parameters of the saw chain gear (including positioning parameters and comparison parameters) are input into the model through numerical simulation or physical model to simulate the processing process. For example, the positioning parameters (such as tooth top height) and comparison parameters (such as tooth surface roughness and tooth profile error) are input into the simulation software to simulate the processing process, calculate the actual gear parameters and structure, and generate the restoration result.

[0066] Compare the simulation restoration results with the design requirements or the measured data of the actual gear. If the restoration results meet the requirements (usually the design requirements or the measured data of the actual gear), perform a reduction analysis on the comparison parameters and gradually reduce the number of parameters until the minimum comparison parameters that meet the restoration requirements are found. According to the simulation restoration results, analyze the contribution of each comparison parameter to the gear quality. Use an optimization algorithm or a step-by-step deletion method to remove those comparison parameters that have little effect on the gear quality and retain the core parameters that best reflect the gear quality. The minimum comparison parameters refer to the minimum set of comparison parameters obtained through reduction analysis while meeting the accuracy requirements. The minimum comparison parameters help simplify the quality inspection process and improve efficiency.

[0067] Confirm all the comparison parameters, evaluate the importance of each comparison parameter, and determine which parameters have a greater impact on the gear quality and which have a smaller impact. Gradually remove minor parameters and optimize the model. Use statistical analysis, sensitivity analysis and other methods to quantify the contribution of each parameter to the gear quality, remove the comparison parameters with small contribution, and finally determine the minimum comparison parameters. Through reduction analysis, retain the minimum core parameters.

[0068] If the restoration result does not meet the requirements, it means that some parameters of the gear have deviated. At this time, incremental analysis is performed to increase or adjust certain positioning parameters or comparison parameters until the design requirements are met. According to the deviation position obtained by simulation, the incremental parameters that need to be increased are determined. By re-simulating and restoring, check whether the adjusted results meet the design requirements. By continuously performing incremental and decremental analysis, the parameters of the gear are gradually adjusted to ensure that the final gear design fully matches the production requirements. By using positioning parameters and comparison parameters for simulation restoration, the actual performance of the gear is accurately predicted. Simulation restoration helps identify potential design problems and optimize them before production.

[0069] Further, step three of this application includes:

[0070] Based on the number of levels and the hierarchical relationship of the detection parameter hierarchical structure, a module space of the quality detection module is established, the module space corresponds to the detection parameter hierarchical structure and has a spatial identification of the structural parameters; the detection target and the collaborative following amount are used as indexes to search and match with the spatial identification, and the detection target and the collaborative following amount are fitted to the corresponding module space according to the spatial matching relationship; the data sensing platform is connected, and the detection target and the collaborative following amount are used to match the sensor source, and a transmission channel between the sensor source and the module space is established, and the data collected by the matching sensor source is synchronized to the module space through the transmission channel, the detection target and the collaborative following amount obtained by positioning and monitoring are located, and the monitoring amount is compared with the standard amount of the detection target-collaborative following amount to obtain the parameter deviation amount.

[0071] Specifically, according to the hierarchical structure of the detection parameters, the number of levels and the hierarchical relationship are determined. The number of levels is the number of levels. The hierarchical relationship refers to the mutual relationship and organizational structure between the levels in the hierarchical structure of the detection parameters. The number of levels and the hierarchical structure are determined based on the importance of the parameters and the degree of influence on the gear performance. According to the hierarchical structure of the detection parameters, the module space of the quality detection module is constructed. The module space is a conceptual or physical space dedicated to organizing and storing all parameters and their data used in the quality detection process. Each parameter has its position in this space, which is organized through the hierarchical structure of the parameters to facilitate quick search and data analysis.

[0072] Each parameter (such as the outer diameter, pitch, tooth shape, etc. of the gear) has a unique identifier in the module space. This identifier is the spatial identifier, which helps to identify different parameters and accurately locate and compare data. The spatial identifier is not only a unique code, but also should be able to clearly indicate the level and function of the parameter. According to the level of each parameter and its importance in detection, the structure of the spatial identifier can be further adjusted so that each parameter can be quickly identified and located according to the spatial identifier during real-time monitoring and detection.

