Compression resistance detection method and system for sensor shell

Abstract

The invention provides a sensor shell compression resistance detection method and system, and relates to the technical field of compression resistance detection of sensor shells. In the process that an external pressure applying device applies external pressure to a sensor shell, stress or strain data at a plurality of preset positions are obtained in real time, and an index reflecting stress or strain distribution heterogeneity is calculated according to the data, so that the applying speed of the external pressure is dynamically determined and controlled according to the index; the problem that efficiency and reliability are difficult to consider in a traditional fixed rate testing method is solved, and particularly, the recognition capability of early-stage local damage is improved.

Description

Technical Field

[0001] The present application relates to the technical field of pressure resistance detection of sensor housings, and in particular to a method and system for detecting pressure resistance of sensor housings. Background Art

[0002] In the automated batch manufacturing environment of sensor housings, compressive performance testing is a key step in ensuring product quality. The traditional fixed-rate compressive testing method has encountered a bottleneck in the pursuit of high production efficiency. Those skilled in the art understand that automated production lines have strict requirements on test rhythm, and a time-consuming test will limit overall production capacity. The traditional testing process usually sets a constant pressure loading rate until the preset pressure is reached or macroscopic damage occurs to the housing. This method is common for basic performance testing of materials or structures in laboratory environments, but its efficiency issues become prominent on high-speed batch production lines.

[0003] Simply increasing the rate at which external pressure is applied to improve efficiency can lead to new technical challenges. Sensor housings are not simple homogeneous bodies; their interiors contain complex structural features such as varying wall thicknesses, connection interfaces, and openings. According to the principles of structural mechanics, these structural discontinuities are prone to stress concentration when subjected to external pressure. When external pressure increases rapidly, the local stress in these stress concentration areas increases at a higher rate. Before reaching macroscopic failure, materials often experience damage at the microscopic level, such as microyielding, microcrack initiation, or interfacial debonding. These early stages of damage often first appear in stress concentration areas. During rapid loading, traditional testing systems may not be able to capture these transient local stress anomalies or weak early damage signals through data acquisition speeds or macroscopic inspection methods (such as visual inspection). For example, a tiny internal defect can initiate a crack under a specific local stress. Rapid loading can cause the crack to rapidly grow to macroscopic dimensions, while the system fails to record the precise stress state at the time of crack initiation. This can lead to housings that should have potential defects being misclassified as conforming, increasing the risk of premature failure under complex real-world operating conditions. This lack of ability to identify early local damage is an inherent limitation of traditional fixed-rate testing methods when dealing with complex structures and high reliability requirements.

[0004] Furthermore, even shells produced within the same batch exhibit individual variations, such as subtle fluctuations in material properties or manufacturing tolerances. These variations lead to differences in internal stress distribution and local stress growth rates among different shells when subjected to the same external pressure. Traditional fixed-rate loading methods cannot be tailored to the specific mechanical response of each shell. For individual shells with sensitive stress responses or minor defects, fixed-rate loading may be too rapid, failing to expose defects or provide detailed analysis of their behavior at critical stress levels. This makes it difficult to fully assess the true performance margins of each shell using traditional methods.

[0005] Therefore, the compression test process needs to be able to pay real-time attention to the mechanical response of the shell interior or key local areas, and intelligently adjust the rate of external pressure application based on these response data, so as to take into account the efficiency of mass production and the reliability of test results, especially to improve the ability to identify early local damage and potential defects.

[0006] There is currently no effective technical solution to the above problems. Summary of the Invention

[0007] The purpose of this application is to provide a sensor housing pressure resistance detection method and system, which can monitor the mechanical response of key areas of the sensor housing in real time and dynamically adjust the external pressure application rate according to the response, thereby improving the ability to identify early local damage and taking into account both test efficiency and reliability.

[0008] The present application provides a method for detecting the pressure resistance of a sensor housing, comprising the steps of:

[0009] S1. In the process of applying external pressure to the sensor housing by the external pressure applying device, real-time stress data or strain data is obtained at multiple preset positions of the sensor housing;

[0010] S2. Calculating an index reflecting the non-uniformity of stress or strain distribution at a plurality of preset positions based on the real-time stress data or strain data;

[0011] S3. Determine the rate of application of the external pressure based on the comparison result of the indicator with the preset threshold;

[0012] S4. According to the determined application rate, control the external pressure applying device to apply external pressure to the sensor housing.

[0013] Through the above scheme, the mechanical response of the key areas of the sensor housing can be monitored in real time, and the external pressure application rate can be dynamically adjusted according to the response, thereby improving the ability to identify early local damage and taking into account both test efficiency and reliability.

[0014] Optionally, before step S1, the method further includes:

[0015] S0.1. Analyze the structure and expected failure modes of the sensor housing and identify key areas of the sensor housing. Expected failure modes refer to the potential damage or performance loss that may occur when the sensor housing is subjected to external pressure, as determined or analyzed in advance based on its material properties, process, and structural characteristics.

[0016] S0.2. Determine the number and distribution of the plurality of preset positions based on the key area, and determine whether the data to be acquired corresponding to each preset position is stress data or strain data.

[0017] Through the above scheme, the structure and failure mode are analyzed in advance, the key areas and monitoring locations are determined, and the data collection is made more targeted.

