A Compliance Verification Method and System Based on the Slope of Anchor Point Neighborhood of Experimental Curves

Abstract

This invention discloses a method and system for verifying the compliance of slope in the neighborhood of test curve anchor points, belonging to the field of digital supervision technology for engineering testing and inspection. This invention acquires and preprocesses the raw force, displacement, and time data output by the testing equipment, identifies mechanical characteristic inflection points in the preprocessed data and extracts corresponding neighborhood datasets, collects real-time location information of inflection point adjustment operations, calculates the slope change trajectory and slope characteristic parameters of the final position during the adjustment process, conducts dual compliance verification based on a slope characteristic library of material mechanical properties, records the entire process adjustment information to generate a trace log, and binds and stores it with the original data. This invention achieves full-process control of manual adjustment operations of test curves, effectively identifies adjustment behaviors that do not conform to material mechanical properties, ensures the authenticity and traceability of test data, and improves the supervision level of engineering testing and inspection data.

Description

Technical Field

[0001] This invention relates to the field of digital supervision technology for engineering testing and inspection, and in particular to a method and system for compliance verification based on the slope of the neighborhood of anchor points on test curves. Background Technology

[0002] Materials mechanics testing in the engineering construction field is a core step in evaluating the quality of engineering materials, directly affecting the overall safety and reliability of the project. With the development of digital technology, the testing and inspection industry has gradually transitioned from manual recording to automated data acquisition, and various digital acquisition systems for testing machines are widely used. These systems can collect force, displacement, and time data in real time during the test using sensors, automatically generating test curves reflecting changes in the mechanical properties of materials. They also allow operators to manually adjust key feature points in the test curves to correct deviations caused by equipment system errors, environmental interference, and other factors. Currently, most digital testing systems have local data storage, cloud-based synchronous upload, and automatic test report generation functions, meeting the basic workflow requirements of testing and inspection work.

[0003] Existing manual adjustment monitoring technologies for test curves have significant shortcomings. They only verify threshold ranges for key characteristic point values ​​after adjustment, failing to consider the inherent nature of test curves as a continuous expression of material mechanical properties. This makes them unable to identify covert attempts to falsify compliance data through single-point jump adjustments. Furthermore, existing technologies do not monitor the entire adjustment process, focusing only on the final result. They cannot reflect the operator's adjustment methods and process characteristics, making it difficult to distinguish between legitimate corrections and malicious tampering. In addition, existing systems record adjustment operations in a limited way, failing to fully preserve the slope change trajectory and overall operation information during the adjustment process. The traceability of adjustment traces is insufficient, and they are not linked to the original test data for storage, failing to form a complete chain of evidence and thus failing to meet the requirements for monitoring the authenticity of test data. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for verifying the compliance of the slope of the anchor point neighborhood of the test curve.

[0005] The objective of this invention is achieved through the following technical solution: A method for compliance verification based on the slope of the neighborhood of anchor points on a test curve is provided, which includes the following steps: S1. Obtain the raw force data, raw displacement data and raw time data output by the test equipment, construct the raw test curve dataset in chronological order, preprocess the raw test curve dataset, and generate the preprocessed test curve dataset. S2. Identify the mechanical feature inflection points in the preprocessed test curve dataset, extract discrete data points within a set interval before and after the mechanical feature inflection points, and generate a mechanical feature inflection point neighborhood dataset. S3. Receive the real-time position information of the mechanical feature inflection point adjustment operation, calculate the slope sequence of the neighborhood of the mechanical feature inflection point corresponding to each intermediate position during the adjustment process, generate the slope change trajectory, calculate the slope sequence and slope change rate sequence of the neighborhood of the mechanical feature inflection point at the final position after the adjustment is completed, perform dual compliance verification on the slope change trajectory and the slope feature of the final position based on the material mechanical property slope feature library, and generate the slope verification result. S4. Execute the corresponding operation based on the slope verification result, record the full process information of the mechanical feature inflection point adjustment operation, mouse movement trajectory characteristics and slope change trajectory, generate adjustment trace log, and bind and store the adjustment trace log with the preprocessed test curve dataset.

[0006] Furthermore, step S1 includes the following sub-steps: S1.1. Acquire the raw force data, raw displacement data, and raw time data output by the test equipment through an independent acquisition channel; S1.2. Link the original force data, original displacement data and original time data in chronological order to generate the original test curve dataset; S1.3. Verify the consistency of the time intervals of data points in the original experimental curve dataset and remove abnormal data points whose time intervals exceed the set range; S1.4. The sliding window filtering method is used to remove noisy data points from the original experimental curve dataset to generate a preprocessed experimental curve dataset; S1.5. Bind the preprocessed test curve dataset with the test equipment information, sample information, operator information and timestamp to generate the original curve data block.

[0007] Furthermore, step S2 includes the following sub-steps: S2.1. Based on the variation characteristics of the first and second derivatives of the curve, generate a candidate set of mechanical feature inflection points for the preprocessed experimental curve dataset; S2.2. Perform feature matching and screening on the candidate inflection points in the candidate set of mechanical feature inflection points to determine the final mechanical feature inflection points and mark the correlation between the mechanical feature inflection points; S2.3. Receive the mechanical feature inflection point adjustment operation command, and lock the corresponding mechanical feature inflection point and related mechanical feature inflection points; S2.4. Extract all discrete data points within the set interval before and after the mechanical feature inflection point to generate a mechanical feature inflection point neighborhood dataset; S2.5. Standardize the neighborhood dataset of mechanical feature inflection points to unify data dimensions and coordinate system.

[0008] Furthermore, step S3 includes the following sub-steps: S3.1. Real-time acquisition of position information of mechanical feature inflection point adjustment operation, and extraction of intermediate position coordinates during the adjustment process according to the set time interval; S3.2. Calculate the slope sequence of the neighborhood of each intermediate mechanical feature inflection point to generate the slope change trajectory; S3.3. Calculate the slope sequence and slope change rate sequence of the neighborhood of the mechanical characteristic inflection point of the final position after adjustment, and generate the slope characteristic parameters of the final position; S3.4. Obtain the compliance slope change trajectory characteristics and compliance final position slope characteristic parameters for the corresponding test type and material type from the material mechanical property slope characteristic library; S3.5. Compare the slope change trajectory with the compliant slope change trajectory features, compare the final position slope feature parameters with the compliant final position slope feature parameters, and generate slope verification results.

[0009] Furthermore, step S4 includes the following sub-steps: S4.1. When the slope verification result is compliant, save the adjusted test curve data; S4.2. When the slope verification result is non-compliant, an early warning will be triggered, and saving the adjusted test curve data will be prohibited; S4.3. Record the coordinates of the mechanical feature inflection point before adjustment, the coordinates of the mechanical feature inflection point after adjustment, the operation sequence, the operator information, the mouse movement trajectory characteristics and the slope change trajectory, and generate an adjustment trace log. S4.4. Bind and store the adjustment trace log with the preprocessed test curve dataset, the adjusted test curve dataset, and the slope verification results.