[0073] With the detection target as the core parameter and the collaborative following amount as the auxiliary parameter, the corresponding positions of these parameters in the module space are searched and fitted through the spatial matching relationship. When the matching relationship between the detection target and the collaborative following amount is determined, these parameters will be fitted into the module space to ensure that the real-time data is compared with the design standards (such as the ideal size of the outer diameter and the pitch). The detection target and the collaborative following amount are used as indexes and matched with the module space through the spatial identifier. The quality inspection module will use the spatial identifiers of the positioning parameters and the comparison parameters to find the corresponding positions in the module space, match the detected real-time data with the defined standard parameters, and ensure the correctness and real-time nature of the data. According to the matching relationship of the spatial identifiers, the inspection module compares the data of the positioning parameters and the collaborative following amount with the corresponding standard data.

[0074] Connect the data sensing platform to collect gear inspection data in real time. The data sensing platform includes sensors and data transmission channels, which can transmit monitoring data to the module space for analysis in real time. The data sensing platform is a data acquisition and processing platform used to collect, store, and transmit data from different sensors. Each sensor source is responsible for collecting real-time data of a specific detection target or collaborative following quantity. During the detection process, the real-time data collected by the sensor will enter the module space synchronously through the transmission channel. During this process, the detection target (such as the outer diameter of the gear) and the collaborative following quantity (such as the pitch) will be synchronously transmitted to the module space for analysis and comparison. The sensor source refers to the sensor or detection equipment used to collect various data in the gear production process.

[0075] Establish a data transmission channel between the sensor source and the module space to ensure real-time synchronization of data. Collect real-time data through the matching sensor source, and synchronize the collected data to the module space through the transmission channel. According to the data of positioning parameters and collaborative following quantity, use standard quantity to compare the monitoring quantity. By comparing with the standard quantity, the quality inspection module calculates the deviation of the parameter to evaluate whether the quality of the gear meets the requirements. If the deviation exceeds the tolerance range, an early warning or alarm message needs to be issued. The standard quantity of the detection target-collaborative following quantity is the parameter value obtained by design standard or experiment, which is usually the ideal size or functional parameter of the gear.

[0076] By matching the detection target and collaborative following quantity with the parameter identifier in the module space, quality problems can be quickly located. Combined with the real-time data synchronization of the data sensing platform and the rapid matching of the module space, the quality data of the gear can be obtained at any time and compared with the standard quantity, so as to detect deviations and issue early warnings in a timely manner.

[0077] Further, step 4 of this application includes:

[0078] According to the parameter type of the detection parameter hierarchy structure, the failure probability of the parameter type is obtained and the type risk coefficient is determined; according to the hierarchical relationship, the hierarchical coefficient is set; the type risk coefficient, the hierarchical coefficient and the parameter deviation are superimposed and matched with the preset risk level list to obtain the quality risk warning level.

[0079] Specifically, the failure probability of the parameter type is determined according to the parameter type of the parameter hierarchy. Different parameters (such as tooth shape, tooth pitch, tooth top height, etc.) have different probabilities of failure during the production process. Estimate the failure probability of each parameter type through historical data, empirical rules or statistical methods. Calculate the risk factor based on the failure probability of each parameter, and determine it based on the failure probability of different parameters and the impact of the parameter on the overall quality. It is obtained by weighting or amplifying the failure probability to indicate the degree of influence of different types of parameters on product quality when they fail.

[0080] In the test parameter hierarchy, each parameter is assigned a hierarchy factor, which indicates its importance in the overall quality inspection system. If a parameter is located at the upper level of the hierarchy (i.e., a parameter that directly affects the performance of the gear), the hierarchy factor will be higher. The hierarchy factor helps determine the degree of influence of each parameter in multi-level quality inspection, ensuring that more important parameters have a greater impact on quality assessment. The hierarchy factor is a weight given to the parameter based on its position in the test parameter hierarchy. Generally, parameters located at the upper level of the hierarchy have a greater impact on product quality, so their hierarchy factors will be higher.

