Spring fatigue detection method and device for spring production

Abstract

The present application relates to the technical field of spring testing, in particular to a spring fatigue detection method and device for spring production. The method can analyze the thermal balance correlation between temperature and load loading in the historical detection environment parameters by combining the target working condition of the spring, evaluate the target running frequency of the current operation, integrate the processes under different batches, uniformly analyze and compare the error changes of the spring load pressure between different historical batches, obtain the load error characteristics of the equipment, and correct the frequency to obtain the current optimal detection frequency for fatigue detection. The present application analyzes the thermal balance relationship between temperature and load application in the historical data, and the system error degree of the equipment in different detection stages, corrects the running frequency of the current detection for fatigue detection, reduces the influence of implicit bias, improves the accuracy of load application in fatigue detection, and makes the fatigue detection more real and accurate.

Description

Technical Field

[0001] This invention relates to the field of spring testing technology, and specifically to a method and apparatus for testing the fatigue of springs used in spring production. Background Technology

[0002] Springs play a crucial role in equipment, often providing cushioning and resetting functions. Fatigue fracture can lead to equipment downtime or component damage, potentially causing a series of safety incidents. Therefore, to prevent substandard springs from entering the market and causing losses during use, quality control is implemented during mass production to screen out products with insufficient fatigue life.

[0003] Typically, spring fatigue monitoring utilizes a fatigue testing machine to apply sinusoidal cyclic loads and records the number of cycles and actual force at which the spring fails on the machine, thus detecting the spring's fatigue degree. However, under high-frequency cyclic loads, the deformation friction with the clamps and internal material losses cause the spring temperature to rise rapidly. High temperatures reduce the elastic limit and fatigue resistance of the spring material, leading to an inflated perceived fatigue degree. Furthermore, the high-frequency loads applied by the testing machine, influenced by inertia and its own vibrations, can cause the applied load to deviate from expectations, affecting the final fatigue test results. Summary of the Invention

[0004] To address the technical problem in existing technologies where high-frequency loads on testing machines lead to inaccurate spring fatigue testing due to frictional internal losses, temperature variations, and equipment interference, the present invention aims to provide a method and apparatus for testing spring fatigue in spring production. The specific technical solution adopted is as follows:

[0005] This invention provides a method for testing the fatigue of springs used in spring production, the method comprising:

[0006] Obtain the environmental parameters and load loading times for each historical batch of tests, as well as the load pressure of the tested springs. The environmental parameters include temperature and humidity data.

[0007] Based on the current target working condition environmental parameters of the spring, the influence of the environmental parameters of historical batch detection and the current environmental parameters is analyzed, and the current initial target detection frequency is obtained by combining thermal balance analysis with the number of load loading times.

[0008] The fatigue process of different historical batches of tests is evaluated by the number of loads applied and then normalized and aligned. The current load error index is obtained by the load pressure error distribution between process points in the fatigue process of each historical batch of tests after alignment.

[0009] The initial target detection frequency is corrected by the load error index to obtain the current detection frequency; fatigue testing is then performed on the spring to be tested based on the current detection frequency.

[0010] Furthermore, the method for obtaining the initial target detection frequency includes:

[0011] Based on the initial changes in temperature data and the number of load loading times in all historical batches, and combined with the deviation from the currently detected temperature data, the loading temperature rise coefficient is obtained;

[0012] Based on the humidity data deviation between the current environmental parameters and the environmental parameters of historical batches, combined with the current heating requirements and loading heating coefficient, the current target number of loading times is obtained.

[0013] The current initial target detection frequency is obtained based on the ratio of the current number of target loading times to the average detection time of historical batches.

[0014] Furthermore, the method for obtaining the loading temperature rise coefficient includes:

[0015] For each historical batch, the correlation coefficient is calculated as the ratio of the temperature change at the start of the detection period to the total number of load loadings.

