Method for predicting rivet die life, rivet die life prediction device, and storage medium

Abstract

This invention discloses a method, device, and storage medium for predicting the lifespan of a riveting die. The method includes: acquiring monitoring data of riveting operations during the use of a target riveting die, the monitoring data including a first riveting time, a first riveting distance, and a first riveting force; determining the predicted lifespan of the target riveting die based on the first riveting time, the first riveting distance, and the first riveting force; and outputting a prompt message based on the predicted lifespan. This invention aims to improve the accuracy of riveting die lifespan prediction and ensure riveting quality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of machinery, in particular to a rivet die life prediction method, a rivet die life prediction device and a storage medium. BACKGROUND

[0002] Self-piercing riveting technology is a new type of plate cold connection technology. A rivet is directly pressed into a to-be-riveted plate by a hydraulic cylinder or a servo motor, the to-be-riveted plate is plastically deformed under the pressure of the rivet, the rivet is formed and filled in the rivet die, and a stable connection plate connection structure is formed, which is suitable for the connection between metal plates and metal plates, metal plates and non-metal plates and the connection of various plates. The number of connected plates can be two layers or up to several layers. Among them, the rivet die will crack, collapse and other adverse conditions after reaching the service life, causing riveting quality problems.

[0003] At present, the service life of the rivet die is generally evaluated by artificial inspection, however, such a way is easily affected by human errors or insufficient experience and other factors, the evaluated rivet die life has a large error with the actual life, and it is difficult to guarantee the riveting quality. SUMMARY

[0004] The main purpose of the present application is to provide a rivet die life prediction method, a rivet die life prediction device and a storage medium, which aims to improve the prediction accuracy of the rivet die life and guarantee the riveting quality.

[0005] To achieve the above purpose, the present application provides a rivet die life prediction method, which comprises the following steps:

[0006] Obtaining monitoring data of riveting operation in the use process of a target rivet die, the monitoring data comprising a first riveting time, a first riveting distance and a first riveting force;

[0007] Determining the predicted life of the target rivet die according to the first riveting time, the first riveting distance and the first riveting force;

[0008] Outputting prompt information according to the predicted life.

[0009] Optionally, the number of monitoring data is multiple, and the step of determining the predicted life of the target rivet die according to the first riveting time, the first riveting distance and the first riveting force comprises:

[0010] Obtaining the theoretical life of the target rivet die and corresponding theoretical riveting time, theoretical riveting distance and theoretical riveting force;

[0011] determining a time difference value between each of the first riveting time and the theoretical riveting time, to obtain a plurality of time difference values; determining a distance difference value between each of the first riveting distance and the theoretical riveting distance, to obtain a plurality of distance difference values; determining a force difference value between each of the first riveting force and the theoretical riveting force, to obtain a plurality of force difference values;

[0012] determining a life correction value according to the plurality of time difference values, the plurality of distance difference values and the plurality of force difference values;

[0013] correcting the theoretical life according to the life correction value, to obtain the predicted life.

[0014] Optionally, before the step of obtaining the theoretical life of the target riveting die and corresponding theoretical riveting time, theoretical riveting distance and theoretical riveting force, the method further comprises:

[0015] obtaining a plurality of second riveting time, a plurality of second riveting distance and a plurality of second riveting force detected in the process of using the riveting die to reach the theoretical life;

[0016] determining the theoretical riveting time according to the plurality of second riveting time, determining the theoretical riveting distance according to the plurality of second riveting distance, and determining the theoretical riveting force according to the plurality of second riveting force.

[0017] Optionally, the step of determining a life correction value according to the plurality of time difference values, the plurality of distance difference values and the plurality of force difference values comprises:

[0018] determining a first KL divergence between the plurality of first riveting time and the plurality of second riveting time, determining a second KL divergence between the plurality of first riveting distance and the plurality of second riveting distance, and determining a third KL divergence between the plurality of first riveting force and the plurality of second riveting force;

[0019] determining the life correction value according to the plurality of time difference values and corresponding first KL divergence, the plurality of distance difference values and corresponding second KL divergence, and the plurality of force difference values and corresponding third KL divergence.