[0018] Optionally, step S2 includes:

[0019] Calculating the local change rate of stress or strain at each of the plurality of preset positions as the external pressure changes based on the real-time stress data or strain data;

[0020] According to the preset structural features corresponding to each position, the local change rate of each position is weighted to obtain a weighted local change rate;

[0021] Calculating, based on the weighted local change rate, differences in weighted local change rates between different positions in the plurality of preset positions;

[0022] An index reflecting the non-uniformity of stress or strain distribution at the plurality of preset positions is calculated based on the differences in the weighted local change rates between different positions.

[0023] Through the above scheme, the non-uniformity of stress or strain distribution can be quantified more finely by calculating the local variation rate and its difference.

[0024] Optionally, the step of performing weighted processing on the local change rate of each position according to the preset structural features corresponding to each position to obtain the weighted local change rate includes:

[0025] According to the preset structural features corresponding to each position, the structural feature information of each position is obtained, where the structural feature information includes the geometric, material, process characteristics or micro-defect characteristics of the position;

[0026] Determining a weighting factor for each position based on the structural feature information and with reference to preset association data, wherein the preset association data is data of a mapping relationship between the structural feature and the sensitivity of the local change rate to the structural feature;

[0027] The weighted local change rate of each position is calculated according to the local change rate of each position and the weighting factor corresponding to each position.

[0028] Through the above scheme, the influence of structural characteristics on the local change rate is weighted, so that the non-uniformity index can better reflect the actual structural weaknesses.

[0029] Optionally, the step of determining the weighting factor for each position based on the structural feature information and referring to preset association data, wherein the preset association data is data of a mapping relationship between the structural feature and the sensitivity of the local change rate to the structural feature, includes:

[0030] Perform matching or interpolation calculation in the preset associated data according to the structural feature information;

[0031] The weighting factor of each position is determined according to the matching or interpolation calculation result.

[0032] Optionally, the step of determining the weighting factor of each position according to the matching or interpolation calculation result includes:

[0033] Obtaining preset criticality information for each of the plurality of preset positions, the preset criticality information being determined based on a structural analysis of the sensor housing or with reference to historical test data, and reflecting the degree of influence of the position on the overall compressive performance or failure mode of the sensor housing;

[0034] Based on the matching or interpolation calculation results and the preset criticality information, a weighting factor for each position is calculated through a preset comprehensive calculation rule. The preset comprehensive calculation rule combines the matching or interpolation calculation results with the preset criticality information to quantify the relative importance of the position in the calculation of the non-uniformity index.

[0035] Optionally, the step of calculating the weighting factor of each position according to the matching or interpolation calculation result and the preset criticality information using a preset comprehensive calculation rule, wherein the preset comprehensive calculation rule combines the matching or interpolation calculation result with the preset criticality information to quantify the relative importance of the position in calculating the non-uniformity index includes:

[0036] Obtaining the matching or interpolation calculation result and the preset criticality information;

[0037] Normalizing the matching or interpolation calculation results to obtain a normalized sensitivity value;

[0038] Normalizing the preset criticality information to obtain a normalized criticality value;

[0039] According to a preset combination function, the normalized sensitivity value and the normalized criticality value are combined and calculated to obtain a weighting factor for each position.

[0040] Optionally, the step of calculating the local change rate of stress or strain at each of the plurality of preset positions as the external pressure changes based on the real-time stress data or strain data includes:

[0041] For each of the plurality of preset positions, acquiring the stress data or strain data and corresponding external pressure data collected in real time within a preset time interval;

[0042] According to the preset time interval, the stress data or strain data and the corresponding external pressure data, the ratio of the stress change or strain change at the position to the external pressure change is calculated to obtain the local change rate of the position.

[0043] Optionally, step S3 includes:

[0044] Obtaining historical data of the indicator collected within a preset time period;

[0045] Calculating a change trend of the indicator based on the current indicator and the historical data, wherein the change trend includes a change rate or a change acceleration of the indicator;

[0046] Comparing the current indicator with a preset indicator threshold to obtain a first comparison result;

[0047] Comparing the change trend of the indicator with a preset change trend threshold to obtain a second comparison result;

[0048] The rate of applying the external pressure is determined according to the first comparison result and the second comparison result by a preset rate determination rule.

[0049] In a second aspect, the present application provides a sensor housing pressure resistance detection system, comprising:

[0050] a data acquisition module, configured to acquire real-time stress or strain data at a plurality of preset positions of the sensor housing during a process in which an external pressure applying device applies external pressure to the sensor housing;

[0051] an index calculation module, configured to calculate an index reflecting the non-uniformity of stress or strain distribution at the plurality of preset positions based on the real-time stress or strain data;

[0052] a rate determination module, configured to determine a rate of application of the external pressure based on a comparison result of the indicator with a preset threshold;

[0053] The pressure control module is configured to control the external pressure applying device to apply external pressure to the sensor housing according to the determined application rate.

[0054] From the above, it can be seen that the present application provides a sensor housing pressure resistance detection method and system, which obtains stress or strain data at multiple preset positions in real time during the process of an external pressure applying device applying external pressure to the sensor housing, and calculates an indicator reflecting the non-uniformity of the stress or strain distribution based on these data, thereby dynamically determining and controlling the rate of application of external pressure based on the indicator, solving the problem of traditional fixed-rate testing methods that are difficult to strike a balance between efficiency and reliability, and especially improving the ability to identify early local damage.