[0016] The time weight is determined based on the time elapsed from each historical batch to the present, and the time elapsed from the historical batch to the present is negatively correlated with the time weight. The difference between the temperature data in the current environmental parameters and the initial temperature data in the environmental parameters of each historical batch is used as the confidence coefficient, and the confidence coefficient is negatively correlated with the difference. The association weight of each historical batch is obtained by combining the time weight and the confidence coefficient.

[0017] The current loading and heating coefficient is obtained by weighting and averaging the correlation coefficients of historical batches based on the correlation weights.

[0018] Furthermore, the method for obtaining the target number of loads includes:

[0019] The ratio of the humidity data in the current environmental parameters to the average humidity data in all historical batches of environmental testing is used as the current impact of the heating efficiency.

[0020] The deviation between the current environmental temperature data and the target operating temperature data is taken as the temperature rise requirement; the initial loading count is obtained by multiplying the value of the negative correlation mapping by the loading temperature rise coefficient with the temperature rise requirement.

[0021] The target number of loading times is obtained by rounding down the product of the initial loading times and the effect of heating efficiency.

[0022] Furthermore, the method for obtaining the load error index includes:

[0023] After each change in the number of load loading times in the time sequence during each historical batch of testing, the ratio of the number of load loading times to the total number of load loading times is taken as each fatigue process;

[0024] By detecting the load pressure error between different historical batches at the same fatigue process, the average load pressure error at each fatigue process can be obtained.

[0025] The application deviation of each historical batch is determined by the deviation of the load pressure error from the load pressure error mean at each fatigue process.

[0026] By combining the applied deviations from all historical batch tests, the current load error index is obtained.

[0027] Furthermore, the method for obtaining the uniformity of the load pressure error includes:

[0028] The difference between the load pressure and the input pressure is analyzed at each fatigue stage of each historical batch to obtain the load pressure error; the average load pressure error of all historical batches at each fatigue stage is taken as the load pressure error mean at each fatigue stage.

[0029] Furthermore, the method for obtaining the applied deviation includes:

[0030] For any given historical batch, the difference between the load pressure error and the corresponding average load pressure error at each fatigue process in that historical batch is analyzed and used as the local deviation of each fatigue process in that historical batch.

[0031] The applied deviation of this historical batch is obtained by combining the local deviation of all fatigue processes in this historical batch.

[0032] Furthermore, the current load error index is obtained by combining the applied deviation from all historical batch detections, including:

[0033] The average applied deviation of all historical batches is used as the current load error index.

[0034] Furthermore, the method for obtaining the detection frequency includes:

[0035] The correction degree is obtained by combining the initial target detection frequency and the load error index; the difference between the initial target frequency and the correction degree is taken as the current detection frequency.

[0036] The present invention also provides a spring fatigue testing device for spring production, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a spring fatigue testing method for spring production as described in any of the above claims.

[0037] The present invention has the following beneficial effects:

[0038] This invention, by combining the target operating conditions of the spring and analyzing the thermal balance relationship between temperature and load application in historical testing environmental parameters, assesses the current target operating frequency. It counteracts potential temperature variations that could distort test results, making the load level more closely match the actual operating conditions and more accurately reflecting the spring's actual fatigue resistance. Simultaneously, it integrates and aligns the processes of different batches, uniformly analyzing and comparing the error changes in spring load pressure across different historical batches. This reflects the load error characteristics of the equipment at the same fatigue process testing stage. Analyzing the equipment's own error, it corrects load deviations caused by inertial forces or vibrations, improving load application accuracy and obtaining a more optimal testing operating frequency. This invention, by analyzing the thermal balance relationship between temperature and load application in historical data and the degree of systematic error of the equipment at different testing stages, corrects the current testing operating frequency for fatigue testing, reducing the influence of implicit deviations and improving the accuracy of load application in fatigue testing, making fatigue testing more realistic and accurate. Attached Figure Description

[0039] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0040] Figure 1 A flowchart of a spring fatigue testing method for spring production provided in one embodiment of the present invention;

[0041] Figure 2 This is a flowchart illustrating a method for obtaining an initial target detection frequency according to an embodiment of the present invention.

