A soil density compaction degree compaction detection method and system

Abstract

The application discloses a kind of soil density compaction degree compaction detection method and system, it is related to soil detection related technical field, method includes: through scanning module, unique identification code on sample carrier is automatically identified, and is associated with test task;Sample is placed after automatically triggering weighing, mass data is bound and transmitted to data processing terminal with identification code;Terminal calls corresponding reference parameter from pre-stored parameter library according to identification code, then utilizes built-in multilevel calculation engine, combines mass data, reference parameter and dynamically selected physical formula and parallelly calculates soil density, compaction degree and compaction characteristic parameter;Cross logic verification output detection result.Solve the technical problems that exist in prior art, detection data record is prone to misplacement, parameter matching efficiency is low, calculation process lacks automatic check, achieve to realize detection whole process automation, data accurate association, parameter intelligent matching, improve soil detection efficiency, result reliability and consistency technical effect.

Description

Technical Field

[0001] This invention relates to the field of soil testing technology, specifically to a method and system for testing soil density and compaction. Background Technology

[0002] Density compaction and compaction tests in geotechnical testing are crucial for evaluating the quality of backfill projects and controlling the compaction effect during construction. Traditional testing mainly relies on manual operation, discrete data recording, and step-by-step calculations, which suffers from cumbersome procedures, high risk of human error, poor data traceability, and low computational efficiency. This is especially true in high-volume, high-frequency engineering testing scenarios, where testing is not efficient or accurate enough. Currently, most testing equipment is semi-automated, and sample information recording relies on manual input, which is prone to confusion in numbering and data misalignment. The separation of quality measurement and calculation processes requires testers to repeatedly check and transcribe between different devices or paper records, which may lead to inaccurate final results due to oversights in intermediate steps. In addition, soil types vary, and corresponding benchmark material parameters such as maximum dry density and optimum moisture content differ. Manually finding matching parameters is prone to errors, and real-time logical verification of the calculation process is not possible, affecting the reliability and consistency of soil test results.

[0003] Therefore, current technologies suffer from technical problems such as easy misalignment of detection data records, low efficiency of parameter matching, and lack of automatic verification in the calculation process. Summary of the Invention

[0004] This application provides a soil density and compaction degree testing method and system, which solves the technical problems of easy misalignment of test data recording, low parameter matching efficiency, and lack of automatic verification in the calculation process in the prior art. It achieves the technical effects of realizing full automation of the testing process, accurate data correlation, intelligent parameter matching, and improving soil testing efficiency, reliability and consistency.

[0005] This application provides a method for testing soil density, compaction degree, and compaction characteristics. The method includes: automatically identifying a unique identifier attached to a sample carrier using a scanning module and uniquely associating the identifier with the test task; automatically triggering a weighing module after sample placement to collect mass data, binding the mass data with the unique identifier, and transmitting it to a data processing terminal; the data processing terminal receiving the mass data and automatically retrieving and calling the corresponding reference parameter set from a pre-stored standard material parameter library based on the unique identifier; and using a multi-level calculation engine built into the data processing terminal, performing parallel calculations of soil density, compaction degree, and compaction characteristics-related parameters at the core calculation layer based on the mass data, the reference parameter set, and dynamically selected physical calculation formulas, wherein the calculations involve cross-logic verification of intermediate results to obtain the test results.

[0006] In a possible implementation, the soil density compaction test method further performs the following processing: integrating the unique identifier, original mass data, reference parameters, all intermediate calculation results and final parameters, automatically generating a structured complete test record and storing or outputting it.

[0007] In a possible implementation, the soil density compaction degree test method further performs the following processing: real-time monitoring of the sensor status of the weighing module; when the status indicator confirms that the module is in a stable and ready state, triggering a data acquisition action, wherein the acquired mass data includes at least the stable mass value and the corresponding unit of measurement information, and together with the acquisition timestamp, constitutes a data packet.

[0008] In a possible implementation, the soil density compaction degree compaction detection method further performs the following processing: the data processing terminal parses the unique identifier and the reference parameter set called to determine the target parameter set to be calculated. The target parameter set includes at least: soil wet density, dry density, moisture content, compaction degree, and compaction curve characteristic parameters. Based on the physical dependencies and computational complexity between the parameters, the calculation task of the target parameter set is dynamically mapped to a core computing layer composed of at least two independent logical computing units. The first logical computing unit is dedicated to direct density calculation based on mass and volume, and the second logical computing unit is dedicated to iterative optimization calculation based on physical models and historical data.

[0009] In a possible implementation, the soil density and compaction degree compaction detection method further performs the following processing: parsing the unique identifier and the reference parameter set called by the data processing terminal to determine the target parameter set to be calculated, which includes at least: soil wet density, dry density, moisture content, compaction degree, and compaction curve characteristic parameters; determining the physical calculation formula based on the physical dependencies and computational complexity between the parameters in the target parameter set, deploying it to a multi-level core computing layer, taking the quality data and the reference parameter set as input, and performing parallel computation processing through the core computing layer to obtain intermediate calculation results.

