Constant force spring life prediction method and device, electronic equipment, medium and product

Abstract

The application discloses a constant force spring life prediction method and device, electronic equipment, medium and product. The method comprises the following steps: in the case that it is detected that the collected life-related data of the to-be-tested spring meets a preset life prediction condition, the use scenario information corresponding to the to-be-tested spring is determined according to the collected life-related data, wherein the preset life prediction condition at least comprises that the data quantity meets a preset data quantity threshold; and the residual use life corresponding to the to-be-tested spring at the current time is determined according to the collected life-related data and the use scenario information. The technical scheme of the embodiment realizes the effect of accurately predicting the residual use life corresponding to the to-be-tested spring in combination with the life-related data and the use scenario information, and improves the spring life prediction precision.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and in particular to a method, device, electronic device, medium, and product for predicting the lifespan of a constant force spring. Background Technology

[0002] With the continuous advancement of medical technology, surgical robots are increasingly widely used in clinical surgery, and their stability and reliability are crucial to the success of the operation. The lifting joint of a surgical robot, as one of its core components, relies heavily on constant-force springs, which play a key role due to their stable output force and excellent resilience. However, with increased use, the performance of constant-force springs gradually declines, leading to instability in joint movement and consequently affecting the stability and safety of preoperative positioning. Therefore, accurately predicting the lifespan of constant-force springs is of great significance for improving the safety and reliability of surgical robots.

[0003] In related technologies, the prediction of the lifespan of constant force springs in the lifting joints of surgical robots mainly relies on empirical formulas and offline calibration. However, when lifespan prediction is based on empirical formulas, these formulas are often based on idealized conditions and cannot accurately reflect the complexities of actual use, leading to a significant deviation between the predicted results and the actual lifespan, resulting in low prediction accuracy. Furthermore, when lifespan prediction is based on offline calibration, it is impossible to dynamically monitor and predict the lifespan of the constant force springs during use. Summary of the Invention

[0004] This invention provides a method, device, electronic device, medium, and product for predicting the lifespan of a constant force spring, thereby achieving the effect of accurately predicting the remaining lifespan of the spring under test by combining lifespan correlation data and usage scenario information, thus improving the accuracy of spring lifespan prediction.

[0005] According to one aspect of the present invention, a method for predicting the life of a constant force spring is provided, the method comprising:

[0006] If the collected life-related data corresponding to the spring under test meets the preset life-related conditions, the usage scenario information corresponding to the spring under test is determined based on the collected life-related data; wherein, the preset life-related conditions include at least one data quantity that meets a preset data quantity threshold; the life-related data includes at least one parameter associated with the life-related service life of the spring under test.

[0007] The remaining service life of the spring under test is determined based on the collected life-related data and the usage scenario information.

[0008] According to another aspect of the present invention, a constant force spring life prediction device is provided, the device comprising:

[0009] The usage scenario determination module is used to determine the usage scenario information corresponding to the spring under test based on the collected lifespan correlation data when the collected lifespan correlation data corresponding to the spring under test meets the preset lifespan prediction conditions. The preset lifespan prediction conditions include the data volume meeting a preset data volume threshold. The lifespan correlation data includes at least one parameter associated with the lifespan of the spring under test.

[0010] The remaining service life determination module is used to determine the remaining service life of the spring under test based on the collected service life correlation data and the usage scenario information.

[0011] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0012] At least one processor; and

[0013] A memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the constant force spring life prediction method according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the constant force spring life prediction method according to any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the constant force spring life prediction method according to any embodiment of the present invention.

[0017] The technical solution of this embodiment determines the usage scenario information of the spring under test based on the collected life correlation data when the collected life correlation data meets the preset life prediction conditions. Furthermore, it determines the remaining service life of the spring under test based on the collected life correlation data and usage scenario information. This solves the problem that related technologies for predicting the life of constant force springs are difficult to accurately reflect the complexities of actual use, resulting in low prediction accuracy and the inability to dynamically detect and predict the life of constant force springs during use. This solution achieves the effect of accurately predicting the remaining service life of the spring under test by combining life correlation data and usage scenario information, improving the accuracy of spring life prediction. Moreover, it enables dynamic detection and prediction of the remaining service life of the spring during use, thereby helping to promptly identify potential spring problems and take measures to avoid malfunctions.