[0042] Figure 3 A flowchart illustrating a method for obtaining load error indices according to an embodiment of the present invention;

[0043] Figure 4 A schematic diagram illustrating the stiffness and temperature variation trend of a normal spring sample provided in an embodiment of the present invention;

[0044] Figure 5 A schematic diagram illustrating the stiffness and temperature variation trend of an early-failure spring sample provided in an embodiment of the present invention;

[0045] Figure 6 This is a schematic diagram illustrating the stiffness and temperature variation trends of a typical fatigue spring sample provided in an embodiment of the present invention. Detailed Implementation

[0046] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a spring fatigue testing method and apparatus for spring production based on the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0048] The following description, in conjunction with the accompanying drawings, details the specific scheme of the spring fatigue testing method and apparatus provided by the present invention.

[0049] Please see Figure 1 The diagram illustrates a flowchart of a spring fatigue testing method for spring production according to an embodiment of the present invention. The method includes the following steps:

[0050] S1: Obtain the environmental parameters and load loading times for each historical batch of tests, as well as the load pressure of the tested spring. The environmental parameters include temperature and humidity data.

[0051] During the fatigue testing of springs, random sampling is conducted during the finished product outbound process of the spring production line. Random number tables or production line random sampling tools can be used to uniformly draw samples from the finished product bins. It is important to note that this should cover all production periods of the current batch to avoid distorted results from sampling only a single sample. The sampling quantity is determined according to industry standards or production line requirements, and the implementer adjusts it based on the specific implementation scenario; further details and limitations are not provided here.

[0052] In this embodiment of the invention, the pre-treated sample is mounted on the fixture of the testing machine. The testing ends and the test data is collected and processed when the sample breaks, the permanent deformation of the spring exceeds the allowable range, or the preset target number of cycles is reached. A high-sensitivity force sensor is used to collect the load pressure on the spring during the testing process in real time. Temperature and humidity sensors are used to collect real-time temperature and humidity data in the working area where the spring is being tested. A frequency counter is used to record the number of load applications of the fatigue testing machine, which is also the number of complete cycles.

[0053] It should be noted that the relevant test data, after preprocessing, is stored in the test database, providing a basis for data analysis through the collection of test data from multiple historical batches. Preprocessing includes data cleaning, completion, and time-scale normalization to facilitate unified data analysis. Data preprocessing is a technique well-known to those skilled in the art, and the implementation team can adjust the sampling frequency as needed; therefore, it will not be elaborated upon or limited here.

[0054] S2: Based on the current target working condition environmental parameters of the spring, analyze the influence of the environmental parameters of historical batch detection on the current environmental parameters, and obtain the current initial target detection frequency through thermal balance analysis combined with the number of load loading times.

[0055] When conducting fatigue testing on springs, in addition to considering the temperature rise caused by high-frequency loads, it is also necessary to consider the spring's own operating environment. When the operating environment temperature of the spring is high, the high temperature itself has a wear effect. For example, the target operating environment for springs used in electronic devices is a normal temperature and humidity environment, with a temperature of 15℃-40℃ and a humidity of 40%-70%. Industrial springs, on the other hand, are targeted to operate in a high-temperature environment, with a temperature of 80℃-250℃ and a humidity of less than or equal to 40%.

[0056] Considering that different temperatures can cause changes in the stiffness of the spring, this invention compares normal spring samples, early-failure spring samples, and typical fatigue spring samples to analyze the trend of stiffness variation with temperature, thereby verifying that the working state of the spring is related to temperature. Please refer to [link / reference]. Figure 4 , Figure 4 This is a schematic diagram illustrating the trend of stiffness versus temperature variation for a normal spring sample; please refer to [link / reference]. Figure 5 , Figure 5 This is a schematic diagram showing the stiffness and temperature variation trends of spring samples with early failure; please refer to [link / reference]. Figure 6 , Figure 6 This is a schematic diagram showing the stiffness and temperature variation trends of a typical fatigue spring sample.

