Intelligent screw drill service life prediction method and system

Abstract

The invention provides an intelligent screw drill life prediction method and system, and relates to the technical field of screw drill life prediction. The method comprises the steps that historical screw drill life data are collected, clustering division based on use duration characteristics is carried out, and life data sets with different duration characteristics are formed; performing data extraction analysis based on use characteristics on the life data sets with different duration characteristics, and establishing a corresponding clustering life prediction model; performing consistency analysis on different clustering life prediction models, and determining a life prediction model of the screw drill; and obtaining the current use data of the target screw drill, and determining the effective residual life of the target screw drill according to the screw drill life prediction model. According to the method, a reasonable and accurate service life prediction model is established by performing reasonable service life prediction analysis through historical use of big data so as to predict the service life of the screw drill.

Description

Technical Field

[0001] The present invention relates to the technical field of screw drill tool life prediction, and in particular to an intelligent screw drill tool life prediction method and system. Background Art

[0002] Screw drill is a type of volumetric downhole power drill that uses drilling fluid as power and converts liquid pressure energy into mechanical energy. Since screw drill works in a high-pressure and high-temperature working environment, it is more susceptible to damage to the drill itself, especially fatigue damage caused by different factors, which accumulates over a long period of time and affects the service life of the screw drill. At the same time, screw drill is a high-value drilling tool, so its reasonable preservation and use is an effective way to improve its utilization rate.

[0003] The life prediction of screw drill tools can provide enterprises with reasonable cost planning and work arrangements. However, at present, there is no established and reasonable and accurate way to predict the life of screw drill tools. Currently, the life prediction of screw drill tools is basically based on visual observation and judgment based on experience, and the prediction results have large deviations.

[0004] Therefore, it is an urgent problem to design an intelligent screw drill life prediction method and system, and to establish a reasonable and accurate life prediction model by utilizing historical usage big data to conduct reasonable life prediction analysis to predict the life of screw drill tools. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent method for predicting the life of screw drill tools. By acquiring big data of historical screw drill tools, a life prediction model analysis based on factors affecting the life of screw drill tools is performed, and then a life prediction model of these factors affecting the life of screw drill tools is established to achieve a reasonable and accurate prediction of the remaining life of the screw drill tools. This method is based on big data analysis and processing, and adopts a cluster analysis method. It can, to a certain extent, make reasonable and accurate life predictions for different usage scenarios, covering a larger range of screw drill tool life predictions for usage scenarios. At the same time, with big data as a reference, the accuracy of screw drill tool life prediction is greatly improved, providing a more reasonable and accurate reference basis for planning the use of screw drill tools.

[0006] The purpose of the present invention is also to provide an intelligent screw drill tool life prediction system, which is a complete system for predicting the life of screw drill tools by configuring it to utilize historical big data for clustering life prediction model analysis, thereby fully ensuring the efficient and accurate implementation of screw drill tool life prediction, and is an important material basis for realizing reasonable and accurate life prediction of screw drill tools.

[0007] In a first aspect, the present invention provides an intelligent screw drill life prediction method, comprising: collecting historical screw drill life data, performing clustering division based on usage time characteristics, and forming characteristic life data sets of different time lengths; performing data extraction and analysis based on usage characteristics on different characteristic life data sets, and establishing corresponding cluster life prediction models; performing consistency analysis on different cluster life prediction models to determine a screw drill life prediction model; obtaining the current usage data of the target screw drill, and determining the effective remaining life of the target screw drill according to the screw drill life prediction model.

[0008] In the present invention, the method obtains big data of historical screw drill tools to perform life prediction model analysis based on factors affecting the life of screw drill tools, and then establishes a life prediction model of these factors affecting the life of screw drill tools, so as to achieve reasonable and accurate prediction of the remaining life of the screw drill tools. This method is based on big data analysis and processing, and adopts a cluster analysis method. It can make reasonable and accurate life predictions for different usage scenarios to a certain extent, covering a larger range of screw drill tool life prediction scenarios. At the same time, with big data as a reference, the accuracy of screw drill tool life prediction is greatly improved, providing a more reasonable and accurate reference basis for planning the use of screw drill tools.

[0009] As a possible implementation method, historical screw drill life data is collected and clustered based on the usage time characteristics to form different time characteristic life data sets, including: extracting the usage time characteristic information of different historical screw drills based on the historical screw drill life data; clustering different historical screw drills based on the usage time characteristic information of different historical screw drills to form different time characteristic life data sets.

[0010] In the present invention, the historical screw drill life data includes data from the beginning of use to the entire effective service life of different screw drills. Considering that the impact on the life of the screw drill is closely related to the usage situation, before establishing a prediction model that affects the life of the screw drill, it is necessary to reasonably cluster the data according to different usage situations, so that the subsequent life prediction model is more targeted and accurate. Of course, a more reasonable way can also cluster the specific product form of the screw drill, so that the life prediction of different types of screw drill products can be more accurately ensured. It should be noted that for the clustering division performed for different screw drill usage situations, since the impact on the life of the screw drill is caused by the main factors acting on a certain time dimension span, the clustering division is performed with reference to the usage time characteristics, which can ensure the rationality and correctness of the clustering.

[0011] As a possible implementation method, based on the historical screw drill life data, the usage time characteristic information of different historical screw drills is extracted, including: for different historical screw drills, the historical single usage time of each use within the historical life is determined. And historical single idle time , Indicates the number of times it has been used within the corresponding historical lifespan. Indicates the number of times the device has been idle within the corresponding historical lifespan; based on the historical single idle duration , determine the average idle time of the historical screw drill ,in, ; Based on the historical single usage duration , determine the average single use time of the historical screw drill ,in, .

