A soil sample testing system for water conservancy projects

Abstract

This invention discloses a soil sample testing system for hydraulic engineering, belonging to the field of geotechnical testing technology. It includes a data input module, an intelligent outlier identification and correction module, a multi-parameter linkage calculation module, a multi-condition parameter adaptation module, a result judgment and output module, a data management module, and a terminal interaction module. The system uses the weighted Grubbs test combined with historical test data to automatically identify and process outliers. It can automatically perform correlation calculations and multi-factor corrections for moisture content, wet density, dry density, and compaction degree, and can automatically match standard parameters according to different soil types and engineering locations. The system is integrated into a portable testing box, enabling on-site completion of the entire testing process. It also generates regional quality distribution charts based on Kriging interpolation, achieving a comprehensive assessment of engineering quality. This invention effectively solves the problems of low efficiency, poor accuracy, and non-standard data management in traditional geotechnical testing, significantly improving testing efficiency and result reliability.

Description

Technical Field

[0001] This invention relates to the field of testing system technology, specifically a soil sample testing system for water conservancy projects. Background Technology

[0002] Embankment engineering is the core barrier of a watershed's flood control and disaster reduction system, encompassing various engineering structures such as main stream embankments, flood detention area embankments, and flood inlet / outlet embankments. The quality of soil filling and compaction directly determines the seepage resistance, anti-sliding stability, and is a core control indicator for the safe operation of flood control projects. Moisture content, wet density, dry density, and compaction degree are core indicators for testing the quality of embankment soil filling. Currently, on-site testing mainly uses the ring cutter method to determine density and the alcohol combustion method to determine moisture content, supplemented by manual calculation and acceptance judgment. This is the conventional method for on-site quality control of embankment projects. However, in the context of comprehensive quality control of embankment projects, especially for the special working conditions of flood detention area embankments, existing testing methods have clear shortcomings: 1. There is no unified statistical standard for outlier identification, and data quality is uncontrollable under small sample sizes. Parallel field tests for levee projects typically only set up two groups. The traditional Grubbs test is statistically insufficient under small sample sizes, and outlier removal relies entirely on the personal experience of the testing personnel. The judgment results of different personnel for the same sample can vary by more than 5%. Furthermore, the soil properties of levees in flood storage and detention areas fluctuate greatly, and historical data is scattered. Most tests do not combine historical data from the same region and soil type for comprehensive verification, resulting in high rates of missed and false positives. The outlier identification process is undocumented, and there is no basis for tracing quality responsibility.

[0003] 2. Lack of parameter correction dimensions leads to systematic calculation bias. Current testing methods generally use the 20℃ calibration of the ring cutter to calculate density directly, without considering the volumetric expansion error of the stainless steel ring cutter under high summer outdoor conditions; it also fails to consider the compaction effect caused by the ring cutter being driven into the compacted soil, resulting in a systematic overestimation of the density calculation results; at the same time, the soil of the dikes in flood storage and detention areas is mostly river and lake alluvial soil, with generally high organic matter content. The alcohol combustion method will include the loss of organic matter on ignition in the moisture content, resulting in an overestimation of the moisture content by 1% to 2%. This bias has a significant cumulative effect in the testing of the entire dike line, directly affecting the accuracy of the compaction degree qualification judgment.

[0004] 3. Manual matching of standardized parameters is prone to errors, and the judgment standards are inconsistent throughout the entire line. The dike project covers a variety of engineering parts, including the main body of the dike, the road on the top of the dike, the slope protection layer, and the roadbed of the flood storage and detention area floodgates, which correspond to various soil properties such as fine-grained soil, sandy loam, and gravelly soil. The allowable values ​​for parallel difference and the judgment threshold for compaction are different for different working conditions. In the field operation environment, it is inconvenient to consult the standard provisions, and the mismatch rate of manually selected parameters can reach more than 15%, resulting in inconsistent judgment standards among different testing personnel and different batches, and inconsistent quality control standards throughout the entire line.

[0005] 4. Single-point detection is limited and cannot support comprehensive quality assessment. Traditional testing only outputs isolated single-point compaction results. However, embankment projects have long routes and scattered measuring points, and flood storage and detention area embankments are even more extensive and long. Discrete measuring points cannot reflect the compaction quality distribution of continuous areas, making it difficult to quickly locate weak quality sections. This makes it impossible to support the acceptance of compaction quality of the entire embankment and the overall risk assessment, and it is also difficult to meet the control requirements of segmented acceptance and full-line compliance of embankment projects.

[0006] 5. Poor adaptability to field operations, and testing efficiency is difficult to meet control requirements. Traditional on-site testing requires carrying multiple sets of equipment such as weighing tools, sampling equipment, and recording forms. Dike projects are mostly located in unpaved areas in the suburbs, and the site conditions in flood storage and detention areas are even more complex. Strong winds and high temperatures significantly interfere with the accuracy of measurement. Data recording relies on handwritten input, which is inconvenient for one-handed operation in the field. The complete testing process for a single point, including manual calculation, takes about 45 to 60 minutes, which is difficult to adapt to the large-scale and high-frequency on-site sampling needs of dike projects, and cannot support the control requirements of rapid quality inspection before the flood season. Summary of the Invention

[0007] This invention discloses a soil sample testing system for water conservancy projects, which is a closed-loop on-site testing system with deep integration of hardware and software functions. The hardware layer uses a portable integrated testing box as a carrier, integrating various functional modules. The software layer deploys seven major functional modules: data acquisition and storage, intelligent identification and correction of outliers, multi-parameter linkage calculation, multi-condition parameter adaptation, result judgment and analysis, full-link data management, and visual terminal interaction. The system can operate independently without an external network, completing the entire testing process on-site, from soil sample preparation, data acquisition, calculation and analysis to qualification determination and report generation.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a soil sample testing system for water conservancy projects, comprising a data input module, an intelligent outlier identification and correction module, a multi-parameter linkage calculation module, a multi-condition parameter adaptation module, a result judgment and output module, a data management module, and a terminal interaction module; The data input module is used to receive the measured basic data and environmental parameters of soil sample testing input by the user, supports QR code input and voice input, and performs preliminary verification of the format and validity of the input data; The intelligent outlier identification and correction module is based on the weighted Grubbs test and historical detection data model. It performs statistical analysis on the input measured basic data, automatically identifies outliers and provides correction suggestions, and records the process of identifying outliers. The multi-parameter linkage calculation module has a pre-stored full-process calculation formula that conforms to the standard of geotechnical testing methods. It can automatically complete the correlation calculation of moisture content, wet density, dry density and compaction degree, error propagation analysis and integration of parallel test results based on the input measured basic data and environmental parameters. The multi-condition parameter adaptation module has a built-in library of standard parameters for different soil types (fine-grained soil, sandy soil, gravelly soil) and different engineering parts (embankment, roadbed, dam body), which can automatically adjust the calculation parameters, parallel difference threshold and judgment criteria according to the conditions selected by the user. The result determination and output module is used to compare the calculated test results with the preset standard threshold and engineering design requirements, automatically generate a pass / fail judgment conclusion including confidence level assessment, and output standardized test results and visualization charts in real time. The data management module is used for unified storage, categorized querying, batch management and secure encryption of all detection records; The terminal interaction module provides a visual interactive interface between the user and the system, enabling data input, function selection, result display, and system settings operations. Preferably, the data input module supports receiving measured basic data including box number and box weight. Quality of wet soil in boxes , box plus dry soil quality Ring cutter number, ring cutter standard volume at 20℃ quality of ring cutter quality of the ring cutter and moistened soil Maximum dry density of soil sample Design compaction requirements and soil organic matter content Supported environmental parameters include ambient temperature. On-site wind speed and atmospheric pressure at the scene ; The data input module has a built-in device QR code recognition function. Scanning the QR code on the ring cutter or aluminum box can automatically retrieve the pre-stored device number and standard parameters. It also has a built-in voice recognition engine that supports voice data input during one-handed operation in the field. When inputting data, it automatically associates the current inspection number, engineering location, and GPS positioning information to achieve one-to-one binding of inspection data with physical objects. It also supports automatic collection of environmental parameters through external temperature and humidity sensors and barometric pressure sensors, eliminating the need for manual input.