[0020] Optionally, the step of determining a first KL divergence between the plurality of first riveting time and the plurality of second riveting time comprises:

[0021] determining a first distribution characteristic value of a Gaussian distribution corresponding to the plurality of first riveting time, and determining a second distribution characteristic value of a Gaussian distribution corresponding to the plurality of second riveting time;

[0022] determining the first KL divergence according to the first distribution characteristic value and the second distribution characteristic value;

[0023] And / or, the step of determining the second KL divergence between the plurality of first riveting distances and the plurality of second riveting distances includes:

[0024] Determine the third distribution characteristic value of the Gaussian distribution corresponding to the plurality of first riveting distances, and determine the fourth distribution characteristic value of the Gaussian distribution corresponding to the plurality of second riveting distances;

[0025] The second KL divergence is determined based on the third and fourth distribution characteristic values;

[0026] And / or, the step of determining the third KL divergence between the plurality of first riveting forces and the plurality of second riveting forces includes:

[0027] Determine the fifth distribution characteristic value of the Gaussian distribution corresponding to the plurality of first riveting forces, and determine the sixth distribution characteristic value of the Gaussian distribution corresponding to the plurality of second riveting forces;

[0028] The third KL divergence is determined based on the fifth and sixth distribution characteristic values.

[0029] Optionally, the step of obtaining the theoretical lifespan of the target riveting die and the corresponding theoretical riveting time, theoretical riveting distance, and theoretical riveting force includes:

[0030] Obtain the riveting feature information of the riveting operation corresponding to the target riveting die;

[0031] The theoretical lifespan, theoretical riveting time, theoretical riveting distance, and theoretical riveting force are obtained based on the riveting feature information.

[0032] Optionally, the riveting feature information includes first information about the target riveting die, second information about the rivet corresponding to the target riveting die, and third information about the riveting plate corresponding to the target riveting die.

[0033] Optionally, after the step of correcting the theoretical lifetime according to the lifetime correction value to obtain the predicted lifetime, the method further includes:

[0034] When the predicted lifespan is greater than the theoretical lifespan, and the actual lifespan of the target rivet reaches the predicted lifespan, a new theoretical lifespan is determined based on the predicted lifespan.

[0035] Return to the step of obtaining monitoring data of riveting operations during the use of the target riveting die.

[0036] Optionally, the step of outputting prompt information based on the predicted lifespan includes:

[0037] When the predicted lifespan is less than the preset minimum lifespan, the prompt message is output.

[0038] In addition, to achieve the above objectives, this application also proposes a rivet die life prediction device, which includes: a memory, a processor, and a rivet die life prediction program stored in the memory and executable on the processor. When the rivet die life prediction program is executed by the processor, it implements the steps of the rivet die life prediction method as described in any of the preceding claims.

[0039] In addition, to achieve the above objectives, this application also proposes a storage medium storing a rivet die life prediction program, which, when executed by a processor, implements the steps of the rivet die life prediction method as described in any of the preceding claims.

[0040] This invention proposes a method for predicting the lifespan of a riveting die. This method determines the predicted lifespan of the riveting die based on the riveting time, riveting distance, and riveting force detected during the riveting operation. It then outputs a prompt message based on the predicted lifespan. In this process, the predicted lifespan of the riveting die is determined by data monitored during its use, enabling digital prediction of the die's lifespan. Compared to manual inspection, this method effectively avoids lifespan prediction errors caused by human error or lack of experience, significantly improving the accuracy of the prediction. Production personnel can then promptly and accurately know the lifespan of the riveting die through the prompt message and manage production, equipment maintenance, and other aspects, thereby effectively ensuring riveting quality. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the hardware structure involved in the operation of an embodiment of the riveting die life prediction device of the present invention;

[0042] Figure 2 This is a flowchart illustrating an embodiment of the riveting die life prediction method of the present invention;

[0043] Figure 3 This is a flowchart illustrating another embodiment of the riveting die life prediction method of the present invention;

[0044] Figure 4 This is a flowchart illustrating another embodiment of the riveting die life prediction method of the present invention;

[0045] Figure 5 This is a flowchart illustrating another embodiment of the riveting die life prediction method of the present invention.