[0057] Therefore, when analyzing the current load applied during testing, the target operating environment parameters of the spring are combined with the thermal balance characteristics of the spring temperature and the applied load in historical test data. The rationality of the spring temperature change during the test is analyzed, and the expected operating frequency is initially obtained.

[0058] Preferably, in this embodiment of the invention, the method for obtaining the initial target detection frequency is described in [reference needed]. Figure 2 The diagram illustrates a flowchart of a method for obtaining an initial target detection frequency according to an embodiment of the present invention. The method includes the following steps:

[0059] S201: Based on the initial changes in temperature data and the number of load loading times in all historical batches, and combined with the deviation from the currently detected temperature data, the loading temperature rise coefficient is obtained.

[0060] By measuring the changes in the initial temperature during the testing process, we can reflect the temperature rise performance of the spring in the fatigue test. Combined with the load application, we can analyze the balance relationship between load application and temperature rise.

[0061] In this embodiment of the invention, for each historical batch, the ratio of the temperature change amplitude at the beginning of the detection period to the total number of load applications is used as the correlation coefficient for each historical batch. That is, the difference in temperature data between the start and end times of the detection period for each historical batch is used as the temperature change amplitude, reflecting the required temperature rise of the spring in each historical test. The ratio of the temperature change amplitude to the total number of load applications during the test is used as the correlation coefficient to reflect the balance between load application and temperature rise in a single historical batch. Here, the difference is the absolute value of the difference between the data.

[0062] Considering that different historical batches have different credibility to the current time, the longer the time interval, the lower the credibility, a time weight is determined based on the time interval from each historical batch to the current time. The time interval from the historical batch to the current time is negatively correlated with the time weight. The time interval between each historical batch and the current time is obtained, and a negative correlation mapping is performed on the time interval to obtain the time weight.

[0063] Because the initial temperatures vary during different testing processes, there may be issues with the degree of compatibility. The closer the initial temperatures are, the higher the reliability. Therefore, the difference between the temperature data in the current environmental parameters and the initial temperature data in each historical batch environmental parameters is used as the reliability coefficient. The reliability coefficient is negatively correlated with the difference. In this embodiment of the invention, the difference between the temperature data at the initial time of the historical batch detection period and the temperature data at the current time is negatively correlated to obtain the reliability coefficient.

[0064] Then, by combining time weight and credibility coefficient, the association weight of each historical batch is obtained. In this embodiment of the invention, the product of time weight and credibility coefficient is normalized to obtain the association weight of each historical batch. The larger the association weight, the more reliable the loading and heating coefficient of the historical batch.

[0065] It should be noted that negative correlation mapping and normalization are both techniques well known to those skilled in the art. Negative correlation mapping can take the form of inverse proportional value or negative exponential power function, etc. The choice of normalization can be maximum-minimum value normalization, linear normalization or standard normalization, etc. The specific method is not limited here.

[0066] Finally, the correlation coefficients of historical batches are weighted and averaged based on the correlation weights to obtain the current loading and heating coefficient. The correlation coefficients of all historical batches are combined, and the product of the correlation weight and correlation coefficient of each historical batch is calculated. The average of the corresponding products of all historical batches is used as the loading and heating coefficient.

[0067] S202: Based on the humidity data deviation between the current environmental parameters and the environmental parameters of historical batches, and combined with the current heating requirements and loading heating coefficient, the current target number of loading times is obtained.

[0068] Considering the heating demand of the spring and the impact of humidity on heating efficiency, higher humidity leads to greater suppression of heating. The ratio of the current humidity data to the average humidity data from all historical batches of environmental testing is used as the degree of influence on the current heating efficiency. In this embodiment of the invention, the greater the deviation of the current humidity data from historical humidity levels, the stronger the suppression effect.