[0009] Preferably, the workflow of the intelligent outlier identification and correction module includes the following steps: S11. Receive data transmitted from the data input module. Measured data sequence of parallel experiments ,in This represents the current number of parallel experimental groups. ; S12. Call the data management module to obtain historical test data sequences for the same project location and soil type over the past 30 days. ,in This represents the number of historical test data sets. ; S13. Calculate the arithmetic mean of a historical data series. and standard deviation ; S14. Calculate the arithmetic mean of the current data sequence. and standard deviation ; S15. Calculate the weighted composite standard deviation : ; In the formula: The weighted average standard deviation is calculated by incorporating historical data. This represents the standard deviation of the current parallel test data; The standard deviation of historical testing data; This represents the current number of parallel experimental groups; This represents the number of historical test data sets. S16. Calculate the weighted Grubbs statistic for each data point. : ; In the formula: For the first The weighted Grubbs statistic for each data point; For the first Group current parallel test data; This is the arithmetic mean of the current parallel experimental data; The arithmetic mean of historical detection data; The weighted average standard deviation; This represents the current number of parallel experimental groups; This represents the number of historical test data sets. S17. The maximum weighted Grubbs statistic The corresponding Grubbs critical value at the confidence level (95%) Compare: like If all data are valid, the data sequence is transmitted to the multi-parameter linkage calculation module. like If the data point is identified as an outlier, it will be automatically removed and a message indicating that an outlier has been detected and automatically removed will be displayed on the terminal interaction module. At the same time, the outlier and its identification process will be recorded. S18. Repeat steps S14-S17 until there are no outliers in the data sequence or the number of remaining data sets is less than 2. If the number of remaining data sets is less than 2, prompt the user to repeat the experiment.

[0010] Preferably, the moisture content calculation process of the multi-parameter linkage calculation module includes the following steps: S21. Receive the first value processed by the intelligent outlier identification and correction module. Box mass of parallel tests Quality of wet soil in boxes The quality of the box plus dry soil ,in This refers to the parallel test group number. ; S22. Calculate the first [item] based on the difference between the mass of the box containing moist soil and the mass of the box containing dry soil. Moisture mass of group experiment The first calculation is based on the difference between the mass of the box plus dry soil and the mass of the box. Dry soil mass of the group test ; S23. Calculate the first The original single-group moisture content of the group experiment : ; In the formula: For the first The original moisture content (%) of the soil samples. For the first Mass of water in the soil sample (g); For the first Mass of dry soil in the soil sample group (g); S24. Call the multi-condition parameter adaptation module to obtain the corresponding organic matter combustion loss coefficient based on the current soil type being tested. Allowable value of parallel difference with moisture content ; S25. Receive the soil sample organic matter content transmitted by the data input module. Calculate the first Corrected single-group moisture content of group experiments : ; In the formula: For the first Moisture content (%) of soil samples after correction for organic matter combustion loss; For the first The original moisture content (%) of the soil samples. The organic matter combustion loss coefficient (dimensionless) is the corresponding soil type. Organic matter content (%) of the soil sample; S26. Calculate the range of all corrected individual moisture contents. Compare the range with the allowable value of parallel difference: ; In the formula: The range (%) of moisture content for a single group after correction; The maximum value (%) of all corrected single-group moisture content; The minimum value (%) among all corrected single-group moisture contents; like Then calculate the arithmetic mean of the corrected moisture content of all parallel test groups. This is used as the final moisture content result and transmitted to the dry density calculation unit; like If the result is negative, the user will be prompted that the moisture content parallel difference exceeds the allowable range of the specification and the test should be repeated.

[0011] Preferably, the calculation process for wet density and dry density of the multi-parameter linkage calculation module includes the following steps: S31. Receive the first value processed by the intelligent outlier identification and correction module. Standard volume of ring cutter at 20°C for group density test quality of ring cutter and the quality of the ring cutter and moist soil ,in This is the density test group number. ; S32. Receive the ambient temperature transmitted by the data input module. Calculate the actual volume of the ring cutter at the ambient temperature. : ; In the formula: For the on-site temperature Actual volume of the lower ring cutter (cm²) 3 ); The calibration volume (cm³) of the ring cutter at a standard temperature of 20°C. 3 ); The linear expansion coefficient (°C) of the ring cutter material -1 Stainless steel ring cutter ; The ambient temperature (°C) is the actual temperature at the site. S33. Call the multi-condition parameter adaptation module to adjust the parameters according to the current soil type and design compaction requirements. Obtain the corresponding ring cutter compression coefficient. ; S34. Calculate the effective volume of the ring cutter after compression correction. : ; In the formula: To correct the effective volume (cm³) after the compression effect of the ring cutter driven into the soil. 3 ); The actual volume of the ring cutter at the ambient temperature (cm²) 3 ); The compression coefficient (dimensionless) is applied to the ring cutter corresponding to the soil type and compaction degree. Design compaction degree requirement (%); S35. Calculate the first [item name] based on the difference between the mass of the wetted soil and the mass of the ring cutter. Wet mass of soil samples from the group test ; S36. Calculate the first Single group wet density of group test : ; In the formula: For the first Wet density of soil samples (g / cm³) 3 ); For the first Wet mass (g) of soil sample from the ring cutter; The corrected effective volume of the ring cutter (cm³) 3 ); S37. Call the multi-condition parameter adaptation module to obtain the corresponding allowable value of parallel difference in wet density based on the current soil type being tested. ; S38. Calculate the range of all individual wet densities. Compare the range with the allowable value of parallel difference:

[0012] In the formula: The range of wet density for a single group (g / cm³) 3 ); The maximum value (g / cm³) among all individual wet densities. 3 ); The minimum wet density among all individual groups (g / cm³) 3 ); like Then calculate the arithmetic mean of the wet density of all parallel test groups. This is used as the final wet density result and transferred to the dry density calculation unit; like If the wet density parallel difference exceeds the allowable range specified, please repeat the test. S39 automatically retrieves the final moisture content result obtained in step S6. Calculate the final dry density of the soil sample. And transmit it to the compaction degree calculation unit: ; In the formula: The dry density of the soil sample (g / cm³) 3 ); The wet density of the soil sample (g / cm³) 3 ); The moisture content (%) of the soil sample.

[0013] Preferably, the compaction degree calculation and regional quality assessment process of the multi-parameter linkage calculation module includes the following steps: S41. Receive the maximum dry density of the soil sample transmitted by the data input module. and design compaction requirements ; S42. Automatically retrieve the final dry density result obtained in step S39. Calculate the actual compaction degree of the soil sample. : ; In the formula: The actual compaction degree of the soil sample (%). The dry density of the soil sample (g / cm³) 3 ); The maximum dry density of the soil sample (g / cm³) 3 ); S43. Calculate the 95% confidence interval of compaction results based on the dispersion of parallel tests: Calculate the standard deviation of dry density : ; In the formula: The standard deviation of dry density (g / cm³) 3 ); For the first Dry density of soil samples (g / cm³) 3 ); For the final dry density result (g / cm³) 3 ); The number of valid parallel experimental groups; Calculate the 95% confidence interval for compaction degree: ; In the formula: The actual compaction degree of the soil sample (%). For degrees of freedom The critical value of the t-distribution at a confidence level of 95%; The standard deviation of dry density (g / cm³) 3 ); The maximum dry density of the soil sample (g / cm³) 3 ); The number of valid parallel experimental groups; S44. Evaluate the confidence level of the results based on the confidence interval width: If the confidence interval width is ≤2%, the confidence level of the judgment result is high; If 2% < confidence interval width ≤ 5%, then the confidence level of the judgment result is medium. If the confidence interval width is greater than 5%, the confidence level of the result is considered low, and the user is prompted to increase the number of parallel test groups to improve the reliability of the result. S45. Call the data management module to obtain the compaction degree data and GPS coordinates of all detected points in the same engineering location, and use ordinary Kriging interpolation to calculate the compaction degree of any point in the engineering location. The estimated compaction degree : Calculate the experimental semivariogram : ; In the formula: The distance is The experimental semivariogram value at time; The spatial distance (m) between the two detection points; The distance is The number of detection points; For the first Compaction degree (%) at each test point; In order to be with the first The distance between the detection points is The compaction degree (%) at the test point; Fitting an exponential theoretical semivariogram model: ; In the formula: This represents the theoretical semivariogram value. This is the nugget value (reflecting measurement error and short-distance variation); The sill value (reflecting the overall degree of variation); For variable range (reflecting the range of spatial correlation); The spatial distance (m) between the two detection points; Solving the Kriging equations yields the interpolation weighting coefficients. ; Calculate the estimated compaction degree of the point to be estimated. : ; In the formula: Points to be estimated The estimated degree of compaction (%); For the first Interpolation weight coefficients for each known detection point; For the first Compaction degree (%) at known test points; S46. Calculate the overall compaction pass rate of this part of the project based on the interpolation results. : ; In the formula: The overall compaction pass rate of the engineering section (%) For compaction degree ≥ Area (m) 2 ); Total inspection area of ​​the engineering section (m²) 2 ); S47. Calculate the actual compaction degree at a single point. The confidence interval, confidence level, regional compaction distribution, and overall pass rate are transmitted to the result judgment and output module.