[0055] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A flow chart of a sensor housing pressure resistance detection method provided in an embodiment of the present application.

[0057] Figure 2 This is a schematic structural diagram of the sensor housing pressure resistance detection system provided in an embodiment of the present application.

[0058] Explanation of reference numerals: 21. Data acquisition module; 22. Index calculation module; 23. Rate determination module; 24. Pressure control module. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0060] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0061] Please refer to Figure 1-Figure 2 The present application provides a sensor housing pressure resistance detection method and system, which can monitor the mechanical response of key areas of the sensor housing in real time and dynamically adjust the external pressure application rate according to the response, thereby improving the ability to identify early local damage and taking into account both test efficiency and reliability.

[0062] The present application provides a method for detecting the pressure resistance of a sensor housing, comprising the steps of:

[0063] S1. In the process of applying external pressure to the sensor housing by the external pressure applying device, real-time stress data or strain data is obtained at multiple preset positions of the sensor housing;

[0064] S2. Calculating an indicator reflecting the non-uniformity of stress or strain distribution at multiple preset locations based on real-time stress or strain data;

[0065] S3. Determine the rate of application of external pressure based on the comparison result of the indicator with the preset threshold;

[0066] S4. According to the determined application rate, control the external pressure applying device to apply external pressure to the sensor housing.

[0067] Real-time stress or strain data at multiple preset locations refers to the acquisition of dynamic internal force or deformation measurements at specific points on the sensor housing during compression. This can be achieved using strain gauges, fiber optic sensors, or digital image correlation technology. An indicator reflecting the non-uniformity of stress or strain distribution at multiple preset locations is a quantitative value that indicates the degree of stress or strain difference at the selected locations.

[0068] Specifically, while the external pressure-applying device applies external pressure to the sensor housing, real-time stress or strain data is continuously acquired from multiple predetermined locations on the housing. This data reflects the mechanical response of different regions within the housing under the current pressure. Next, based on the real-time local mechanical response data, an index is calculated that quantifies the degree of variation in stress or strain distribution between these locations. This index serves as a basis for assessing potential risks in the housing's current state. Non-uniformity in stress or strain distribution indicates stress concentration or the presence of structural weaknesses. The calculated non-uniformity index is then compared with a preset threshold. This comparison result is used to determine whether the housing's current mechanical state is stable or abnormal. Based on this determination, the system determines the rate at which to apply external pressure next. For example, if the non-uniformity index is high, the application rate may need to be reduced; if it is low, the application rate may be maintained or increased. Finally, based on the determined application rate, the external pressure-applying device is controlled to adjust its loading behavior. This entire process forms a closed loop, where the housing's real-time mechanical response influences the subsequent loading strategy, allowing the testing process to be tailored to the specific housing's conditions, thereby optimizing test efficiency while ensuring reliability.

[0069] Through the above scheme, the present application can monitor the local mechanical response of the sensor housing during compression in real time and dynamically adjust the pressure application rate based on the non-uniformity of these responses. This allows the testing process to be adjusted according to the actual mechanical behavior of each housing, improving the ability to identify early local damage and potential defects, thereby enhancing the reliability of compression resistance testing. At the same time, for housings with uniform and stable mechanical responses, the loading rate can be appropriately increased, taking into account the test efficiency requirements of mass production.

[0070] In some embodiments, before step S1, the method further includes:

[0071] S0.1. Analyze the structure and expected failure modes of the sensor housing and identify critical areas of the sensor housing. Expected failure modes refer to the potential damage or performance loss that may occur when the sensor housing is subjected to external pressure, as determined or analyzed in advance based on its material properties, process, and structural conditions.

[0072] S0.2. Determine the number and distribution of multiple preset positions based on the key area, and determine whether the data to be obtained at each preset position is stress data or strain data.

[0073] Among them, the structure of the sensor housing refers to the physical structural features of the housing, such as its geometry, size, wall thickness variation, internal cavity, connection interface, opening, reinforcement ribs, etc., which can be represented by three-dimensional model data, engineering drawings, scan data or actual measurement data. The expected failure mode refers to the possible form of damage to the housing under the action of external pressure, such as material yielding, plastic deformation, fracture, fatigue damage, interface debonding, structural instability (such as buckling), etc., which can be determined based on material mechanics theory, finite element analysis, historical test data or failure analysis reports. The critical area refers to the specific part of the sensor housing where the stress or strain level is high, the stress concentration is obvious, and it is prone to early damage or overall failure when subjected to external pressure. It can be identified based on the structural analysis results, the expected failure mode analysis results or historical failure data.

[0074] Stress data refers to the internal force per unit area, reflecting the material's ability to resist deformation. It can be obtained using a stress sensor or calculated through strain data. Strain data refers to the relative deformation of a material under stress, reflecting its deformation state. It can be measured using strain gauges or displacement sensors.