[0069] By analyzing the temperature of the target operating condition of the spring, the deviation between the current environmental temperature data and the target operating condition temperature data is used as the temperature rise requirement, reflecting the expected loading situation for the actual temperature rise demand. The initial loading number is obtained by multiplying the value of the negative correlation mapping of the loading temperature rise coefficient with the temperature rise requirement. Based on the current operating condition temperature demand, the required number of loading cycles is determined according to the temperature rise situation.

[0070] Finally, considering the suppression of temperature rise, the number of loading cycles is adjusted. The greater the influence, the greater the impact of temperature rise suppression, and the more loading cycles need to be increased. Therefore, the product of the initial number of loading cycles and the influence of temperature rise efficiency is rounded to obtain the target number of loading cycles, which reflects the number of load cycles applied at the temperature required for the current detection spring.

[0071] S203: Based on the current number of target loadings and the historical batch detection duration, obtain the current initial target detection frequency.

[0072] The operating frequency of the testing machine is determined by the cycle and average detection time. In this embodiment of the invention, the average detection time of historical batches is obtained, and the ratio of the current number of target loading times to the average detection time of historical batches is used to obtain the current initial target detection frequency, which is the operating frequency of the initial analysis and detection device.

[0073] S3: Normalize and align the fatigue process of different historical batches of tests by evaluating the number of loads applied. Obtain the current load error index by the load pressure error distribution between process points in the fatigue process of each historical batch of tests after alignment.

[0074] Since the fatigue of springs gradually accumulates with the increase of usage, based on historical data samples of spring fatigue testing, the error between the system spring load and the actual spring load is analyzed, taking into account the error situation under different deformation stages. The systematic error situation is then analyzed, and the load application of the compensation equipment is corrected to improve the accuracy of subsequent testing.

[0075] Preferably, in this embodiment of the invention, the method for obtaining the load error index is described in [reference needed]. Figure 3 The diagram illustrates a method for obtaining load error indices according to an embodiment of the present invention, which includes the following steps:

[0076] S301: Based on the changes in the number of times the load is applied in the time sequence during each historical batch of testing, obtain each fatigue process; by using the load pressure error between different historical batches of testing in the same fatigue process, obtain the average load pressure error at each fatigue process.

[0077] Alignment analysis is performed on the number of loads applied for different historical test batches. Considering the impact of load application, in this embodiment of the invention, after each change in the number of loads applied in the time sequence of each historical batch test, the ratio of the number of loads applied to the total number of loads applied is used as each fatigue process to quantify the performance of the fatigue test process. That is, when the fatigue process is zero, it indicates the start of the test, and when the fatigue process is 1, it indicates the end of the spring failure.

[0078] Furthermore, a unified analysis of error performance under the same fatigue process is performed. In this embodiment of the invention, the difference between the load pressure and the input pressure is analyzed at each fatigue process of each historical batch to obtain the load pressure error. The input pressure is the preset applied load value of the equipment. The error degree at each process is reflected by the actual data deviation. The average load pressure error of all historical batches at each fatigue process is taken as the load pressure error mean of each fatigue process. The error is analyzed uniformly for the same fatigue process to characterize the typical value of the load error at each fatigue process.

[0079] S302: Determine the applied deviation of each historical batch by the deviation of the load pressure error from the load pressure error mean at each fatigue process for each historical batch.

[0080] By analyzing the overall deviation of typical values ​​at different fatigue stages in historical batches, the overall deviation of the applied load on the equipment can be reflected, and the performance of systematic errors in each historical batch can be evaluated.