[0014] Preferably, the determination and output process of the result determination and output module includes the following steps: S51. Receive the actual compaction degree at a single point transmitted by the multi-parameter linkage calculation module. Confidence interval, confidence level, regional compaction distribution, and overall pass rate; S52. Call the multi-condition parameter adaptation module to obtain the corresponding design compaction requirements based on the current engineering location. and risk classification thresholds; S53. A four-level risk classification assessment of single-point compaction degree is performed: like If the result is satisfactory, a green qualified label and the words "test qualified" will be generated. like If the compaction is slightly below the design requirements, it will be judged as a minor non-compliance, generating a yellow warning label and a message indicating that the compaction degree is slightly lower than the design requirements and that it is recommended to strengthen subsequent construction control. like If the compaction degree is lower than the design requirement, it will be judged as a general non-compliance, and an orange warning label and the words "double sampling and re-inspection are required near this point" will be generated. like If the compaction degree is significantly lower than the design requirements, it will be judged as a serious non-compliance, generating a red non-compliance mark and the words "compliance degree is significantly lower than the design requirements, and immediate rework is required". The GPS location of the non-compliance point will be automatically marked on the electronic map. S54. Conduct a three-level assessment of the overall quality of the engineering components: If the overall pass rate is ≥95% and there are no serious non-conformities, the overall quality is judged to be excellent. If the overall pass rate is 90% ≤ overall pass rate < 95% and there are no serious non-conformities, then the overall quality is deemed to be qualified. If the overall pass rate is less than 90% or there are serious non-conformities, the overall quality is deemed unqualified, and an overall quality rectification notice will be generated. S55. Integrate the raw data, calculation process, parallel difference verification, confidence assessment results, single-point judgment conclusions and overall quality judgment conclusions of moisture content, wet density, dry density and compaction degree to generate a standardized test result page; S56. Automatically generate visualization charts of test data, including single-group data bar charts, parallel test trend charts, regional compaction contour maps, and engineering part quality heat maps, and display them in real time on the terminal interaction module.

[0015] Preferably, the data management module uses AES-256 encrypted non-volatile memory, which can store at least 100,000 complete test records; each test record includes a unique test number, test time, GPS location information, tester's name, project location, soil type, environmental parameters, measured basic data, outlier identification record, intermediate calculation results, final test results, confidence level, single-point judgment conclusion, overall quality judgment conclusion, and operation log; The data management module supports multi-condition queries and searches based on testing time, soil sample number, testing personnel, project location, and testing results; it supports modification and deletion of testing records, with all modification and deletion operations automatically generating immutable audit logs; and it supports automatic backup and cloud synchronization of testing data, automatically synchronizing local data to the cloud server after network recovery. The data management module has a built-in quality trend analysis unit that can automatically analyze the test data of the past 7 days, 30 days and 90 days, calculate the moving average, standard deviation and coefficient of variation of compaction, and draw quality trend curves. When the standard deviation of compaction exceeds the preset threshold or shows a downward trend for 3 consecutive days, it will automatically issue an abnormal construction process warning.

[0016] The soil sampling testing system is integrated into a portable integrated testing box. The portable integrated testing box includes a box body, the interior of which is divided into an equipment storage area, a shock-absorbing weighing area, and a sealed, windproof working area. The equipment storage area is equipped with multiple EVA foam fixing slots that are adapted to the shape of the soil sampling testing tools, and each slot corresponds to a unique QR code. The shock-absorbing weighing area is equipped with a flexible silicone shock-absorbing base and an electronic balance, which is communicatively connected to the soil sampling testing system. The sealed, windproof working area is equipped with an openable and closable magnetic transparent windproof barrier, and temperature and humidity sensors and air pressure sensors are installed in the working area.

[0017] This invention provides a soil sample testing system for water conservancy projects, which has the following beneficial effects: 1. By using the weighted Grubbs test combined with historical data under the same working conditions to identify outliers, the common problem of strong subjectivity in manual anomaly identification under small-sample parallel tests in dike engineering is solved. The identification process is fully traceable and documented. Simultaneously, it achieves correction of three systematic errors: ring sampler temperature and volume, soil compression effect, and organic matter combustion loss. It is suitable for accurate testing of conventional soil properties in ordinary dikes and can also effectively offset the detection deviation caused by the high organic matter content of alluvial soil in flood storage areas. With the addition of a graded evaluation mechanism for compaction confidence, the reliability of the results is further guaranteed, and it can meet the differentiated testing requirements of different parts and different soil types in dike engineering.

[0018] 2. Based on the Kriging interpolation method, a regional compaction distribution heat map is generated, breaking through the limitations of traditional single-point detection. It can quickly locate areas with weak quality, which is suitable for the overall quality assessment of long lines and large-scale dikes in flood storage and detention areas, and can also meet the control needs of conventional dike segment acceptance and full-line inspection. All detection data is stored in encrypted form and all operations are fully traceable, supporting multi-condition traceability query. The built-in quality trend analysis unit can automatically warn of quality fluctuations, promoting the extension of dike quality control from post-inspection to process prediction.

[0019] 3. The integrated portable testing box integrates a complete set of equipment including weighing, sensing, and terminals, and is equipped with a shock-absorbing base and a sealed windproof working area, effectively reducing the interference of field vibration and strong wind on the measurement accuracy. It is suitable for ordinary dike and field operation scenarios, and can also meet the usage requirements of complex field environments in flood storage and detention areas. It supports QR code parameter adjustment, automatic sensor data collection, and voice input, simplifying the on-site operation process and significantly shortening the detection time of a single point. It can meet the operational needs of large-scale high-frequency sampling inspection of dike projects and rapid investigation before the flood season.

[0020] 4. Built-in multi-condition standard parameter library, which can automatically match the judgment thresholds of different soil types and engineering parts, covering all types of dike working conditions such as the main body of the dike, the road on the top of the dike, the slope protection layer and the flood storage and detention area flood discharge facilities, avoiding manual parameter mismatch and unifying the detection and judgment scale of the whole line; forming a closed-loop automatic process from data collection to result output, reducing human operation errors; equipped with a standardized four-level risk classification and overall quality judgment mechanism, automatically generating rectification opinions, and constructing a complete closed loop for dike engineering quality control. Attached Figure Description

[0021] Figure 1 This is a diagram showing the overall architecture of the soil sample testing system. Figure 2 This is a schematic diagram of the entire process of soil sample testing; Figure 3 A heat map showing the compaction distribution in the engineering area; Figure 4 This is a trend analysis chart of compaction quality. Figure 5 This is a schematic diagram of the structure of a portable integrated testing box. Detailed Implementation

[0022] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0023] This embodiment provides a soil sample testing system for water conservancy projects, which is integrated into a portable integrated testing box. It can complete the entire process of testing on-site, from soil sample collection and data processing to result output, solving the problems of low efficiency, poor accuracy, and non-standard data management in traditional geotechnical testing.

