A method and system for predicting the resource utilization of shield muck

Abstract

The present application relates to the technical field of shield muck treatment, and discloses a method and system for predicting the resource utilization of shield muck, which comprises collecting samples and pretreating, measuring quality and particle size distribution, obtaining initial parameters through initial pressure compression testing, obtaining parameters under different pressures through step-by-step pressure testing, inputting data into a prediction model to determine the availability and determine the optimal utilization mode, and generating a report for storage. The system comprises sample selection and pretreatment, fixation and measurement, initial parameter collection, test parameter collection, and resource utilization prediction modules. The method and system can accurately predict the resource utilization potential of shield muck, determine the optimal utilization mode, improve the resource utilization efficiency, and have good economic and environmental benefits.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of shield muck treatment, in particular to a method and system for predicting the resource utilization of shield muck. BACKGROUND

[0002] With the rapid development of urban underground space, shield construction technology is widely used in subway, tunnel and other engineering. In the process of shield construction, a large amount of shield muck is produced. According to statistics, the shield muck produced in the construction of one kilometer of subway tunnel can reach tens of thousands of tons. Traditionally, these mucks are mostly directly landfilled or stacked as construction waste, which not only occupies a large amount of land resources, but also may cause environmental pollution, such as soil pollution and water pollution. Shield muck is not a worthless waste. In fact, it contains a variety of useful components and has certain resource utilization potential. If the shield muck can be reasonably utilized, not only the problem of muck treatment can be solved, but also new raw materials can be provided for engineering construction, which has significant economic and environmental benefits.

[0003] At present, in the resource utilization of shield muck, there are many challenges: the nature of shield muck is complex and changeable, and the physical and chemical properties of muck produced under different engineering and different geological conditions are quite different, which makes it difficult to accurately judge whether it is suitable for resource utilization and suitable for which utilization method; lack of scientific and effective prediction methods and systems, unable to quickly and accurately assess the resource utilization potential of shield muck, resulting in low efficiency of resource utilization and difficult to be widely applied.

[0004] Some existing muck treatment methods often only simply screen, crush and other treatments, without fully considering the influence of key parameters such as physical and mechanical properties of muck on resource utilization. Moreover, these methods mostly lack systematicness and scientificalness, and cannot provide comprehensive and accurate guidance for the resource utilization of shield muck.

[0005] In addition, there are also some problems in the testing process of shield muck. For example, the testing method is not standardized, the testing parameters are not comprehensive, which leads to inaccurate test results and cannot truly reflect the resource utilization potential of muck. At the same time, the automation degree of testing equipment is low, the data acquisition and processing efficiency is low, which is difficult to meet the needs of engineering practice. SUMMARY

[0006] The purpose of the present application is to provide a method and system for predicting the resource utilization of shield muck to solve the problems raised in the background.

[0007] To achieve the above purpose, the present application provides the following technical scheme: a method for predicting the resource utilization of shield muck, the method comprising:

[0008] Step S10: Collect the shield muck sample to be tested, visually check whether there are impurities or abnormal substances in the appearance, replace the muck sample to be tested when there are abnormalities, and pretreat the muck sample when there are no abnormalities to remove surface moisture and loose particles;

[0009] Step S20: After the pretreatment is completed, the shield muck sample is placed in a test container, the sample mass is weighed by using an electronic balance, and the particle size distribution of the muck particles is measured by using a laser particle size analyzer;

[0010] Step S30: The test equipment is started to compress the muck sample at a set initial pressure, and the initial compression displacement value D0 and the initial compressive strength value S0 are measured by using a sensor installed on the equipment;

[0011] Step S40: A step-by-step pressure test is adopted, the test pressure is increased according to a set gradient, the compression displacement value Dn and the compressive strength value Sn under each pressure value are measured after being stably maintained for 60 seconds at each pressure value;

[0012] Step S50: The measured data values are input into a pre-designed resource utilization prediction model, whether the muck is suitable for resource utilization is judged in combination with the physical parameters of the muck, the optimal resource utilization mode is calculated, a data report is generated from the test result and the calculated optimal utilization mode, and the data report is stored in a database;

[0013] The test container in the step S20 is designed according to the volume of the shield muck and test requirements, the particle size distribution of the muck particles is measured by using a laser particle size analyzer, and the measurement error range is controlled to be ±0.1%.