[0010] In a possible implementation, the soil density compaction degree testing method further performs the following processing: the multi-level core calculation layer includes at least a first logical calculation unit and a second logical calculation unit, wherein the first logical calculation unit and the second logical calculation unit synchronously start calculation based on the shared mass data and reference parameter set, the first logical calculation unit is used to directly calculate the density based on mass and volume, and the second logical calculation unit is used to iteratively optimize the calculation based on the physical model and historical data.

[0011] In a possible implementation, the soil density and compaction degree compaction test method further performs the following processing: the first logical calculation unit directly calculates and generates a first intermediate result set based on the physical formula corresponding to the dynamically selected volume measurement method, which includes at least: a first wet density, a first dry density, and a first compaction degree reference value calculated based on the first dry density and the benchmark maximum dry density; the second logical calculation unit calls a preset soil-water characteristic relationship model and a compaction curve prediction model, and performs iterative inversion with the quality data as constraints to generate a second intermediate result set, which includes at least: a second moisture content, a second dry density, a second compaction degree, and a compaction curve curvature coefficient; the first intermediate result set and the second intermediate result set are pushed to the shared area in real time, with timestamps and calculation path tags attached.

[0012] In a possible implementation, the soil density compaction test method further performs the following processing: continuously monitoring the shared area, acquiring the first intermediate result set and the second intermediate result set, and performing parameter cross-validation through built-in validation rules, including moisture content-density correlation validation, compaction degree convergence validation, and compaction curve trend validation; based on the conflict combinations triggered by the validation results of each rule, issuing feedback instructions to the core computing layer to eliminate conflicts, and after multiple rounds of validation until all conflict flags are eliminated, or after reaching the maximum number of iterations, outputting a set of optimal values ​​from the final stable intermediate result set as the final test result according to a predetermined synthesis strategy, and assigning a quantitative confidence level to the test result based on the number of conflict triggers, the final deviation size, and the iteration convergence speed during the validation process; wherein, the predetermined synthesis strategy is to preferentially use the second dry density value and the second moisture content value calculated by the second logic computing unit as the final dry density and moisture content; the final compaction degree is calculated from the final dry density and the standard maximum dry density in the benchmark parameter set; the compaction curve characteristic parameters are directly taken from the second intermediate result set.

[0013] In a possible implementation, the soil density compaction degree test method further performs the following processing: using a barcode scanning component with a three-dimensional spatial position adjustment structure to acquire an image of the unique identifier; controlling an auxiliary positioning device to project a positioning pattern onto the unique identifier area to assist in focusing and positioning; recognizing and analyzing the acquired image; and comparing the data fields identified and analyzed with predefined test task information to confirm the validity of the sample binding status.

[0014] The validity identification code will be uniquely associated with the test task.

[0015] This application also provides a soil density and compaction degree compaction testing system, the system comprising: an identification code recognition unit, used to automatically identify a unique identification code attached to the sample carrier through a scanning module, and uniquely associate the identification code with the test task; a mass data acquisition unit, used to automatically trigger a weighing module after the sample is placed, acquire mass data, bind the mass data with the unique identification code, and transmit it to a data processing terminal; a parameter retrieval unit, used by the data processing terminal to receive the mass data, and automatically retrieve and call the corresponding benchmark parameter set from a pre-stored standard material parameter library according to the unique identification code; and a test result acquisition unit, used by the multi-level calculation engine built into the data processing terminal, based on the mass data, the benchmark parameter set, and dynamically selected physical calculation formulas, to perform parallel calculations of soil density, compaction degree, and compaction characteristics-related parameters in the core calculation layer, wherein the calculation is performed by cross-logic verification through intermediate results to obtain the test result.

[0016] This application proposes a method and system for testing soil density, compaction degree, and compaction characteristics. The system automatically identifies a unique identifier on the sample carrier using a scanning module and associates it with the test task. After sample placement, weighing is automatically triggered, and the mass data is bound to the identifier and transmitted to a data processing terminal. The terminal retrieves the corresponding baseline parameters from a pre-stored parameter library based on the identifier. Then, using a built-in multi-level calculation engine, it calculates soil density, compaction degree, and compaction characteristics in parallel, combining the mass data, baseline parameters, and dynamically selected physical formulas. The test results are then output after cross-logic verification. This method solves the technical problems of easy misalignment in data recording, low parameter matching efficiency, and lack of automatic verification in the calculation process in existing technologies. It achieves full automation of the testing process, accurate data association, intelligent parameter matching, and improves soil testing efficiency, reliability, and consistency. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0018] Figure 1 This is a schematic diagram of a soil density compaction test method provided in an embodiment of this application.

[0019] Figure 2 This is a schematic diagram of a soil density compaction degree testing system provided in an embodiment of this application.

[0020] Figure 3 This is a schematic diagram of the terminal structure of a soil density compaction degree testing system provided in an embodiment of this application.

[0021] Figure 4 This is a schematic diagram of the product structure for soil density compaction testing provided in an embodiment of this application.