[0046] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0047] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0048] This invention provides a riveting die life prediction device 1, which is used to predict the life of riveting dies used in riveting.

[0049] The rivet die life prediction device 1 is connected to the automatic riveting equipment corresponding to the rivet die. The rivet die life prediction device 1 can acquire the operating data of the automatic riveting equipment and obtain the monitoring data during the use of the target rivet die. Specifically, the rivet die life prediction device 1 can be built into the automatic riveting equipment or set up independently of the automatic riveting equipment.

[0050] In addition, the rivet die life prediction device 1 can also be connected to the prompting device 2, which can be used to output prompt information.

[0051] In this embodiment of the invention, reference is made to Figure 1 The rivet die life prediction device 1 includes: a processor 1001 (e.g., CPU), a memory 1002, a timer 1003, etc. The components in the control device are connected via a communication bus. The memory 1002 can be a high-speed RAM or a stable, non-volatile memory, such as a disk drive. Optionally, the memory 1002 can also be a storage device independent of the aforementioned processor 1001.

[0052] Those skilled in the art will understand that Figure 1 The device structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0053] like Figure 1 As shown, the memory 1002, which serves as a storage medium, may include a riveting die life prediction program. Figure 1 In the apparatus shown, the processor 1001 can be used to call the rivet die life prediction program stored in the memory 1002 and execute the relevant steps of the rivet die life prediction method in the following embodiments.

[0054] This invention also provides a method for predicting the life of a riveting die, which is applied to the above-mentioned riveting die life prediction device.

[0055] Reference Figure 2 This application proposes an embodiment of the riveting die life prediction method. In this embodiment, the riveting die life prediction method includes:

[0056] Step S10: Obtain monitoring data of the riveting operation during the use of the target riveting die, the monitoring data including the first riveting time, the first riveting distance and the first riveting force;

[0057] The target riveting die body is the riveting die currently used by the automatic riveting equipment that requires life prediction.

[0058] In this embodiment, the number of monitoring data is more than one, including data detected multiple times during the riveting process. In other embodiments, the number of monitoring data may be one, which may be data detected at the current moment or data detected at a preset moment prior to the current moment.

[0059] The monitoring data can be obtained by acquiring the operating data of the automatic riveting equipment corresponding to the target riveting mold.

[0060] The first riveting time is specifically the duration from when the rivet corresponding to the target riveting die reaches its rated speed to when it stops moving.

[0061] The first riveting distance is specifically the distance between the initial position of the rivet corresponding to the target riveting die contacting the plate and the final position after contact with the plate where the movement stops. This first riveting distance can be determined based on the rivet's movement speed and the first riveting time during the riveting process. Alternatively, it can be determined based on the displacement difference between the first displacement of the rivet from its original position to the starting position and the second displacement of the rivet from its original position to the final position.

[0062] The first riveting force is specifically the output force of the automatic riveting die equipment in the riveting process corresponding to the target riveting. In this embodiment, the first riveting force is the peak value of the riveting force during the riveting process. In other embodiments, the first riveting force may also be the average value of the riveting force during the riveting process, etc.

[0063] Step S20: Determine the predicted lifespan of the target riveting die based on the first riveting time, the first riveting distance, and the first riveting force;

[0064] Different first riveting times, different first riveting distances, and different first riveting forces correspond to different predicted lifespans.

[0065] Specifically, the correspondence between riveting time, riveting distance, riveting force, and predicted lifespan can be preset. This correspondence can include calculation formulas, mapping relationships, machine learning models, etc. Based on this correspondence, the lifespan corresponding to the current first riveting time, first riveting distance, and first riveting force can be determined as the current predicted lifespan.

[0066] Specifically, there may be more than one correspondence here. Specifically, the target correspondence can be determined in more than one correspondence based on at least one of the following: the first information of the target riveting die, the second information of the corresponding rivet, and the third information of the corresponding riveting plate. Based on the target correspondence, the lifespan corresponding to the current first riveting time, the first riveting distance, and the first riveting force is determined as the current predicted lifespan.

[0067] Step S30: Output prompt information based on the predicted lifespan.

[0068] The prompts may include voice, text, and / or light.

[0069] The prompts may include maintenance prompts and / or fault prompts corresponding to the predicted lifespan and / or preset lifespan.