[0010] Furthermore, prior to step S3, a slope feature library of material mechanical properties is constructed, compliant test curve data of different test types and different material types are collected, mechanical feature inflection points in each compliant test curve are identified, slope feature parameters and slope change trajectory features of the neighborhood of each mechanical feature inflection point are calculated, and the compliant interval and compliant slope change trajectory range of the neighborhood of each mechanical feature inflection point of various materials are determined by statistical analysis methods. The slope feature library of material mechanical properties is then incrementally calibrated in real time based on compliant test curve data of the same batch and equipment.

[0011] Furthermore, in step S4, the original curve data block, adjustment trace log, and slope verification result are simultaneously stored in the local encrypted database and the cloud encrypted database. A hash value is generated for each stored data. The hash values ​​of adjacent stored data within the same batch are associated to form a batch hash chain. The first and last hash values ​​of the batch hash chain are associated with the global hash chain. The hash value consistency of the data stored in the local encrypted database and the data stored in the cloud encrypted database is compared periodically. When a hash value inconsistency occurs, a data anomaly prompt is triggered.

[0012] Furthermore, in step S3, based on the correlation between mechanical feature inflection points, the linkage relationship of the slope features in the neighborhood of the associated mechanical feature inflection points is calculated. The linkage relationship is compared with the compliant linkage relationship in the material mechanical property slope feature library. When the linkage relationship exceeds the set range, the slope verification result is generated as non-compliant.

[0013] Furthermore, after step S4, the adjustment operation information with non-compliant slope verification results is synchronously uploaded to the supervision platform. The slope change trajectory features and mouse movement trajectory features of the abnormal adjustment operations are extracted and matched with the historical abnormal operation feature database. Approval permissions are automatically assigned according to the matching results and the abnormality level, triggering the abnormal data approval process. The approval results are received, and the corresponding operations are executed according to the approval results. Approved adjustment operations are allowed to be saved and test reports are generated. Approved adjustment operations are prohibited from being saved and adjustment traces are deleted.

[0014] A compliance verification system based on the slope of the anchor point neighborhood of the test curve is provided. The system includes a test curve acquisition and preprocessing module, an inflection point identification and association marking module, a slope dynamic trajectory calculation module, a dual compliance verification module, a multi-inflection point linkage verification module, a full storage module for adjustment traces, a batch hash chain evidence storage module, and an intelligent approval module for abnormal operations. The test curve acquisition and preprocessing module is used to acquire and process raw test data; the inflection point identification and association marking module is used to identify mechanical characteristic inflection points and mark the association relationships; the slope dynamic trajectory calculation module is used to calculate the slope change trajectory during the adjustment process; the dual compliance verification module is used to perform dual verification of slope characteristics and change trajectories; the multi-inflection point linkage verification module is used to verify the slope linkage relationship of associated inflection points; the full-volume adjustment trace storage module is used to record and store full adjustment information; the batch hash chain evidence storage module is used to realize batch-level data anti-tampering storage; and the abnormal operation intelligent approval module is used to trigger and process the abnormal data approval process.

[0015] The beneficial effects of this invention are: (1) By preprocessing the test curve and identifying the mechanical characteristic inflection point, the slope characteristics of the neighborhood of the adjustment process and the final position are calculated to carry out dual compliance verification. At the same time, the adjustment information of the whole process is recorded and stored in conjunction with the original data. (2) Construct a slope feature library of material mechanical properties that can be dynamically calibrated, and combine it with the slope linkage verification mechanism of associated inflection points to effectively identify curve adjustment behaviors that do not conform to the laws of material mechanics; (3) A closed-loop processing system of dual-end hash chain evidence storage and hierarchical approval is adopted to realize the immutable storage of test data and operation traces, and to ensure the traceability of the entire test process. Attached Figure Description

[0016] Figure 1 The flowchart shows the steps of the compliance verification method based on the slope of the anchor point neighborhood of the test curve. Figure 2 The following is a flowchart illustrating the specific steps of a method for verifying compliance of the slope of the neighborhood of an anchor point on a test curve, provided as an example. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1 See Figure 1 This embodiment provides a method for compliance verification based on the slope of the neighborhood of the anchor point of the test curve. The method includes the following steps: S1. Obtain the raw force data, raw displacement data and raw time data output by the test equipment, construct the raw test curve dataset in chronological order, preprocess the raw test curve dataset, and generate the preprocessed test curve dataset. S2. Identify the mechanical feature inflection points in the preprocessed test curve dataset, extract discrete data points within a set interval before and after the mechanical feature inflection points, and generate a mechanical feature inflection point neighborhood dataset. S3. Receive the real-time position information of the mechanical feature inflection point adjustment operation, calculate the slope sequence of the neighborhood of the mechanical feature inflection point corresponding to each intermediate position during the adjustment process, generate the slope change trajectory, calculate the slope sequence and slope change rate sequence of the neighborhood of the mechanical feature inflection point at the final position after the adjustment is completed, perform dual compliance verification on the slope change trajectory and the slope feature of the final position based on the material mechanical property slope feature library, and generate the slope verification result. S4. Execute the corresponding operation based on the slope verification result, record the full process information of the mechanical feature inflection point adjustment operation, mouse movement trajectory characteristics and slope change trajectory, generate adjustment trace log, and bind and store the adjustment trace log with the preprocessed test curve dataset.

[0019] In some embodiments, step S1 includes the following sub-steps: S1.1. Acquire the raw force data, raw displacement data, and raw time data output by the test equipment through an independent acquisition channel; S1.2. Link the original force data, original displacement data and original time data in chronological order to generate the original test curve dataset; S1.3. Verify the consistency of the time intervals of data points in the original experimental curve dataset and remove abnormal data points whose time intervals exceed the set range; S1.4. The sliding window filtering method is used to remove noisy data points from the original experimental curve dataset to generate a preprocessed experimental curve dataset; S1.5. Bind the preprocessed test curve dataset with the test equipment information, sample information, operator information and timestamp to generate the original curve data block.

[0020] In some embodiments, step S2 includes the following sub-steps: S2.1. Based on the variation characteristics of the first and second derivatives of the curve, generate a candidate set of mechanical feature inflection points for the preprocessed experimental curve dataset; S2.2. Perform feature matching and screening on the candidate inflection points in the candidate set of mechanical feature inflection points to determine the final mechanical feature inflection points and mark the correlation between the mechanical feature inflection points; S2.3. Receive the mechanical feature inflection point adjustment operation command, and lock the corresponding mechanical feature inflection point and related mechanical feature inflection points; S2.4. Extract all discrete data points within the set interval before and after the mechanical feature inflection point to generate a mechanical feature inflection point neighborhood dataset; S2.5. Standardize the neighborhood dataset of mechanical feature inflection points to unify data dimensions and coordinate system.