[0018] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a constant force spring life prediction method provided in Embodiment 1 of the present invention;

[0021] Figure 2 This is a flowchart of a constant force spring life prediction method provided in Embodiment 2 of the present invention;

[0022] Figure 3 This is a schematic diagram of the training process of a usage scenario recognition model according to Embodiment 3 of the present invention;

[0023] Figure 4 This is a schematic diagram of a constant force spring life prediction process provided in Embodiment 3 of the present invention;

[0024] Figure 5 This is a schematic diagram of the structure of a constant force spring life prediction device according to Embodiment 4 of the present invention;

[0025] Figure 6 This is a schematic diagram of the structure of an electronic device that implements the constant force spring life prediction method of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Example 1

[0029] Figure 1 This is a flowchart of a constant force spring life prediction method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where the remaining service life of a spring needs to be predicted. This method can be executed by a constant force spring life prediction device, which can be implemented in hardware and / or software and can be configured in a terminal and / or server. Figure 1 As shown, the method includes:

[0030] S110. If the life correlation data of the spring under test that has been collected meets the preset life prediction conditions, determine the usage scenario information of the spring under test based on the collected life correlation data.

[0031] The spring under test can be any spring for which a lifespan test is to be performed. The spring under test can be any spring, optionally a constant-force spring installed in the lifting joint of a surgical robot. Lifespan-related data can be understood as data affecting the lifespan of the spring under test. Lifespan-related data includes at least one parameter associated with the lifespan of the spring under test. Optionally, lifespan-related data includes the joint position of the lifting joint where the spring under test is located, the joint lifting speed, the joint lifting acceleration, the temperature of the spring under test, and the spring tension data, etc. In this embodiment, lifespan-related data can be used to characterize the usage of the spring under test within a preset time period. The preset lifespan prediction condition can be a pre-set condition used to trigger the prediction of the remaining lifespan of the spring. The preset lifespan prediction condition includes at least one data volume meeting a preset data volume threshold. The preset data volume threshold can be any value, optionally 500, 1000, or 1500 data points, etc. It should be noted that the preset condition may also include a data acquisition duration reaching a preset duration. The usage scenario information can be used to indicate the usage scenario of the spring under test at the current moment. In this embodiment, the usage scenario information may include at least one of factory calibration testing, preoperative automatic deployment, and preoperative positioning and docking. It is understood that for the spring under test installed in the lifting joint, frequent stretching of the lifting joint during the factory calibration stage would necessitate high-intensity use; the preoperative automatic deployment function involves the motor module driving the lifting joint to move a short distance at a uniform speed; and the preoperative positioning and docking involves manually dragging the lifting joint directly to the designated position.

[0032] In this embodiment, during the use of the spring under test, in order to detect its remaining service life, at least one parameter associated with the service life of the spring under test can be detected, and data can be collected according to a preset sampling frequency. Thus, the collected service life-related data corresponding to the spring under test can be obtained. Furthermore, if the collected service life-related data corresponding to the spring under test is found to meet preset service life prediction conditions, the usage scenario information corresponding to the spring under test can be determined based on the collected service life-related data.

[0033] In practical applications, when predicting the remaining service life of a spring under test, it is usually based on the life correlation data of the spring up to the current moment. However, the spring under test can be used in a variety of different scenarios, and the life consumption of the spring under test varies greatly in different scenarios. The life correlation data only includes the usage of the spring under test up to the current moment. Predicting the remaining service life based on the life correlation data may result in low accuracy.

[0034] To address the above situation, in this embodiment, the usage scenario information corresponding to the spring under test can be determined. Furthermore, the remaining service life of the spring under test can be predicted based on the lifespan correlation data and the usage scenario information. Lifespan correlation data characterizes the usage of the spring under test; therefore, to determine the usage scenario information corresponding to the spring under test, it can be determined based on the collected lifespan correlation data.

[0035] As an optional implementation of this embodiment, during the use of the spring under test, encoders and / or sensors installed on the lifting joint where the spring is located can be used to detect the joint position, joint lifting speed, spring temperature, and spring tension data. This yields the joint position, lifting speed, temperature, and spring tension data corresponding to the spring under test, and the acquired data is stored. Furthermore, the joint lifting acceleration can be determined based on the acquired joint position and lifting speed, and the determined joint lifting acceleration is stored. Consequently, the lifespan correlation data corresponding to the collected spring under test can be obtained.