[0070] Specifically, it can output a prompt message when a preset command is received, or it can output a prompt message when the preset lifespan is reached under preset conditions.

[0071] Specifically, in this embodiment, when the predicted lifespan is less than the preset minimum lifespan, a prompt message is output. Based on this, production personnel can promptly replace or maintain the target riveting die based on the output prompt message, thereby ensuring riveting quality.

[0072] This invention proposes a method for predicting the lifespan of a riveting die. This method determines the predicted lifespan of the riveting die based on the riveting time, riveting distance, and riveting force detected during the riveting operation. It then outputs a prompt message based on the predicted lifespan. In this process, the predicted lifespan of the riveting die is determined by data monitored during its use, enabling digital prediction of the die's lifespan. Compared to manual inspection, this method effectively avoids lifespan prediction errors caused by human error or lack of experience, significantly improving the accuracy of the prediction. Production personnel can then promptly and accurately know the lifespan of the riveting die through the prompt message and manage production, equipment maintenance, and other aspects, thereby effectively ensuring riveting quality.

[0073] Furthermore, based on the above embodiments, another embodiment of the riveting die life prediction method of this application is proposed. In this embodiment, the number of monitoring data is multiple, and the multiple monitoring data are obtained by monitoring in different riveting processes after the target riveting die is activated. (Refer to...) Figure 3 Step S20 includes:

[0074] Step S21: Obtain the theoretical lifespan of the target riveting die and the corresponding theoretical riveting time, theoretical riveting distance, and theoretical riveting force;

[0075] The theoretical lifespan is the theoretical value of the target riveting die's lifespan obtained from extensive data analysis up to the current moment. The theoretical riveting time is the theoretical value of the riveting time corresponding to the target riveting die reaching its theoretical lifespan. The theoretical riveting distance is the theoretical value of the riveting distance corresponding to the target riveting die reaching its theoretical lifespan. The theoretical riveting force is the theoretical value of the riveting force corresponding to the target riveting die reaching its theoretical lifespan.

[0076] The theoretical lifespan, theoretical riveting time, theoretical riveting distance, and theoretical riveting force can be preset fixed values ​​or values ​​determined based on the actual riveting situation of the target riveting die.

[0077] Step S22: Determine the time difference between each first riveting time and the theoretical riveting time, and obtain multiple time difference values; determine the distance difference between each first riveting distance and the theoretical riveting distance, and obtain multiple distance difference values; determine the force difference between each first riveting force and the theoretical riveting force, and obtain multiple force difference values.

[0078] In this embodiment, the time difference is specifically the calculated result of subtracting the theoretical riveting time from the first riveting time. The distance difference is the calculated result of subtracting the theoretical riveting distance from the first riveting time. The force difference is the calculated result of subtracting the theoretical riveting force from the first riveting force.

[0079] Step S23: Determine the lifetime correction value based on the plurality of time differences, the plurality of distance differences, and the plurality of force differences;

[0080] Specifically, a first correction value can be determined based on multiple time differences, a second correction value can be determined based on multiple distance differences, and a third correction value can be determined based on multiple force differences. The sum of the first, second, and third correction values ​​is used as the lifetime correction value. In other embodiments, the average of the first, second, and third correction values ​​can also be used as the lifetime correction value.

[0081] Specifically, the first correction value can be determined based on the mean, extreme value, or sum of multiple time differences; the second correction value can be determined based on the mean, extreme value, or sum of multiple distance differences; and the third correction value can be determined based on the mean, extreme value, or sum of multiple force differences.

[0082] Step S24: Correct the theoretical lifetime according to the lifetime correction value to obtain the predicted lifetime.

[0083] Specifically, the sum of the life correction value and the theoretical life is used as the predicted life, which can be greater than, equal to or less than the theoretical life.

[0084] In this embodiment, the theoretical lifespan is corrected based on the deviation between the actual monitored riveting time, riveting distance, and riveting force corresponding to the theoretical values ​​to obtain the predicted lifespan of the riveting die. This achieves an organic combination of theoretical verification and actual usage, effectively improving the accuracy of the riveting die lifespan prediction.