[0075] Specifically, by analyzing the structural features of the sensor housing, such as wall thickness variations, chamfers, and openings, combined with material properties and manufacturing processes, it is possible to predict which areas are prone to stress concentration or significant deformation under external pressure. Simultaneously, analysis of anticipated failure modes, such as whether to focus on brittle fracture or plastic yield, helps determine whether stress or strain monitoring should be prioritized in key areas. Based on these analysis results, the critical areas identified are the most sensitive to the housing's compressive performance and most likely to reflect potential problems. Therefore, data collection locations are concentrated in these critical areas, and stress or strain data is collected selectively based on specific failure modes. This ensures that the collected data is the most representative and effective in capturing early localized abnormal responses. This optimized, targeted, real-time stress or strain data, used as input for the subsequent calculation of nonuniformity indicators, more accurately reflects changes in the housing's local mechanical state during compression, particularly localized stress / strain anomalies caused by minor damage or defects. Compared with simply collecting data at any preset location, this pre-analysis-based data collection strategy enables the subsequently calculated non-uniformity index to more sensitively reflect the shell's true compressive performance and potential risks, thereby improving the entire compressive testing method's ability to identify early local damage and potential defects, and making the strategy of adjusting the application rate based on this index more effective and reliable.

[0076] In practical applications, finite element analysis (FEA) software can be used to analyze the sensor housing's structure and expected failure modes. First, a 3D geometric model of the sensor housing is created, and material properties are imported. Boundary conditions simulating external pressure are then applied to the model, and static or nonlinear analysis is performed. The analysis results can be output as stress and strain distribution contours of the housing under different pressures. By observing these contours, areas with the most pronounced stress or strain concentrations can be identified; these areas are then designated as critical regions. For example, high stress concentrations are often observed at locations with sudden changes in housing wall thickness, at the edges of openings, or at joints. Analyzing expected failure modes can combine material parameters such as yield strength, tensile strength, and fracture toughness to determine the likely failure type in these stress-concentrated areas. For example, plastic deformation may occur when the local stress exceeds the yield strength, while fracture may occur when the stress exceeds the tensile strength. Based on the identified critical areas, such as a chamfered corner with severe stress concentration, multiple preset locations can be determined. The number of preset locations can be determined based on the size of the area and the stress gradient, for example, three locations can be placed along the stress gradient in the chamfered corner. At the same time, based on the material properties and expected failure mode of the region, for example, if the material in the region is brittle ceramic and the expected failure mode is fracture, the data to be acquired at these preset locations is determined to be stress data. If the material in the region is plastic polymer and the expected failure mode is excessive deformation, the data to be acquired is determined to be strain data.

[0077] In some embodiments, step S2 includes:

[0078] Calculating the local change rate of stress or strain at each of a plurality of preset positions as the external pressure changes based on the real-time stress data or strain data;

[0079] According to the preset structural features corresponding to each position, the local change rate of each position is weighted to obtain a weighted local change rate;

[0080] Calculating, based on the weighted local change rates, differences in weighted local change rates between different positions in the plurality of preset positions;

[0081] An index reflecting the non-uniformity of stress or strain distribution at multiple preset positions is calculated based on the differences in weighted local change rates between different positions.

[0082] Specifically, this solution, through the above steps, provides a more refined and sensitive method for calculating a nonuniformity index. During the application of external pressure, stress or strain data is acquired in real time at multiple preset locations. Based on this real-time data, the rate of change of stress or strain relative to the external pressure at each location, known as the local variation rate, is calculated. This dynamic index can capture transient or rapidly occurring local response anomalies. Furthermore, considering the inherent influence of the structural characteristics of different locations in the sensor housing on the stress / strain response, the local variation rates calculated for each location are weighted based on pre-set structural feature information. For example, a higher weight is assigned to the local variation rate of thinner wall thicknesses and areas with a risk of stress concentration. This results in a weighted local variation rate that comprehensively reflects the dynamic response and structural criticality of the location. Subsequently, the weighted local variation rates at different locations are compared to calculate their differences. This difference directly quantifies the degree of spatial nonuniformity in the stress / strain response and highlights areas of abnormal response. Finally, a comprehensive nonuniformity index is calculated based on these differences. This indicator not only reflects the dynamic changes and spatial distribution of stress / strain, but also incorporates the influence of structural characteristics. Therefore, it can more effectively identify early local damage or potential problems that may be caused by structural defects or material unevenness. By comparing this more sensitive and targeted non-uniformity indicator with the preset threshold, it is possible to more accurately determine whether there is an abnormality in the shell under the current loading state, thereby providing a reliable basis for the subsequent intelligent adjustment of the application rate of external pressure. For example, when the indicator exceeds a certain threshold, it can be determined that there is a potential problem, and the application rate can be reduced for more detailed observation and analysis; when the indicator is far below the threshold and the change is slow, the application rate can be appropriately increased to improve test efficiency. This application rate adjustment mechanism based on dynamic, weighted, and differentiated indicators enables the compressive testing process to better balance efficiency and reliability, and especially improves the ability to identify early local damage.

[0083] In some embodiments, the step of weighting the local change rate at each position according to the preset structural features corresponding to each position to obtain the weighted local change rate includes:

[0084] According to the preset structural features corresponding to each position, the structural feature information of each position is obtained, where the structural feature information includes the geometric, material, process characteristics or micro-defect characteristics of the position;

[0085] Determining a weighting factor for each position based on the structural feature information and with reference to preset association data, wherein the preset association data is data on a mapping relationship between the structural feature and the sensitivity of the local change rate to the structural feature;

[0086] The weighted local change rate of each position is calculated based on the local change rate of each position and the weighting factor corresponding to each position.