[0036] In this embodiment, the usage scenario information corresponding to the spring under test can be determined based on the collected lifespan correlation data using a preset scenario determination method. The scenario determination method can be any method capable of determining the usage scenario information of the spring under test; optionally, the usage scenario information of the spring under test can be determined based on a usage scenario recognition model; or, the usage scenario information of the spring under test can be determined based on feature matching, etc.

[0037] Optionally, the usage scenario information corresponding to the spring under test can be determined based on the collected lifespan correlation data, including: processing the collected lifespan correlation data according to the usage scenario identification model to obtain the usage scenario information corresponding to the spring under test.

[0038] The usage scenario recognition model can be understood as a neural network model that takes life-related data as input and identifies the usage scenario information of the spring under test based on the life-related data. The usage scenario recognition model can be a neural network model with any model structure, such as a linear regression model, a decision tree model, or a convolutional neural network model.

[0039] In this embodiment, the usage scenario recognition model is trained on a neural network model using sample association data corresponding to multiple sample springs and actual usage scenario information. It should be noted that before applying the usage scenario recognition model provided in this embodiment, a pre-built neural network model can be trained using supervised or unsupervised methods. Before training the neural network model, multiple training samples can be constructed to train the model based on these samples. To improve the accuracy of the usage scenario recognition model, as many and rich training samples as possible can be constructed. Optionally, the training process of the usage scenario recognition model can be as follows: acquiring multiple training samples; wherein, the training samples include sample association data corresponding to sample springs and actual usage scenario information; for each training sample, inputting the sample association data in the training sample into the pre-built neural network model to obtain predicted usage scenario information; determining a loss value based on the predicted usage scenario information and the actual usage scenario information in the training samples; correcting the model parameters in the neural network model based on the loss value, and using the convergence of the loss function in the neural network model as the training objective, so as to use the trained neural network model as the usage scenario recognition model.

[0040] As an optional implementation of this embodiment, if the collected life correlation data corresponding to the spring under test meets the preset life prediction conditions, the collected life correlation data can be input into the usage scenario identification model. Then, the life correlation data can be processed based on the usage scenario identification model to determine the usage scenario information corresponding to the spring under test.

[0041] Optionally, the usage scenario information corresponding to the spring under test can be determined based on the collected lifespan correlation data, including: extracting features from the collected lifespan correlation data to obtain scenario correlation features; and determining the usage scenario information corresponding to the spring under test based on at least one candidate scenario information and scenario correlation features.

[0042] The scenario-related features can be understood as features associated with the spring's usage scenario. Optionally, scenario-related features may include load features, usage frequency features, temperature features, or environmental features. Candidate scenario information may be pre-determined usage scenario information to be selected. At least one candidate scenario information may include factory calibration testing, preoperative automatic deployment, and preoperative positioning docking.

[0043] As another optional implementation of this embodiment, if the collected life-related data meets the preset life-prediction conditions, feature extraction can be performed on the collected life-related data to extract feature information related to the usage scenario. Then, the extracted feature information can be used as scenario-related features. Furthermore, at least one candidate scenario information can be matched with the scenario-related features. Then, based on the matching result, the usage scenario information corresponding to the spring under test can be determined from at least one candidate scenario information.

[0044] Optionally, the usage scenario information corresponding to the spring under test is determined based on at least one candidate scenario information and scenario association features, including: for at least one candidate scenario information, obtaining feature information of the candidate scenario information, and if the feature information matches the scenario association features, using the candidate scenario information as the usage scenario information corresponding to the spring under test.

[0045] Among them, the feature information can be features in the candidate scene information that are associated with the usage scene.

[0046] As an optional implementation of this embodiment, for at least one candidate scene information, feature information corresponding to the candidate scene information can be obtained. Further, this feature information can be matched with scene-related features. Then, if the feature information matches the scene-related features, the candidate scene information corresponding to this feature information can be used as the usage scene information corresponding to the spring under test.