[0024] The soil sample testing system described in this embodiment includes seven core functional modules: a data input module, an intelligent outlier identification and correction module, a multi-parameter linkage calculation module, a multi-condition parameter adaptation module, a result judgment and output module, a data management module, and a terminal interaction module. The modules interact with each other and transmit commands through an internal bus.

[0025] like Figure 1As shown, this system adopts a modular, layered, closed-loop architecture design: the data input module serves as the front-end data entry point, transmitting the collected measured basic data and environmental parameters to the intelligent outlier identification and correction module for data quality verification; the valid data after outlier removal enters the multi-parameter linkage calculation module to complete the full-process correlation calculation and correction of moisture content, wet density, dry density, and compaction degree; the multi-condition parameter adaptation module serves as the parameter support layer, automatically matching the corresponding soil type and engineering part's standard parameters for the calculation and result judgment stages; the calculation results are transmitted to the result judgment and output module, generating standardized qualification judgment conclusions and visualization charts; all raw data, intermediate calculation results, and operation records are uniformly stored in the data management module, which also feeds back historical test data under the same conditions to the intelligent outlier identification and correction module, supporting the outlier judgment of the weighted Grubbs test; the terminal interaction module serves as the sole human-computer interaction entry point, realizing the full-process operation of data input, function selection, result display, and system settings.

[0026] The data input module is used to receive the measured basic data and environmental parameters of soil sample testing input by the user. It supports QR code input and voice input, and performs preliminary verification of the format and validity of the input data.

[0027] Supported received basic measured data includes: box number, box quality. Quality of wet soil in boxes , box plus dry soil quality Ring cutter number, ring cutter standard volume at 20℃ quality of ring cutter quality of the ring cutter and moistened soil Maximum dry density of soil sample Design compaction requirements and soil organic matter content ; Supported environmental parameters to receive include: ambient temperature. On-site wind speed and atmospheric pressure at the scene ; Built-in device QR code recognition function: scanning the QR code on the ring cutter or aluminum box can automatically retrieve the pre-stored device number and standard parameters; Built-in voice recognition engine supports voice data input during one-handed operation in the field; When data is input, it is automatically associated with the current inspection number, engineering location, and GPS positioning information, so as to achieve a one-to-one binding between inspection data and physical objects; It supports automatic collection of environmental parameters via external temperature and humidity sensors and barometric pressure sensors, eliminating the need for manual input.

[0028] The intelligent outlier identification and correction module, based on the weighted Grubbs test and historical detection data model, performs statistical analysis on the input measured basic data, automatically identifies outliers and provides correction suggestions, while recording the outlier identification process. Its workflow includes the following steps: S11. Receive data transmitted from the data input module. Measured data sequence of parallel experiments ,in This represents the current number of parallel experimental groups. ; S12. Call the data management module to obtain historical test data sequences for the same project location and soil type over the past 30 days. ,in This represents the number of historical test data sets. ; S13. Calculate the arithmetic mean of a historical data series. and standard deviation ; S14. Calculate the arithmetic mean of the current data sequence. and standard deviation ; S15. Calculate the weighted composite standard deviation : ; In the formula: The weighted average standard deviation is calculated by incorporating historical data. This represents the standard deviation of the current parallel test data; The standard deviation of historical testing data; This represents the current number of parallel experimental groups; This represents the number of historical test data sets. Numerical Calculation Example: Setting the Number of Parallel Test Groups for this Field Test Current data standard deviation Number of historical testing groups under the same working conditions in the past 30 days Historical data standard deviation Substitute into the formula to calculate: Molecular part: ; Denominator: ; Weighted composite standard deviation:

[0029] S16. Calculate the weighted Grubbs statistic for each data point. : ; In the formula: For the first The weighted Grubbs statistic for each data point; For the first Group current parallel test data; This is the arithmetic mean of the current parallel experimental data; The arithmetic mean of historical detection data; The weighted average standard deviation; This represents the current number of parallel experimental groups; This represents the number of historical test data sets. Numerical calculation example: Continuing with the parameters mentioned above, the mean of the two sets of measured data is... Historical data average Weighted average Take a single set of measured data Substitute calculate: ; S17. The maximum weighted Grubbs statistic The corresponding Grubbs critical value at the confidence level (95%) Compare: like If all data are valid, the data sequence is transmitted to the multi-parameter linkage calculation module. like If the data point is identified as an outlier, it will be automatically removed and a message indicating that an outlier has been detected and automatically removed will be displayed on the terminal interaction module. At the same time, the outlier and its identification process will be recorded. S18. Repeat steps S14-S17 until there are no outliers in the data sequence or the number of remaining data sets is less than 2. If the number of remaining data sets is less than 2, prompt the user to repeat the experiment.

[0030] The multi-parameter linkage calculation module pre-stores complete calculation formulas conforming to geotechnical testing method standards. Based on input measured baseline data and environmental parameters, it can automatically perform correlation calculations of moisture content, wet density, dry density, and compaction degree, error propagation analysis, and integration of parallel test results. Its core calculation process is as follows: Moisture content calculation process: S21. Receive the first value processed by the intelligent outlier identification and correction module. Box mass of parallel tests Quality of wet soil in boxes The quality of the box plus dry soil ,in This refers to the parallel test group number. ; S22. Calculate the first [item] based on the difference between the mass of the box containing moist soil and the mass of the box containing dry soil. Moisture mass of group experiment The first calculation is based on the difference between the mass of the box plus dry soil and the mass of the box. Dry soil mass of the group test ; S23. Calculate the first The original single-group moisture content of the group experiment : ; In the formula: For the first The original moisture content (%) of the soil samples. For the first Mass of water in the soil sample (g); For the first Mass of dry soil in the soil sample group (g); Numerical calculation example: Take the data from the first set of parallel experiments, the mass of the aluminum box Quality of wet soil in box Box plus dry soil quality .

[0031] Moisture content: ; Dry soil quality: ; Original moisture content: ; S24. Call the multi-condition parameter adaptation module to obtain the corresponding organic matter combustion loss coefficient based on the current soil type being tested. Allowable value of parallel difference with moisture content ; S25. Receive the soil sample organic matter content transmitted by the data input module. Calculate the first Corrected single-group moisture content of group experiments : ; In the formula: For the first Moisture content (%) of soil samples after correction for organic matter combustion loss; For the first The original moisture content (%) of the soil samples. The organic matter combustion loss coefficient (dimensionless) is the corresponding soil type. Organic matter content (%) of the soil sample; Numerical calculation example: The soil type tested was fine-grained soil, and the organic matter combustion loss coefficient was... Soil organic matter content Substitute the above original moisture content data into the calculation: ; S26. Calculate the range of all corrected individual moisture contents. Compare the range with the allowable value of parallel difference: ; In the formula: The range (%) of moisture content for a single group after correction; The maximum value (%) of all corrected single-group moisture content; The minimum value (%) among all corrected single-group moisture contents; Numerical calculation example: After correction, the moisture contents of two sets of parallel experiments are 16.70% and 16.58%, respectively. Then... , Extremely poor moisture content: Allowable value of parallel difference in moisture content of fine-grained soil It meets the requirements of the specifications.

[0032] like Then calculate the arithmetic mean of the corrected moisture content of all parallel test groups. This is used as the final moisture content result and transmitted to the dry density calculation unit; like If the result is negative, the user will be prompted that the moisture content parallel difference exceeds the allowable range of the specification and the test should be repeated.