[0087] Among them, structural feature information refers to data that describes the physical properties of a specific position of the sensor housing. These properties may affect the mechanical response of that position when under pressure. They may include the geometric dimensions of that position (such as wall thickness, radius of curvature), the type or brand of material used, features introduced during the manufacturing process (such as welding areas, heat treatment areas, surface treatment status), or internal or surface microscopic defects discovered through non-destructive testing (such as tiny cracks, inclusions, pores).

[0088] Pre-established correlation data refers to a pre-established database, lookup table, or mathematical model that reflects the relationship between different structural features and the sensitivity of the local stress or strain change rate in the region of the structural feature to changes in external pressure. This sensitivity quantifies the relative change in the local change rate expected at a location with a certain structural feature for a unit change in external pressure. The weighting factor is a multiplier used to adjust or amplify / reduce the original local change rate value. Its value is determined based on the structural feature information and its corresponding sensitivity at the location, reflecting the relative importance of the location in the overall non-uniformity assessment or the potential impact of its local changes on the overall structural integrity.

[0089] Specifically, by determining the weighting factor based on the specific structural feature information of each position and referring to the preset correlation data between the structural features and the sensitivity of the local change rate, weights can be provided for the local change rates at different positions. This weighted processing based on structural sensitivity enables the calculated weighted local change rate to more accurately reflect the differences in mechanical response sensitivity exhibited by different positions due to structural differences when subjected to external pressure. Therefore, the subsequent non-uniformity index calculated based on the weighted local change rate can more effectively highlight the local abnormal changes in those areas of the structure that are more prone to stress concentration or damage, thereby improving the accuracy of the non-uniformity index, helping to identify potential weak links or early signs of damage to the sensor housing during the pressure resistance process earlier and more accurately, and improving the reliability of pressure resistance detection.

[0090] In some embodiments, the step of determining a weighting factor for each position based on the structural feature information and referring to preset association data, where the preset association data is data of a mapping relationship between the structural feature and the sensitivity of the local change rate to the structural feature, includes:

[0091] According to the structural feature information, matching or interpolation calculation is performed in the preset associated data;

[0092] Based on the matching or interpolation calculation results, a weighting factor is determined for each position.

[0093] Specifically, based on the acquired structural feature information, matching or interpolation calculations are performed within the preset associated data. This allows interpolation to yield a reasonable quantitative value reflecting the sensitivity of the structural feature, even if the structural feature value is not at a discrete point in the preset associated data. This matching or interpolation method overcomes the discrete nature of the preset associated data, ensuring a more accurate quantitative sensitivity value for any given structural feature information. Based on this, a weighting factor is determined for each location based on the matching or interpolation calculation results. This weighting factor accurately reflects the sensitivity of the structural feature at that location to the local stress or strain rate of change. By applying this more accurate weighting factor to the local rate of change, a more precise weighted local rate of change can be obtained. The calculated weighted local rate of change difference and the resulting non-uniformity index more accurately reflect the stress or strain distribution in critical areas of the sensor housing, improving the ability to identify early-stage local damage and potential defects. Therefore, by optimizing the weighting factor determination process, this solution improves the accuracy of the overall non-uniformity index calculation, providing a more reliable basis for subsequent pressure application rate adjustments based on this index.

[0094] In a specific embodiment, it is assumed that the structural feature information is the wall thickness value at a preset position of the sensor housing. The preset associated data can be a table that records the quantitative values ​​of the sensitivity of the local change rate corresponding to different wall thickness values. For example, the table may contain data points with a sensitivity of X corresponding to a wall thickness of 1 mm and a sensitivity of Y corresponding to a wall thickness of 2 mm. When the actual wall thickness of a certain position is 1.5 mm, since 1.5 mm is not at a discrete point in the table, a linear interpolation method can be used to calculate the quantitative value of the sensitivity corresponding to the wall thickness of 1.5 mm based on the data points corresponding to 1 mm and 2 mm. If the wall thickness of a certain position happens to be a value recorded in the table, such as 2 mm, the corresponding quantitative value of the sensitivity Y can be directly matched. Based on the quantitative value of the sensitivity obtained by this matching or interpolation calculation, the weighting factor of the position can be determined according to the preset rules.

[0095] In some embodiments, the step of determining a weighting factor for each position based on the matching or interpolation calculation results includes:

[0096] Obtaining preset criticality information for each of a plurality of preset positions, where the preset criticality information is determined based on a structural analysis of the sensor housing or with reference to historical test data, and reflects the degree of influence of the position on the overall compressive performance or failure mode of the sensor housing;

[0097] According to the matching or interpolation calculation results and the preset criticality information, the weighting factor of each position is calculated through the preset comprehensive calculation rules. The preset comprehensive calculation rules combine the matching or interpolation calculation results with the preset criticality information to quantify the relative importance of the position in the calculation of the non-uniformity index.

[0098] Among them, preset criticality information refers to predetermined data that reflects the importance of a specific position in the sensor housing to the overall compressive performance or potential failure mode; structural analysis refers to the evaluation of the stress distribution, strain distribution, weak links or potential failure areas of the sensor housing when subjected to external pressure through mechanical modeling, simulation calculations or experimental testing; historical test data refers to the data accumulated from past pressure tests on similar or similar sensor housings, including the stress or strain response during the test, the pressure value when failure occurs, the failure location, the failure mode and other information.