[0022] Explanation of reference numerals in the attached figures: Identification code unit 10, quality data acquisition unit 20, parameter retrieval unit 30, and detection result acquisition unit 40. Detailed Implementation

[0023] To further illustrate the technical means and effects adopted by the present invention in order to achieve the intended purpose, the following detailed description is provided in conjunction with the accompanying drawings and preferred embodiments, based on the specific implementation methods, structures, features and effects of the present invention.

[0024] This application provides a method for detecting soil density and compaction degree, such as... Figure 1 As shown, the method includes:

[0025] Step S100: The scanning module automatically identifies the unique identification code attached to the sample carrier and uniquely associates the identification code with the test task.

[0026] Preferably, in the initial stage of detection, the optical scanner or radio frequency reader built into the scanning module is used to automatically read the displacement identification code, such as a QR code, barcode or RFID tag, pre-set on the surface of the container or vessel carrying the soil sample. The code is then parsed into a character sequence and uniquely associated with the detection test task. That is, the sample container and all its related measurement data and calculation process are associated with a specific, non-repeatable test task record through a unique code, ensuring the integrity and traceability of the data chain.

[0027] Furthermore, step S100 also includes step S110, using a barcode scanning component with a three-dimensional spatial position adjustment structure to acquire an image of the unique identifier; step S120, controlling an auxiliary positioning device to project a positioning pattern onto the unique identifier area to assist in focusing and positioning, and recognizing and analyzing the acquired image; step S130, comparing the recognized and analyzed data fields with predefined test task information to confirm the validity of the sample binding status; and step S140, uniquely associating the valid identifier with the test task.

[0028] Preferably, a barcode scanning assembly equipped with a three-dimensional spatial position adjustment mechanism is used. For example, it may be equipped with a robotic arm or slide capable of vertical, horizontal, forward, backward, and angular rotation. This automatically adjusts the camera's spatial position and angle relative to the sample carrier, ensuring that the camera can clearly capture the surface regardless of the carrier's placement. Then, the unique identifier on the surface is imaged. At this point, an auxiliary positioning device, such as a laser or structured light projector, projects a positioning pattern, such as crosshairs, a grid, or a dot matrix, onto the unique identifier area to assist focusing and positioning. This helps visual recognition quickly and accurately locate the boundary and center of the identifier, and provides... The camera's autofocus system provides distance and contour references to ensure that the acquired images are clear and distortion-free. Next, the acquired images are analyzed to extract the data fields encoded in the identifier. These data fields are then compared with predefined experimental task information to verify whether the information represented by the identifier matches the expected information of the currently valid, pending experimental task. Finally, if the identifier information completely matches a predefined experimental task in a "pending binding" or "in progress" state, the identifier is deemed valid, and a unique one-to-one association is established between this unique identifier and the experimental task in the database.

[0029] Step S200: After the sample is placed, the weighing module is automatically triggered to collect mass data, and the mass data is bound with the unique identifier and transmitted to the data processing terminal.

[0030] Preferably, when the operator places the soil sample carrier on the weighing module's platform, the photoelectric sensor and pressure sensor switch automatically detect the "sample in place" status change and immediately send a command to the weighing module to start weighing. The internal sensors measure the mass data and continuously read the sensor output until the measurement value stabilizes, obtaining stable mass data, units of measurement, and recording the corresponding timestamp. Then, the mass data is associated with the carrier's unique identification code and finally automatically sent to the data processing terminal via USB, Ethernet, Wi-Fi, or Bluetooth, ensuring that each piece of mass data is correctly assigned to the correct sample and test task.

[0031] Furthermore, step S200 also includes real-time monitoring of the sensor status of the weighing module. When the status indicator confirms that the module is in a stable and ready state, a data acquisition action is triggered. The acquired mass data includes at least the stable mass value and the corresponding unit of measurement information, and together with the acquisition timestamp, they form a data packet.

[0032] Preferably, the sensor status of the weighing module is monitored in real time. When the measurement signal no longer fluctuates or drifts, does not exceed the effective range, and is not affected by environmental vibration or airflow, and the change amplitude of the mass reading output by the sensor is continuously less than the preset minimum stability threshold, it is determined that the status indicator of the weighing module has reached a stable and ready state. Then, the data acquisition action is triggered to record and lock the current stable mass reading as the valid measurement value. The acquired mass data is a structured data packet, which includes at least the stable mass value, the corresponding unit of measurement information, and the acquisition timestamp. This fundamentally avoids random errors introduced by instrument fluctuations, premature human readings, or environmental interference, while ensuring the integrity and traceability of the data.

[0033] In step S300, the data processing terminal receives the quality data and automatically retrieves and calls the corresponding reference parameter set from the pre-stored standard material parameter library according to the unique identifier.

[0034] Preferably, the data processing terminal receives and parses quality data through a communication interface, extracts a unique identifier as a key to locate and identify all relevant information of the sample, and then automatically retrieves it from a pre-stored standard material parameter library using the unique identifier as a query condition. The standard material parameter library stores standard physical parameters corresponding to various soil types, test standards, or specific engineering tasks. It identifies and determines the records associated with the unique identifier and extracts the reference parameters from them, including at least the standard maximum dry density, optimum moisture content, soil type, specific parameters of the compaction standard used, and particle size distribution necessary for calculating compaction degree.