[0012] In the present invention, the time dimension characteristics to be considered for clustering the screw drill tools according to different screw drill tool usage conditions based on the time characteristic information of the screw drill tools mainly include three aspects. The first aspect is the working time characteristics of the screw drill tools during their effective life cycle. Considering that the environmental conditions when the screw drill tools are working are completely different from those when they are not in use and idle, and the impact on the life of the screw drill tools during operation is much greater than when they are idle, the time characteristic information of the use in the entire service life cycle needs to be considered first. It can be understood that the use time of the screw drill tools will occur multiple times during the entire service life cycle, and the use time of each time is not the same. The characterization of the overall use time by the average use time of each time is representative of the impact of the use time on the life of the screw drill tools, so the average use time of a single time needs to be considered. The second aspect is the length of time that the screw drill is idle during its entire service life. When it is idle, the different storage methods and environmental conditions will affect the life of the screw drill to varying degrees. After all, the humidity and temperature of the environment during idle time can affect the performance of the screw drill, and thus affect the life of the screw drill. Similarly, the screw drill will be idle many times during its entire service life, and it will intersect with the length of time it is in operation. The average idle time of a single time can reflect to a certain extent the degree to which the life of the screw drill is affected by the idle time, so the average idle time of a single time needs to be considered. The third aspect is the utilization rate of the screw drill. It is understandable that the more times the screw drill is used, the shorter its life will be. Therefore, the number of times it is used is also an important factor in determining the screw drill, and the number of operations and the number of times it is idle need to be considered in order to accurately achieve a reasonable definition of the use of the screw drill.

[0013] As a possible implementation method, different historical screw drills are clustered according to their usage time characteristics to form different time characteristic life data sets, including: setting cluster idle time difference threshold , Clustering using time difference threshold , Clustering times difference threshold , and perform clustering in the following way: For any two different historical screw drills, if the following four conditions are met at the same time, the historical life data of the corresponding two historical screw drills will be regarded as the same category: the average single idle time of the two different historical screw drills The difference does not exceed the cluster idle time difference threshold ; The average single use time of two different historical screw drilling tools The difference does not exceed the clustering time difference threshold ; The number of times two screw drills are used in different histories The difference does not exceed the clustering number difference threshold ; Idle times of two different screw drill tools The difference does not exceed the clustering number difference threshold The historical life data of all historical screw drill tools of the same category will be clustered to form corresponding time-length characteristic life data.

[0014] In the present invention, it should be noted that the timely use of different screw drill tools is roughly the same, and it is impossible to maintain a high degree of consistency or even the same in the average single use time, the average single idle time, and the number of idle and used times. Therefore, when clustering different screw drill tools based on these time characteristics, determining whether to cluster them by determining the difference in the time characteristics of different screw drill tools is a reasonable clustering method. The cluster idle time difference threshold, the cluster use time difference threshold, and the cluster number difference threshold can be set according to actual needs, and can also be determined based on big data analysis.

[0015] As a possible implementation method, data extraction and analysis based on usage characteristics are performed on different time-length characteristic life data sets to establish a corresponding cluster life prediction model, including: for different time-length characteristic life data sets, the usage time characteristic information and usage characteristic information of different historical screw drill tools are extracted to form corresponding life prediction relationships; according to the life prediction relationships corresponding to different historical screw drill tools, relationship confirmation analysis is performed to form a cluster life prediction model corresponding to the time-length characteristic life data set.

[0016] In the present invention, after completing the clustering of different historical screw drills, targeted life prediction analysis can be performed on different cluster data to establish a reasonable life prediction model. To establish a prediction model for the life prediction of screw drills, it is necessary to consider the factors that affect the life of the screw drills, and thus establish a comprehensive life prediction model by determining the influence of these factors on the life. Therefore, the analysis of these factors needs to be extracted from the usage feature information.

[0017] As a possible implementation method, for different time characteristic life data sets, the use time characteristic information and usage characteristic information of different historical screw drills are extracted to form corresponding life prediction relationships, including: for different historical screw drills, the corresponding single average idle time is extracted and idle times , and establish the idle-life impact relationship ,in: , represents the idle life influencing factor, and , represents the idle life influencing factor, k is 1, 2, 3; for different historical screw drill tools, the corresponding single average usage time is extracted and idle times , and establish the use-life impact relationship ,in: , represents the service life influencing factor, and , Indicates the service life influencing factor; for different historical screw drill tools, extract the total working time Total working hours The corresponding ambient temperature change factor is and total working hours The corresponding drill temperature change factor is , and establish the temperature-life effect relationship ,in: , , Indicates the factors affecting the operating temperature life, Indicates the temperature conversion life influencing factor; for different historical screw drills, extract the total working time and total working hours The corresponding workload lifespan influence factor is , and establish the load-life influence relationship ,in: , Indicates that the conversion life influencing factor is met; for different historical screw drills, the total working time is extracted Total working hours The corresponding environmental humidity change factor is and total working hours The corresponding drilling tool humidity change factor is , and establish the humidity-lifespan relationship ,in: , , Indicates the factor affecting the life of working humidity, Indicates the humidity conversion life impact factor; based on the idle-life impact relationship , Use-life impact relationship , Temperature-life relationship , Load-life influence relationship And the humidity-lifespan relationship , the following life prediction relationship is established: , represents the predicted life span value, represents the total number of factors affecting lifespan included in the lifespan prediction relationship, and γ represents the prediction-adjusted lifespan impact factor.

[0018] In the present invention, it can be understood that the life of the screw drill is mainly the process of functional failure of the screw drill, and the main factors affecting the function of the screw drill are the environmental factors and operating factors of the screw drill. In this application, the environmental factors mainly consider humidity and temperature, and the operating factors consider workload. What needs life is that there may be many factors affecting the life of the screw drill, and different factors have different influences on the life of the screw drill, and these factors are indirect and direct, such as regional factors, which mainly affect the life of the screw drill through the difference in humidity and temperature. Therefore, through observation and analysis, it is reasonable to use humidity and temperature as the two most important direct influencing factors for life prediction analysis. Of course, for the workload, it can be a direct operating load, or it can be a combination of various static and kinetic forces to which the screw drill is subjected during operation. In addition, when establishing the life prediction relationship, it is considered that different factors affecting the life of the screw drill have different influences on the life of the screw drill, so it is necessary to establish corresponding influence relationships respectively before the life prediction relationship of the screw drill can be formed comprehensively. Here, for idle time and use time, since these time periods do not have an equal impact on the life of the screw drill, there is a conversion relationship between life and time. In order to avoid overly complex data processing and to ensure that this conversion relationship can be determined more accurately, the life influence factors under the established influence relationship are all characterized by quadratic terms. Similarly, for humidity, temperature, and workload, the impact on life is not equal, so conversion is required, so different conversion life influence factors are provided for different influencing factors. Of course, for humidity and temperature, since the changes in humidity and temperature during operation are much greater than the humidity and temperature of the environment, there is no need to consider the ambient temperature and humidity, and the impact of ambient temperature and humidity will not have a significant impact on the life during the operation. The actual temperature and humidity obtained during the operation are indeed the result of the superposition of environmental conditions, so the impact of environmental conditions needs to be removed when the influence factor is made. For temperature, humidity, and workload, the accumulation of these factors can be obtained based on historical data, but these factors also change over time.