[0047] It should be noted that during the use of the spring under test, the device for collecting life-related data continuously collects data from the spring under test according to a preset sampling frequency. If the life-related data corresponding to the spring under test that has already been collected meets the preset life-related prediction conditions, the previously collected life-related data can be re-detected starting from the currently collected data. Furthermore, if it is detected that the previously collected life-related data again meets the preset life-related prediction conditions, the steps of determining the usage scenario information and the following life-related prediction steps can be repeated.

[0048] S130. Determine the remaining service life of the spring under test based on the collected life-related data and usage scenario information.

[0049] The remaining service life can be understood as the time period from the current state of the spring under test until it fails or needs to be replaced. Optionally, the remaining service life can be represented based on the remaining number of uses of the spring under test; or it can be represented based on the remaining elongation of the spring under test. In this embodiment, in order to enable relevant personnel to more clearly understand the remaining service life of the spring under test, so as to process the spring under test based on the remaining service life, the remaining service life can be represented based on the remaining elongation of the spring under test.

[0050] In this embodiment, after obtaining the life-related data and usage scenario information corresponding to the spring under test, the life-related data and usage scenario information can be processed according to a preset life-prediction method to obtain the remaining life-use of the spring under test. The preset life-prediction method can be any method capable of predicting the remaining life-use of the spring under test. Optionally, the remaining life-use of the spring under test can be predicted based on a life-prediction model; or, the remaining life-use of the spring under test can be predicted through statistical analysis and trend prediction, etc.

[0051] As an optional implementation of this embodiment, after obtaining the life-related data and usage scenario information corresponding to the spring under test, the obtained life-related data and usage scenario information can be input into the life-prediction model. Then, based on the life-prediction model, the life-related data and usage scenario information are processed to predict the remaining service life of the spring under test.

[0052] As another optional implementation of this embodiment, after obtaining the life-related data and usage scenario information corresponding to the spring under test, statistical analysis and trend prediction can be performed on the obtained life-related data and usage scenario information. Then, the remaining service life of the spring under test can be determined based on the statistical analysis and trend prediction results.

[0053] In this embodiment, after obtaining the remaining service life of the spring under test, the processing method corresponding to the spring under test can be determined based on the remaining service life and the preset service life threshold, so as to process the spring under test based on the processing method.

[0054] As an optional implementation of this embodiment, after obtaining the remaining service life of the spring under test, the remaining service life can be compared with a preset service life threshold. Further, if the remaining service life is greater than the preset service life threshold, the handling method for the spring under test can be determined as no handling or spring maintenance. If the remaining service life is not greater than the preset service life threshold, the handling method for the spring under test can be determined as spring replacement. The preset service life threshold can be the maximum elongation that the spring under test can withstand.

[0055] The technical solution of this embodiment determines the usage scenario information of the spring under test based on the collected life correlation data when the collected life correlation data meets the preset life prediction conditions. Furthermore, it determines the remaining service life of the spring under test based on the collected life correlation data and usage scenario information. This solves the problem that related technologies for predicting the life of constant force springs are difficult to accurately reflect the complexities of actual use, resulting in low prediction accuracy and the inability to dynamically detect and predict the life of constant force springs during use. This solution achieves the effect of accurately predicting the remaining service life of the spring under test by combining life correlation data and usage scenario information, improving the accuracy of spring life prediction. Moreover, it enables dynamic detection and prediction of the remaining service life of the spring during use, thereby helping to promptly identify potential spring problems and take measures to avoid malfunctions.

[0056] Example 2

[0057] Figure 2 This is a flowchart of a constant force spring life prediction method provided in Embodiment 2 of the present invention. Based on the aforementioned embodiments, it determines the remaining service life of the spring under test at the current moment according to life-related data and usage scenario information. This includes: inputting the life-related data and usage scenario information into a life prediction model to obtain the remaining service life of the spring under test at the current moment. Specific implementation details can be found in the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0058] like Figure 2 As shown, the method includes:

[0059] S210. If the life correlation data of the spring under test that has been collected meets the preset life prediction conditions, determine the usage scenario information of the spring under test based on the collected life correlation data.

[0060] S220. Input the collected life-related data and usage scenario information into the life prediction model to obtain the remaining lifespan of the spring under test.