[0081] The preset risk level list is a risk level standard predefined based on experience, statistical analysis or historical data. Different deviations, risk factors and hierarchical factors will determine the corresponding quality risk warning level according to the preset risk level list. The type risk factor, hierarchical factor and parameter deviation are superimposed and calculated, and the type risk factor, hierarchical factor and parameter deviation are weighted according to their corresponding weights, and the corresponding quality risk warning level is matched in the preset risk level list.

[0082] Each parameter is calculated and risk matched to determine the corresponding quality risk warning level, such as low, medium, and high risk, to indicate the severity of product quality problems. By using the type risk coefficient, level coefficient, and parameter deviation for weighted calculation and matching with the preset risk level list, an accurate quality risk warning level can be obtained to help the production line identify potential quality problems in real time, respond quickly, and optimize the production process, thereby improving production efficiency, reducing defective products, and reducing costs.

[0083] Furthermore, step five of this application includes:

[0084] According to the parameter type and the type risk coefficient, the impact coverage range is determined, and the impact coverage range includes multiple partitions, including a direct range, an adjacent related range, and a process impact range; according to the quality risk warning level, the warning range level is obtained; the impact coverage range is matched using the warning range level to determine a matching warning range, and the warning communication connection path of the matching warning range is obtained as the warning path.

[0085] Specifically, the scope of impact that may be caused by problems with each quality parameter is determined based on the parameter type and type risk factor. Parameter type refers to the various physical quantities or structural characteristics that need to be monitored. The type risk factor is a weight value assigned to each parameter type, which is used to indicate the potential risk level of the parameter type to product quality. Impact coverage refers to the scope of impact that may be caused to other areas or links when a quality parameter has a problem.

[0086] The impact coverage includes multiple partitions, such as direct scope, adjacent associated scope, and process impact scope. The direct scope means that the deviation of a certain parameter directly affects the product quality at that location, and an immediate warning may be required; the adjacent associated scope means that the deviation of the parameter not only affects the location, but may also affect the next process link or part of the product area; the process impact scope means that due to the deviation of a certain parameter, the stability of the entire production process may be affected, and it may even affect the accuracy of subsequent processes. For example, if the outer diameter parameter of the gear deviates, the scope of its direct impact is the meshing position of the gear and the chain gear (ie, the direct scope). If there is a problem with the pitch of the gear, it may affect the meshing accuracy of the adjacent gears, thereby affecting the operation of the entire gear system. This belongs to the adjacent associated scope.

[0087] According to the quality risk warning level, the warning range level of each parameter is determined. The warning range level corresponds to the quality risk warning level, indicating the range of countermeasures taken when risks are found in quality inspection. Usually, the warning range level will vary according to the warning level. For example, high risk may trigger a comprehensive production line shutdown inspection, while low risk may only require monitoring of individual links.

[0088] Use the warning range level to match the impact coverage, that is, determine the specific areas or links that need warnings based on the relationship between the quality risk warning level and the impact coverage. Different warning levels correspond to different processing ranges. Through matching, it is ensured that potential quality problems are responded to in a timely manner during the production process. The warning communication connection path refers to the warning information generated according to the test results during the quality inspection process, which is transmitted to relevant personnel, systems or equipment through a specific communication channel so that timely measures can be taken to deal with potential quality risks, ensuring that the warning information can be quickly and accurately transmitted from the data acquisition source (such as sensors, monitoring systems, etc.) to the target that needs to respond (such as operators, quality control personnel, automation control systems, etc.).