[0099] Specifically, pre-determined criticality information for each critical location is determined through structural analysis or historical test data. This information, independent of the current real-time test data, reflects the inherent importance of the location. During real-time testing, matching or interpolation calculation results are obtained based on the sensitivity of the structural features. This sensitivity result is then combined with the pre-determined criticality information using a pre-defined comprehensive calculation rule. The comprehensive calculation rule aims to balance local sensitivity and overall importance. For example, even if a location has low local sensitivity, its weighting factor will be increased if it is located in a critical load-bearing area or a failure-prone region. Conversely, a region with high local sensitivity but little impact on the overall structure may not be given an excessively high weighting. This method of combining local sensitivity with overall criticality to determine the weighting factor ensures that the calculated non-uniformity index more accurately reflects the stress or strain distribution in areas that are critical to overall performance. This improved index calculation, combined with a framework for dynamically adjusting the application rate, enables earlier and more accurate identification of potential local damage or defects, particularly in areas that have a significant impact on overall structural reliability. This ensures that the entire compressive testing method improves the ability to identify early-stage local damage and potential defects while maintaining efficiency.

[0100] In some embodiments, a weighting factor for each position is calculated based on the matching or interpolation calculation results and preset criticality information using a preset comprehensive calculation rule. The preset comprehensive calculation rule combines the matching or interpolation calculation results with the preset criticality information to quantify the relative importance of the position in calculating the non-uniformity index. The steps include:

[0101] Obtain matching or interpolation calculation results and preset criticality information;

[0102] Normalizing the matching or interpolation calculation results to obtain a normalized sensitivity value;

[0103] Normalizing the preset criticality information to obtain a normalized criticality value;

[0104] According to the preset combination function, the normalized sensitivity value and the normalized criticality value are combined and calculated to obtain the weighting factor of each position.

[0105] Normalization refers to the process of converting a set of values ​​into a specific range or distribution, such as by using a minimum-maximum normalization method. A preset combination function refers to a mathematical function that has been determined and set before the weighting factor calculation. The function accepts normalized sensitivity values ​​and normalized criticality values ​​as input and outputs a single weighting factor, such as a weighted sum, product, or exponential function.

[0106] Specifically, the entire process first obtains the raw data required for the calculation: matching or interpolation calculation results and preset criticality information. Because this data may come from different analysis or evaluation processes, their numerical ranges and dimensions may differ, and direct combination may lead to biased results. Therefore, these two types of data need to be normalized separately. Through normalization, sensitivity information is converted into standardized sensitivity values, and criticality information is converted into standardized criticality values, placing them on a unified basis for comparison. These two normalized values ​​are then mathematically combined using a predefined combination function. This combination function, determined based on an in-depth analysis of the sensor housing's structural characteristics, potential failure modes, and test objectives, defines how sensitivity and criticality jointly determine the relative importance of a location. This clear normalization and combination calculation process eliminates the reliance on vague "preset rules" for calculating weighting factors. The calculated weighting factors are then used in the subsequent calculation of the weighted local rate of change and the non-uniformity index. Their accuracy and stability directly impact the reliability of the non-uniformity index. A reliable non-uniformity index can more accurately reflect the stress or strain distribution of a shell during compression, particularly highlighting changes in critical or sensitive areas. Dynamically adjusting the external pressure application rate based on this more accurate index allows the testing process to better adapt to the specific mechanical responses of different shells. For example, when changes in stress concentration areas intensify, the loading rate can be appropriately slowed to more precisely capture early damage signals.

[0107] In practical applications, the minimum-maximum normalization method can be used to scale the matching or interpolation calculation results of all positions to a sensitivity value between 0 and 1, and at the same time scale the preset criticality information of all positions to a criticality value between 0 and 1. After normalization, a preset combination function can be used to calculate the weighting factor. For example, the combination function can be a weighted summation function, such as: weighting factor = W1*normalized sensitivity value + W2*normalized criticality value, where W1 and W2 are pre-set weight coefficients, for example, W1+W2=1 can be set. By adjusting the ratio of W1 and W2, the relative influence of sensitivity or criticality in the final weighting factor can be controlled. The calculated weighting factor is then used to calculate the weighted local change rate.

[0108] In some embodiments, the step of calculating the local rate of change of stress or strain at each of a plurality of preset positions as a function of external pressure based on real-time stress data or strain data includes:

[0109] For each of the plurality of preset positions, obtaining stress data or strain data and corresponding external pressure data collected in real time within a preset time interval;

[0110] According to the preset time interval, stress data or strain data and corresponding external pressure data, the ratio of the stress change or strain change at the position to the external pressure change is calculated to obtain the local change rate of the position.