[0061] The lifespan prediction model can be understood as a neural network model that takes lifespan-related data and usage scenario information as input to predict the remaining lifespan of the spring under test. The lifespan prediction model can be a deep learning model, such as a recurrent neural network, used to predict the remaining lifespan of a spring. Optionally, the lifespan prediction model can be a Long Short-Term Memory (LSTM) network, a gated recurrent unit (GRU), a bidirectional recurrent neural network (Bi-RNN), or a deep recurrent neural network (DRNN). It should be noted that the advantage of using recurrent neural networks for lifespan prediction is that, by utilizing the time series processing capabilities of recurrent neural networks, the degradation trend of the spring under test can be accurately captured, improving the accuracy of lifespan prediction.

[0062] In this embodiment, the lifespan prediction model is trained on a deep learning model using sample association data, sample usage scenario information, and actual remaining lifespan of the sample springs across multiple historical time intervals. The prediction time can be the moment when the remaining lifespan prediction is to be performed. Optionally, the prediction time corresponding to a historical time interval can be the last moment within that historical time interval; or it can be any moment after the historical time interval, etc.

[0063] It should be noted that before applying the lifespan prediction model provided in this embodiment, a pre-built deep learning model can be trained in a supervised or unsupervised manner. Before training the deep learning model, multiple training samples can be constructed to train the model based on these samples. To improve the accuracy of the lifespan prediction model, as many and rich training samples as possible can be constructed. Optionally, the training process of the lifespan prediction model can be as follows: Multiple training samples are acquired, including sample association data and usage scenario information corresponding to sample springs in multiple historical time intervals, as well as the actual remaining lifespan at the prediction time corresponding to each historical time interval; for each training sample, the sample association data and usage scenario information corresponding to the current historical time interval in the training sample are input into the pre-built deep learning model to obtain the predicted remaining lifespan of the sample spring at the prediction time; a loss value is determined based on the predicted remaining lifespan and the actual remaining lifespan at the prediction time corresponding to the current historical time interval in the training sample; the model parameters in the deep learning model are corrected based on the loss value, and the convergence of the loss function in the deep learning model is taken as the training objective, so that the trained deep learning model becomes the lifespan prediction model.

[0064] The historical time interval can be any time interval within the life cycle of the sample spring. The historical time interval can be of any length, optionally a time interval of the same length as a preset time period. Sample-related data can be the parameter value corresponding to at least one life-related parameter associated with the lifespan within the historical time interval. Sample usage information can be the usage scenario information of the sample spring within the historical time interval. The true remaining lifespan can be the actual remaining lifespan of the sample spring at the corresponding predicted time. In this embodiment, the true remaining lifespan can be determined by the spring elongation at each time point. Optionally, the true remaining lifespan can be determined by the following steps: For multiple sample springs, acquire life cycle data corresponding to the sample springs, where the life cycle data includes the spring elongation at multiple times; the multiple times include the time from the start of spring use to the end of spring use; for multiple times, determine the true remaining lifespan of the sample spring at time point based on the spring elongation at time point, the spring elongation at the end of spring use, and a preset elongation.

[0065] Lifecycle data can be understood as data characterizing the usage of a sample spring from the start of its use until its end. Lifecycle data can include spring elongation at multiple points in time, from the start of use to the end. Spring elongation refers to the difference between the length of the spring after stretching and its initial length. Spring elongation characterizes the degree of deformation of the spring under external force. The preset elongation can be a predetermined value or any value.

[0066] As an optional implementation of this embodiment, for multiple sample springs, lifecycle data corresponding to the sample springs can be obtained from a database. This lifecycle data can include spring elongation at multiple points in time, ranging from the start of spring use to the end of spring use. Further, for each of these multiple points, the spring elongation at all points in time from the start of the spring use until the end of the spring use can be obtained, and the obtained spring elongation values ​​can be summed to obtain the total spring elongation at that point. Further, the ratio between this total spring elongation and a preset elongation value can be determined, and this ratio can be used as the actual remaining service life of the sample spring at that point.

[0067] For example, the true remaining lifespan of a sample spring at any given time can be determined using the following formula:

[0068]

[0069] Among them, life t The sample spring is characterized by its actual remaining service life at time t; T represents the time when the spring ends its service life; Δd t d represents the spring elongation; d represents the preset elongation.