[0019] As a possible implementation method, according to the life prediction relationship corresponding to different historical screw drilling tools, a relationship confirmation analysis is performed to form a cluster life prediction model corresponding to the time characteristic life data set, including: +1 is the unit of grouping quantity. Different historical life data in the duration characteristic life data set are arbitrarily extracted to form different duration characteristic life data groups. For different duration characteristic life data groups, all life influencing factors are determined according to the life prediction relationship corresponding to different historical life data in the group. For all life influencing factors determined for different duration characteristic life data groups, the average value of the same life influencing factors in different groups is obtained. The average value of different life influencing factors is used as the numerical value of the corresponding life influencing factor in the life prediction relationship to form a clustered life prediction model corresponding to the duration characteristic life data set.

[0020] In the present invention, after establishing the corresponding cluster life prediction model, it is necessary to determine the life influencing factors set in the established model, so as to form a life prediction model without unknown quantities. The number of cluster life prediction models established in the data set is greater than the size of the unknown quantity, so as long as the minimum number of models that can determine the life influencing factors is provided, the determination of the life influencing factors in the model can be achieved. Here, it should be noted that in the cluster life prediction relationship, different life prediction values ​​can be considered to be the same, which serves as a standard reference for the total life under the corresponding usage conditions. Since the prediction model is always used when the screw drill has not reached its life limit, the prediction result obtained by the prediction model is numerically smaller than the life prediction value, but the comprehensive life prediction value and the prediction result obtained by the prediction model analysis can determine the remaining effective life of the screw drill.

[0021] As a possible implementation method, different cluster life prediction models are analyzed for consistency to determine the life prediction model of screw drill tools, including: extracting the corresponding different life influencing factors for different cluster life prediction models to form a corresponding cluster life influencing factor set; for different cluster life influencing factor sets, the following model consistency judgment is performed: setting the factor consistency judgment threshold corresponding to different influencing factors and factored total deviation threshold , x takes α, β, w, f, c; arbitrarily extract two sets of cluster life influencing factors, and determine the cumulative difference of the corresponding influencing factors under the corresponding time length ; If the cumulative difference for any factor All meet the following requirements: ,and ≤ , it is determined that the two cluster life prediction models are consistent, and the life influence factor with the largest factor accumulation of the same cluster life influence factor in the corresponding time is used as the representative life influence factor of the two cluster life prediction models, and a new life prediction model is formed according to different representative life influence factors; the formed new life prediction model is also taken as the object of consistency judgment, and the consistency judgment is continued until no new life prediction model is formed; all life prediction models after the consistency judgment are determined as screw drill tool life prediction models.

[0022] In the present invention, different cluster life prediction models represent the situation of screw drill tools under different conditions of use. However, considering that different conditions of use may also produce similar life development situations, in order to improve the applicability of the life prediction model, it is necessary to conduct consistency analysis on different cluster life prediction models to complete comprehensive merging. This consistency analysis mainly considers that different influencing factors may have similar predictions on life in general, so the analysis is also based on the close accumulation of all influencing factors in the time dimension to make consistency judgments. Of course, in order to fully guarantee the accuracy of the prediction results of the prediction model, the present application also makes separate difference judgments on different influencing factors as one of the conditions that need to be considered in the consistency analysis. For the factor consistency judgment threshold and the factor total deviation threshold, it can be set according to the actual situation, or it can be obtained based on big data analysis. In addition, for the prediction model with consistency, the factor with the largest factor accumulation is used as the representative factor, so that a more conservative life estimate can be achieved.

[0023] As a possible implementation method, the current usage data of the target screw drill is obtained, and the effective remaining life of the target screw drill is determined according to the screw drill life prediction model, including: determining the current working time according to the current usage data of the target screw drill The accumulated temperature value under , Humidity cumulative value and workload accumulation ; Based on current working hours , determine the following equivalent cumulative values ​​corresponding to all screw drill tool life prediction models: Working temperature life influence factor in current working time Equivalent temperature life accumulation value under , i represents the number of different screw drill life prediction models; the working humidity life influencing factor is the current working time Equivalent humidity life accumulation value under ; Workload life factors affecting current working hours Equivalent load life accumulation value under ;According to the temperature accumulation value And the equivalent temperature life accumulation value , determine the temperature influence overlap ratio ,in, ;According to humidity accumulation value And the equivalent humidity life accumulation value , determine the humidity impact overlap ratio ,in, ;According to the workload accumulation value and the cumulative value of equivalent load life , determine the load impact overlap ratio ,in, ;According to the temperature influence the overlap ratio , Humidity affects the overlap ratio And the load impact overlap ratio , determine the total proportion ,in, ; Different total proportions The largest total share The corresponding screw drill tool life prediction model is determined as the screw drill tool life prediction model corresponding to the target screw drill tool; according to the determined screw drill tool life prediction model and in combination with current usage data, the effective remaining life of the target screw drill tool is determined.

[0024] In the present invention, after obtaining the life prediction model of the screw drill, a reasonable and accurate life prediction can be made for the screw drill being used. The life prediction of the target screw drill first requires determining whether the use conditions of the target screw drill meet the corresponding screw drill life prediction model, and then using the determined corresponding screw drill life prediction model to make a reasonable and accurate life prediction for it. Here, the matching of the screw torque life prediction model is mainly judged by the overlap between the cumulative amount of different factors in the time dimension and the cumulative amount corresponding to different models, that is, the closer the cumulative amount of all factors is, the more it can match the use conditions of the current target screw drill. Of course, after determining the matching screw drill prediction model, the current usage data can be used to determine the life consumption, and then the effective remaining life can be determined based on the life prediction value provided by the model.