[0021] In some embodiments, step S3 includes the following sub-steps: S3.1. Real-time acquisition of position information of mechanical feature inflection point adjustment operation, and extraction of intermediate position coordinates during the adjustment process according to the set time interval; S3.2. Calculate the slope sequence of the neighborhood of each intermediate mechanical feature inflection point to generate the slope change trajectory; S3.3. Calculate the slope sequence and slope change rate sequence of the neighborhood of the mechanical characteristic inflection point of the final position after adjustment, and generate the slope characteristic parameters of the final position; S3.4. Obtain the compliance slope change trajectory characteristics and compliance final position slope characteristic parameters for the corresponding test type and material type from the material mechanical property slope characteristic library; S3.5. Compare the slope change trajectory with the compliant slope change trajectory features, compare the final position slope feature parameters with the compliant final position slope feature parameters, and generate slope verification results.

[0022] In some embodiments, step S4 includes the following sub-steps: S4.1. When the slope verification result is compliant, save the adjusted test curve data; S4.2. When the slope verification result is non-compliant, an early warning will be triggered, and saving the adjusted test curve data will be prohibited; S4.3. Record the coordinates of the mechanical feature inflection point before adjustment, the coordinates of the mechanical feature inflection point after adjustment, the operation sequence, the operator information, the mouse movement trajectory characteristics and the slope change trajectory, and generate an adjustment trace log. S4.4. Bind and store the adjustment trace log with the preprocessed test curve dataset, the adjusted test curve dataset, and the slope verification results.

[0023] In some embodiments, before step S3, a slope feature library of material mechanical properties is constructed, compliant test curve data of different test types and different material types are collected, mechanical feature inflection points in each compliant test curve data are identified, slope feature parameters and slope change trajectory features of the neighborhood of each mechanical feature inflection point are calculated, and the compliant interval and compliant slope change trajectory range of the neighborhood of each mechanical feature inflection point of various materials are determined by statistical analysis methods. The slope feature library of material mechanical properties is then incrementally calibrated in real time based on compliant test curve data of the same batch and equipment.

[0024] In some embodiments, in step S4, the original curve data block, adjustment trace log, and slope verification result are stored simultaneously in a local encrypted database and a cloud encrypted database. A hash value is generated for each stored data. The hash values ​​of adjacent stored data within the same batch are associated to form a batch hash chain. The first and last hash values ​​of the batch hash chain are associated with the global hash chain. The hash value consistency is compared between the data stored in the local encrypted database and the data stored in the cloud encrypted database periodically. When a hash value inconsistency occurs, a data anomaly prompt is triggered.

[0025] In some embodiments, in step S3, based on the correlation between mechanical feature inflection points, the linkage relationship of the slope features in the neighborhood of the associated mechanical feature inflection points is calculated, and the linkage relationship is compared with the compliant linkage relationship in the material mechanical property slope feature library. When the linkage relationship exceeds the set range, the slope verification result is generated as non-compliant.

[0026] In some embodiments, after step S4, the adjustment operation information with non-compliant slope verification results is synchronously uploaded to the supervision platform, the slope change trajectory features and mouse movement trajectory features of the abnormal adjustment operation are extracted, matched with the historical abnormal operation feature database, approval permissions are automatically assigned according to the matching results and the abnormality level, the abnormal data approval process is triggered, the approval results are received, and the corresponding operation is executed according to the approval results. The approved adjustment operation is allowed to be saved and a test report is generated, while the unapproved adjustment operation is prohibited from being saved and the adjustment trace is deleted.

[0027] A compliance verification system based on the slope of the anchor point neighborhood of the test curve is provided. The system includes a test curve acquisition and preprocessing module, an inflection point identification and association marking module, a slope dynamic trajectory calculation module, a dual compliance verification module, a multi-inflection point linkage verification module, a full storage module for adjustment traces, a batch hash chain evidence storage module, and an intelligent approval module for abnormal operations. The test curve acquisition and preprocessing module is used to acquire and process raw test data; the inflection point identification and association marking module is used to identify mechanical characteristic inflection points and mark the association relationships; the slope dynamic trajectory calculation module is used to calculate the slope change trajectory during the adjustment process; the dual compliance verification module is used to perform dual verification of slope characteristics and change trajectories; the multi-inflection point linkage verification module is used to verify the slope linkage relationship of associated inflection points; the full-volume adjustment trace storage module is used to record and store full adjustment information; the batch hash chain evidence storage module is used to realize batch-level data anti-tampering storage; and the abnormal operation intelligent approval module is used to trigger and process the abnormal data approval process.

[0028] Example 2 This embodiment provides a specific implementation process for a method for verifying the compliance of the slope of the neighborhood of the anchor point of the test curve. This method is applicable to the compliance control of manual adjustment operations of test curves in various material mechanics tests. It can effectively identify curve adjustment behaviors that do not conform to the mechanical properties of materials and ensure the authenticity and objectivity of test data.

[0029] This embodiment is implemented based on a B / S and C / S collaborative architecture. The C / S side is responsible for real-time acquisition of experimental data, curve interaction, and local verification, while the B / S side is responsible for feature library management, global monitoring, and approval process handling. The two sides synchronize data through an encrypted communication channel. The anchor point referred to in this embodiment is the mechanical characteristic inflection point, which is the point on the experimental curve where the material's mechanical properties undergo a sudden change, corresponding to the material's proportional limit point, yield point, ultimate load point, failure point, etc. Figure 2 As shown, the specific implementation process is as follows: Step 1. Data Acquisition and Preprocessing of Experimental Curves: Step 1.1. Acquire the raw force data, raw displacement data, and raw time data output by the testing equipment through an independent acquisition channel: An independent acquisition channel refers to a data acquisition channel that runs parallel to the testing equipment controller. It does not rely on the data output of the original controller and can directly acquire raw data from the equipment sensors, avoiding data distortion caused by tampering with the equipment controller. In this embodiment, the independent acquisition channel is connected to the force and displacement sensors of the testing equipment via serial communication, acquiring the analog signals output by the sensors in real time and converting them into digital signals to generate raw force data, raw displacement data, and raw time data. The raw force data represents the magnitude of the force borne by the material during the test, the raw displacement data represents the amount of deformation of the material during the test, and the raw time data represents the acquisition time corresponding to each data point.

[0030] The independent acquisition channel includes a signal conditioning module, an analog-to-digital conversion module, and a data transmission module. The signal conditioning module amplifies and filters the weak analog signal output by the sensor, the analog-to-digital conversion module converts the processed analog signal into a digital signal, and the data transmission module transmits the digital signal to the C / S terminal processing unit through an encrypted communication protocol.