[0070] In this embodiment, after obtaining the actual remaining service life of the sample spring at each time point, the sample association data and sample usage scenario information corresponding to the sample spring in each historical time interval can be obtained, and training samples corresponding to the sample spring can be constructed based on the sample association data, sample usage scenario information and actual remaining service life.

[0071] As an optional implementation in this embodiment, for multiple training samples, sample association data and sample usage scenario information corresponding to the current historical time interval in the training samples can be input into a pre-built deep learning model. Then, the sample association data and sample usage scenario information can be processed based on the deep learning model, and the predicted remaining lifespan of the sample spring at the predicted time corresponding to the current historical time interval can be output. Furthermore, the predicted remaining lifespan can be compared with the actual remaining lifespan of the training samples at the predicted time corresponding to the current historical time interval to determine the loss value. Then, the model parameters in the deep learning model can be corrected based on the loss value. Afterwards, the training error of the loss function in the deep learning model, i.e., the loss parameter, can be used as a condition to detect whether the current loss function has reached convergence, for example, whether the training error is less than a preset error or whether the error change trend tends to be stable, or whether the current model iteration number is equal to a preset number, etc. If the convergence condition is met, for example, the training error of the loss function is less than the preset error or the error change tends to be stable, it indicates that the deep learning model training is complete. At this time, iterative training can be stopped, and the trained deep learning model can be used as the lifespan prediction model. If the convergence condition is not met, further training samples can be obtained to train the deep learning model until the training error of the loss function is within a preset range. When the training error of the loss function converges, the trained life prediction model can be obtained.

[0072] The technical solution of this embodiment determines the usage scenario information of the spring under test based on the collected life correlation data when the collected life correlation data meets the preset life prediction conditions. Furthermore, the collected life correlation data and usage scenario information are input into the life prediction model to obtain the remaining service life of the spring under test. This achieves the effect of accurately predicting the remaining service life of the spring under test at the corresponding time based on the life correlation data and usage scenario information. In addition, the life prediction model can accurately capture the degradation trend of the spring and improve the accuracy of spring life prediction.

[0073] Example 3

[0074] Figure 3 This is a schematic diagram of the training process of a usage scenario recognition model provided in Embodiment 3 of the present invention. Figure 4 This is a schematic diagram of a constant force spring life prediction process according to Embodiment 3 of the present invention. This embodiment is an optional embodiment of the above-described embodiments. Figure 3 and Figure 4 As shown, the method in this embodiment of the invention may specifically include:

[0075] like Figure 3 As shown, firstly, offline historical data corresponding to the sample spring is acquired. Then, the offline historical data is preprocessed to obtain sample association data corresponding to the sample spring. Next, the sample association data is categorized and labeled according to usage scenarios to obtain actual usage scenario information corresponding to the sample spring. Furthermore, training samples can be constructed based on the sample association data and actual usage scenario information corresponding to the sample spring, and a scene recognition model can be trained based on these training samples.

[0076] like Figure 4 As shown, during the use of the spring under test, life-related data corresponding to the spring is collected. When the number of collected life-related data reaches a preset data threshold, the collected life-related data is processed according to the scene recognition model to obtain the usage scene information corresponding to the spring under test. Furthermore, the life-related data and usage scene information can be input into the life-prediction model. Then, based on the feature fusion layer, long short-term memory network layer, fully connected layer, and output layer in the life-prediction model, the life-related data and usage scene information are processed sequentially to obtain the remaining service life of the spring under test.

[0077] The technical solution of this embodiment achieves the effect of accurately predicting the remaining service life of the spring under test at the corresponding time based on the service life correlation data and usage scenario information through the service life prediction model. Furthermore, the service life prediction model can accurately capture the degradation trend of the spring and improve the accuracy of spring service life prediction.

[0078] Example 4

[0079] Figure 5 This is a schematic diagram of a constant force spring life prediction device provided in Embodiment 4 of the present invention. Figure 5 As shown, the device includes: a usage scenario determination module 310 and a remaining service life determination module 320.