[0089] The matching warning range is determined and the warning path is established. The warning path refers to how to effectively transmit the warning information to the relevant staff or system after the matching warning range is determined, including the route of data transmission and the way to notify the relevant personnel (such as alarm system, email or text message, etc.). By accurately setting the impact coverage and warning range level, it is ensured that potential quality problems are located and handled in time during the production process, and the warning path is established so that the warning information is transmitted to the operators on the production line at the first time, and measures are taken when the problem just occurs, thereby reducing the production of unqualified products, reducing rework and scrap costs, and improving the product qualification rate.

[0090] In summary, the production quality detection and early warning method for saw chain gears provided in this application has the following technical effects:

[0091] By analyzing the loss parameters of the saw chain gear, the loss values ​​of the gear structural parameters are located according to the loss parameters, and a detection parameter hierarchical structure is constructed; according to the detection parameter hierarchical structure, a core detection parameter combination is extracted, and the core detection parameter combination is a quality parameter combination that can quickly locate the structural loss, and the core detection parameter combination has a positioning parameter and a comparison parameter; the positioning parameter is used as a detection target, and the comparison parameter is used as a collaborative following quantity, which is fitted into a quality detection module, and the quality detection module is connected to a data sensing platform, and is used to perform quality detection on production monitoring data using the structural standard quantity of the detection target and the collaborative following quantity to obtain a parameter deviation; according to the parameter deviation and the hierarchical relationship between the detection target and the detection parameter hierarchical structure, a warning level analysis is performed to obtain a quality risk warning level; according to the quality risk warning level, a warning path is matched, and warning feedback information is generated based on the warning path. In other words, by constructing a hierarchical structure of detection parameters, the production process is evaluated in layers according to the detection standards of different levels. The positioning parameters and comparison parameters are combined to accurately identify and locate the specific structural parameters where quality problems occur. The quality risk warning level is derived based on the hierarchical structure analysis, and the warning path is automatically generated, which improves the accuracy of quality detection and thus the accuracy of warning information.

[0092] Embodiment 2, based on the same inventive concept as the production quality detection and early warning method of the saw chain gear in the aforementioned embodiment 1, the present application also provides a production quality detection and early warning device for the saw chain gear, please refer to the attached Figure 2 The production quality detection and early warning device of the saw chain gear comprises:

[0093] A loss positioning module 11, the loss positioning module 11 is used to analyze the loss parameters of the saw chain gear, locate the loss value of the gear structure parameter according to the loss parameters, and construct a detection parameter hierarchical structure; a detection parameter extraction module 12, the detection parameter extraction module 12 is used to extract a core detection parameter combination according to the detection parameter hierarchical structure, the core detection parameter combination is a quality parameter combination that can quickly locate the structural loss, and the core detection parameter combination has a positioning parameter and a comparison parameter; a deviation determination module 13, the deviation determination module 13 is used to use the positioning parameter as a detection target and the comparison parameter as a collaborative follow-up amount to fit into the quality detection module, the quality detection module is connected to the data sensing platform, and is used to use the detection target and the structural standard amount of the collaborative follow-up amount to perform quality detection on the production monitoring data to obtain the parameter deviation; an early warning level determination module 14, the early warning level determination module 14 is used to perform early warning level analysis according to the parameter deviation and the hierarchical relationship between the detection target and the detection parameter hierarchical structure, and obtain the quality risk early warning level; an early warning feedback module 15, the early warning feedback module 15 is used to match the early warning path according to the quality risk early warning level, and generate early warning feedback information based on the early warning path.

[0094] Furthermore, the loss location module 11 in the production quality detection and early warning device for saw chain gears is also used for:

[0095] A detection sample database is obtained, wherein the detection sample database includes static samples and time-series dynamic samples, wherein the static samples reflect the size detection results of the target saw chain gear and the design drawings, and the time-series dynamic samples reflect the time-series abnormal size detection results of the target saw chain gear during use; according to the detection sample database, abnormal sample cases are extracted for the static samples and the time-series dynamic samples respectively to obtain quality abnormal deviation data; loss parameter clustering is performed on the quality abnormal deviation data of the static samples, and weighted average calculation of the loss amount is performed according to the clustering results to obtain static loss parameters; the quality abnormal deviation data of the time-series dynamic samples are sorted according to time series, the loss time series curves of each quality deviation parameter are fitted, and dynamic loss parameters are obtained according to the loss time series curves; the static loss parameters are integrated with the dynamic loss parameters to obtain the loss parameters.