[0111] Specifically, for each preset location where the local rate of change needs to be calculated, the system continuously collects real-time stress or strain data at that location during the application of external pressure within a preset time interval, and simultaneously collects the corresponding external pressure data. Next, using the data collected within the preset time interval, the difference between the stress or strain values ​​at the beginning and end of the preset time interval is calculated to obtain the stress change or strain change. Simultaneously, the difference between the external pressure values ​​at the beginning and end of the preset time interval is calculated to obtain the external pressure change. Finally, the calculated stress change or strain change is divided by the corresponding external pressure change to obtain the local rate of change for that location within the preset time interval. This method effectively filters out transient noise and fluctuations, making the calculated local rate of change more stable and accurate. This accurate and stable local rate of change calculation results serve as the basis for subsequent non-uniformity index calculations, ensuring that subsequent weighting processing, difference calculations, and the final non-uniformity index more reliably reflect the true mechanical response non-uniformity of the sensor housing. Based on these reliable non-uniformity indicators, a strategy for dynamically adjusting the external pressure application rate based on the indicators can be effectively implemented, thereby accurately evaluating the compressive performance of the sensor housing and effectively identifying potential early damage.

[0112] In a specific embodiment, within a preset time interval, for example, from time point T1 to time point T2, the stress or strain values ​​and the corresponding external pressure values ​​at the location at time T1 and time T2 are obtained. The difference between the stress or strain values ​​at time T2 and time T1 can be calculated as the stress change or strain change, and the difference between the external pressure values ​​at time T2 and time T1 can be calculated as the external pressure change. The stress change or strain change is then divided by the external pressure change to obtain the local change rate of the location within the preset time interval.

[0113] In some embodiments, step S3 includes:

[0114] Get historical data of indicators collected within a preset time period;

[0115] Calculate the indicator's changing trend based on the current indicator and historical data. The changing trend includes the indicator's changing rate or acceleration.

[0116] Comparing the current indicator with a preset indicator threshold to obtain a first comparison result;

[0117] Comparing the change trend of the indicator with a preset change trend threshold to obtain a second comparison result;

[0118] The rate of application of the external pressure is determined according to the first comparison result and the second comparison result by a preset rate determination rule.

[0119] Among them, the changing trend of the indicator refers to the speed or acceleration of the change of the stress or strain distribution non-uniformity indicator with time or external pressure. It can be obtained by calculating the ratio of the difference between the indicators at adjacent time points or pressure points to the time or pressure difference, or by calculating the ratio of the difference in the change rate with time or pressure to the time or pressure difference to obtain the change acceleration.

[0120] Specifically, by comprehensively judging and determining the rate of application of external pressure through preset rate determination rules, the mechanical response state of the sensor housing under the current pressure and its development trend can be more comprehensively evaluated. For example, the rule can be set as follows: when any of the current indicators or change trends exceeds the threshold, the pressure rate is reduced or the pressure is stopped; when neither exceeds the threshold, the pressure rate can be maintained or appropriately increased. By dynamically adjusting the pressure rate based on the current state and change trend, the test process can respond more sensitively to the actual mechanical behavior of the shell, which helps to detect potential early damage or defects earlier. This method is combined with the basic method of real-time acquisition of stress or strain data and calculation of non-uniformity indicators, making the entire pressure resistance testing process more adaptable and forward-looking, and can dynamically adjust the test rhythm according to the actual mechanical response of the shell.

[0121] In a second aspect, the present application provides a sensor housing pressure resistance detection system, comprising:

[0122] The data acquisition module 21 is used to acquire real-time stress or strain data at multiple preset positions of the sensor housing when the external pressure applying device applies external pressure to the sensor housing;

[0123] An index calculation module 22 is used to calculate an index reflecting the non-uniformity of stress or strain distribution at multiple preset positions based on real-time stress or strain data;

[0124] a rate determination module 23 for determining the rate of application of the external pressure based on a comparison result of the indicator with a preset threshold;

[0125] The pressure control module 24 is configured to control the external pressure applying device to apply external pressure to the sensor housing according to the determined application rate.

[0126] Specifically, this system acquires stress or strain data at multiple locations of the sensor housing in real time, and calculates indicators reflecting the non-uniformity of stress or strain distribution based on these data, which can identify local stress anomalies or weak early damage signals that appear in the housing during the pressure process earlier and more sensitively. By dynamically adjusting the rate of application of external pressure according to the non-uniformity index, the system can perform adaptive testing based on the individual differences of different housings, avoiding misjudgments that may be caused by fixed-rate loading and reducing the risk of early failure. This dynamic adjustment mechanism can maintain high test efficiency when the sensor housing performs normally, and slow down the loading to facilitate detailed analysis when signs of abnormality appear. This significantly improves the reliability of the pressure test while taking into account mass production efficiency, and can more fully evaluate the true performance margin of each sensor housing.

[0127] The sensor housing pressure resistance detection system provided in this embodiment is used to execute the steps in the sensor housing pressure resistance detection method provided in the first aspect above. The principle of the sensor housing pressure resistance detection system provided in this embodiment is the same as the principle of the sensor housing pressure resistance detection method provided in the first aspect above, and will not be discussed in detail here.

[0128] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0129] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for detecting the pressure resistance of a sensor housing, characterized in that: Including steps: S1. In the process of applying external pressure to the sensor housing by the external pressure applying device, real-time stress data or strain data is obtained at multiple preset positions of the sensor housing; S2. Calculating an index reflecting the non-uniformity of stress or strain distribution at a plurality of preset positions based on the real-time stress data or strain data; S3. Determine the rate of application of the external pressure based on the comparison result of the indicator with the preset threshold; S4. According to the determined application rate, control the external pressure applying device to apply external pressure to the sensor housing.