[0033] S31. Receive the first value processed by the intelligent outlier identification and correction module. Standard volume of ring cutter at 20°C for group density test quality of ring cutter and the quality of the ring cutter and moist soil ,in This is the density test group number. ; S32. Receive the ambient temperature transmitted by the data input module. Calculate the actual volume of the ring cutter at the ambient temperature. : ; In the formula: For the on-site temperature Actual volume of the lower ring cutter (cm²) 3 ); The calibration volume (cm³) of the ring cutter at a standard temperature of 20°C. 3 ); The linear expansion coefficient (°C) of the ring cutter material -1 Stainless steel ring cutter ; The ambient temperature (°C) is the actual temperature at the site. Numerical Calculation Example: Standard Ring Cutter Volume On-site temperature detection Substitute into the formula: ; S33. Call the multi-condition parameter adaptation module to adjust the parameters according to the current soil type and design compaction requirements. Obtain the corresponding ring cutter compression coefficient. ; S34. Calculate the effective volume of the ring cutter after compression correction. : ; In the formula: To correct the effective volume (cm³) after the compression effect of the ring cutter driven into the soil. 3 ); The actual volume of the ring cutter at the ambient temperature (cm²) 3 ); The compression coefficient (dimensionless) is applied to the ring cutter corresponding to the soil type and compaction degree. Design compaction degree requirement (%); Numerical calculation example: Compression coefficient of fine-grained soil ring cutter Design compaction degree Following the above temperature-corrected volume calculation: ; S35. Calculate the first [item name] based on the difference between the mass of the wetted soil and the mass of the ring cutter. Wet mass of soil samples from the group test ; S36. Calculate the first Single group wet density of group test : ; In the formula: For the first Wet density of soil samples (g / cm³) 3 ); For the first Wet mass (g) of soil sample from the ring cutter; The corrected effective volume of the ring cutter (cm³) 3 ); Numerical Calculation Example: Ring Cutter Mass quality of ring cutter and moistened soil Soil sample wet quality Substitute the corrected volume into the formula: ; S37. Call the multi-condition parameter adaptation module to obtain the corresponding allowable value of parallel difference in wet density based on the current soil type being tested. ; S38. Calculate the range of all individual wet densities. Compare the range with the allowable value of parallel difference: ; In the formula: The range of wet density for a single group (g / cm³) 3 ); The maximum value (g / cm³) among all individual wet densities. 3 ); The minimum wet density among all individual groups (g / cm³) 3 ); Numerical calculation example: Two sets of parallel wet densities are 1.80 g / cm³. 3 1.79g / cm 3 Extreme Allowable value of parallel difference in wet density of fine-grained soil It meets the requirements of the specifications.

[0034] like Then calculate the arithmetic mean of the wet density of all parallel test groups. This is used as the final wet density result and transferred to the dry density calculation unit; like If the wet density parallel difference exceeds the allowable range specified, please repeat the test. S39. Automatically retrieve the final moisture content result obtained in step S26. Calculate the final dry density of the soil sample. And transmit it to the compaction degree calculation unit: ; In the formula: The dry density of the soil sample (g / cm³) 3 ); The wet density of the soil sample (g / cm³) 3 ); The moisture content (%) of the soil sample.

[0035] Numerical calculation example: final average wet density Finally, the moisture content was corrected. Substitute into the formula: ; Compaction Degree Calculation and Regional Quality Assessment Process: S41. Receive the maximum dry density of the soil sample transmitted by the data input module. and design compaction requirements ; S42. Automatically retrieve the final dry density result obtained in step S39. Calculate the actual compaction degree of the soil sample. : ; In the formula: The actual compaction degree of the soil sample (%). The dry density of the soil sample (g / cm³) 3 ); The maximum dry density of the soil sample (g / cm³) 3 ); Numerical calculation example: Maximum dry density of this soil sample Measured dry density Substitute into the calculation: ; S43. Calculate the 95% confidence interval of compaction results based on the dispersion of parallel tests: Calculate the standard deviation of dry density : ; In the formula: The standard deviation of dry density (g / cm³) 3 ); For the first Dry density of soil samples (g / cm³) 3 ); For the final dry density result (g / cm³) 3 ); The number of valid parallel experimental groups; Numerical calculation example: The effective parallel dry density of the two groups of experiments is 1.54 g / cm³. 3 1.53g / cm 3 final mean , Substitute into the calculation: Sum of squared differences: ; ; Calculate the 95% confidence interval for compaction degree: ; In the formula: The actual compaction degree of the soil sample (%). For degrees of freedom The critical value of the t-distribution at a confidence level of 95%; The standard deviation of dry density (g / cm³) 3 ); The maximum dry density of the soil sample (g / cm³) 3 ); The number of valid parallel experimental groups; Numerical calculus example: Degrees of freedom At a 95% confidence level Calculate the range fluctuation value based on the above parameters: ; compaction The final confidence interval is [95.85%, 96.65%]; S44. Evaluate the confidence level of the results based on the confidence interval width: If the confidence interval width is ≤2%, the confidence level of the judgment result is high; If 2% < confidence interval width ≤ 5%, then the confidence level of the judgment result is medium. If the confidence interval width is greater than 5%, the confidence level of the result is considered low, and the user is prompted to increase the number of parallel test groups to improve the reliability of the result. S45. Call the data management module to obtain the compaction degree data and GPS coordinates of all detected points in the same engineering location, and use ordinary Kriging interpolation to calculate the compaction degree of any point in the engineering location. The estimated compaction degree : Calculate the experimental semivariogram : ; In the formula: The distance is The experimental semivariogram value at time; The spatial distance (m) between the two detection points; The distance is The number of detection points; For the first Compaction degree (%) at each test point; In order to be with the first The distance between the detection points is The compaction degree (%) at the test point; Numerical Calculation Example: Taking the Spacing The number of corresponding detection points The squared differences in compaction degree among the three groups are 0.16, 0.25, and 0.09, respectively. Substituting these values ​​into the calculation: ; Fitting an exponential theoretical semivariogram model: ; In the formula: This represents the theoretical semivariogram value. This is the nugget value (reflecting measurement error and short-distance variation); The sill value (reflecting the overall degree of variation); For variable range (reflecting the range of spatial correlation); The spatial distance (m) between the two detection points; Numerical fitting example: Fitting to obtain nugget value sill value Variable range Substitute verify: The results showed a good match with the experimental values.

[0036] Solving the Kriging equations yields the interpolation weighting coefficients. ; Calculate the estimated compaction degree of the point to be estimated. : ; In the formula: Points to be estimated The estimated degree of compaction (%); For the first Interpolation weight coefficients for each known detection point; For the first Compaction degree (%) at known test points; Numerical calculation example: Select three neighboring detection points with weighting coefficients of 0.4, 0.35, and 0.25, corresponding to compaction degrees of 96.25%, 95.80%, and 96.10%, respectively. Substitute these values ​​into the calculation: ; S46. Calculate the overall compaction pass rate of this part of the project based on the interpolation results. : ; In the formula: The overall compaction pass rate of the engineering section (%) For compaction degree ≥ Area (m) 2 ); Total inspection area of ​​the engineering section (m²) 2 ); Numerical calculation example: Total inspection area of ​​the project qualified area Substitute into the calculation: ; S47. Calculate the actual compaction degree at a single point. The confidence interval, confidence level, regional compaction distribution, and overall pass rate are transmitted to the result judgment and output module. For example... Figure 3As shown, the system generates a heat map of compaction distribution in the engineering area based on ordinary Kriging interpolation. It uses a six-level color gradient to visually display the compaction range of different areas (legend labels: <94%, 94%-95%, 95%-96%, 96%-97%, 97%-98%, ≥98%). The map also indicates the specific measured compaction values ​​at each testing point. Below the map, the system automatically calculates and displays the overall compaction pass rate and quality grade of the engineering area. This heat map allows for visual querying of compaction at any point, helping inspectors quickly locate areas with low compaction potential, solving the problem that traditional testing methods can only obtain results from isolated testing points.