[0096] Furthermore, the loss location module 11 in the production quality detection and early warning device for saw chain gears is also used for:

[0097] The loss timing curves of the quality deviation parameters are aligned according to the timing relationship, and the nodes of the loss timing curve are marked according to the slope of the curve; the timing chain time window is divided based on the timing alignment relationship, and the timing weight of the time window is configured, wherein the timing weight of the early time is greater than the timing weight of the late time; according to the aligned time window, all nodes of the loss timing curve are continuously weightedly calculated using the timing weight to obtain the loss value of each quality deviation parameter, and the quality deviation parameter whose loss value meets the basic timing change threshold is used as the dynamic loss parameter.

[0098] Furthermore, the detection parameter extraction module 12 in the production quality detection and early warning device for saw chain gears is also used for:

[0099] According to the detection parameter hierarchical structure, the gear structural parameters corresponding to the structural loss parameters of each hierarchical level are obtained, and the parameter type of the gear structural parameters is located, and the parameter type includes tooth shape, tooth pitch, and tooth top height; the production process of the saw chain gear is analyzed to obtain the processing change state of the parameter type, and the positioning connection position of the corresponding processing process is determined; according to the positioning connection position and the processing change state, the positioning parameter is determined; with the positioning parameter as the center, the position relationship of the structural contour of the parameter type is analyzed to obtain the comparison parameter, and the comparison parameter is a parameter or parameter combination used to locate the structural contour with the relative size parameter of the positioning parameter; according to the positioning parameter and the comparison parameter, the core detection parameter combination is obtained.

[0100] Furthermore, the detection parameter extraction module 12 in the production quality detection and early warning device for saw chain gears is also used for:

[0101] The saw chain gear is simulated and restored according to the positioning parameters and the comparison parameters to obtain a restoration result; when the restoration result meets the requirements, the comparison parameters are reduced and analyzed to obtain a reduced restoration result, and the minimum comparison parameters are determined; when the restoration result does not meet the requirements, the deviation position is determined according to the restoration result, and the positioning parameters or the comparison parameters are incrementally analyzed according to the deviation position to obtain incremental parameters; and the simulation restoration is re-performed using the incremental parameters until the requirements are met.

[0102] Furthermore, the deviation determination module 13 in the production quality detection and early warning device for saw chain gears is also used for:

[0103] Based on the number of levels and the hierarchical relationship of the detection parameter hierarchical structure, a module space of the quality detection module is established, the module space corresponds to the detection parameter hierarchical structure and has a spatial identification of the structural parameters; the detection target and the collaborative following amount are used as indexes to search and match with the spatial identification, and the detection target and the collaborative following amount are fitted to the corresponding module space according to the spatial matching relationship; the data sensing platform is connected, and the detection target and the collaborative following amount are used to match the sensor source, and a transmission channel between the sensor source and the module space is established, and the data collected by the matching sensor source is synchronized to the module space through the transmission channel, the detection target and the collaborative following amount obtained by positioning and monitoring are located, and the monitoring amount is compared with the standard amount of the detection target-collaborative following amount to obtain the parameter deviation amount.

[0104] Furthermore, the warning level determination module 14 in the saw chain gear production quality detection and warning device is also used for:

[0105] According to the parameter type of the detection parameter hierarchy structure, the failure probability of the parameter type is obtained and the type risk coefficient is determined; according to the hierarchical relationship, the hierarchical coefficient is set; the type risk coefficient, the hierarchical coefficient and the parameter deviation are superimposed and matched with the preset risk level list to obtain the quality risk warning level.