[0031] In some embodiments, the independent acquisition channel can also be connected to the test equipment via Ethernet or wireless communication, making it suitable for different types of test equipment and field environments.

[0032] Step 1.2. Associate the original force data, original displacement data, and original time data in chronological order to generate the original test curve dataset: Use each original time data point as an index to associate the corresponding original force and displacement data, forming data points. Then, arrange all data points in chronological order to generate the original test curve dataset. The original test curve dataset is the foundation for all subsequent processing steps, containing all information about the changes in the material's mechanical properties during the experiment. Each data point uses a structured storage format, including timestamp, force, displacement, and equipment identifier fields, ensuring the data's structure and parsability.

[0033] Step 1.3. Verify the consistency of the time intervals of data points in the original experimental curve dataset, and remove abnormal data points whose time intervals exceed the set range: The time interval refers to the time difference between two adjacent data points. Under normal circumstances, the data acquisition frequency of the experimental equipment is fixed, so the time interval between adjacent data points should be kept within a stable range.

[0034] If the time interval of a data point exceeds the set range, it indicates that the data point may be due to equipment failure, communication interference, or other abnormal conditions, and it needs to be removed to ensure the accuracy of the original data. In this embodiment, the time difference between adjacent data points is calculated and compared with the set time interval range. Data points exceeding the range are marked as abnormal data points and removed. For consecutively occurring abnormal data points, the system records the duration and number of abnormal data points, generates a device operation anomaly log, and notifies equipment administrators to conduct an inspection.

[0035] Step 1.4. A sliding window filtering method is used to remove noisy data points from the original experimental curve dataset, generating a preprocessed experimental curve dataset. Sliding window filtering is a commonly used digital signal processing method. It involves sliding a fixed-size window across the data sequence and statistically processing the data within the window to remove noise. In this embodiment, a sliding window mean filtering method is used, where the average value of all data points within the window is used as the new value of the center data point, thereby removing high-frequency noise caused by factors such as electromagnetic interference and mechanical vibration. The size of the sliding window can be adjusted according to the test type and data acquisition frequency to achieve the best filtering effect. During the filtering process, the system retains the overall trend characteristics of the original data to avoid over-filtering that could lead to curve distortion.

[0036] Table 1 Sliding Window Filter Parameter Configuration Table In some specific implementations, the parameter configurations for sliding window filtering are shown in Table 1. The corresponding filter type and window size are selected based on the noise characteristics of different test types. For steel bar tensile tests, high-frequency electromagnetic interference is generated during equipment operation; using a mean filter with a window size of 5 can effectively remove high-frequency noise while preserving the mechanical characteristics of the curve. For concrete compressive strength tests, mechanical vibration during equipment loading generates impulse noise; using a median filter with a window size of 7 can effectively suppress the influence of impulse noise. For cement flexural strength tests, random interference in the test environment generates white noise; using a Gaussian filter with a window size of 3 can remove noise while maintaining the smoothness of the curve. For asphalt ductility tests, environmental vibration and electromagnetic interference during the test generate mixed noise; using a mean filter with a window size of 5 can comprehensively remove various types of noise. After the parameter configuration is completed, the system will automatically filter the original test curve dataset to generate a preprocessed test curve dataset.

[0037] In some embodiments, a sliding window midpoint filtering method or a Gaussian filtering method can also be used to remove noise data, which is suitable for different types of noise characteristics.

[0038] Step 1.5. Bind the preprocessed test curve dataset with the test equipment information, sample information, operator information, and timestamp to generate the original curve data block: the test equipment information includes the model, number, calibration date, etc. of the test equipment; the sample information includes the sample number, material type, strength grade, batch, etc.; the operator information includes the operator's name, employee number, etc.; the timestamp indicates the time when the original curve data block was generated.

[0039] Binding this information to the preprocessed experimental curve dataset to generate raw curve data blocks ensures the traceability of experimental data and facilitates subsequent monitoring and retrieval. The raw curve data blocks are encrypted using a symmetric encryption algorithm, with the encryption key automatically generated and periodically updated by the system to prevent unauthorized access and tampering.

[0040] Step 2. Anchor point identification and neighborhood extraction: Step 2.1. Based on the variation characteristics of the first and second derivatives of the curve, generate a candidate set of anchor points for the preprocessed experimental curve dataset: the first derivative of the curve represents the slope of the curve, reflecting the deformation rate of the material; the second derivative of the curve represents the rate of change of the slope of the curve, reflecting the change in the deformation rate of the material. When the curve passes through an anchor point, the sign of the second derivative changes.

[0041] In this embodiment, by calculating the first and second derivatives of the preprocessed test curves, points where the second derivative is zero and the signs of the second derivatives on both sides are opposite are identified, and these points are used as the anchor point candidate set. The first derivative is calculated using the central difference method. For the i-th data point, its first derivative is the ratio of the force difference to the displacement difference between two adjacent data points; the second derivative is the ratio of the difference between two adjacent first derivatives to the displacement difference. After the calculation is completed, the system smooths the second derivative sequence to remove spurious zeros caused by data fluctuations.

[0042] Step 2.2. Perform feature matching and filtering on the candidate anchor points in the anchor point candidate set to determine the final anchor points and mark the relationships between the anchor points: Due to the influence of factors such as noise, the anchor point candidate set may contain some false inflection points, which need to be filtered. In this embodiment, an inflection point feature template is established based on the mechanical characteristics of different material types and test types. The features of the candidate anchor points are matched with the template to remove false inflection points that do not meet the features, and the final anchor points are determined.

[0043] Simultaneously, based on the material's mechanical properties, the relationships between anchor points are marked. For example, there is a sequential and numerical correlation between the yield point and the ultimate load point, and the failure point should appear after the ultimate load point. These relationships are stored in a directed graph, with each anchor point as a node and the relationships as edges, facilitating subsequent linkage verification.

[0044] Step 2.3. Receive Anchor Point Adjustment Command and Lock the Corresponding and Associated Anchor Points: When operators need to manually adjust the test curve, they select the anchor point to be adjusted by clicking or dragging the mouse. After receiving the adjustment command, the system locks the corresponding anchor point and its associated anchor points to prevent other inflection points from being accidentally manipulated during the adjustment process. Locked inflection points can only undergo the specified adjustment operation and cannot be deleted or moved to unreasonable positions. The locked state will automatically be released after the adjustment operation is completed or canceled, restoring the normal operation permissions of the anchor point.