[0085] Furthermore, based on the above embodiments, another embodiment of the riveting die life prediction method of this application is proposed. In this embodiment, referring to... Figure 4 Before the step of obtaining the theoretical lifespan of the target riveting die and the corresponding theoretical riveting time, theoretical riveting distance, and theoretical riveting force, the method further includes:

[0086] Step S01: Obtain multiple second riveting times, multiple second riveting distances, and multiple second riveting forces detected during the use of the riveting die to reach the theoretical lifespan.

[0087] The definitions and detection methods of the second riveting time, second riveting distance, and second riveting force can be compared with the first riveting time, first riveting distance, and first riveting force mentioned above, and will not be repeated here.

[0088] Step S02: Determine the theoretical riveting time based on the plurality of second riveting times, determine the theoretical riveting distance based on the plurality of second riveting distances, and determine the theoretical riveting force based on the plurality of second riveting forces.

[0089] Specifically, the average of multiple second riveting times can be used as the theoretical riveting time, the average of multiple second riveting distances can be used as the theoretical riveting distance, and the average of multiple second riveting forces can be used as the theoretical riveting force. Alternatively, the average of multiple second riveting times that satisfy a Gaussian distribution can be determined as the theoretical riveting time, the average of multiple second riveting distances that satisfy a Gaussian distribution can be determined as the theoretical riveting distance, and the average of multiple second riveting forces that satisfy a Gaussian distribution can be determined as the theoretical riveting force.

[0090] In this embodiment, determining the theoretical riveting time, theoretical riveting distance, and theoretical riveting force in the manner described above ensures that the corresponding time difference, distance difference, and force difference determined based on the theoretical riveting time, theoretical riveting distance, and theoretical riveting force accurately reflect the relationship between the predicted life and the theoretical life, thereby effectively improving the accuracy of the riveting die life prediction.

[0091] Furthermore, based on the above steps S01 and S02, referring to Figure 4 Step S23 includes:

[0092] Step S231: Determine a first KL divergence between a plurality of first riveting times and a plurality of second riveting times, determine a second KL divergence between a plurality of first riveting distances and a plurality of second riveting distances, and determine a third KL divergence between a plurality of first riveting forces and a plurality of second riveting forces.

[0093] A first probability distribution parameter is determined for multiple first riveting times, and a second probability distribution parameter is determined for multiple second riveting times. A first KL divergence is calculated based on the first and second probability distribution parameters. The first KL divergence characterizes the similarity between the riveting time distribution corresponding to the theoretical lifespan and the riveting time distribution of the current target riveting die. The first and second probability distribution parameters can be calculated based on probability distribution models such as Gaussian distribution, empirical distribution, and binomial distribution.

[0094] Specifically, in this embodiment, a first distribution characteristic value of a Gaussian distribution corresponding to a plurality of first riveting times is determined, and a second distribution characteristic value of a Gaussian distribution corresponding to a plurality of second riveting times is determined. It should be noted that the plurality of second riveting times are riveting time data from the riveting time data corresponding to the theoretical lifespan that have been verified to satisfy a Gaussian distribution. The first distribution characteristic value includes a first mean and / or a first variance. The second distribution characteristic value includes a second mean and / or a second variance. Specifically, a first KL divergence is calculated using the first mean, the first variance, the second mean, and the second variance.

[0095] A third probability distribution parameter is determined for multiple first riveting distances, and a fourth probability distribution parameter is determined for multiple second riveting distances. A second KL divergence is then calculated based on these third and fourth probability distribution parameters. The second KL divergence characterizes the similarity between the riveting distance distribution corresponding to the theoretical lifespan and the riveting distance distribution of the current target riveting die. The third and fourth probability distribution parameters can be calculated based on probability distribution models such as Gaussian distribution, empirical distribution, and binomial distribution.

[0096] Specifically, in this embodiment, a third distribution characteristic value of the Gaussian distribution corresponding to the plurality of first riveting distances is determined, and a fourth distribution characteristic value of the Gaussian distribution corresponding to the plurality of second riveting distances is determined; the second KL divergence is determined based on the third and fourth distribution characteristic values. It should be noted that the plurality of second riveting distances are riveting distance data from the theoretical lifespan data that have been verified to satisfy a Gaussian distribution. The third distribution characteristic value includes a third mean and / or a third difference. The fourth distribution characteristic value includes a fourth mean and / or a fourth variance. Specifically, the second KL divergence is calculated using the third mean, the third difference, the fourth mean, and the fourth variance.