[0106] Furthermore, the early warning feedback module 15 in the production quality detection and early warning device for the saw chain gear is also used for:

[0107] According to the parameter type and the type risk coefficient, the impact coverage range is determined, and the impact coverage range includes multiple partitions, including a direct range, an adjacent related range, and a process impact range; according to the quality risk warning level, the warning range level is obtained; the impact coverage range is matched using the warning range level to determine a matching warning range, and the warning communication connection path of the matching warning range is obtained as the warning path.

[0108] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The production quality detection and early warning method and specific examples of the saw chain gear in the first embodiment are also applicable to the production quality detection and early warning device of the saw chain gear in this embodiment. Through the above detailed description of the production quality detection and early warning method of the saw chain gear, those skilled in the art can clearly know the production quality detection and early warning device of the saw chain gear in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0109] 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.

[0110] 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 production quality detection and early warning method for saw chain gears, characterized in that: include: Analyze the loss parameters of the saw chain gear, locate the loss values ​​of the gear structure parameters according to the loss parameters, and build a hierarchical structure of detection parameters; Extracting a core detection parameter combination according to the detection parameter hierarchical structure, wherein the core detection parameter combination is a quality parameter combination capable of quickly locating structural losses, and the core detection parameter combination has a positioning parameter and a comparison parameter; The positioning parameter is used as the detection target, and the comparison parameter is used as the collaborative following amount, which is fitted into the quality detection module. The quality detection module is connected to the data sensing platform and is used to perform quality detection on the production monitoring data using the structural standard amount of the detection target and the collaborative following amount to obtain the parameter deviation amount; Performing an early warning level analysis based on the parameter deviation and the hierarchical relationship between the detection target and the detection parameter hierarchical structure to obtain a quality risk early warning level; Matching a warning path according to the quality risk warning level, and generating warning feedback information based on the warning path; The obtaining of the parameter deviation comprises: Based on the number of levels and the relationship between the levels of the detection parameter hierarchical structure, a module space of the quality detection module is established, wherein the module space corresponds to the detection parameter hierarchical structure and has a space identifier of a structural parameter; Using the detection target and the collaborative following amount as indexes to search and match with the spatial identifier, and fitting the detection target and the collaborative following amount to the corresponding module space according to the spatial matching relationship; Connecting to the data sensing platform, using the detection target and the collaborative following amount to match the sensor source, establishing a transmission channel between the sensor source and the module space, synchronizing the data collected by the matching sensor source to the module space through the transmission channel, positioning and monitoring the detection target and the collaborative following amount, and using the standard amount of the detection target-collaborative following amount to compare the monitoring amount, and obtain the parameter deviation amount; The loss parameters of the analyzed saw chain gear include: Obtaining a detection sample database, the detection sample database comprising static samples and sequential dynamic samples, the static samples reflecting the size detection