[0080] The usage scenario determination module 310, upon detecting that the collected life-related data corresponding to the spring under test meets the preset life-prediction conditions, determines the usage scenario information corresponding to the spring under test based on the collected life-related data; wherein, the preset life-prediction conditions include at least one data volume satisfying a preset data volume threshold; the life-related data includes at least one parameter associated with the lifespan of the spring under test; the remaining lifespan determination module 320 is used to determine the remaining lifespan of the spring under test based on the collected life-related data and the usage scenario information.

[0081] The technical solution of this embodiment determines the usage scenario information of the spring under test based on the collected life correlation data when the collected life correlation data meets the preset life prediction conditions. Furthermore, it determines the remaining lifespan of the spring under test based on the collected life correlation data and usage scenario information. This solves the problem that related technologies for predicting the lifespan of constant force springs are difficult to accurately reflect the complexities of actual use, resulting in low prediction accuracy and the inability to dynamically detect and predict the lifespan of constant force springs during use. This solution achieves the effect of accurately predicting the remaining lifespan of the spring under test at the corresponding time by combining life correlation data and usage scenario information, thus improving the accuracy of spring life prediction. Furthermore, it enables dynamic detection and prediction of the remaining lifespan of the spring during use, thereby helping to promptly identify potential spring problems and take measures to avoid malfunctions.

[0082] Optionally, the usage scenario determination module 310 is specifically used to process the collected lifespan correlation data according to the usage scenario identification model to obtain the usage scenario information corresponding to the spring under test; wherein, the usage scenario identification model is obtained by training a neural network model through sample correlation data corresponding to multiple sample springs and actual usage scenario information.

[0083] Optionally, the usage scenario determination module 310 includes a feature extraction unit and a usage scenario information determination unit.

[0084] The feature extraction unit is used to extract features from the collected lifetime-related data to obtain scene-related features;

[0085] The usage scenario information determination unit is used to determine the usage scenario information corresponding to the spring under test based on at least one candidate scenario information and the scenario association features.

[0086] Optionally, the scenario information determination unit is specifically used to obtain feature information of at least one candidate scenario information, and if the feature information matches the scenario-related features, use the candidate scenario information as the scenario information corresponding to the spring under test.

[0087] Optionally, the remaining service life determination module 320 is specifically used to input the collected service life association data and the usage scenario information into the service life prediction model to obtain the remaining service life corresponding to the spring under test; wherein, the service life prediction model is trained by the deep learning model through the sample association data, sample usage scenario information and actual remaining service life of the sample spring in multiple historical time intervals.

[0088] Optionally, the device further includes: a training sample acquisition module, a prediction remaining lifetime determination module, a loss value determination module, and a model parameter adjustment module.

[0089] The training sample acquisition module is used to acquire multiple training samples, wherein the training samples include sample association data and sample usage scenario information corresponding to sample springs in multiple historical time intervals, as well as the actual remaining service life at the predicted time corresponding to each of the historical time intervals.

[0090] The module for predicting remaining lifespan is used to input the sample association data and sample usage scenario information corresponding to the current historical time interval in each training sample into a pre-built deep learning model to obtain the predicted remaining lifespan of the sample spring at the prediction time.

[0091] The loss value determination module is used to determine the loss value based on the predicted remaining lifespan and the actual remaining lifespan of the predicted time corresponding to the current historical time interval in the training samples.

[0092] The model parameter adjustment module is used to correct the model parameters in the deep learning model based on the loss value, and to take the convergence of the loss function in the deep learning model as the training objective, so as to use the trained deep learning model as a lifespan prediction model.

[0093] Optionally, the device further includes a lifecycle data acquisition module and a true remaining useful life determination module.

[0094] The life cycle data acquisition module is used to acquire life cycle data corresponding to multiple sample springs, wherein the life cycle data includes spring elongation at multiple times; the multiple times include the time from the time when the spring starts to be used to the time when the spring ends to be used.

[0095] The module for determining the true remaining service life is used to determine the true remaining service life of the sample spring at a given time based on the spring elongation corresponding to that time, the spring elongation corresponding to the time when the spring ends its use, and a preset elongation.

[0096] The constant force spring life prediction device provided in the embodiments of the present invention can execute the constant force spring life prediction method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0097] Example 5

[0098] Figure 6 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0099] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0100] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0101] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the constant force spring life prediction method.

[0102] In some embodiments, the constant force spring life prediction method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the constant force spring life prediction method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the constant force spring life prediction method by any other suitable means (e.g., by means of firmware).