[0081] In this embodiment of the invention, for any historical batch, the difference between the load pressure error and the corresponding average load pressure error at each fatigue process in that historical batch is analyzed and used as the local deviation of each fatigue process in that historical batch. Combining the local deviations of all fatigue processes in that historical batch, the applied deviation of that historical batch is obtained. In one specific embodiment of the invention, the average of the local deviations of all fatigue processes in that historical batch is used to calculate the applied deviation of that historical batch. When the applied deviation is positive, it indicates that the overall load applied by the equipment in this historical test is too large; when the applied deviation is negative, it indicates that the overall load applied by the equipment in this historical test is too small.

[0082] S303: Combine the applied deviation of all historical batch tests to obtain the current load error index.

[0083] By comprehensively analyzing the deviations in the applied loads of the equipment during historical testing, the magnitude of the current system's load error is reflected. In this embodiment of the invention, the average applied load deviation of all historical batches of testing is used as the current load error index. A positive load error index indicates that the equipment is applying an excessively large load, while a negative load error index indicates that the equipment is applying an excessively small load.

[0084] S4: Correct the initial target detection frequency using the load error index to obtain the current detection frequency; perform fatigue testing on the spring to be tested based on the current detection frequency.

[0085] The ideal analytical load frequency is corrected by measuring the load error of the testing machine. When the load error index is positive and large, it indicates that the load is too large, and the input frequency needs to be reduced. This reduces the total force and improves the detection accuracy by decreasing the number of load applications. Conversely, when the load error index is negative and small, it indicates that the load is too small, and the input frequency needs to be increased. This increases the total force and improves the detection accuracy by increasing the number of load applications.

[0086] In this embodiment of the invention, a correction degree is obtained by combining the initial target detection frequency and the load error index. That is, the correction degree is obtained by multiplying the initial target detection frequency and the load error index. The difference between the initial target frequency and the correction degree is then used as the current detection frequency. It should be noted that the adjusted detection frequency must be within the allowable physical range of the equipment to ensure safe operation. When the detection frequency exceeds the physical range, the nearest boundary value will be used for operation.

[0087] Based on the current testing frequency as the load operating frequency of the testing machine, more accurate fatigue testing is performed on the spring to be tested through compensation and correction.

[0088] In summary, this invention, by combining the target operating conditions of the spring and analyzing the thermal balance relationship between temperature and load application in historical testing environmental parameters, assesses the current target operating frequency. It counteracts potential temperature variations that could distort test results, making the loading level more closely match the actual operating conditions and more accurately reflecting the spring's actual fatigue resistance. Simultaneously, by integrating and aligning processes from different batches, it uniformly analyzes and compares the error changes in spring load pressure across different historical batches, reflecting the load error characteristics of the equipment at the same fatigue process testing stage. Analyzing the equipment's own error, it corrects load deviations caused by inertial forces or vibrations, improving load application accuracy and obtaining a more optimal testing operating frequency. This invention, by analyzing the thermal balance relationship between temperature and load application in historical data and the degree of systematic error of the equipment at different testing stages, corrects the current testing operating frequency for fatigue testing, reducing the influence of implicit deviations and improving the accuracy of load application in fatigue testing, making fatigue testing more realistic and accurate.

[0089] The present invention also provides a spring fatigue testing device for spring production, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the spring fatigue testing method for spring production as described above.

[0090] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0091] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for testing the fatigue degree of springs used in spring production, characterized in that, The method includes: Obtain the environmental parameters and load loading times for each historical batch of tests, as well as the load pressure of the tested springs. The environmental parameters include temperature and humidity data. Based on the current target working condition environmental parameters of the spring, the influence of the environmental parameters of historical batch detection and the current environmental parameters is analyzed, and the current initial target detection frequency is obtained by combining thermal balance analysis with the number of load loading times. The fatigue process of different historical batches of tests is evaluated by the number of loads applied and then normalized and aligned. The current load error index is obtained by the load pressure error distribution between process points in the fatigue process of each historical batch of tests after alignment. The initial target detection frequency is corrected by the load error index to obtain the current detection frequency; fatigue testing is then performed on the spring to be tested based on the current detection frequency. The method for obtaining the initial target detection frequency includes: obtaining a loading heating coefficient based on the initial changes in temperature data and the number of load loadings in all historical batches, combined with the deviation from the current detected temperature data; obtaining the current target loading count based on the humidity data deviation between the current detected environmental parameters and the environmental parameters of historical batches, combined with the current detection heating requirement and the loading heating coefficient; and obtaining the current initial target detection frequency based on the ratio of the current target loading count to the average detection duration of historical batches.