[0035] Furthermore, step S300 also includes the data processing terminal parsing the unique identifier and the reference parameter set called to determine the target parameter set to be calculated. The target parameter set includes at least: soil wet density, dry density, moisture content, compaction degree, and compaction curve characteristic parameters. Based on the physical dependencies and computational complexity between the parameters, the calculation task of the target parameter set is dynamically mapped to a core computing layer composed of at least two independent logical computing units. The first logical computing unit is dedicated to the direct calculation of density based on mass and volume, and the second logical computing unit is dedicated to the iterative optimization calculation based on physical models and historical data.

[0036] Preferably, the data processing terminal determines the multiple final output parameters to be solved in this experiment, i.e., the target parameter set, based on the received unique identifier and the reference parameter set. These parameters include at least soil wet density, dry density, moisture content, compaction degree, and compaction curve characteristic parameters. Soil wet density is the mass of a unit volume of wet soil, dry density is the mass of a unit volume of dry soil, moisture content is the ratio of the mass of water in the soil to the mass of dry soil, compaction degree is the ratio of the measured dry density to the standard maximum dry density, and compaction curve characteristic parameters are characteristic values ​​describing the relationship between moisture content and dry density in the compaction test, such as optimum moisture content, maximum dry density, and curve shape coefficient. Then, the physical dependencies between the parameters are analyzed. For example, calculating dry density requires first determining wet density and moisture content, and calculating compaction degree requires first determining dry density and the standard maximum dry density. The complexity of calculating each parameter is then analyzed. For instance, wet density can be directly obtained from mass and volume, which is a simple calculation, while accurately fitting the compaction curve through a limited number of measuring points and obtaining the characteristic parameters constitutes a complex calculation.

[0037] Preferably, based on the analysis results of physical dependencies and computational complexity, the entire computational task is dynamically decomposed and mapped to different processing units of its core computational layer. The core computational layer consists of at least two independent logical computational units. The first logical computational unit is dedicated to the direct calculation of density based on mass and volume, that is, using the known total mass of wet soil and sample volume, directly calculating the wet density, dry density, and preliminary compaction degree according to physical formulas. The second logical computational unit is dedicated to the iterative optimization calculation based on physical models and historical data, that is, using known mass data, combined with a pre-set soil-water characteristic physical model and possible historical test data, iteratively inverting and optimizing to solve for the water content and dry density, and further calculating the characteristic parameters of the compaction curve. Through parallel computing processing, the computational efficiency is improved while taking into account the accuracy and reliability of the test results.

[0038] Step S400: Using the multi-level computing engine built into the data processing terminal, based on the quality data, the benchmark parameter set, and the dynamically selected physical calculation formula, the calculation of soil density, compaction degree, and compaction characteristics related parameters is performed in parallel at the core computing layer. During the calculation, the intermediate results are cross-validated to obtain the detection results.

[0039] Step S400 further includes parsing the unique identifier and the reference parameter set called by the data processing terminal to determine the target parameter set to be calculated, which includes at least: soil wet density, dry density, moisture content, compaction degree, and compaction curve characteristic parameters; determining the physical calculation formula based on the physical dependencies and computational complexity between the parameters in the target parameter set, deploying it to a multi-level core computing layer, taking the quality data and the reference parameter set as input, and performing parallel computation processing through the core computing layer to obtain intermediate calculation results.

[0040] Preferably, the data processing terminal parses the unique identifier and the reference parameter set to be called to determine the target parameter set to be calculated, and analyzes the physical dependencies and computational complexity among the parameters in the target parameter set. Then, it dynamically selects the physical calculation formula to be used from the formula library. For example, for different volume measurement methods such as the ring cutter method and the sand cone method, the corresponding density calculation formula is selected and deployed to the multi-level core calculation layer. Then, through the multi-level calculation engine built into the data processing terminal, the mass data and the reference parameter set are used as input. In the two independent logical calculation units contained in the core calculation layer, the calculation of soil density, compaction degree and compaction characteristics related parameters are performed in parallel. This includes calculating wet density from directly measured volume and mass, quickly estimating water content and corresponding dry density, and determining water content, dry density and fitting compaction curve curvature coefficient through iterative inversion of soil-water characteristic model, as intermediate calculation results. Then, cross-logic verification is performed on the intermediate results. This involves cross-comparing and checking the logical consistency of intermediate results from different parallel logic computing units. For example, it verifies the reasonableness of the range of moisture content and density values, whether the calculated dry density and moisture content satisfy the mass-volume-moisture content relationship, and whether the relationship between the calculated compaction degree and moisture content conforms to the compaction curve trend. If cross-verification finds logical conflicts between different intermediate calculation results, such as the difference in calculated dry density values ​​exceeding the allowable error, it may trigger the logic computing unit to recalculate or iteratively fine-tune, or to weight and fuse the intermediate calculation results according to different calculation accuracies. When all intermediate calculation results reach logical consistency, they are then synthesized into the final detection result output, including soil wet density, dry density, moisture content, compaction degree, and compaction characteristic parameters, thereby ensuring the reliability and scientific validity of the final result.