[0045] Step 2.4. Extract all discrete data points within the defined interval before and after the anchor point to generate the anchor point neighborhood dataset: The anchor point neighborhood refers to the curved region within a certain range before and after the inflection point. The slope variation characteristics of this region can reflect the changes in the mechanical properties of the material near the inflection point. In this embodiment, the interval range of the inflection point neighborhood is set according to the test type and material type, and all discrete data points within this interval are extracted to generate the anchor point neighborhood dataset. The size of the neighborhood interval should be moderate, containing enough information to reflect the characteristics of the inflection point, while avoiding including too many irrelevant data points that would affect the calculation results.

[0046] Table 2. Types of Mechanical Characteristic Inflection Points and Neighborhood Intervals In some specific implementations, the neighborhood interval configuration of mechanical feature inflection points is shown in Table 2. Corresponding neighborhood ranges are set for mechanical features with different inflection point types. For the proportional limit point, its neighborhood interval is 10 data points before and after it, applicable to reinforced concrete materials, with the associated inflection point being the yield point; for the yield point, its neighborhood interval is 15 data points before and after it, applicable to reinforced steel materials, with the associated inflection point being the ultimate load point; for the ultimate load point, its neighborhood interval is 12 data points before and after it, applicable to all materials, with the associated inflection point being the failure point; for the failure point, its neighborhood interval is 8 data points before and after it, applicable to all materials, with no associated inflection point; for the elastic modulus point, its neighborhood interval is 6 data points before and after it, applicable to cement and mortar materials, with the associated inflection point being the proportional limit point. After the neighborhood interval configuration is completed, the system automatically extracts the discrete data points within the corresponding interval to generate the anchor point neighborhood dataset.

[0047] Step 2.5. Standardize the anchor point neighborhood dataset to unify data dimensions and coordinate systems: Since different experimental equipment and different experimental types may have different data dimensions and coordinate systems, it is necessary to standardize the anchor point neighborhood dataset to convert it into a unified dimension and coordinate system for subsequent slope calculation and verification. In this embodiment, the min-max standardization method is used to map the force and displacement data in the neighborhood dataset to a set interval, eliminating the influence of dimensions. During the standardization process, the system records the maximum and minimum values ​​of the original data for subsequent data reconstruction and verification.

[0048] Step 3. Slope feature calculation and dual compliance verification: Step 3.1. Construct a slope feature library for material mechanical properties, collect compliant test curve data for different test types and different material types, identify anchor points in each compliant test curve data, calculate the slope feature parameters and slope change trajectory characteristics of the neighborhood of each anchor point, determine the compliant interval and compliant slope change trajectory range of the anchor point neighborhood corresponding to different material types through statistical analysis methods, and perform real-time incremental calibration of the slope feature library for material mechanical properties based on compliant test curve data of the same batch and the same equipment: The slope feature library for material mechanical properties is an important part of this embodiment, storing the slope feature information of compliant test curves for different material types and test types.

[0049] When constructing the feature library, a large amount of validated compliance test curve data was first collected. Then, anchor point identification was performed on each curve, and slope characteristic parameters of the neighborhood of each inflection point were calculated, including the average slope, slope standard deviation, slope change rate, etc., as well as slope change trajectory characteristics, that is, the change law of the neighborhood slope during the inflection point adjustment process. Through statistical analysis of a large amount of compliance data, the compliance interval and compliance slope change trajectory range of the neighborhood slope of each anchor point for various materials were determined.

[0050] Meanwhile, to accommodate differences in equipment and material batches, the feature library is incrementally calibrated in real time based on compliant test curve data from the same batch and equipment, continuously optimizing its accuracy and applicability. The feature library is stored in a relational database and includes tables for material types, test types, inflection point features, and compliance intervals, facilitating data retrieval and management.

[0051] In some specific implementations, the real-time incremental calibration of the material mechanical property slope feature library adopts a batch processing approach to address the issue of decreased calibration accuracy caused by differences in mechanical properties between different devices and batches of materials. First, the system aggregates compliant test curve data generated by the same batch and equipment to form a batch calibration dataset. This batch calibration dataset contains a number of compliant test curves that meet statistical requirements, ensuring the statistical significance of the calibration results.

[0052] Then, anchor point identification and neighborhood slope feature calculation are performed on each curve in the batch calibration dataset to generate batch slope feature statistics, including batch average slope, batch slope standard deviation and batch slope change rate range.

[0053] The batch slope feature statistics are compared with the corresponding original compliance interval in the feature library, and the deviation value between the two is calculated. If the deviation value is within the set allowable range, the feature library is not updated; if the deviation value exceeds the allowable range, the original compliance interval is adjusted based on the batch slope feature statistics to generate a new compliance interval.

[0054] Finally, the new compliance range is updated to the feature library, while the historical versions of the original compliance range are retained for easy traceability and comparison in the future. The feature library update operation will generate an update log, which records the update time, batch information, adjustment content and operator information.

[0055] In some embodiments, the material mechanical property slope feature library can also be customized according to industry standards and specifications in different regions to meet the regulatory requirements of different regions.

[0056] Step 3.2. Real-time acquisition of anchor point adjustment operation position information, and extraction of intermediate position coordinates during the adjustment process at set time intervals: When the operator drags the anchor point for adjustment, the system acquires the mouse position information in real time, i.e., the real-time coordinates of the inflection point. At set time intervals, the intermediate position coordinates during the adjustment process are extracted, and the movement trajectory of the inflection point during the adjustment process is recorded. The size of the time interval can be adjusted according to the precision requirements of the adjustment operation, ensuring complete recording of the adjustment process while avoiding excessive data volume. The acquired position information is bound to the corresponding timestamp to generate an adjustment trajectory dataset, which is stored in a local temporary database.

[0057] Step 3.3. Calculate the slope sequence of the anchor point neighborhood corresponding to each intermediate position to generate a slope change trajectory: For each extracted intermediate position coordinate, recalculate the slope sequence of the anchor point neighborhood at that position. A slope sequence refers to the sequence of slope values ​​between every two adjacent data points within the neighborhood. By calculating the slope sequence corresponding to each intermediate position, a slope change trajectory is generated, which represents the change in the neighborhood slope during the inflection point adjustment process. The slope change trajectory reflects the method and process by which the operator adjusts the inflection point and is an important basis for judging whether the adjustment operation is compliant. The slope change trajectory is stored in a time series format, with each time point corresponding to a slope sequence, facilitating subsequent comparison and analysis.

[0058] Step 3.4. Calculate the slope sequence and slope change rate sequence of the final position after adjustment, and generate the slope feature parameters of the final position: When the operator completes the inflection point adjustment and releases the mouse, the system calculates the slope sequence of the anchor point neighborhood after adjustment, and then calculates the rate of change between every two adjacent slope values ​​in the slope sequence to generate the slope change rate sequence.

[0059] Based on the slope sequence and the slope change rate sequence, the slope characteristic parameters of the final position are calculated, including the average slope, slope standard deviation, average change rate, and change rate standard deviation. The average slope reflects the overall inclination of the curve in the neighborhood, the slope standard deviation reflects the fluctuation of the slope in the neighborhood, the average change rate reflects the average rate of change of the slope in the neighborhood, and the change rate standard deviation reflects the fluctuation of the slope change in the neighborhood.