[0097] A fifth probability distribution parameter corresponding to multiple first riveting forces is determined, and a sixth probability distribution parameter corresponding to multiple second riveting forces is determined. The third KL divergence is then calculated based on the fifth and sixth probability distribution parameters. The third KL divergence characterizes the similarity between the riveting force distribution corresponding to the theoretical lifespan and the riveting force distribution of the current target riveting die. The third and fourth probability distribution parameters can be calculated based on probability distribution models such as Gaussian distribution, empirical distribution, and binomial distribution.

[0098] Specifically, in this embodiment, a fifth distribution characteristic value of the Gaussian distribution corresponding to the plurality of first riveting forces is determined, and a sixth distribution characteristic value of the Gaussian distribution corresponding to the plurality of second riveting forces is determined; the third KL divergence is determined based on the fifth and sixth distribution characteristic values. It should be noted that the plurality of second riveting forces are riveting force data from the riveting force data corresponding to the theoretical life that have been verified to satisfy a Gaussian distribution. The fifth distribution characteristic value includes the fifth mean and / or the fifth variance. The sixth distribution characteristic value includes the sixth mean and / or the sixth variance. Specifically, the third KL divergence is calculated using the fifth mean, the fifth variance, the sixth mean, and the sixth variance.

[0099] Step S232: Determine the lifetime correction value based on the plurality of time differences and their corresponding first KL divergence, the plurality of distance differences and their corresponding second KL divergence, and the plurality of force differences and their corresponding third KL divergence.

[0100] Specifically, a first correction value is determined based on multiple time differences, a second correction value is determined based on multiple distance differences, a third correction value is determined based on multiple force differences, a first weight of the first correction value is determined based on a first KL divergence, a second weight of the second correction value is determined based on a second KL divergence, a third weight of the third correction value is determined based on a third KL divergence, and a weighted average of the first correction value, the second correction value, and the third correction value is calculated based on the first weight, the second weight, and the third weight to obtain the lifetime correction value.

[0101] In this embodiment, the first KL divergence, second KL divergence, and third KL divergence accurately characterize the similarity between the measured data distribution and the theoretical data distribution of the corresponding parameters. Based on this, a life correction value is determined by combining multiple time differences and their corresponding first KL divergences, multiple distance differences and their corresponding second KL divergences, and multiple force differences and their corresponding third KL divergences. The life correction value accurately reflects the deviation between the actual and theoretical values, thereby improving the accuracy of rivet die life prediction. Furthermore, comparing and calculating the actual and theoretical data based on a Gaussian distribution effectively reduces the influence of random factors on life prediction, further enhancing the accuracy of rivet die life prediction.

[0102] Furthermore, based on any of the above embodiments, another embodiment of the riveting die life prediction method of this application is proposed. In this embodiment, referring to... Figure 5 Step S21 includes:

[0103] Step S211: Obtain the riveting feature information of the riveting operation corresponding to the target riveting die;

[0104] Riveting feature information includes the feature information of the target riveting die itself and / or the feature information of other components outside the target riveting die that cooperate with the target riveting die to achieve riveting. Riveting feature information can be obtained by acquiring information manually input or by reading the operating information from automatic riveting equipment.

[0105] In this embodiment, the riveting feature information includes first information of the target riveting die, second information of the rivet corresponding to the target riveting die, and third information of the riveting plate corresponding to the target riveting die.

[0106] The first information may include the dimensions, material, and / or usage duration of the target riveting die. The rivet specifically includes its dimensions, material, and / or type. The third information specifically includes the type of sheet metal being riveted (e.g., whether it is a metal sheet) and / or its dimensions.

[0107] In other embodiments, the riveting feature information may also include one or two of the first information, the second information, and the third information, or may include other riveting operation-related information other than the first information, the second information, and the third information, such as the number of riveting operations of the target riveting die.