[0037] The multi-condition parameter adaptation module has a built-in library of standard parameters for different soil types (fine-grained soil, sandy soil, gravelly soil) and different engineering parts (embankment, roadbed, dam body). It can automatically adjust the calculation parameters, parallel difference threshold and judgment criteria according to the working conditions selected by the user, avoiding the misuse of manual parameters.

[0038] The result determination and output module compares the calculated test results with preset standard thresholds and engineering design requirements, automatically generates a pass / fail judgment conclusion including confidence level assessment, and outputs standardized test results and visualization charts in real time. Its determination and output process includes the following steps: S51. Receive the actual compaction degree at a single point transmitted by the multi-parameter linkage calculation module. Confidence interval, confidence level, regional compaction distribution, and overall pass rate; S52. Call the multi-condition parameter adaptation module to obtain the corresponding design compaction requirements based on the current engineering location. and risk classification thresholds; S53. A four-level risk classification assessment of single-point compaction degree is performed: like If the result is satisfactory, a green qualified label and the words "test qualified" will be generated. like If the compaction is slightly below the design requirements, it will be judged as a minor non-compliance, generating a yellow warning label and a message indicating that the compaction degree is slightly lower than the design requirements and that it is recommended to strengthen subsequent construction control. like If the compaction degree is lower than the design requirement, it will be judged as a general non-compliance, and an orange warning label and the words "double sampling and re-inspection are required near this point" will be generated. like If the compaction degree is significantly lower than the design requirements, it will be judged as a serious non-compliance, generating a red non-compliance mark and the words "compliance degree is significantly lower than the design requirements, and immediate rework is required". The GPS location of the non-compliance point will be automatically marked on the electronic map. S54. Conduct a three-level assessment of the overall quality of the engineering components: If the overall pass rate is ≥95% and there are no serious non-conformities, the overall quality is judged to be excellent. If the overall pass rate is 90% ≤ overall pass rate < 95% and there are no serious non-conformities, then the overall quality is deemed to be qualified. If the overall pass rate is less than 90% or there are serious non-conformities, the overall quality is deemed unqualified, and an overall quality rectification notice will be generated. S55. Integrate the raw data, calculation process, parallel difference verification, confidence assessment results, single-point judgment conclusions and overall quality judgment conclusions of moisture content, wet density, dry density and compaction degree to generate a standardized test result page; S56. Automatically generate visualization charts of test data, including single-group data bar charts, parallel test trend charts, regional compaction contour maps, and engineering part quality heat maps, and display them in real time on the terminal interaction module.

[0039] The data management module is used for unified storage, categorized querying, batch management, and secure encryption of all detection records. Employing AES-256 encrypted non-volatile memory, it can store at least 100,000 complete detection records; Each test record includes a unique test number, test time, GPS location information, tester's name, project location, soil type, environmental parameters, measured basic data, outlier identification record, intermediate calculation results, final test result, confidence level, single-point judgment conclusion, overall quality judgment conclusion, and operation log; It supports multi-condition search and retrieval by testing time, soil sample number, testing personnel, project location, and testing results; It supports modification and deletion of detection records, and all modification and deletion operations automatically generate tamper-proof audit logs; It supports automatic backup and cloud synchronization of detection data, and automatically synchronizes local data to the cloud server after the network is restored; The built-in quality trend analysis unit can automatically analyze the test data of the past 7, 30, and 90 days, calculate the moving average, standard deviation, and coefficient of variation of compaction, and plot the quality trend curve; when the standard deviation of compaction exceeds the preset threshold or shows a downward trend for 3 consecutive days, it automatically issues an early warning of abnormal construction process. Figure 4 As shown, the compaction quality trend analysis chart generated by the system includes three core curves: the solid line represents the daily measured compaction trend line, the dashed line represents the compaction moving average line, and the dotted line represents the design requirement baseline. The chart displays the dynamic changes in compaction over 13 consecutive days, and automatically generates trend analysis conclusions below the chart. This chart can intuitively reflect the fluctuation patterns and overall trends in construction quality, providing quantitative data support for optimizing construction processes and adjusting quality control measures.

[0040] The terminal interaction module is used to provide a visual interactive interface between the user and the system, enabling data input, function selection, result display, and system setting operations.

[0041] The soil sampling system is integrated into a portable integrated testing box. The portable integrated testing box includes a main body, the interior of which is divided into an equipment storage area, a shock-absorbing weighing area, and a sealed, windproof operating area. Figure 5 As shown, the portable integrated testing box adopts an integrated design. The exterior of the box features a foldable pull rod, casters, and leveling feet for easy field transport and on-site leveling. The interior is divided into four functional areas: the equipment storage area has EVA foam fixing slots precisely adapted to the shape of the testing tools, used to store specialized tools such as ring cutters, aluminum boxes, soil shovels, and soil cutting knives; each slot corresponds to a unique QR code. The shock-absorbing weighing area is equipped with a flexible silicone shock-absorbing base and a 0.01g precision electronic balance, effectively isolating the impact of field vibrations on weighing accuracy. The electronic balance communicates with the system for automatic data transmission. The terminal interaction area is equipped with a touch screen and an industrial tablet computer, integrating a high-capacity mobile power supply to provide continuous power to all equipment. The enclosed windproof operating area features an openable magnetic transparent windproof enclosure with built-in temperature, humidity, and air pressure sensors, enabling the determination of moisture content using the alcohol combustion method in harsh field environments such as strong winds.

[0042] The equipment storage area is equipped with multiple EVA foam fixing slots that are compatible with the shape of the soil sample testing tools, and each slot corresponds to a unique QR code. The vibration-damping weighing area is equipped with a flexible silicone vibration-damping base and an electronic balance, which is connected to the soil sample testing system. The enclosed, windproof work area is equipped with an openable, magnetically attached transparent windproof enclosure, and temperature, humidity, and air pressure sensors are installed inside the work area.

[0043] Field application methods of soil sampling testing systems: The field application method of the soil sample testing system includes the following steps: like Figure 2 As shown, this system realizes a closed-loop operation of the entire geotechnical testing process, from site preparation to data archiving, consisting of 10 standardized core steps: site preparation and equipment handling, system startup and equipment calibration, scanning codes to retrieve equipment parameters, undisturbed soil sample collection and preparation, ring sampler and aluminum box weighing, alcohol combustion method for moisture content determination, input of working condition parameters and initiation of testing, automatic system calculation and result judgment, generation of test reports and blockchain notarization, and equipment cleaning and data synchronization to the cloud. The entire process requires no manual calculation or report compilation and can complete all testing work at a single site within 30 minutes.