[0060] Step 3.5. Obtain the compliant slope change trajectory features and compliant final position slope feature parameters corresponding to the test type and material type from the material mechanical property slope feature library. Compare the slope change trajectory with the compliant slope change trajectory features, and compare the final position slope feature parameters with the compliant final position slope feature parameters to generate slope verification results: Based on the current test type and material type, obtain the corresponding compliant slope change trajectory features and compliant final position slope feature parameters from the material mechanical property slope feature library.

[0061] First, the slope change trajectory generated during the adjustment process is compared with the compliant slope change trajectory characteristics to determine whether the adjustment process conforms to the material's mechanical properties. Then, the slope characteristic parameters at the final position are compared with the compliant final position slope characteristic parameters to determine whether the adjusted curve conforms to the material's mechanical properties. If both comparisons pass, the slope verification result is compliant; if either comparison fails, the slope verification result is non-compliant.

[0062] Table 3. Slope Feature Parameter Compliance Range Configuration Table In some specific implementations, the compliance range configurations for the slope characteristic parameters are shown in Table 3. Corresponding compliance ranges for the average slope and standard deviation of the slope are set for different material types and inflection point types. For the yield point of HRB400 steel reinforcement, the average slope compliance range is 0.8-1.2, and the standard deviation compliance range is 0.05-0.15; for the ultimate load point of C30 concrete, the average slope compliance range is 0.6-1.0, and the standard deviation compliance range is 0.08-0.20; for the flexural failure point of P.O42.5 cement, the average slope compliance range is 0.5-0.9, and the standard deviation compliance range is 0.06-0.18; for the ductility inflection point of AH-70 asphalt, the average slope compliance range is 0.3-0.7, and the standard deviation compliance range is 0.04-0.12. When performing slope verification, the system retrieves the compliance range of the corresponding parameter from the slope feature library of material mechanical properties, compares the calculated final position slope feature parameter with the compliance range, and if the parameter value is within the compliance range, the verification passes; otherwise, the verification fails.

[0063] Step 3.6. Based on the correlation between anchor points, calculate the linkage relationship of the slope features of the neighborhood of the associated anchor points, and compare the linkage relationship with the compliant linkage relationship in the slope feature library of material mechanical properties. When the linkage relationship exceeds the set range, the slope verification result is generated as non-compliant: because there is a correlation between anchor points, the adjustment of one inflection point will affect the slope features of other associated inflection points.

[0064] In this embodiment, based on the correlation relationships marked in step 2.2, the linkage relationship of the slope features of the neighborhood of the associated anchor points is calculated, such as the ratio between the neighborhood slope of the yield point and the neighborhood slope of the ultimate load point. The calculated linkage relationship is compared with the compliant linkage relationships in the material mechanical property slope feature library. If the linkage relationship exceeds the set range, it indicates that the adjustment operation has caused a contradiction in the mechanical properties between the associated inflection points, and the generated slope verification result is non-compliant. The calculation of the linkage relationship adopts a combination of ratio method and difference method, comprehensively considering the relative and absolute changes of the slope features.

[0065] Step 4. Adjust the solidification of operation traces and data storage: Step 4.1. Perform the corresponding operation based on the slope verification result. When the slope verification result is compliant, save the adjusted test curve data. When the slope verification result is non-compliant, trigger an early warning and prohibit saving the adjusted test curve data. If the slope verification result is compliant, it means that the adjustment operation conforms to the mechanical properties of the material. The system saves the adjusted test curve data and updates the original curve data block.

[0066] If the slope verification result is non-compliant, it indicates that the adjustment operation may have involved data tampering. The system will trigger an alert on the user interface, informing the operator that the adjustment operation is non-compliant and prohibiting the saving of the adjusted test curve data. Simultaneously, the system will record the abnormal operation information. The alert uses a combination of pop-up windows and sound to ensure that operators receive timely notifications.

[0067] Step 4.2. Record the anchor point coordinates before and after adjustment, operation sequence, operator information, mouse movement trajectory characteristics, and slope change trajectory for the anchor point adjustment operation, generating an adjustment trace log. Regardless of whether the slope verification result is compliant, the system will record the entire process information of the adjustment operation, including the coordinates of the inflection point before and after adjustment, the start and end times of the operation, operator information, mouse movement trajectory characteristics, and slope change trajectory during the adjustment process. This information is integrated to generate the adjustment trace log, which is stored in an immutable format to ensure the traceability of the adjustment operation. The adjustment trace log includes a unique operation identifier field, an experiment number field, an operator field, an operation time field, coordinates before and after adjustment fields, trajectory feature fields, and a verification result field, facilitating subsequent querying and auditing.

[0068] In some specific implementations, the system performs multi-dimensional feature extraction and compliance verification on the mouse movement trajectory of anchor point adjustment operations to address the issue of unnatural adjustment behavior that cannot be identified by slope verification alone. First, it extracts the speed, acceleration, direction change, and pause features of the mouse movement trajectory. Speed ​​features are obtained by calculating the ratio of the distance between two adjacent intermediate coordinates to the time interval; acceleration features are obtained by calculating the ratio of the difference between two adjacent speed values ​​to the time interval; direction change features are obtained by calculating the angle between two adjacent movement directions; and pause features are obtained by identifying segments where the mouse stays at a certain position for a period exceeding a set range. Then, the extracted trajectory features are compared with a normal adjustment trajectory feature library. This library stores a large number of verified, compliant manual adjustment operation trajectory features, including normal adjustment trajectories from different operators and different test types. If the matching degree between trajectory features and normal features is lower than the set threshold, it indicates that the adjustment operation may be abnormal. For example, a jump trajectory caused by directly modifying coordinates through the background program, or a straight trajectory without natural pauses, etc. The system will mark the adjustment operation as a high-risk anomaly, increase the anomaly level, add a trajectory anomaly mark in the adjustment trace log, and upload it to the monitoring platform simultaneously.

[0069] Step 4.3. Bind and store the adjustment trace log with the preprocessed experimental curve dataset, the adjusted experimental curve dataset, and the slope verification results: Bind the generated adjustment trace log with the corresponding preprocessed experimental curve dataset, the adjusted experimental curve dataset, and the slope verification results to form a complete experimental data record. The bound record is stored in an encrypted manner to prevent data tampering or deletion. Simultaneously, the bound record is stored in both a local encrypted database and a cloud encrypted database to achieve dual-end backup and ensure data security and reliability. The local database uses an embedded database, suitable for offline operation in a field environment; the cloud database uses a distributed database, suitable for centralized monitoring across multiple sites.