[0025] In a second aspect, the present invention provides an intelligent screw drill tool life prediction system, which is configured to: collect historical screw drill tool life data, perform clustering based on usage time characteristics, and form characteristic life data sets of different durations; perform data extraction and analysis based on usage characteristics for different characteristic life data sets, and establish corresponding cluster life prediction models; perform consistency analysis on different cluster life prediction models to determine a screw drill tool life prediction model; obtain the current usage data of the target screw drill tool, and determine the effective remaining life of the target screw drill tool based on the screw drill tool life prediction model.

[0026] In the present invention, the system is configured as a complete system that can predict the life of screw drill tools by utilizing historical big data for clustering life prediction model analysis, which fully guarantees the efficient and accurate implementation of screw drill tool life prediction and is an important material basis for achieving reasonable and accurate life prediction of screw drill tools.

[0027] The beneficial effects of the intelligent screw drill tool life prediction method and system provided by the present invention are as follows: This method obtains big data of historical screw drill tools and performs life prediction model analysis based on factors affecting the life of screw drill tools, and then establishes a life prediction model of these factors affecting the life of screw drill tools, so as to achieve reasonable and accurate prediction of the remaining life of screw drill tools. This method is based on big data analysis and processing, and adopts cluster analysis. It can make reasonable and accurate life predictions for different usage scenarios to a certain extent, covering a larger range of screw drill tool life prediction scenarios. At the same time, with big data as a reference, it greatly improves the accuracy of screw drill tool life prediction, and provides a more reasonable and accurate reference basis for planning the use of screw drill tools.

[0028] The system is configured as a complete system that can predict the life of screw drill tools by using historical big data for clustering life prediction model analysis, which fully guarantees the efficient and accurate implementation of screw drill tool life prediction and is an important material basis for achieving reasonable and accurate life prediction of screw drill tools. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 A step diagram of an intelligent screw drill tool life prediction method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0032] Screw drill is a type of volumetric downhole power drill that uses drilling fluid as power and converts liquid pressure energy into mechanical energy. Since screw drill works in a high-pressure and high-temperature working environment, it is more susceptible to damage to the drill itself, especially fatigue damage caused by different factors, which accumulates over a long period of time and affects the service life of the screw drill. At the same time, screw drill is a high-value drilling tool, so its reasonable preservation and use is an effective way to improve its utilization rate.

[0033] The life prediction of screw drill tools can provide enterprises with reasonable cost planning and work arrangements. However, at present, there is no established and reasonable and accurate way to predict the life of screw drill tools. Currently, the life prediction of screw drill tools is basically based on visual observation and judgment based on experience, and the prediction results have large deviations.

[0034] refer to Figure 1 The embodiment of the present invention provides an intelligent screw drill life prediction method. The method obtains big data of historical screw drills to perform life prediction model analysis based on factors affecting the life of screw drills, and then establishes a life prediction model of these factors affecting the life of screw drills, so as to achieve reasonable and accurate prediction of the remaining life of screw drills. This method is based on big data analysis and processing, and adopts a cluster analysis method. It can make reasonable and accurate life predictions for different usage scenarios to a certain extent, covering a larger range of screw drill life prediction scenarios. At the same time, with big data as a reference, it greatly improves the accuracy of screw drill life prediction, and provides a more reasonable and accurate reference basis for planning the use of screw drills.

[0035] The intelligent screw drill tool life prediction method specifically includes the following steps: S1: Collect historical screw drill life data, perform clustering based on usage time characteristics, and form characteristic life data sets of different durations.

[0036] The historical screw drill life data is collected, and clustering is performed based on the usage time characteristics to form different time characteristic life data sets, including: extracting the usage time characteristic information of different historical screw drills according to the historical screw drill life data; clustering different historical screw drills according to the usage time characteristic information of different historical screw drills to form different time characteristic life data sets.

[0037] The historical screw drill life data includes data from the beginning of use to the entire effective service life of different screw drills. Considering that the impact on the life of screw drills is closely related to the usage conditions, before establishing a prediction model that affects the life of screw drills, it is necessary to reasonably cluster the data according to different usage conditions, so that the subsequent life prediction model is more targeted and accurate. Of course, a more reasonable way is to cluster the specific product forms of screw drills, so that the life prediction of different types of screw drill products can be more accurately ensured. It should be noted that for the clustering division based on different screw drill usage conditions, since the impact on the life of screw drills is caused by the main factors acting on a certain time dimension span, the clustering division is based on the usage time characteristics, which can ensure the rationality and correctness of the clustering.

[0038] According to the historical screw drill life data, the use time characteristic information of different historical screw drills is extracted, including: for different historical screw drills, the historical single use time of each use within the historical life is determined And historical single idle time , Indicates the number of times it has been used within the corresponding historical lifespan. Indicates the number of times the device has been idle within the corresponding historical lifespan; based on the historical single idle duration , determine the average idle time of the historical screw drill ,in, ; Based on the historical single usage duration , determine the average single use time of the historical screw drill ,in, .

[0039] The time dimension characteristics to be considered for clustering screw drills according to different screw drill usage conditions based on the time characteristic information of screw drills mainly include three aspects. The first aspect is the working time characteristics of the screw drills during their effective life cycle. Considering that the environmental conditions when the screw drills are working are completely different from those when they are not in use and idle, and the impact on the life of the screw drills during operation is much greater than when they are idle, the time characteristic information of the use during the entire service life cycle needs to be considered first. It is understandable that the use time of the screw drills will occur multiple times during the entire service life cycle, and the use time of each time is different. The characterization of the overall use time by the average use time of each time is representative of the impact of the use time on the life of the screw drills, so the average use time of a single time needs to be considered. The second aspect is the length of time that the screw drill is idle during its entire service life. When it is idle, the different storage methods and environmental conditions will affect the life of the screw drill to varying degrees. After all, the humidity and temperature of the environment during idle time can affect the performance of the screw drill, and thus affect the life of the screw drill. Similarly, the screw drill will be idle many times during its entire service life, and it will intersect with the length of time it is in operation. The average idle time of a single time can reflect to a certain extent the degree to which the life of the screw drill is affected by the idle time, so the average idle time of a single time needs to be considered. The third aspect is the utilization rate of the screw drill. It is understandable that the more times the screw drill is used, the shorter its life will be. Therefore, the number of times it is used is also an important factor in determining the screw drill, and the number of operations and the number of times it is idle need to be considered in order to accurately achieve a reasonable definition of the use of the screw drill.