results of the target saw chain gear and the design drawing, and the sequential dynamic samples reflecting the sequential abnormal size detection results of the target saw chain gear during use; According to the detection sample database, abnormal sample cases are extracted for static samples and time series dynamic samples respectively to obtain quality abnormal deviation data; Performing loss parameter clustering on the quality abnormal deviation data of the static sample, and performing weighted average calculation of the loss amount according to the clustering result to obtain the static loss parameter; The quality anomaly deviation data of the time series dynamic sample are sorted according to the time series, the loss time series curve of each quality deviation parameter is fitted, and the dynamic loss parameter is obtained according to the loss time series curve; Integrate the static loss parameter with the dynamic loss parameter to obtain the loss parameter; Obtaining dynamic loss parameters according to the loss timing curve includes: Aligning the loss time series curves of the quality deviation parameters according to the time series relationship, and marking the nodes of the loss time series curves according to the slope of the curves; Based on the timing alignment relationship, the timing chain time window is divided and the timing weight of the time window is configured, wherein the timing weight of the early time is greater than the timing weight of the late time; According to the aligned time window, all nodes of the loss time series curve are subjected to continuous weighted calculation of loss amounts using the time series weights to obtain loss values ​​of various quality deviation parameters, and the quality deviation parameters whose loss values ​​meet the basic time series change threshold are used as the dynamic loss parameters; Extracting a core detection parameter combination according to the detection parameter hierarchical structure includes: According to the detection parameter hierarchical structure, the gear structure parameters corresponding to the loss parameters of each hierarchical structure are obtained, and the parameter types of the gear structure parameters are located, where the parameter types include tooth shape, tooth pitch, and tooth addendum height; Analyze the production process of the saw chain gear, obtain the processing change state of the parameter type, and determine the positioning connection position corresponding to the processing process; Determining the positioning parameters according to the positioning connection position and the processing change state; Taking the positioning parameter as the center, performing position relationship analysis on the structural profile of the parameter type to obtain the comparison parameter, wherein the comparison parameter is a parameter or a combination of parameters used to locate the structural profile with a relative size parameter of the positioning parameter; Obtaining the core detection parameter combination according to the positioning parameter and the comparison parameter; According to the parameter deviation and the hierarchical relationship between the detection target and the detection parameter hierarchical structure, a warning level analysis is performed to obtain a quality risk warning level, including: According to the parameter type of the detection parameter hierarchy structure, the failure probability of the parameter type is obtained, and the type risk coefficient is determined; According to the hierarchical relationship, a hierarchical coefficient is set; The type risk coefficient, the level coefficient and the parameter deviation are superimposed and matched with a preset risk level list to obtain the quality risk warning level; Matching the warning path according to the quality risk warning level includes: Determine an impact coverage range according to the parameter type and the type risk coefficient, wherein the impact coverage range includes multiple partitions, including a direct range, an adjacent associated range, and a process impact range; According to the quality risk warning level, obtain the warning range level; The impact coverage range is matched using the warning range level to determine a matching warning range, and a warning communication connection path of the matching warning range is obtained as the warning path.