[0070] Step 4.4. Generate a hash value for each stored data record. Associate the hash values ​​of adjacent stored data within the same batch to form a batch hash chain. Associate the first and last hash values ​​of the batch hash chain with the global hash chain. Periodically compare the hash values ​​of data stored in the local encrypted database and data stored in the cloud encrypted database. When a hash value inconsistency is found, trigger a data anomaly alert. A hash value is a fixed-length string calculated from data using a hash algorithm. It is unique and irreversible, effectively verifying the integrity and authenticity of the data. In this embodiment, a hash value is generated for each stored test data record, and then the hash values ​​of adjacent data records within the same batch are associated to form a batch hash chain.

[0071] The characteristic of batch hash chains is that if any piece of data is tampered with, its hash value changes, causing all subsequent data to change their hash values, thus enabling rapid detection of data tampering. Furthermore, associating the first and last hash values ​​of the batch hash chain with the global hash chain further enhances data tamper resistance. The system periodically performs consistency comparisons of the hash values ​​of data stored in the local encrypted database and the cloud encrypted database. If a hash value inconsistency is found, it indicates that the data may have been tampered with or corrupted, triggering a data anomaly alert and notifying administrators for handling.

[0072] In some specific implementations, the system enables breakpoint resumption and automatic data conflict handling between the local encrypted database and the cloud encrypted database, resolving data synchronization interruptions and inconsistencies caused by unstable on-site networks. When the network is interrupted, the system automatically records the current synchronization progress, temporarily stores unsynchronized data in a local temporary cache, and continues local data collection and storage operations without affecting the normal progress of on-site testing. When the network is restored, the system automatically resumes the transmission of unsynchronized data from the recorded synchronization progress, without needing to retransmit all data, thus improving synchronization efficiency. During data synchronization, if inconsistencies occur between local and cloud data, the system will determine the conflict based on the data's generation time, hash value, and operation logs, prioritizing the retention of data with a later generation time and verified hash value. Detailed information about the conflicting data is recorded in the data conflict log, including the conflicting data's number, generation time, local hash value, cloud hash value, and reason for the conflict, and the system notifies management personnel for manual review. After review, the system updates the corresponding data based on the review results and updates the batch hash chain and global hash chain to ensure the continuity and integrity of the hash chain.

[0073] In some embodiments, blockchain technology can also be used for data notarization, storing the hash value of the test data on the blockchain, and leveraging the immutability of the blockchain to further improve the security and credibility of the data.

[0074] Step 5. Approval and closed-loop processing of abnormal operations: Adjustment operations with non-compliant slope verification results are synchronously uploaded to the monitoring platform. The slope change trajectory features and mouse movement trajectory features of abnormal adjustment operations are extracted and matched with the historical abnormal operation feature database. Approval permissions are automatically assigned based on the matching results and abnormality level, triggering the abnormal data approval process. The approval results are received, and corresponding operations are executed based on the approval results. Approved adjustment operations are allowed to be saved and test reports are generated. Adjustment operations that are not approved are prohibited from being saved and adjustment traces are deleted. When an adjustment operation with non-compliant slope verification results occurs, the system synchronously uploads the abnormal operation information to the B / S terminal monitoring platform.

[0075] The monitoring platform extracts the slope change trajectory features and mouse movement trajectory features of abnormal adjustment operations, matches them with the historical abnormal operation feature database, and determines the type and level of the abnormal operation. Based on the matching results and the level of abnormality, the system automatically assigns the approval task to the administrator with the corresponding permissions, triggering the abnormal data approval process. Administrators can view detailed information about the abnormal operation, including the curves before and after the adjustment, adjustment trace logs, slope verification results, etc., and then make an approval decision. If the approval is approved, it indicates that the adjustment operation is reasonable, and the system allows the saving of the adjusted test curve data and generates a test report; if the approval is not approved, it indicates that there is a problem with the adjustment operation, and the system prohibits the saving of the adjusted test curve data and deletes the trace of the adjustment operation. The historical abnormal operation feature database is updated regularly, adding new abnormal operation features to the database to continuously improve the accuracy of abnormality identification.

[0076] In some specific implementations, the abnormal operation approval process adopts a hierarchical workflow mechanism, while simultaneously ensuring full traceability of the approval process, thus resolving issues of unreasonable allocation of approval authority and lack of traceability in the approval process. The system assigns approval tasks to different levels of management personnel based on the severity of the abnormal operation: low-level abnormalities are assigned to the laboratory director, medium-level abnormalities to the project technical lead, and high-level abnormalities to the company's quality control department. The approval process follows a serial flow; approval at each level must be completed before proceeding to the next. Each level of approver can view all relevant information about the abnormal operation, including the experimental curves before and after adjustment, adjustment logs, slope verification results, and mouse trajectory characteristics.

[0077] After an approver makes an approval decision, the system records the approver's information, approval time, approval comments, and approval result, generating an approval log. This log is bound to corresponding abnormal operation information and stored in an unalterable manner. If the approval process is not completed within the set time, the system will automatically send a reminder notification to the corresponding approver and escalate the approval task to the next higher-level manager to ensure that abnormal operations are handled promptly.

[0078] This embodiment effectively controls manual adjustments to test curves by verifying the compliance of the slope in the neighborhood of anchor points. It can identify adjustments that do not conform to the material's mechanical properties, thus ensuring the authenticity and objectivity of test data to a certain extent. This embodiment employs a dual verification method, combining the slope change trajectory during the adjustment process with the slope characteristics of the final position. This not only determines whether the adjusted curve is compliant but also monitors the adjustment process, improving the accuracy and comprehensiveness of the verification. Simultaneously, multi-anchor point linkage verification can detect correlation anomalies that cannot be identified by single-inflection-point verification, further enhancing the verification effect. This embodiment uses dual-end hash chain evidence storage technology to achieve tamper-proof storage of test data and adjustment traces, ensuring data traceability. Furthermore, the material mechanical property slope feature library constructed in this embodiment supports real-time incremental calibration, adapting to differences in different equipment and material batches, exhibiting good applicability and scalability. The technical solution of this embodiment can effectively improve the supervision level of engineering test data, reduce the generation of false test data, and provide reliable data support for engineering quality assessment.

[0079] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for checking the compliance of the slope of the neighborhood of an anchor point based on a trial curve, characterized in that, Includes the following steps: S1. Obtain the raw force data, raw displacement data and raw time data output by the test equipment, construct the raw test curve dataset in chronological order, preprocess the raw test curve dataset, and generate the preprocessed test curve dataset. S2. Identify the mechanical feature inflection points in the preprocessed test curve dataset, extract discrete data points within a set interval before and after the mechanical feature inflection points, and generate a mechanical feature inflection point neighborhood dataset. Step S2 includes the following sub-steps: S2.