[0040] According to the usage time characteristic information of different historical screw drills, different historical screw drills are clustered and divided to form different time characteristic life data sets, including: setting cluster idle time difference threshold , Clustering using time difference threshold , Clustering times difference threshold , and perform clustering in the following way: For any two different historical screw drills, if the following four conditions are met at the same time, the historical life data of the corresponding two historical screw drills will be regarded as the same category: the average single idle time of the two different historical screw drills The difference does not exceed the cluster idle time difference threshold ; The average single use time of two different historical screw drilling tools The difference does not exceed the clustering time difference threshold ; The number of times two screw drills are used in different histories The difference does not exceed the clustering number difference threshold ; Idle times of two different screw drill tools The difference does not exceed the clustering number difference threshold The historical life data of all historical screw drill tools of the same category will be clustered to form corresponding time-length characteristic life data.

[0041] It should be noted that the timely use of different screw drills is generally the same, and it is impossible to maintain a high degree of consistency or even the same average single use time, average single idle time, and idle and use times. Therefore, when clustering different screw drills based on these time characteristics, determining the difference in the time characteristics of different screw drills to determine whether clustering is a reasonable clustering method. The cluster idle time difference threshold, cluster use time difference threshold, and cluster number difference threshold can be set according to actual needs, or can be determined based on big data analysis.

[0042] S2: For different time-length characteristic life data sets, perform data extraction and analysis based on usage characteristics and establish corresponding clustering life prediction models.

[0043] For different time-characteristic life data sets, data extraction and analysis based on usage characteristics are performed to establish corresponding cluster life prediction models, including: for different time-characteristic life data sets, the usage time characteristic information and usage characteristic information of different historical screw drill tools are extracted to form corresponding life prediction relationships; according to the life prediction relationships corresponding to different historical screw drill tools, relationship confirmation analysis is performed to form a cluster life prediction model corresponding to the time-characteristic life data set.

[0044] After completing the clustering of different historical screw drills, targeted life prediction analysis can be performed on different cluster data to establish a reasonable life prediction model. To establish a prediction model for the life prediction of screw drills, it is necessary to consider the factors that affect the life of screw drills. Therefore, a comprehensive life prediction model is established by determining the impact of these factors on the life. Therefore, the analysis of these factors needs to be extracted from the usage feature information.

[0045] For different time characteristic life data sets, extract the usage time characteristic information and usage characteristic information of different historical screw drills to form corresponding life prediction relationships, including: for different historical screw drills, extract the corresponding single average idle time and idle times , and establish the idle-life impact relationship ,in: , represents the idle life influencing factor, and , represents the idle life influencing factor, k is 1, 2, 3; for different historical screw drill tools, the corresponding single average usage time is extracted and idle times , and establish the use-life impact relationship ,in: , represents the service life influencing factor, and , Indicates the service life influencing factor; for different historical screw drill tools, extract the total working time Total working hours The corresponding ambient temperature change factor is and total working hours The corresponding drill temperature change factor is , and establish the temperature-life effect relationship ,in: , , Indicates the factors affecting the operating temperature life, Indicates the temperature conversion life influencing factor; for different historical screw drills, extract the total working time and total working hours The corresponding workload lifespan influence factor is , and establish the load-life influence relationship ,in: , Indicates that the conversion life influencing factor is met; for different historical screw drills, the total working time is extracted Total working hours The corresponding environmental humidity change factor is and total working hours The corresponding drilling tool humidity change factor is , and establish the humidity-lifespan relationship ,in: , , Indicates the factor affecting the life of working humidity, Indicates the humidity conversion life impact factor; based on the idle-life impact relationship , Use-life impact relationship , Temperature-life relationship , Load-life influence relationship And the humidity-lifespan relationship , the following life prediction relationship is established: , represents the predicted life span value, represents the total number of factors affecting lifespan included in the lifespan prediction relationship, and γ represents the prediction-adjusted lifespan impact factor.

[0046] It is understandable that the life of the screw drill is mainly the process of the functional failure of the screw drill, and the main factors affecting the function of the screw drill are the environmental factors and operating factors of the screw drill. In this application, the environmental factors mainly consider humidity and temperature, and the operating factors consider the workload. What needs life is that there may be many factors affecting the life of the screw drill, and different factors have different influences on the life of the screw drill, and these factors are indirect and direct, such as regional factors, which mainly affect the life of the screw drill through the difference in humidity and temperature. Therefore, through observation and analysis, it is reasonable to use humidity and temperature as the two most important direct influencing factors for life prediction analysis. Of course, for the workload, it can be a direct operating load, or it can be a combination of various static and kinetic forces to which the screw drill is subjected during operation. In addition, when establishing the life prediction relationship, it is considered that different factors affecting the life of the screw drill have different influences on the life of the screw drill, so it is necessary to establish corresponding influence relationships respectively before the life prediction relationship of the screw drill can be formed comprehensively. Here, for idle time and use time, since these time periods do not have an equal impact on the life of the screw drill, there is a conversion relationship between life and time. In order to avoid overly complex data processing and to ensure that this conversion relationship can be determined more accurately, the life influence factors under the established influence relationship are all characterized by quadratic terms. Similarly, for humidity, temperature, and workload, the impact on life is not equal, so conversion is required, so different conversion life influence factors are provided for different influencing factors. Of course, for humidity and temperature, since the changes in humidity and temperature during operation are much greater than the humidity and temperature of the environment, there is no need to consider the ambient temperature and humidity, and the impact of ambient temperature and humidity will not have a significant impact on the life during the operation. The actual temperature and humidity obtained during the operation are indeed the result of the superposition of environmental conditions, so the impact of environmental conditions needs to be removed when the influence factor is made. For temperature, humidity, and workload, the accumulation of these factors can be obtained based on historical data, but these factors also change over time.