[0041] Furthermore, step S400 also includes a multi-level core computing layer comprising at least: a first logical computing unit and a second logical computing unit, wherein the first logical computing unit and the second logical computing unit synchronously start computing based on the shared mass data and reference parameter set, the first logical computing unit is used to directly calculate the density based on mass and volume, and the second logical computing unit is used to iteratively optimize the calculation based on the physical model and historical data.

[0042] Preferably, the multi-level core computing layer includes at least a first logical computing unit and a second logical computing unit. The first logical computing unit and the second logical computing unit start computing synchronously based on shared mass data and a benchmark parameter set to achieve task-level parallel processing and improve computing efficiency. The first logical computing unit is used for direct density calculation based on mass and volume. The second logical computing unit is used for iterative optimization calculation based on physical models and historical data. For example, it calls a pre-set soil-water characteristic relationship physical model, uses measured mass data as constraints, and repeatedly adjusts the water content and dry density in the model using the least squares method until the difference between the model-predicted data and the measured data is minimized. During the iteration process, reasonable initial values ​​or constraint boundaries may be set with reference to historical experimental data.

[0043] Furthermore, step S400 also includes the following: the first logical calculation unit directly calculates and generates a first intermediate result set based on the physical formula corresponding to the dynamically selected volume measurement method, which includes at least: a first wet density, a first dry density, and a first compaction reference value calculated based on the first dry density and the benchmark maximum dry density; the second logical calculation unit calls a preset soil-water characteristic relationship model and a compaction curve prediction model, and performs iterative inversion with the quality data as constraints to generate a second intermediate result set, which includes at least: a second moisture content, a second dry density, a second compaction degree, and a compaction curve curvature coefficient; the first intermediate result set and the second intermediate result set are pushed to the shared area in real time, with timestamps and calculation path tags attached.

[0044] Preferably, the volume measurement method used in the current test is determined, and a physical calculation formula that precisely corresponds to it is dynamically selected from the formula library. Then, the calculation is performed using mass data and sample volume. This includes, for example, directly calculating the first wet density using the formula wet density = total mass of wet soil / sample volume, calculating the first dry density using the formula dry density = wet density (1 + moisture content), and calculating the first compaction reference value = (first dry density / standard maximum dry density) × 100% using the standard maximum dry density in the reference parameter set, and directly generating the first intermediate result set. The second logical calculation unit invokes a pre-set soil-water characteristic relationship model and a compaction curve prediction model. The soil-water characteristic relationship model describes the physical relationship between soil moisture content and its state parameters, while the compaction curve prediction model describes the variation of soil dry density with moisture content under compaction energy. Iterative inversion is then performed using mass data as constraints, continuously adjusting the moisture content and dry density in the model until the error between the model's predicted soil sample mass and the measured mass data is minimized. This generates a second intermediate result set, which includes at least the second moisture content, second dry density, second compaction degree, and compaction curve curvature coefficient. Finally, the first and second intermediate result sets are pushed to the shared area in real time for integration, with timestamps and calculation path tags added to obtain the final intermediate calculation results.

[0045] Furthermore, step S400 also includes continuously monitoring the shared area, acquiring the first intermediate result set and the second intermediate result set, and performing parameter cross-validation through built-in validation rules, including moisture content-density correlation validation, compaction degree convergence validation, and compaction curve trend validation; based on the conflict combinations triggered by the validation results of each rule, issuing feedback instructions to the core computing layer to eliminate conflicts, and after multiple rounds of validation until all conflict flags are eliminated, or after reaching the maximum number of iterations, outputting a set of optimal values ​​from the final stable intermediate result set as the final detection result according to a predetermined synthesis strategy, and assigning a quantitative confidence level to the detection result based on the number of conflict triggers, the final deviation size, and the iteration convergence speed during the validation process; wherein, the predetermined synthesis strategy is to preferentially use the second dry density value and the second moisture content value calculated by the second logic computing unit as the final dry density and moisture content; the final compaction degree is calculated from the final dry density and the standard maximum dry density in the benchmark parameter set; the compaction curve characteristic parameters are directly taken from the second intermediate result set.

[0046] Preferably, the first and second intermediate result sets in the shared area are continuously monitored, and parameter cross-validation is performed using built-in validation rules. This involves comparing and logically checking the two sets of results based on built-in validation rules derived from soil mechanics principles. These built-in validation rules include moisture content-density correlation validation, compaction degree convergence validation, and compaction curve trend validation. Specifically, the moisture content-density correlation validation checks whether the moisture content and dry density in the two sets of results satisfy the basic physical relationship. For example, the wet density of the first intermediate result set and the moisture content of the second intermediate result set are used to calculate the validation dry density, which is then compared with the dry density of the second intermediate result set to determine if the deviation is within the allowable range. The compaction degree convergence validation checks whether the difference between the first compaction degree reference value and the second compaction degree is reasonable. The compaction curve trend validation checks whether the moisture content and dry density values ​​obtained from the second result set conform to the basic trend of the compaction curve.