[0044] Step 1: Transport the portable integrated testing box to the testing site using the foldable pull rod and casters, place it on a level and solid ground, step on the brake device of the casters to fix the box, and adjust the leveling feet at the bottom of the box to keep the box level. Step 2: Open the top cover of the box, turn on the soil sample testing system and electronic balance. The system will automatically perform self-test and connect the equipment. The temperature and humidity sensor and the air pressure sensor will automatically collect the on-site environmental parameters and transmit them to the data input module. Step 3: Use the terminal interaction module to scan the QR codes on the ring cutter and aluminum box in the device storage area. The system will automatically retrieve the pre-stored standard volume of the ring cutter and the weight of the box. Step 4: Place the standard weight on the electronic balance for calibration, and confirm that the weighing accuracy of the electronic balance reaches 0.01g; Step 5: Take out the soil shovel, soil cutting knife and brush from the equipment storage area, dig a test pit in the area to be tested according to the specifications, collect a representative original soil sample, and smooth the surface of the soil sample. Step 6: Apply a thin layer of Vaseline evenly to the inner wall of the ring cutter. Place the ring cutter vertically with the cutting edge facing down on the prepared soil sample surface. Use a rubber mallet to slowly and vertically drive the ring cutter into the soil until the soil sample is completely extended from the top of the ring cutter. Step 7: Carefully flatten the soil sample at both ends of the ring cutter with a soil-cutting knife, wipe the outer wall of the ring cutter clean, place the ring cutter containing the soil sample on an electronic balance for weighing, and the electronic balance will automatically transmit the weighing result to the soil sample testing system. Step 8: Open the ring cutter, use a soil cutting knife to take 20-30g of representative soil sample from the middle of the ring cutter and put it into an aluminum box with known mass. Cover the box and place the aluminum box on an electronic balance for weighing. The electronic balance will automatically transmit the weighing result to the soil sample testing system. Step 9: Flip up to open the magnetic transparent windproof enclosure of the sealed windproof work area, place the aluminum box containing the soil sample into the work area, open the lid, and use a dropper to add an appropriate amount of alcohol to the soil sample in the aluminum box until the soil sample is completely soaked in alcohol. Step 10: Ignite the alcohol with a lighter and wait for the flame to extinguish naturally. Then, gently stir the soil sample with a glass rod, add a small amount of alcohol again and ignite it. Repeat this burning process 3 times until the soil sample is completely dry. Step 11: After the aluminum box has cooled to room temperature, close the lid and place the aluminum box on the electronic balance for weighing. The electronic balance will automatically transmit the weighing result to the soil sample testing system. Step 12: On the terminal interaction module, select the soil type and engineering location to be tested, and input the maximum dry density of the soil sample, the design compaction requirement, and the organic matter content of the soil sample; Step 13: Click the Start Detection button. The system will automatically start the intelligent outlier identification and correction module, the multi-parameter linkage calculation module, and the result judgment and output module to complete the entire process of calculation and judgment. Step 14: After the test is completed, the system automatically generates a standardized test result page and test report. Users can view the detailed calculation process, confidence assessment results, single-point judgment conclusions, regional compaction distribution and overall quality judgment conclusions. Step 15: If you need to export the report, click the Export Report button, select the export format and export method, and the system will automatically generate and export a test report containing the blockchain evidence number. Step 16: If the test result is unqualified, the system will automatically generate a rectification notice and prompt the user to double the sampling near the unqualified point for retesting; Step 17: Clean up the combustion residue in the enclosed and windproof work area, pour the waste liquid in the waste liquid collection box into the designated container, wipe all tools and equipment clean and put them back into the corresponding slots in the equipment storage area, close the top cover of the box, release the universal wheel brakes, and move the box to the designated storage location. Step 18: The system automatically synchronizes the test data to the cloud server to complete data backup and blockchain evidence storage; Step 19: The cloud server automatically summarizes and analyzes all the test data for this part of the project, generates a monthly quality analysis report, and sends it to the project administrator and the supervision unit.

[0045] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0046] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A hydraulic earth sample detection system, characterized in that, It includes a data input module, an intelligent outlier identification and correction module, a multi-parameter linkage calculation module, a multi-condition parameter adaptation module, a result judgment and output module, a data management module, and a terminal interaction module; The data input module is used to receive the measured basic data and environmental parameters of soil sample testing input by the user, supports QR code input and voice input, and performs preliminary verification of the format and validity of the input data; The intelligent outlier identification and correction module is based on the weighted Grubbs test and historical detection data model. It performs statistical analysis on the input measured basic data, automatically identifies outliers and provides correction suggestions, and records the process of identifying outliers. The multi-parameter linkage calculation module has a pre-stored full-process calculation formula that conforms to the standard of geotechnical testing methods. It can automatically complete the correlation calculation of moisture content, wet density, dry density and compaction degree, error propagation analysis and integration of parallel test results based on the input measured basic data and environmental parameters. The multi-condition parameter adaptation module has a built-in library of standard parameters for different soil types and different engineering parts, and can automatically adjust the calculation parameters, parallel difference threshold and judgment criteria according to the conditions selected by the user. The result determination and output module is used to compare the calculated test results with the preset standard threshold and engineering design requirements, automatically generate a pass / fail judgment conclusion including confidence level assessment, and output standardized test results and visualization charts in real time. The data management module is used for unified storage, categorized querying, batch management and secure encryption of all detection records; The terminal interaction module is used to provide a visual interactive interface between the user and the system, enabling data input, function selection, result display, and system setting operations.

2. The soil sample testing system for water conservancy projects according to claim 1, characterized in that, The data input module supports receiving measured basic data including box number and box quality. Quality of wet soil in boxes , box plus dry soil quality Ring cutter number, ring cutter standard volume at 20℃ quality of ring cutter quality of the ring cutter and moistened soil Maximum dry density of soil sample Design compaction requirements and soil organic matter content Supported environmental parameters include ambient temperature. On-site wind speed and atmospheric pressure at the scene ; The data input module has a built-in device QR code recognition function. Scanning the QR code on the ring cutter or aluminum box can automatically retrieve the pre-stored device number and standard parameters. It has a built-in voice recognition engine that supports voice data input during one-handed operation in the field; when inputting data, it automatically associates the current inspection number, engineering location and GPS positioning information to achieve one-to-one binding of inspection data with physical objects; it supports automatic collection of environmental parameters through external temperature and humidity sensors and barometric pressure sensors, without the need for manual input.

3. The soil sample testing system for water conservancy projects according to claim 2, characterized in that, The workflow of the intelligent outlier identification and correction module includes the following steps: S11. Receive data transmitted from the data input module. Measured data sequence of parallel experiments ,in This represents the current number of parallel experimental groups. ; S12. Call the data management module to obtain historical test data sequences for the same project location and soil type over the past 30 days. ,in This represents the number of historical test data sets. ; S13. Calculate the arithmetic mean of a historical data series. and standard deviation ; S14. Calculate the arithmetic mean of the current data sequence. and standard deviation ; S15. Calculate the weighted composite standard deviation : ； In the formula: The weighted average standard deviation is calculated by incorporating historical data. This represents the standard deviation of the current parallel test data; The standard deviation of historical testing data; This represents the current number of parallel experimental groups; This represents the number of historical test data sets. S16. Calculate the weighted Grubbs statistic for each data point. : ； In the formula: For the first The weighted Grubbs statistic for each data point; For the first Group current parallel test data; This is the arithmetic mean of the current parallel experimental data; The arithmetic mean of historical detection data; The weighted average standard deviation; This represents the current number of parallel experimental groups; This represents the number of historical test data sets. S17. The maximum weighted Grubbs statistic and the Grubbs critical value at the corresponding confidence level Compare: like If all data are valid, the data sequence is transmitted to the multi-parameter linkage calculation module. like If the data point is identified as an outlier, it will be automatically removed and a message indicating that an outlier has been detected and automatically removed will be displayed on the terminal interaction module. At the same time, the outlier and its identification process will be recorded. S18. Repeat steps S14-S17 until there are no outliers in the data sequence or the number of remaining data sets is less than 2. If the number of remaining data sets is less than 2, prompt the user to repeat the experiment.

4. The soil sample testing system for water conservancy projects according to claim 3, characterized in that, The moisture content calculation process of the multi-parameter linkage calculation module includes the following steps: S21. Receive the first value processed by the intelligent outlier identification and correction module. Box mass of parallel tests Quality of wet soil in boxes The quality of the box plus dry soil ,in This refers to the parallel experimental group number. ; S22. Calculate the first [item] based on the difference between the mass of the box containing moist soil and the mass of the box containing dry soil. Moisture mass of group experiment The first calculation is based on the difference between the mass of the box plus dry soil and the mass of the box. Dry soil mass of the group test ; S23. Calculate the first The original single-group moisture content of the group experiment : ； In the formula: For the first The original moisture content of the soil samples; For the first The mass of water in the soil sample group; For the first The mass of dry soil in the soil sample group; S24. Call the multi-condition parameter adaptation module to obtain the corresponding organic matter combustion loss coefficient based on the current soil type being tested. Allowable value of parallel difference with moisture content ; S25. Receive the soil sample organic matter content transmitted by the data input module. Calculate the first Corrected single-group moisture content of group experiments : ； In the formula: For the first Moisture content of soil samples after correction for organic matter combustion loss; For the first The original moisture content of the soil samples; This represents the organic matter combustion loss coefficient for the corresponding soil type. This refers to the organic matter content of the soil sample; S26. Calculate the range of all corrected individual moisture contents. Compare the range with the allowable value of parallel difference: ； In the formula: This represents the range of moisture content in a single group after correction. This represents the maximum value among all corrected single-group moisture contents; It is the minimum value among all corrected single-group moisture contents; like Then calculate the arithmetic mean of the corrected moisture content of all parallel test groups. This is used as the final moisture content result and transmitted to the dry density calculation unit; like If the result is negative, the user will be prompted that the moisture content parallel difference exceeds the allowable range of the specification and the test should be repeated.