[0047] According to the life prediction relationship corresponding to different historical screw drilling tools, the relationship confirmation analysis is carried out to form a cluster life prediction model corresponding to the time characteristic life data set, including: +1 is the unit of grouping quantity. Different historical life data in the duration characteristic life data set are arbitrarily extracted to form different duration characteristic life data groups. For different duration characteristic life data groups, all life influencing factors are determined according to the life prediction relationship corresponding to different historical life data in the group. For all life influencing factors determined for different duration characteristic life data groups, the average value of the same life influencing factors in different groups is obtained. The average value of different life influencing factors is used as the numerical value of the corresponding life influencing factor in the life prediction relationship to form a clustered life prediction model corresponding to the duration characteristic life data set.

[0048] After establishing the corresponding cluster life prediction model, it is necessary to determine the life influencing factors set in the established model, so as to form a life prediction model without unknown quantities. The number of cluster life prediction models established in the data set is greater than the size of the unknown quantity, so as long as the minimum number of models that can determine the life influencing factors is provided, the determination of the life influencing factors in the model can be achieved. Here, it should be noted that in the cluster life prediction relationship, different life prediction values ​​can be considered to be the same, which serves as a standard reference for the total life under the corresponding usage conditions. Since the prediction model is always used for prediction when the screw drill has not reached its life limit, the prediction result obtained by the prediction model is numerically smaller than the life prediction value, but the comprehensive life prediction value and the prediction result obtained by the prediction model analysis can determine the remaining effective life of the screw drill.

[0049] S3: Perform consistency analysis on different clustering life prediction models to determine the screw drill tool life prediction model.

[0050] The consistency analysis of different cluster life prediction models is carried out to determine the life prediction model of screw drill tools, including: extracting the corresponding different life influencing factors for different cluster life prediction models to form the corresponding cluster life influencing factor set; for different cluster life influencing factor sets, the model consistency judgment is carried out in the following ways: setting the factor consistency judgment threshold corresponding to different influencing factors and factored total deviation threshold , x takes α, β, w, f, c; arbitrarily extract two sets of cluster life influencing factors, and determine the cumulative difference of the corresponding influencing factors under the corresponding time length ; If the cumulative difference for any factor All meet the following requirements: ,and ≤ , it is determined that the two cluster life prediction models are consistent, and the life influence factor with the largest factor accumulation of the same cluster life influence factor in the corresponding time is used as the representative life influence factor of the two cluster life prediction models, and a new life prediction model is formed according to different representative life influence factors; the formed new life prediction model is also taken as the object of consistency judgment, and the consistency judgment is continued until no new life prediction model is formed; all life prediction models after the consistency judgment are determined as screw drill tool life prediction models.

[0051] Different cluster life prediction models represent the situation of screw drill tools under different conditions of use. However, considering that different conditions of use may also produce similar life development situations, in order to improve the applicability of the life prediction model, it is necessary to conduct consistency analysis on different cluster life prediction models to complete comprehensive merging. This consistency analysis mainly considers that different influencing factors may have similar predictions on life in general, so the analysis is also based on the close accumulation of all influencing factors in the time dimension to make consistency judgments. Of course, in order to fully guarantee the accuracy of the prediction results of the prediction model, this application also makes separate difference judgments on different influencing factors as one of the conditions that need to be considered in the consistency analysis. For the factor consistency judgment threshold and the factor total deviation threshold, it can be set according to the actual situation, or it can be obtained based on big data analysis. In addition, for the consistent prediction model here, the factor with the largest factor accumulation is used as the representative factor, so that a more conservative life estimate can be achieved.

[0052] S4: obtaining current usage data of the target screw drill, and determining the effective remaining life of the target screw drill according to the screw drill life prediction model.

[0053] Obtain the current usage data of the target screw drill, and determine the effective remaining life of the target screw drill according to the screw drill life prediction model, including: determining the current working time according to the current usage data of the target screw drill The accumulated temperature value under , Humidity cumulative value and workload accumulation ; Based on current working hours , determine the following equivalent cumulative values ​​corresponding to all screw drill tool life prediction models: Working temperature life influence factor in current working time Equivalent temperature life accumulation value under , i represents the number of different screw drill life prediction models; the working humidity life influencing factor is the current working time Equivalent humidity life accumulation value under ; Workload life factors affecting current working hours Equivalent load life accumulation value under ;According to the temperature accumulation value And the equivalent temperature life accumulation value , determine the temperature influence overlap ratio ,in, ;According to humidity accumulation value And the equivalent humidity life accumulation value , determine the humidity impact overlap ratio ,in, ;According to the workload accumulation value and the cumulative value of equivalent load life , determine the load impact overlap ratio ,in, ;According to the temperature influence the overlap ratio , Humidity affects the overlap ratio And the load impact overlap ratio , determine the total proportion ,in, ; Different total proportions The largest total share The corresponding screw drill tool life prediction model is determined as the screw drill tool life prediction model corresponding to the target screw drill tool; according to the determined screw drill tool life prediction model and in combination with current usage data, the effective remaining life of the target screw drill tool is determined.

[0054] After obtaining the life prediction model of the screw drill, a reasonable and accurate life prediction can be made for the screw drill being used. The life prediction of the target screw drill first requires determining whether the use conditions of the target screw drill meet the corresponding screw drill life prediction model, and then using the determined corresponding screw drill life prediction model to make a reasonable and accurate life prediction for it. Here, the matching of the screw torque life prediction model is mainly judged by the overlap between the cumulative amount of different factors in the time dimension and the cumulative amount corresponding to different models, that is, the closer the cumulative amount of all factors is, the more it can match the use conditions of the current target screw drill. Of course, after determining the matching screw drill prediction model, the current usage data can be used to determine the life consumption, and then the effective remaining life can be determined based on the life prediction value provided by the model.

[0055] The present invention also provides an intelligent screw drill tool life prediction system, which is configured to: collect historical screw drill tool life data, perform clustering based on usage time characteristics, and form different time characteristic life data sets; perform data extraction and analysis based on usage characteristics on different time characteristic life data sets, and establish corresponding cluster life prediction models; perform consistency analysis on different cluster life prediction models to determine the screw drill tool life prediction model; obtain the current usage data of the target screw drill tool, and determine the effective remaining life of the target screw drill tool according to the screw drill tool life prediction model.