[0108] Step S212: Obtain the theoretical lifespan and the corresponding theoretical riveting time, theoretical riveting distance, and theoretical riveting force based on the riveting feature information.

[0109] Different riveting feature information can be associated with different theoretical lifespans and their corresponding theoretical riveting time, theoretical riveting distance, and theoretical riveting force. Based on this, the theoretical lifespan associated with the current riveting feature information and its corresponding theoretical riveting time, theoretical riveting distance, and theoretical riveting force are used as the theoretical values ​​for determining the predicted lifespan of the target riveting die.

[0110] In this embodiment, theoretical lifespan, theoretical riveting time, theoretical riveting distance, and theoretical riveting force are obtained based on riveting feature information. This allows the theoretical lifespan, theoretical riveting time, theoretical riveting distance, and theoretical riveting force to be set according to different riveting scenarios in which the target riveting die is applied, which helps to further improve the accuracy of the predicted lifespan of the target riveting die determined based on the theoretical lifespan, theoretical riveting time, theoretical riveting distance, and theoretical riveting force.

[0111] Furthermore, based on any of the above embodiments, in this embodiment, after the step of correcting the theoretical lifetime according to the lifetime correction value to obtain the predicted lifetime, it further includes:

[0112] When the predicted lifespan is greater than the theoretical lifespan, and the actual lifespan of the target rivet reaches the predicted lifespan, a new theoretical lifespan is determined based on the predicted lifespan.

[0113] Return to the step of obtaining monitoring data of riveting operations during the use of the target riveting die.

[0114] Specifically, the theoretical lifespan associated with the riveting feature information corresponding to the target riveting die can be updated to a new theoretical lifespan.

[0115] In this embodiment, when the actual lifespan of the riveting die reaches a predicted lifespan that is greater than the theoretical lifespan, the current predicted lifespan can be considered the optimal lifespan of the riveting die. At this time, the theoretical lifespan is updated according to the predicted lifespan, so that the prediction process of the riveting die is more in line with its actual application scenario, which is conducive to improving the accuracy of the predicted lifespan of the target riveting die or the riveting die with the same riveting characteristic parameters as the target riveting die.

[0116] Furthermore, in this embodiment, in addition to updating the theoretical lifespan, when the predicted lifespan is greater than the theoretical lifespan and the actual lifespan of the target riveting die reaches the predicted lifespan, a new theoretical riveting time is determined based on multiple first riveting times, a new theoretical riveting distance is determined based on multiple first riveting distances, and a new theoretical riveting force is determined based on multiple first riveting forces. Specifically, the theoretical riveting time, theoretical riveting distance, and theoretical riveting force associated with the riveting feature information corresponding to the target riveting die can be updated to new theoretical riveting time, new theoretical riveting distance, and new theoretical riveting force. Based on this, it is beneficial to further improve the accuracy of the predicted lifespan of the target riveting die or a riveting die with the same riveting feature parameters as the target riveting die.

[0117] Furthermore, this invention also proposes a storage medium storing a riveting die life prediction program, which, when executed by a processor, implements the relevant steps of any of the above embodiments of the riveting die life prediction method.

[0118] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0119] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0120] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, riveting mold life prediction device, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0121] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A rivet die life prediction method characterized by comprising: The rivet die life prediction method comprises the following steps: obtaining monitoring data of riveting operation in the use process of the target rivet die, the monitoring data comprising a first riveting time, a first riveting distance and a first riveting force; determining the predicted life of the target rivet die according to the first riveting time, the first riveting distance and the first riveting force; outputting prompt information according to the predicted life; wherein the number of monitoring data is multiple, and the step of determining the predicted life of the target rivet die according to the first riveting time, the first riveting distance and the first riveting force comprises: obtaining the theoretical life of the target rivet die and the corresponding theoretical riveting time, theoretical riveting distance and theoretical riveting force; determining the time difference value between each first riveting time and the theoretical riveting time to obtain multiple time difference values; determining the distance difference value between each first riveting distance and the theoretical riveting distance to obtain multiple distance difference values; determining the force difference value between each first riveting force and the theoretical riveting force to obtain multiple force difference values; determining a life correction value according to the multiple time difference values, the multiple distance difference values and the multiple force difference values; correcting the theoretical life according to the life correction value to obtain the predicted life.