2. The sensor housing pressure resistance detection method according to claim 1, characterized in that: Before step S1, the method further includes: S0.

1. Analyze the structure and expected failure modes of the sensor housing and identify key areas of the sensor housing. Expected failure modes refer to the potential damage or performance loss that may occur when the sensor housing is subjected to external pressure, as determined or analyzed in advance based on its material properties, process, and structural characteristics. S0.

2. Determine the number and distribution of the plurality of preset positions based on the key area, and determine whether the data to be acquired corresponding to each preset position is stress data or strain data.

3. The sensor housing pressure resistance detection method according to claim 1, characterized in that: Step S2 includes: Calculating the local change rate of stress or strain at each of the plurality of preset positions as the external pressure changes based on the real-time stress data or strain data; According to the preset structural features corresponding to each position, the local change rate of each position is weighted to obtain a weighted local change rate; Calculating, based on the weighted local change rate, differences in weighted local change rates between different positions in the plurality of preset positions; An index reflecting the non-uniformity of stress or strain distribution at the plurality of preset positions is calculated based on the differences in the weighted local change rates between different positions.

4. The sensor housing pressure resistance detection method according to claim 3, characterized in that: The step of performing weighted processing on the local change rate of each position according to the preset structural features corresponding to each position to obtain the weighted local change rate includes: According to the preset structural features corresponding to each position, the structural feature information of each position is obtained, where the structural feature information includes the geometric, material, process characteristics or micro-defect characteristics of the position; Determining a weighting factor for each position based on the structural feature information and with reference to preset association data, wherein the preset association data is data of a mapping relationship between the structural feature and the sensitivity of the local change rate to the structural feature; The weighted local change rate of each position is calculated according to the local change rate of each position and the weighting factor corresponding to each position.

5. The sensor housing pressure resistance detection method according to claim 4, characterized in that: The step of determining the weighting factor of each position according to the structural feature information and referring to preset association data, wherein the preset association data is data of a mapping relationship between the structural feature and the sensitivity of the local change rate to the structural feature, comprises: Perform matching or interpolation calculation in the preset associated data according to the structural feature information; The weighting factor of each position is determined according to the matching or interpolation calculation result.

6. The sensor housing pressure resistance detection method according to claim 5, characterized in that: The step of determining the weighting factor of each position according to the matching or interpolation calculation result includes: Obtaining preset criticality information for each of the plurality of preset positions, the preset criticality information being determined based on a structural analysis of the sensor housing or with reference to historical test data, and reflecting the degree of influence of the position on the overall compressive performance or failure mode of the sensor housing; Based on the matching or interpolation calculation results and the preset criticality information, a weighting factor for each position is calculated through a preset comprehensive calculation rule. The preset comprehensive calculation rule combines the matching or interpolation calculation results with the preset criticality information to quantify the relative importance of the position in the calculation of the non-uniformity index.

7. The sensor housing pressure resistance detection method according to claim 6, characterized in that: The step of calculating the weighting factor of each position according to the matching or interpolation calculation result and the preset criticality information using a preset comprehensive calculation rule, wherein the preset comprehensive calculation rule combines the matching or interpolation calculation result with the preset criticality information to quantify the relative importance of the position in calculating the non-uniformity index includes: Obtaining the matching or interpolation calculation result and the preset criticality information; Normalizing the matching or interpolation calculation results to obtain a normalized sensitivity value; Normalizing the preset criticality information to obtain a normalized criticality value; According to a preset combination function, the normalized sensitivity value and the normalized criticality value are combined and calculated to obtain a weighting factor for each position.

8. The sensor housing pressure resistance detection method according to claim 3, characterized in that: The step of calculating the local change rate of stress or strain at each of the plurality of preset positions as the external pressure changes based on the real-time stress data or strain data includes: For each of the plurality of preset positions, acquiring the stress data or strain data and corresponding external pressure data collected in real time within a preset time interval; According to the preset time interval, the stress data or strain data and the corresponding external pressure data, the ratio of the stress change or strain change at the position to the external pressure change is calculated to obtain the local change rate of the position.

9. The sensor housing pressure resistance detection method according to claim 1, characterized in that: Step S3 includes: Obtaining historical data of the indicator collected within a preset time period; Calculating a change trend of the indicator based on the current indicator and the historical data, wherein the change trend includes a change rate or a change acceleration of the indicator; Comparing the current indicator with a preset indicator threshold to obtain a first comparison result; Comparing the change trend of the indicator with a preset change trend threshold to obtain a second comparison result; The rate of applying the external pressure is determined according to the first comparison result and the second comparison result by a preset rate determination rule.

10. A sensor housing pressure resistance detection system, characterized in that: include: a data acquisition module, configured to acquire real-time stress or strain data at a plurality of preset positions of the sensor housing during a process in which an external pressure applying device applies external pressure to the sensor housing; an index calculation module, configured to calculate an index reflecting the non-uniformity of stress or strain distribution at the plurality of preset positions based on the real-time stress or strain data; a rate determination module, configured to determine a rate of application of the external pressure based on a comparison result of the indicator with a preset threshold; The pressure control module is configured to control the external pressure applying device to apply external pressure to the sensor housing according to the determined application rate.

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