[0103] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0104] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0105] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0106] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0107] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0108] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0109] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0110] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A constant force spring life prediction method, characterized by, Comprise: In the case that it is detected that the collected life-related data of the to-be-tested spring meets a preset life prediction condition, determine the use scenario information corresponding to the to-be-tested spring according to the collected life-related data; wherein the preset life prediction condition at least includes that the data quantity meets a preset data quantity threshold; the life-related data includes at least one parameter associated with the service life of the to-be-tested spring; Determine the remaining service life corresponding to the to-be-tested spring according to the collected life-related data and the use scenario information.

2. The constant force spring life prediction method of claim 1, wherein The determination of the use scenario information corresponding to the to-be-tested spring according to the collected life-related data comprises: Processing the collected life-related data according to a use scenario recognition model to obtain the use scenario information corresponding to the to-be-tested spring; Wherein the use scenario recognition model is obtained by training a neural network model according to sample-related data and actual use scenario information of a plurality of sample springs.

3. The constant force spring life prediction method of claim 1, wherein The determination of the use scenario information corresponding to the to-be-tested spring according to the collected life-related data comprises: Feature extraction is performed on the collected life-related data to obtain scenario-related features; Determine the use scenario information corresponding to the to-be-tested spring according to at least one candidate scenario information and the scenario-related features.

4. The constant force spring life prediction method of claim 3, wherein The determination of the use scenario information corresponding to the to-be-tested spring according to at least one candidate scenario information and the scenario-related features comprises: For at least one candidate scenario information, obtain the feature information of the candidate scenario information, and in the case that the feature information matches the scenario-related features, take the candidate scenario information as the use scenario information corresponding to the to-be-tested spring.

5. The constant force spring life prediction method of claim 1, wherein The determination of the remaining service life corresponding to the to-be-tested spring according to the collected life-related data and the use scenario information comprises: Input the collected life-related data and the use scenario information into a life prediction model to obtain the remaining service life corresponding to the to-be-tested spring; Wherein the life prediction model is obtained by training a deep learning model according to sample-related data, sample use scenario information and actual remaining service life of a sample spring.

6. The constant force spring life prediction method of claim 5, wherein, Further comprise: Obtain a plurality of training samples, wherein the training samples include sample-related data and sample use scenario information of a sample spring corresponding to a plurality of historical time intervals, and real remaining service life at a prediction time corresponding to each of the historical time intervals; For each of the training samples, input the sample-related data and sample use scenario information corresponding to the current historical time interval in the training sample into a pre-constructed deep learning model to obtain the predicted remaining service life of the sample spring at the prediction time; Determine a loss value according to the predicted remaining service life and the real remaining service life at the prediction time corresponding to the current historical time interval in the training sample; Based on the loss value, modify the model parameters in the deep learning model, and take the convergence of the loss function in the deep learning model as a training target to take the trained deep learning model as a life prediction model.

7. The constant force spring life prediction method of claim 6, wherein, Also comprising: For a plurality of sample springs, acquiring life cycle data corresponding to the sample springs, wherein the life cycle data comprises spring elongation corresponding to a plurality of time points, and the plurality of time points comprises time points from a start-of-use time point of the spring to an end-of-use time point of the spring; For a plurality of the time points, determining a real remaining service life of the sample spring corresponding to the time points according to the spring elongation corresponding to the time points, the spring elongation corresponding to the end-of-use time point of the spring, and a preset elongation.

8. A constant force spring life prediction device, characterized by, Comprising: A use scenario determination module configured to, in a case where it is detected that collected life-related data corresponding to a to-be-tested spring meets a preset life prediction condition, determine use scenario information corresponding to the to-be-tested spring according to the collected life-related data, wherein the preset life prediction condition at least includes that a data amount meets a preset data amount threshold, and the life-related data includes at least one parameter associated with a service life of the to-be-tested spring; A remaining service life determination module configured to determine a remaining service life corresponding to the to-be-tested spring according to the collected life-related data and the use scenario information.

9. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the constant force spring life prediction method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing the processor to execute when the constant force spring life prediction method of any one of claims 1-7 is implemented.

11. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program implements the constant force spring life prediction method according to claims 1-7 when executed by the processor.

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