[0047] Preferably, if any built-in verification rule fails, it indicates a logical conflict. In this case, a specific conflict flag is generated, and a feedback instruction is sent to the core computing layer to eliminate the conflict based on the triggered conflict combination. This may include adjusting model parameters, changing the initial iteration value, narrowing the search range, or requiring the logic computing unit to recalculate. After receiving the feedback instruction, the second logic computing unit performs a new round of iterative optimization calculation based on the adjusted conditions, generates an updated second intermediate result set, and pushes it back into the shared area. The verification is repeated until all conflict flags are eliminated or the maximum number of iterations is reached. According to the predetermined synthesis strategy, a set of optimal values ​​is output from the final stable intermediate result set as the final detection result. This includes prioritizing the use of the second dry density value and the second moisture content value calculated by the second logic computing unit as the final dry density and moisture content, recalculating the final compaction degree using the final dry density and the standard maximum dry density in the benchmark parameter set, and directly obtaining the compaction curve characteristic parameters from the second intermediate result. The confidence level of the detection result is assigned based on the number of conflict triggers, the final deviation, and the iteration convergence speed during the verification process. The fewer the number of conflict triggers in the verification loop, the higher the confidence level; the smaller the final deviation remaining at the end of the loop, the higher the confidence level; and the faster the iteration convergence speed of the second calculation unit result tends to be stable, the higher the confidence level.

[0048] In the above text, refer to Figure 1 A method for testing soil density and compaction degree according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 A soil density compaction degree testing system according to an embodiment of the present invention is described.

[0049] A soil density compaction degree testing system according to an embodiment of the present invention addresses the technical problems in existing technologies, such as easy misalignment of test data recording, low parameter matching efficiency, and lack of automatic verification in the calculation process. It achieves the technical effects of automating the entire testing process, accurately associating data, and intelligently matching parameters, thereby improving soil testing efficiency, result reliability, and consistency. Figure 2 As shown, a soil density compaction degree testing system includes: an identification code recognition unit 10, a quality data acquisition unit 20, a parameter retrieval unit 30, and a test result acquisition unit 40.

[0050] The identification code recognition unit 10 is used to automatically identify the unique identification code attached to the sample carrier through the scanning module and uniquely associate the identification code with the test task; the mass data acquisition unit 20 is used to automatically trigger the weighing module after the sample is placed, collect mass data, bind the mass data with the unique identification code, and transmit it to the data processing terminal; the parameter retrieval unit 30 is used to receive the mass data from the data processing terminal and automatically retrieve and call the corresponding benchmark parameter set from the pre-stored standard material parameter library according to the unique identification code; the test result acquisition unit 40 is used to perform parallel calculations of soil density, compaction degree, and compaction characteristics-related parameters in the core calculation layer based on the mass data, the benchmark parameter set, and dynamically selected physical calculation formulas through the multi-level calculation engine built into the data processing terminal, wherein the intermediate results are cross-validated during the calculation to obtain the test result.

[0051] The specific configuration of the test result acquisition unit 40 will be described in detail below. The test result acquisition unit 40 further includes: integrating the unique identifier, raw quality data, reference parameters, all intermediate calculation results and final parameters, automatically generating a structured complete test record and storing or outputting it.

[0052] The specific configuration of the mass data acquisition unit 20 will be described in detail below. The mass data acquisition unit 20 further includes: real-time monitoring of the sensor status of the weighing module; triggering a data acquisition action when the status indicator confirms that the module is in a stable and ready state; wherein the acquired mass data includes at least the stable mass value and the corresponding unit of measurement information, and together with the acquisition timestamp, constitutes a data packet.

[0053] The specific configuration of the parameter retrieval unit 30 will be described in detail below. The parameter retrieval unit 30 further includes: the data processing terminal parsing the unique identifier and the reference parameter set to be called, determining the target parameter set to be calculated, the target parameter set including at least: soil wet density, dry density, moisture content, compaction degree, and compaction curve characteristic parameters; based on the physical dependencies and computational complexity between the parameters, dynamically mapping the calculation task of the target parameter set to a core computing layer composed of at least two independent logical computing units, wherein the first logical computing unit is dedicated to direct density calculation based on mass and volume, and the second logical computing unit is dedicated to iterative optimization calculation based on physical models and historical data.

[0054] The specific configuration of the detection result acquisition unit 40 will be described in detail below. The detection result acquisition unit 40 further includes: parsing the unique identifier and the reference parameter set called by the data processing terminal to determine the target parameter set to be calculated, which includes at least: soil wet density, dry density, moisture content, compaction degree, and compaction curve characteristic parameters; determining the physical calculation formula based on the physical dependencies and computational complexity between the parameters in the target parameter set; deploying the formula to a multi-level core computing layer; using the quality data and the reference parameter set as input; and performing parallel computation processing through the core computing layer to obtain intermediate calculation results.