1. Based on the variation characteristics of the first and second derivatives of the curve, generate a candidate set of mechanical feature inflection points for the preprocessed experimental curve dataset; S2.

2. Perform feature matching and screening on the candidate inflection points in the candidate set of mechanical feature inflection points to determine the final mechanical feature inflection points and mark the correlation between the mechanical feature inflection points; S2.

3. Receive the mechanical feature inflection point adjustment operation command, and lock the corresponding mechanical feature inflection point and related mechanical feature inflection points; S2.

4. Extract all discrete data points within the set interval before and after the mechanical feature inflection point to generate a mechanical feature inflection point neighborhood dataset; S2.

5. Standardize the dataset of the neighborhood of mechanical feature inflection points to unify the data dimensions and coordinate system; S3. Receive the real-time position information of the mechanical feature inflection point adjustment operation, calculate the slope sequence of the neighborhood of the mechanical feature inflection point corresponding to each intermediate position during the adjustment process, generate the slope change trajectory, calculate the slope sequence and slope change rate sequence of the neighborhood of the mechanical feature inflection point at the final position after the adjustment is completed, perform dual compliance verification on the slope change trajectory and the slope feature of the final position based on the material mechanical property slope feature library, and generate the slope verification result. S4. Execute the corresponding operation based on the slope verification result, record the full process information of the mechanical feature inflection point adjustment operation, mouse movement trajectory characteristics and slope change trajectory, generate adjustment trace log, and bind and store the adjustment trace log with the preprocessed test curve dataset.

2. The method of claim 1, wherein, Step S1 includes the following sub-steps: S1.

1. Acquire the raw force data, raw displacement data, and raw time data output by the test equipment through an independent acquisition channel; S1.

2. Link the original force data, original displacement data and original time data in chronological order to generate the original test curve dataset; S1.

3. Verify the consistency of the time intervals of data points in the original experimental curve dataset and remove abnormal data points whose time intervals exceed the set range; S1.

4. The sliding window filtering method is used to remove noisy data points from the original experimental curve dataset to generate a preprocessed experimental curve dataset; S1.

5. Bind the preprocessed test curve dataset with the test equipment information, sample information, operator information and timestamp to generate the original curve data block.

3. The method of claim 1, wherein, Step S3 includes the following sub-steps: S3.

1. Real-time acquisition of position information of mechanical feature inflection point adjustment operation, and extraction of intermediate position coordinates during the adjustment process according to the set time interval; S3.

2. Calculate the slope sequence of the neighborhood of each intermediate mechanical feature inflection point to generate the slope change trajectory; S3.

3. Calculate the slope sequence and slope change rate sequence of the neighborhood of the mechanical characteristic inflection point of the final position after adjustment, and generate the slope characteristic parameters of the final position; S3.

4. Obtain the compliance slope change trajectory characteristics and compliance final position slope characteristic parameters for the corresponding test type and material type from the material mechanical property slope characteristic library; S3.

5. Compare the slope change trajectory with the compliant slope change trajectory features, compare the final position slope feature parameters with the compliant final position slope feature parameters, and generate slope verification results.

4. The method of claim 1, wherein, Step S4 includes the following sub-steps: S4.

1. When the slope verification result is compliant, save the adjusted test curve data; S4.

2. When the slope verification result is non-compliant, an early warning will be triggered, and saving the adjusted test curve data will be prohibited; S4.

3. Record the coordinates of the mechanical feature inflection point before adjustment, the coordinates of the mechanical feature inflection point after adjustment, the operation sequence, the operator information, the mouse movement trajectory characteristics and the slope change trajectory, and generate an adjustment trace log. S4.

4. Bind and store the adjustment trace log with the preprocessed test curve dataset, the adjusted test curve dataset, and the slope verification results.

5. The method of claim 1, wherein, Before step S3, a slope feature library of material mechanical properties is constructed, compliant test curve data of different test types and different material types are collected, mechanical feature inflection points in each compliant test curve are identified, slope feature parameters and slope change trajectory features of the neighborhood of each mechanical feature inflection point are calculated, and the compliant interval and compliant slope change trajectory range of the neighborhood of each mechanical feature inflection point of various materials are determined by statistical analysis methods. The slope feature library of material mechanical properties is incrementally calibrated in real time based on compliant test curve data of the same batch and the same equipment.

6. The method of claim 1, wherein, In step S4, the original curve data block, adjustment trace log, and slope verification result are stored simultaneously in the local encrypted database and the cloud encrypted database. A hash value is generated for each stored data. The hash values ​​of adjacent stored data within the same batch are associated to form a batch hash chain. The first and last hash values ​​of the batch hash chain are associated with the global hash chain. The hash value consistency of the data stored in the local encrypted database and the data stored in the cloud encrypted database is compared periodically. When a hash value inconsistency occurs, a data anomaly prompt is triggered.

7. The method of claim 1, wherein, In step S3, based on the correlation between mechanical feature inflection points, the linkage relationship of the slope features in the neighborhood of the associated mechanical feature inflection points is calculated. The linkage relationship is compared with the compliant linkage relationship in the material mechanical property slope feature library. When the linkage relationship exceeds the set range, the slope verification result is generated as non-compliant.

8. The method of claim 1, wherein, After step S4, the adjustment operation information with non-compliant slope verification results is synchronously uploaded to the supervision platform. The slope change trajectory features and mouse movement trajectory features of the abnormal adjustment operation are extracted and matched with the historical abnormal operation feature database. Approval permissions are automatically assigned according to the matching results and abnormality level, triggering the abnormal data approval process. The approval results are received, and the corresponding operations are executed according to the approval results. Approved adjustment operations are allowed to be saved and test reports are generated. Unapproved adjustment operations are prohibited from being saved and adjustment traces are deleted.

9. A system for performing a method according to any one of claims 1 to 8, based on the compliance of the slope of the anchor point neighborhood of a test curve, characterized in that, The system includes a test curve acquisition and preprocessing module, an inflection point identification and association marking module, a slope dynamic trajectory calculation module, a dual compliance verification module, a multi-inflection point linkage verification module, a full storage module for adjustment traces, a batch hash chain evidence storage module, and an abnormal operation intelligent approval module. The test curve acquisition and preprocessing module is used to acquire and process raw test data; The inflection point identification and association marking module is used to identify inflection points of mechanical features and mark the association relationships; the slope dynamic trajectory calculation module is used to calculate the slope change trajectory during the adjustment process; the dual compliance verification module is used to perform dual verification of slope features and change trajectories. The multi-inflection point linkage verification module is used to verify the slope linkage relationship of related inflection points; the full-volume adjustment trace storage module is used to record and store full adjustment information; the batch hash chain evidence storage module is used to realize batch-level data anti-tampering storage; and the abnormal operation intelligent approval module is used to trigger and process the abnormal data approval process.

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