5. The soil sample testing system for water conservancy projects according to claim 4, characterized in that, The calculation process for wet density and dry density of the multi-parameter linkage calculation module includes the following steps: S31. Receive the first value processed by the intelligent outlier identification and correction module. Standard volume of ring cutter at 20°C for group density test quality of ring cutter and the quality of the ring cutter and moist soil ,in This is the density test group number. ; S32. Receive the ambient temperature transmitted by the data input module. Calculate the actual volume of the ring cutter at the ambient temperature. : ； In the formula: For the on-site temperature The actual volume of the lower ring cutter; This refers to the calibration volume of the ring cutter at a standard temperature of 20°C. The coefficient of linear expansion of the ring cutter material is taken as the coefficient of linear expansion for stainless steel ring cutters. ; The ambient temperature at the site; S33. Call the multi-condition parameter adaptation module to adjust the parameters according to the current soil type and design compaction requirements. Obtain the corresponding ring cutter compression coefficient. ; S34. Calculate the effective volume of the ring cutter after compression correction. : ； In the formula: To correct the effective volume after the ring cutter is driven into the soil and compresses; This refers to the actual volume of the ring cutter at the ambient temperature. The compression coefficient is entered into the ring cutter corresponding to the soil type and compaction degree; To meet the design compaction requirements; S35. Calculate the first [item name] based on the difference between the mass of the wetted soil and the mass of the ring cutter. Wet mass of soil samples from the group test ; S36. Calculate the first Single group wet density of group test : ； In the formula: For the first The wet density of the soil samples; For the first Wet mass of soil samples within the ring cutter; This is the corrected effective volume of the ring cutter; S37. Call the multi-condition parameter adaptation module to obtain the corresponding allowable value of parallel difference in wet density based on the current soil type being tested. ; S38. Calculate the range of all individual wet densities. Compare the range with the allowable value of parallel difference: ； In the formula: This represents the range of a single group of wet densities; It is the maximum value among all individual wet densities; It is the minimum value among all individual wet densities; like Then calculate the arithmetic mean of the wet density of all parallel test groups. This is used as the final wet density result and transferred to the dry density calculation unit; like If the wet density parallel difference exceeds the allowable range specified, please repeat the test. S39 automatically retrieves the final moisture content result obtained in step S6. Calculate the final dry density of the soil sample. And transmit it to the compaction degree calculation unit: ； In the formula: This represents the dry density of the soil sample. This represents the wet density of the soil sample. This represents the moisture content of the soil sample.

6. The soil sample testing system for water conservancy projects according to claim 5, characterized in that, The compaction degree calculation and regional quality assessment process of the multi-parameter linkage calculation module includes the following steps: S41. Receive the maximum dry density of the soil sample transmitted by the data input module. and design compaction requirements ; S42. Automatically retrieve the final dry density result obtained in step S39. Calculate the actual compaction degree of the soil sample. : ； In the formula: This represents the actual compaction degree of the soil sample. This represents the dry density of the soil sample. This represents the maximum dry density of the soil sample. S43. Calculate the 95% confidence interval of the compaction degree results based on the dispersion of parallel tests: Standard deviation of dry density calculation : ； In the formula: This represents the standard deviation of the dry density. For the first Dry density of the soil samples; This is the final dry density result; This represents the number of valid parallel experimental groups. Calculate the 95% confidence interval for compaction degree: ； In the formula: This represents the actual compaction degree of the soil sample. For degrees of freedom The critical value of the t-distribution at a confidence level of 95%; This represents the standard deviation of the dry density. This represents the maximum dry density of the soil sample. This represents the number of valid parallel experimental groups. S44. Evaluate the confidence level of the results based on the confidence interval width: If the confidence interval width is ≤2%, the confidence level of the judgment result is high; If 2% < confidence interval width ≤ 5%, then the confidence level of the judgment result is medium. If the confidence interval width is greater than 5%, the confidence level of the result is considered low, and the user is prompted to increase the number of parallel test groups to improve the reliability of the results. S45. Call the data management module to obtain the compaction degree data and GPS coordinates of all detected points in the same engineering location, and use ordinary Kriging interpolation to calculate the compaction degree of any point in the engineering location. The estimated compaction degree : Calculate the experimental semivariogram : ； In the formula: The distance is The experimental semivariogram value at time; The spatial distance between the two detection points; The distance is The number of detection points; For the first The compaction degree at each testing point; In order to be with the first The distance between the detection points is The compaction degree at the test points; Fitting an exponential theoretical semivariogram model: ； In the formula: This is the theoretical semivariogram value; Value of a nugget; This is the base value; For variable range; The spatial distance between the two detection points; Solving the Kriging equations yields the interpolation weighting coefficients. ; Calculate the estimated compaction degree of the point to be estimated. : ； In the formula: Points to be estimated The estimated compaction degree; For the first Interpolation weight coefficients for each known detection point; For the first The compaction degree at a known test point; S46. Calculate the overall compaction pass rate of this part of the project based on the interpolation results. : ； In the formula: The overall compaction pass rate of the engineering section; For compaction degree ≥ The area of ​​the region; This refers to the total inspection area of ​​the engineering section; S47. Calculate the actual compaction degree at a single point. The confidence interval, confidence level, regional compaction distribution, and overall pass rate are transmitted to the result judgment and output module.

7. A soil sample testing system for water conservancy projects according to claim 6, characterized in that, The result determination and output module includes the following steps: S51. Receive the single-point actual compaction degree transmitted by the multi-parameter linkage calculation module. Confidence interval, confidence level, regional compaction distribution, and overall pass rate; S52. Call the multi-condition parameter adaptation module to obtain the corresponding design compaction requirements based on the current engineering location. and risk classification thresholds; S53. A four-level risk classification assessment of single-point compaction degree is performed: like If the result is satisfactory, a green qualified label and the words "test qualified" will be generated. like If the compaction is slightly below the design requirements, it will be judged as a minor non-compliance, generating a yellow warning label and a message indicating that the compaction degree is slightly lower than the design requirements and that it is recommended to strengthen subsequent construction control. like If the compaction is below the design requirement, it will be judged as a general non-compliance, and an orange warning label and the words "double sampling and re-inspection required near this point" will be generated. like If the compaction degree is significantly lower than the design requirements, it will be judged as a serious non-compliance, generating a red non-compliance mark and the words "compliance degree is significantly lower than the design requirements, and immediate rework is required". The GPS location of the non-compliance point will be automatically marked on the electronic map. S54. Conduct a three-level assessment of the overall quality of the engineering components: If the overall pass rate is ≥95% and there are no serious non-conformities, the overall quality is judged to be excellent. If the overall pass rate is 90% ≤ overall pass rate < 95% and there are no serious non-conformities, then the overall quality is deemed to be qualified. If the overall pass rate is less than 90% or there are serious non-conformities, the overall quality is deemed unqualified, and an overall quality rectification notice will be generated. S55. Integrate the raw data, calculation process, parallel difference verification, confidence assessment results, single-point judgment conclusions and overall quality judgment conclusions of moisture content, wet density, dry density and compaction degree to generate a standardized test result page; S56. Automatically generate visualization charts of test data, including single-group data bar charts, parallel test trend charts, regional compaction contour maps, and engineering part quality heat maps, and display them in real time on the terminal interaction module.

8. A soil sample testing system for water conservancy projects according to claim 7, characterized in that, The soil sample testing system for water conservancy projects is integrated and installed inside a portable integrated testing box.

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