[0055] The specific configuration of the detection result acquisition unit 40 will be described in detail below. The detection result acquisition unit 40 further includes a multi-level core computing layer comprising at least a first logical computing unit and a second logical computing unit. The first logical computing unit and the second logical computing unit synchronously initiate calculations based on the shared mass data and reference parameter set. The first logical computing unit is used for direct calculation of density based on mass and volume, and the second logical computing unit is used for iterative optimization calculation based on a physical model and historical data.

[0056] The specific configuration of the detection result acquisition unit 40 will be described in detail below. The detection result acquisition unit 40 further includes: the first logic calculation unit directly calculates and generates a first intermediate result set according to the physical formula corresponding to the dynamically selected volume measurement method, which includes at least: a first wet density, a first dry density, and a first compaction reference value calculated based on the first dry density and the benchmark maximum dry density; the second logic calculation unit calls a preset soil-water characteristic relationship model and a compaction curve prediction model, and performs iterative inversion with the quality data as constraints to generate a second intermediate result set, which includes at least: a second moisture content, a second dry density, a second compaction degree, and a compaction curve curvature coefficient; the first intermediate result set and the second intermediate result set are pushed to the shared area in real time, with timestamps and calculation path tags attached.

[0057] The specific configuration of the detection result acquisition unit 40 will be described in detail below. The detection result acquisition unit 40 further includes: continuously monitoring the shared area, acquiring the first intermediate result set and the second intermediate result set, performing parameter cross-validation through built-in validation rules, including moisture content-density correlation validation, compaction degree convergence validation, and compaction curve trend validation; issuing feedback instructions to the core computing layer to eliminate conflicts based on the conflict combinations triggered by the validation results of each rule, and after multiple rounds of validation until all conflict flags are eliminated or the maximum number of iterations is reached, outputting a set of optimal values ​​from the final stable intermediate result set as the final detection result according to a predetermined synthesis strategy, and assigning a quantitative confidence level to the detection result based on the number of conflict triggers, the final deviation size, and the iteration convergence speed during the validation process;

[0058] The predetermined synthesis strategy prioritizes using the second dry density value and the second moisture content value calculated by the second logic calculation unit as the final dry density and moisture content; the final compaction degree is calculated from the final dry density and the standard maximum dry density in the reference parameter set; and the compaction curve characteristic parameters are directly taken from the second intermediate result set.

[0059] The specific configuration of the identification code recognition unit 10 will be described in detail below. The identification code recognition unit 10 further includes: acquiring an image of the unique identification code using a scanning component with a three-dimensional spatial position adjustment structure; controlling an auxiliary positioning device to project a positioning pattern onto the unique identification code area to assist focusing and positioning; recognizing and analyzing the acquired image; comparing the data fields identified and analyzed with predefined test task information to confirm the validity of the sample binding status; and uniquely associating the valid identification code with the test task.

[0060] The terminal structure of a soil density compaction degree testing system provided in this embodiment of the invention is as follows: Figure 3 As shown, the core terminal structure of the management center consists of RTU terminals and LoRa terminals, which serve as data acquisition and edge computing nodes and distributed sensing nodes, respectively. They work together through multiple communication methods such as 4G / GPRS communication, BeiDou positioning, and LoRa communication to achieve comprehensive data acquisition and remote transmission of key monitoring parameters such as soil moisture, soil temperature, and soil ambient temperature. Among them, BeiDou positioning ensures the accuracy and traceability of the geographical information of the monitoring points, LoRa communication is suitable for large-area, low-power sensor network coverage, and 4G / GPRS ensures reliable remote data transmission in mobile or fixed environments.

[0061] The product structure for soil density compaction testing provided in this embodiment of the invention is as follows: Figure 4As shown, the system mainly includes a host industrial control computer, a weighing module, a scanning module, and a pull-out keyboard. The industrial control computer serves as the control center and data processing terminal, equipped with dedicated software. Below, a high-precision weighing module is integrated for real-time sample mass acquisition. A scanning module is located at the front for rapid identification and input of sample codes, parameters, and other information. The pull-out keyboard facilitates on-site data input and operation. The scanning module automatically identifies sample information, the weighing module accurately acquires mass data, and the industrial control computer processes and calculates key indicators of the soil sample in real time, such as wet density, dry density, moisture content, and compaction degree. This achieves integrated automatic detection from sample identification, weighing measurement, data calculation to result output, significantly improving detection efficiency, accuracy, and result reliability.