2. The rivet die life prediction method of claim 1, wherein, Before the step of obtaining the theoretical life of the target rivet die and the corresponding theoretical riveting time, theoretical riveting distance and theoretical riveting force, it further comprises: obtaining multiple second riveting times, multiple second riveting distances and multiple second riveting forces detected in the use process of the rivet die reaching the theoretical life; determining the theoretical riveting time according to the multiple second riveting times, determining the theoretical riveting distance according to the multiple second riveting distances, and determining the theoretical riveting force according to the multiple second riveting forces.

3. The rivet die life prediction method of claim 2, wherein The step of determining a life correction value according to the multiple time difference values, the multiple distance difference values and the multiple force difference values comprises: determining a first KL divergence between multiple first riveting times and multiple second riveting times, determining a second KL divergence between multiple first riveting distances and multiple second riveting distances, and determining a third KL divergence between multiple first riveting forces and multiple second riveting forces; determining the life correction value according to the multiple time difference values and the corresponding first KL divergences, the multiple distance difference values and the corresponding second KL divergences, and the multiple force difference values and the corresponding third KL divergences.

4. The rivet die life prediction method according to Claim 3, characterized by, The step of determining a first KL divergence between multiple first riveting times and multiple second riveting times comprises: determining first distribution characteristic values of a Gaussian distribution corresponding to multiple first riveting times, and determining second distribution characteristic values of a Gaussian distribution corresponding to multiple second riveting times; determining the first KL divergence according to the first distribution characteristic values and the second distribution characteristic values; and / or, the step of determining a second KL divergence between multiple first riveting distances and multiple second riveting distances comprises: determining a third distribution characteristic value of a Gaussian distribution corresponding to the plurality of first riveting distances, and determining a fourth distribution characteristic value of a Gaussian distribution corresponding to the plurality of second riveting distances; determining the second KL divergence according to the third distribution characteristic value and the fourth distribution characteristic value; and / or, the step of determining the third KL divergence between the plurality of first riveting forces and the plurality of second riveting forces comprises: determining a fifth distribution characteristic value of a Gaussian distribution corresponding to the plurality of first riveting forces, and determining a sixth distribution characteristic value of a Gaussian distribution corresponding to the plurality of second riveting forces; determining the third KL divergence according to the fifth distribution characteristic value and the sixth distribution characteristic value.

5. The rivet die life prediction method of claim 1, wherein, The step of obtaining the theoretical service life of the target riveting die and corresponding theoretical riveting time, theoretical riveting distance, and theoretical riveting force comprises: obtaining riveting characteristic information of a riveting operation corresponding to the target riveting die; obtaining the theoretical service life and corresponding theoretical riveting time, theoretical riveting distance, and theoretical riveting force according to the riveting characteristic information.

6. The rivet die life prediction method of claim 5, wherein, The riveting characteristic information comprises first information of the target riveting die, second information of a rivet corresponding to the target riveting die, and third information of a riveting plate corresponding to the target riveting die.

7. The rivet die life prediction method of claim 1, wherein, The step of correcting the theoretical service life according to the service life correction value to obtain the predicted service life further comprises: when the predicted service life is greater than the theoretical service life and the actual service life of the target riveting die reaches the predicted service life, determining a new theoretical service life according to the predicted service life; returning to the step of obtaining monitoring data of a riveting operation in a use process of the target riveting die.

8. The rivet die life prediction method according to any one of claims 1 to 7, characterized by, The step of outputting prompt information according to the predicted service life comprises: when the predicted service life is less than a preset minimum service life, outputting the prompt information.

9. A rivet die life prediction device characterized by comprising: The riveting die service life prediction device comprises a memory, a processor, and a riveting die service life prediction program stored on the memory and executable on the processor, and the riveting die service life prediction program, when executed by the processor, implements the steps of the riveting die service life prediction method according to any one of claims 1 to 8.

10. A storage medium, characterized by The storage medium stores a riveting die service life prediction program, and the riveting die service life prediction program, when executed by the processor, implements the steps of the riveting die service life prediction method according to any one of claims 1 to 8.

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