[0056] In summary, the intelligent screw drill life prediction method and system provided by the embodiments of the present invention have the following beneficial effects: This method obtains big data of historical screw drill tools and performs life prediction model analysis based on factors affecting the life of screw drill tools, and then establishes a life prediction model of these factors affecting the life of screw drill tools, so as to achieve reasonable and accurate prediction of the remaining life of screw drill tools. This method is based on big data analysis and processing, and adopts cluster analysis. It can make reasonable and accurate life predictions for different usage scenarios to a certain extent, covering a larger range of screw drill tool life prediction scenarios. At the same time, with big data as a reference, it greatly improves the accuracy of screw drill tool life prediction, and provides a more reasonable and accurate reference basis for planning the use of screw drill tools.

[0057] The system is configured as a complete system that can predict the life of screw drill tools by using historical big data for clustering life prediction model analysis, which fully guarantees the efficient and accurate implementation of screw drill tool life prediction and is an important material basis for achieving reasonable and accurate life prediction of screw drill tools.

[0058] In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated can also be indirectly indicated by indicating other information, wherein there is an association between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved by means of the arrangement order of each piece of information agreed in advance (for example, specified by the protocol), thereby reducing the indication overhead to a certain extent. At the same time, the common parts of each piece of information can also be identified and indicated uniformly to reduce the indication overhead caused by indicating the same information separately.

[0059] In addition, the specific indication method may also be various existing indication methods, such as but not limited to the above-mentioned indication methods and various combinations thereof. The specific details of the various indication methods can refer to the prior art and will not be repeated herein. As can be seen from the above, for example, when it is necessary to indicate multiple information of the same type, different indication methods may be used for different information. In the specific implementation process, the desired indication method can be selected according to specific needs. The embodiment of the present application does not limit the selected indication method. In this way, the indication method involved in the embodiment of the present application should be understood to cover various methods that can enable the party to be indicated to obtain the information to be indicated.

[0060] It should be understood that the information to be indicated can be sent as a whole, or divided into multiple sub-information and sent separately, and the sending period and / or sending time of these sub-information can be the same or different. The specific sending method is not limited in the embodiment of the present application. Among them, the sending period and / or sending time of these sub-information can be pre-defined, for example, pre-defined according to a protocol, or can be configured by the sending end device by sending configuration information to the receiving end device.

[0061] "Pre-definition" or "pre-configuration" can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in the device, and the embodiments of the present application do not limit the specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be set separately or integrated in an encoder or decoder, a processor, or a communication device. The one or more memories can also be partially set separately and partially integrated in a decoder, a processor, or a communication device. The type of memory can be any form of storage medium, which is not limited by the embodiments of the present application.

[0062] The "protocol" involved in the embodiments of the present application may refer to a protocol family in the communication field, a standard protocol with a similar protocol family frame structure, or a related protocol used in future communication systems, and the embodiments of the present application do not make specific limitations on this.

[0063] In the embodiments of the present application, descriptions such as "when...", "in the case of...", "if" and "if" all mean that the device will make corresponding processing under certain objective circumstances. It does not limit the time, nor does it require the device to have a judgment action when implementing it, nor does it mean that there are other limitations.

[0064] In the description of the embodiments of the present application, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship. For example, A / B can represent A or B; "and / or" in the embodiments of the present application is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. These three situations, where A and B can be singular or plural. In addition, in the description of the embodiments of the present application, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple. In addition, in order to facilitate the clear description of the technical solution of the embodiments of the present application, in the embodiments of the present application, the words "first" and "second" are used to distinguish the same or similar items with basically the same functions and effects. Those skilled in the art will appreciate that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference. At the same time, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0065] It should be understood that the processor in the embodiments of the present application may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0066] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0067] The above embodiments may be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0068] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0069] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0070] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0071] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0072] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0073] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0074] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0075] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0076] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0077] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An intelligent method for predicting the life of a screw drill, characterized in that: include: Collect historical screw drill life data, perform clustering based on usage time characteristics, and form characteristic life data sets of different durations; For different time-length characteristic life data sets, data extraction and analysis based on usage characteristics are performed to establish corresponding cluster life prediction models; Performing consistency analysis on different cluster life prediction models to determine a screw drill tool life prediction model; The current usage data of the target screw drill is obtained, and the effective remaining life of the target screw drill is determined according to the screw drill life prediction model.

2. The intelligent screw drill tool life prediction method according to claim 1 is characterized in that: The historical screw drill life data is collected and clustered based on the usage time characteristics to form characteristic life data sets of different time lengths, including: Extracting usage time characteristic information of different historical screw drill tools according to the historical screw drill tool life data; According to the usage time characteristic information of different historical screw drill tools, different historical screw drill tools are clustered and divided to form different time characteristic life data sets.

3. The intelligent screw drill tool life prediction method according to claim 2 is characterized in that: The extracting of usage time characteristic information of different historical screw drill tools according to the historical screw drill tool life data includes: For different historical screw drill tools, determine the historical single use duration of each use within the historical lifespan. And historical single idle time , Indicates the number of times it has been used within the corresponding historical lifespan. Indicates the number of times the idle state is left idle within the corresponding historical lifespan; According to the historical single idle time , determine the single average idle time corresponding to the historical screw drill ,in, ; According to the historical single use duration , determine the average single use time of the historical screw drill ,in, .

4. The intelligent screw drill tool life prediction method according to claim 3 is characterized in that: According to the usage time characteristic information of different historical screw drill tools, different historical screw drill tools are clustered and divided to form different time characteristic life data sets, including: Set cluster idle time difference threshold , Clustering using time difference threshold , Clustering times difference threshold , and perform clustering in the following way: For any two different historical screw drill tools, if the following four conditions are met at the same time, the historical life data of the corresponding two historical screw drill tools are regarded as the same category: The single average idle time of two different historical screw drilling tools The difference does not exceed the cluster idle time difference threshold ; The average single use time of two different historical screw drilling tools The difference does not exceed the clustering time difference threshold ; The usage times of two different historical screw drill tools The difference does not exceed the clustering number difference threshold ; The idle times of two different historical screw drilling tools The difference does not exceed the clustering number difference threshold ; The historical life data of all the historical screw drill tools of the same type are clustered to form the corresponding time-length characteristic life data.