2. The production quality detection and early warning method of the saw chain gear according to claim 1, characterized in that: According to the positioning parameters and the comparison parameters, the core detection parameter combination is obtained, including: Performing a simulated restoration of the saw chain gear according to the positioning parameters and the comparison parameters to obtain a restoration result; When the restoration result meets the requirements, performing reduction analysis on the comparison parameters to obtain a reduction restoration result and determine the minimum comparison parameters; When the restoration result does not meet the requirements, determining the deviation position according to the restoration result, performing incremental analysis of positioning parameters or comparison parameters according to the deviation position, and obtaining incremental parameters; Re-simulate using incremental parameters until the requirements are met.

3. The production quality detection and early warning device of the saw chain gear is characterized by: Steps for implementing the method for detecting and warning the production quality of saw chain gears according to any one of claims 1 to 2, wherein the device for detecting and warning the production quality of saw chain gears comprises: A loss location module, the loss location module is used to analyze the loss parameters of the saw chain gear, locate the loss value of the gear structure parameter according to the loss parameters, and construct a detection parameter hierarchical structure; A detection parameter extraction module, the detection parameter extraction module is used to extract a core detection parameter combination according to the detection parameter hierarchical structure, the core detection parameter combination is a quality parameter combination that can quickly locate structural losses, and the core detection parameter combination has a positioning parameter and a comparison parameter; A deviation determination module, wherein the deviation determination module is used to use the positioning parameter as a detection target and the comparison parameter as a collaborative follow-up amount, and fit it into a quality detection module. The quality detection module is connected to a data sensing platform and is used to perform quality detection on production monitoring data using a structural standard amount of a detection target and a collaborative follow-up amount to obtain a parameter deviation amount; An early warning level determination module, the early warning level determination module is used to perform early warning level analysis according to the parameter deviation and the hierarchical relationship between the detection target and the detection parameter hierarchical structure to obtain a quality risk early warning level; An early warning feedback module is used to match an early warning path according to the quality risk early warning level and generate early warning feedback information based on the early warning path; the loss location module is also used to: A detection sample database is obtained, wherein the detection sample database includes static samples and time-series dynamic samples, wherein the static samples reflect the size detection results of the target saw chain gear and the design drawings, and the time-series dynamic samples reflect the time-series abnormal size detection results of the target saw chain gear during use; according to the detection sample database, abnormal sample cases are extracted for the static samples and the time-series dynamic samples respectively to obtain quality abnormal deviation data; loss parameter clustering is performed on the quality abnormal deviation data of the static samples, and weighted average calculation of the loss amount is performed according to the clustering results to obtain static loss parameters; the quality abnormal deviation data of the time-series dynamic samples are sorted according to the time series, and the loss time series curves of each quality deviation parameter are fitted, and dynamic loss parameters are obtained according to the loss time series curves; the static loss parameters are integrated with the dynamic loss parameters to obtain the loss parameters; The loss location module is also used for: The loss timing curves of the quality deviation parameters are aligned according to the timing relationship, and the nodes of the loss timing curve are marked according to the slope of the curve; the timing chain time window is divided based on the timing alignment relationship, and the timing weight of the time window is configured, wherein the timing weight of the early time is greater than the timing weight of the late time; according to the aligned time window, all nodes of the loss timing curve are continuously weightedly calculated using the timing weight to obtain the loss value of each quality deviation parameter, and the quality deviation parameter whose loss value meets the basic timing change threshold is used as the dynamic loss parameter; The detection parameter extraction module is also used for: According to the detection parameter hierarchical structure, the gear structural parameters corresponding to the structural loss parameters of each hierarchical level are obtained, and the parameter type of the gear structural parameters is located, and the parameter type includes tooth shape, tooth pitch, and tooth top height; the production process of the saw chain gear is analyzed to obtain the processing change state of the parameter type, and the positioning connection position of the corresponding processing process is determined; according to the positioning connection position and the processing change state, the positioning parameter is determined; with the positioning parameter as the center, the position relationship analysis of the structural contour of the parameter type is performed to obtain the comparison parameter, and the comparison parameter is a parameter or parameter combination used for positioning the structural contour with the relative size parameter of the positioning parameter; according to the positioning parameter and the comparison parameter, the core detection parameter combination is obtained; The deviation determination module is also used for: Based on the number of levels and the hierarchical relationship of the detection parameter hierarchical structure, a module space of the quality detection module is established, the module space corresponds to the detection parameter hierarchical structure and has a spatial identifier of the structural parameter; the detection target and the collaborative following amount are used as indexes to search and match with the spatial identifier, and according to the spatial matching relationship, the detection target and the collaborative following amount are fitted to the corresponding module space; the data sensing platform is connected, the detection target and the collaborative following amount are used to match the sensor source, a transmission channel between the sensor source and the module space is established, the data collected by the matching sensor source is synchronized to the module space through the transmission channel, the detection target and the collaborative following amount obtained by positioning and monitoring are located, and the monitoring amount is compared with the standard amount of the detection target-collaborative following amount to obtain the parameter deviation amount; The warning level determination module is also used for: According to the parameter type of the detection parameter hierarchical structure, the failure probability of the parameter type is obtained, and the type risk coefficient is determined; according to the hierarchical relationship, the hierarchical coefficient is set; the type risk coefficient, the hierarchical coefficient and the parameter deviation are superimposed, and matched with the preset risk level list to obtain the quality risk warning level; The early warning feedback module is also used for: According to the parameter type and the type risk coefficient, the impact coverage range is determined, and the impact coverage range includes multiple partitions, including a direct range, an adjacent related range, and a process impact range; according to the quality risk warning level, the warning range level is obtained; the impact coverage range is matched using the warning range level to determine a matching warning range, and the warning communication connection path of the matching warning range is obtained as the warning path.

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