[0062] The soil density compaction test system provided in this embodiment of the invention can execute the soil density compaction test method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0063] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for testing soil density and compaction degree, characterized in that, include: The scanning module automatically identifies the unique identification code attached to the sample carrier and uniquely associates the identification code with the test task. When the carrier holding the soil sample is placed on the platform of the weighing module, the weighing module is automatically triggered to collect mass data, and the mass data is bound with the unique identifier and transmitted to the data processing terminal. The data processing terminal receives quality data and, based on the unique identifier, automatically retrieves and calls the corresponding benchmark parameter set from the pre-stored standard material parameter library to determine the target parameter set to be calculated. The target parameter set includes: soil wet density, dry density, moisture content, compaction degree, and compaction curve characteristic parameters. Through the multi-level computing engine built into the data processing terminal, based on the quality data, the benchmark parameter set, and the dynamically selected physical calculation formula, the calculation of soil density, compaction degree, and compaction characteristics related parameters is performed in parallel at the multi-level core computing layer. The multi-level core computing layer includes a first logical computing unit and a second logical computing unit. The first logical computing unit and the second logical computing unit start the calculation synchronously based on the shared quality data and benchmark parameter set. The first logical computing unit directly calculates and generates a first intermediate result set according to the physical formula corresponding to the dynamically selected volume measurement method, which includes at least: a first wet density, a first dry density, and a first compaction degree reference value calculated based on the first dry density and the benchmark maximum dry density. The second logical calculation unit calls the preset soil-water characteristic relationship model and compaction curve prediction model, and performs iterative inversion with the quality data as constraints to generate a second intermediate result set, which includes at least: second moisture content, second dry density, second compaction degree and compaction curve curvature coefficient; The first and second intermediate result sets are pushed to the shared area in real time, with timestamps and computation path labels attached. The calculation involves cross-logic verification of intermediate results to obtain detection results, including: continuously monitoring the shared area, acquiring the first and second intermediate result sets, and performing parameter cross-verification using built-in verification rules. These built-in verification rules include: moisture content-density correlation verification, checking whether the moisture content and dry density of the first and second intermediate result sets satisfy the basic physical relationship; compaction convergence verification, checking whether the difference between the first and second compaction reference values ​​is reasonable; and compaction curve trend verification, checking whether the moisture content and dry density values ​​obtained from the second intermediate result set conform to the basic trend of the compaction curve. Based on the combination of conflicts triggered by the verification results of each rule, feedback instructions are sent to the multi-level core computing layer to eliminate the conflicts. After multiple rounds of verification until all conflict flags are eliminated, or after reaching the maximum number of iterations, a set of optimal values ​​is output from the final stable intermediate result set as the final detection result according to the predetermined synthesis strategy. The detection result is assigned a quantitative confidence level based on the number of conflict triggers, the final deviation size, and the iteration convergence speed during the verification process. The predetermined synthesis strategy prioritizes using the second dry density value and the second moisture content value calculated by the second logic calculation unit as the final dry density and moisture content; the final compaction degree is calculated from the final dry density and the standard maximum dry density in the reference parameter set; and the compaction curve characteristic parameters are directly taken from the second intermediate result set.

2. The method for detecting soil density and compaction degree according to claim 1, characterized in that, After obtaining the test results, the following is also included: By integrating the unique identifier, raw quality data, reference parameters, all intermediate calculation results, and final parameters, a structured and complete test record is automatically generated and stored or output.

3. The method for detecting soil density and compaction degree according to claim 1, characterized in that, The weighing module is automatically triggered when the carrier holding the soil sample is placed on the platform of the weighing module, including: The sensor status of the weighing module is monitored in real time. When the status indicator confirms that the module is in a stable and ready state, the data acquisition action is triggered. The acquired mass data includes at least the stable mass value and the corresponding unit of measurement information, which together with the acquisition timestamp constitute a data packet.

4. The method for detecting soil density and compaction degree according to claim 1, characterized in that, include: Based on the physical dependencies and computational complexity between parameters, the computational task of the target parameter set is dynamically mapped to a multi-level core computational layer consisting of at least two independent logical computation units. The first logical computation unit is dedicated to direct density calculation based on mass and volume, and the second logical computation unit is dedicated to iterative optimization calculation based on physical models and historical data.

5. The method for detecting soil density and compaction degree according to claim 1, characterized in that, The scanning module automatically identifies the unique identification code attached to the sample carrier and uniquely associates the identification code with the test task, including: The unique identifier is captured using a barcode scanning component with a three-dimensional spatial position adjustment structure. The auxiliary positioning device is controlled to project a positioning pattern onto the unique identification code area to assist in focusing and positioning, and to identify and analyze the acquired image; Based on the identified and parsed data fields, a joint comparison is performed with the predefined test task information to confirm the validity of the sample binding status; The validity identification code will be uniquely associated with the test task.

6. A soil density compaction degree testing system, characterized in that, The system is used to implement the soil density compaction degree testing method according to any one of claims 1 to 5, the system comprising: The identification code recognition unit is used to automatically identify the unique identification code attached to the sample carrier through the scanning module, and to uniquely associate the identification code with the test task. The quality data acquisition unit is used to automatically trigger the weighing module when the carrier carrying the soil sample is placed on the carrier platform of the weighing module, collect quality data, bind the quality data with the unique identification code, and transmit it to the data processing terminal. The parameter retrieval unit is used to receive quality data from the data processing terminal and automatically retrieve and call the corresponding reference parameter set from the pre-stored standard material parameter library according to the unique identifier. The detection result acquisition unit is used to perform parallel calculations of soil density, compaction degree and compaction characteristics-related parameters in the multi-level core calculation layer based on the quality data, the reference parameter set and dynamically selected physical calculation formulas through the multi-level calculation engine built into the data processing terminal. The calculation is performed by cross-logic verification through intermediate results to obtain the detection result.

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