5. The intelligent screw drill tool life prediction method according to claim 4 is characterized in that: The method of performing data extraction and analysis based on usage characteristics on different duration characteristic life data sets and establishing a corresponding cluster life prediction model includes: For different time characteristic life data sets, extracting the use time characteristic information and use characteristic information of different historical screw drilling tools to form corresponding life prediction relationships; According to the life prediction relationships corresponding to different historical screw drill tools, a relationship confirmation analysis is performed to form a cluster life prediction model corresponding to the time-length characteristic life data set.

6. The intelligent screw drill tool life prediction method according to claim 5 is characterized in that: The extracting the usage time characteristic information and usage characteristic information of different historical screw drill tools from different time characteristic life data sets to form corresponding life prediction relationships includes: For different historical screw drill tools, extract the corresponding single average idle time and the number of idle times , and establish the idle-life impact relationship ,in: , represents the idle life influencing factor, and , represents the idle life impact factor, k is 1, 2, 3; For different historical screw drill tools, extract the corresponding single average usage time and the number of idle times , and establish the use-life impact relationship ,in: , represents the service life influencing factor, and , represents the service life influencing factor; For different historical screw drill tools, extract the total working time , the total working time The corresponding ambient temperature change factor is and the total working hours The corresponding drill temperature change factor is , and establish the temperature-life effect relationship ,in: , , Indicates the factor affecting the operating temperature life, Indicates the temperature conversion life impact factor; For different historical screw drill tools, extract the total working time and the total working hours The corresponding workload lifespan influence factor is , and establish the load-life influence relationship ,in: , It indicates that it meets the transformation lifespan impact factor; For different historical screw drill tools, extract the total working time , the total working time The corresponding environmental humidity change factor is and the total working hours The corresponding drilling tool humidity change factor is , and establish the humidity-lifespan relationship ,in: , , Indicates the factor affecting the life of working humidity, Indicates the humidity conversion life impact factor; According to the idle-life impact relationship , the usage-life impact relationship , the temperature-life relationship , the load-life influence relationship And the humidity-lifespan relationship , the following life prediction relationship is established: , represents the predicted life span value, represents the total number of factors influencing the lifespan included in the lifespan prediction relationship, and γ represents the predicted adjusted lifespan influencing factor.

7. The intelligent screw drill tool life prediction method according to claim 6 is characterized in that: The method of performing relationship confirmation analysis based on the life prediction relationships corresponding to different historical screw drill tools to form a cluster life prediction model corresponding to the time characteristic life data set includes: The total number of factors +1 is the unit of number of groups, and different historical life data in the duration characteristic life data set are arbitrarily extracted to form different duration characteristic life data groups; For different groups of the time-length characteristic life data, all life influencing factors are determined according to the life prediction relationships corresponding to different historical life data in the groups; For all lifespan influencing factors determined for different groups of characteristic lifespan data, obtaining average values ​​of the same lifespan influencing factors in different groups; The average values ​​of different life-span influencing factors are used as the values ​​of the corresponding life-span influencing factors in the life-span prediction relationship to form the cluster life-span prediction model corresponding to the duration characteristic life-span data set.

8. The intelligent screw drill tool life prediction method according to claim 7 is characterized in that: The consistency analysis of different cluster life prediction models is performed to determine the life prediction model of the screw drill, including: For different cluster life prediction models, extracting corresponding different life influencing factors to form a corresponding cluster life influencing factor set; For different sets of factors influencing the cluster life span, the following model consistency judgment is performed: Set the factor consistency judgment threshold corresponding to different influencing factors and factored total deviation threshold , x takes α, β, w, f, c; Extract any two sets of factors affecting the life span of the clusters, and determine the cumulative difference of the factors of the corresponding factors under the corresponding time lengths. ; If the cumulative difference for any factor All meet the following requirements: ,and ≤ , it is determined that the two cluster life prediction models are consistent, and the life influence factor with the largest factor accumulation of the same cluster life influence factor in the corresponding time length is used as the representative life influence factor of the two cluster life prediction models, and a new life prediction model is formed according to the different representative life influence factors; The newly formed life prediction model is also taken as the object of consistency judgment, and the consistency judgment is continued until no new life prediction model is formed; All the life prediction models after consistency judgment are determined as the screw drill tool life prediction models.

9. The intelligent screw drill tool life prediction method according to claim 8, characterized in that: The obtaining of current usage data of the target screw drill and determining the effective remaining life of the target screw drill according to the screw drill life prediction model includes: According to the current usage data of the target screw drill, determine the current working time The accumulated temperature value under , Humidity cumulative value and workload accumulation ; According to the current working hours , determine the following equivalent cumulative values ​​corresponding to all the screw drill tool life prediction models: The operating temperature life influence factor is the current working time Equivalent temperature life accumulation value under , i represents the serial number of the different screw drill tool life prediction models; The influence factor of working humidity on life is as follows: Equivalent humidity life accumulation value under ; The workload lifespan factor is the current working time Cumulative value of equivalent load life under ; According to the temperature accumulation value and the equivalent temperature life accumulation value , determine the temperature influence overlap ratio ,in, ; According to the humidity accumulation value and the equivalent humidity life accumulation value , determine the humidity impact overlap ratio ,in, ; According to the workload accumulation value and the equivalent load life cumulative value , determine the load impact overlap ratio ,in, ; According to the temperature, the overlap ratio 、The humidity affects the overlap ratio And the load impact overlap ratio , determine the total proportion ,in, ; The total proportion of different The largest proportion of the total The corresponding screw drill tool life prediction model is determined as the screw drill tool life prediction model corresponding to the target screw drill tool; The effective remaining life of the target screw drill is determined based on the determined screw drill life prediction model and combined with the current usage data.

10. An intelligent screw drill life prediction system, characterized in that: is configured as: Collect historical screw drill life data, perform clustering based on usage time characteristics, and form characteristic life data sets of different durations; For different time-length characteristic life data sets, data extraction and analysis based on usage characteristics are performed to establish corresponding cluster life prediction models; Performing consistency analysis on different cluster life prediction models to determine a screw drill tool life prediction model; The current usage data of the target screw drill is obtained, and the effective remaining life of the target screw drill is determined according to the screw drill life prediction model.

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