2. The method for detecting the fatigue degree of springs used in spring production according to claim 1, characterized in that, The method for obtaining the loading temperature rise coefficient includes: For each historical batch, the correlation coefficient is calculated as the ratio of the temperature change at the start of the detection period to the total number of load loadings. The time weight is determined based on the time elapsed from each historical batch to the present, and the time elapsed from the historical batch to the present is negatively correlated with the time weight. The difference between the temperature data in the current environmental parameters and the initial temperature data in the environmental parameters of each historical batch is used as the confidence coefficient, and the confidence coefficient is negatively correlated with the difference. The association weight of each historical batch is obtained by combining the time weight and the confidence coefficient. The current loading and heating coefficient is obtained by weighting and averaging the correlation coefficients of historical batches based on the correlation weights.

3. The method for detecting the fatigue degree of springs used in spring production according to claim 1, characterized in that, The method for obtaining the target number of loads includes: The ratio of the humidity data in the current environmental parameters to the average humidity data in all historical batches of environmental testing is used as the current impact of the heating efficiency. The deviation between the current environmental temperature data and the target operating temperature data is taken as the temperature rise requirement; the initial loading count is obtained by multiplying the value of the negative correlation mapping by the loading temperature rise coefficient with the temperature rise requirement. The target number of loading times is obtained by rounding down the product of the initial loading times and the effect of heating efficiency.

4. The method for detecting the fatigue degree of springs used in spring production according to claim 1, characterized in that, The method for obtaining the load error index includes: After each change in the number of load loading times in the time sequence during each historical batch of testing, the ratio of the number of load loading times to the total number of load loading times is taken as each fatigue process; By detecting the load pressure error between different historical batches at the same fatigue process, the average load pressure error at each fatigue process can be obtained. The application deviation of each historical batch is determined by the deviation of the load pressure error from the load pressure error mean at each fatigue process. By combining the applied deviations from all historical batch tests, the current load error index is obtained.

5. The method for detecting the fatigue degree of springs used in spring production according to claim 4, characterized in that, The method for obtaining the uniformity of the load pressure error includes: The difference between the load pressure and the input pressure is analyzed at each fatigue stage of each historical batch to obtain the load pressure error; the average load pressure error of all historical batches at each fatigue stage is taken as the load pressure error mean at each fatigue stage.

6. The method for detecting the fatigue degree of springs used in spring production according to claim 4, characterized in that, The method for obtaining the applied deviation includes: For any given historical batch, the difference between the load pressure error and the corresponding average load pressure error at each fatigue process in that historical batch is analyzed and used as the local deviation of each fatigue process in that historical batch. The applied deviation of this historical batch is obtained by combining the local deviation of all fatigue processes in this historical batch.

7. The method for detecting the fatigue degree of springs used in spring production according to claim 4, characterized in that, The current load error index is obtained by combining the applied deviation of all historical batches of tests, including: The average applied deviation of all historical batches is used as the current load error index.

8. The method for detecting the fatigue degree of springs used in spring production according to claim 1, characterized in that, The method for obtaining the detection frequency includes: The correction degree is obtained by combining the initial target detection frequency and the load error index; the difference between the initial target frequency and the correction degree is taken as the current detection frequency.

9. A spring fatigue testing device for spring production, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the spring fatigue testing method for spring production as described in any one of claims 1 to 8.

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