[0014] Preferably, the step of pretreating the muck sample to remove surface moisture and loose particles in the step S10 when there are no abnormalities includes:

[0015] Drying: The shield muck sample is placed in a constant-temperature drying box, the temperature is set to 105°C, and the drying time is 2 hours;

[0016] Screening: After the drying is completed, the muck sample is screened by using a standard screen to remove loose particles with a particle size greater than 5 mm;

[0017] Cleaning: After the screening is completed, the surface of the muck sample is cleaned by using a soft brush to remove residual fine particles;

[0018] Weighing: After the cleaning is completed, the net weight of the muck sample is weighed by using an electronic balance, and the initial mass data is recorded.

[0019] Preferably, the step of compressing the muck sample at a set initial pressure and measuring the initial compression displacement value D0 and the initial compressive strength value S0 by using a sensor installed on the equipment in the step S30 includes:

[0020] Device self-checking: after checking the shield muck sample placement, start the test equipment, wait for the built-in self-checking program of the test equipment to complete the device self-checking; the self-checking content includes checking whether the control system, pressure loading unit and various sensors of the test equipment are running normally, when running abnormally, carry out abnormal troubleshooting and repair;

[0021] Parameter setting: set the initial pressure to 50kPa, control the pressure error within ±5kPa, and set the pressure loading rate to uniform speed mode;

[0022] Compression displacement measurement: after the muck sample is stably loaded at a pressure of 50kPa, the displacement sensor on the test equipment is used to collect the compression displacement value at a sampling frequency of 500 times per second based on the photoelectric encoding principle, the real-time collected compression displacement value is transmitted to the data processing unit of the equipment, after removing the noise interference, the processed compression displacement value is transmitted to the equipment control system to record the initial compression displacement value D0;

[0023] Compressive strength measurement: after the muck sample is stably loaded at a pressure of 50kPa, the pressure sensor on the test equipment is used to collect the compressive strength value at a sampling frequency of 500 times per second based on the strain gauge principle, the real-time collected compressive strength value is transmitted to the data processing unit of the equipment, after removing the zero drift error, it is transmitted to the equipment control system to record the initial compressive strength value S0;

[0024] Data review: after the equipment control system receives the initial compression displacement value D0 and the initial compressive strength value S0, the built-in data review program is automatically started, the initial parameter average value in the historical test data of the same type of shield muck is set in the data review program, when the initial compression displacement value D0 or the initial compressive strength value S0 exceeds ±15% of the initial parameter average value, the control system issues an abnormal alarm, and the operator checks the muck sample for abnormalities; when the initial compression displacement value D0 and the initial compressive strength value S0 are within ±15% of the initial parameter average value, it is determined that the initial compression displacement value D0 and the initial compressive strength value S0 are effective, and they are stored as basic data for subsequent resource utilization prediction.

[0025] Preferably, the step S40 adopts a step-by-step pressure test, and the step of increasing the test pressure according to the set gradient includes:

[0026] Pressure parameter setting: input the set pressure gradient value in the control system of the test equipment, increase the pressure by 100kPa each time, and keep stable for 60 seconds according to the new pressure value after the pressure increase is completed;

[0027] Pressure process: when increasing the pressure each time, increase at a speed of 10kPa / s, and the pressure increase time is 10 seconds, which is a buffer time;

[0028] Data acquisition time setting: during each 60-second stable holding phase, when the holding time reaches 50 seconds, the sensor acquires data at a sampling frequency of 500 times per second;

[0029] Whole-process state monitoring: during the step-by-step pressure test process, a fault detection unit on the test equipment performs whole-process state monitoring, the fault detection unit includes a temperature sensor and a pressure fluctuation sensor, and the temperature and pressure fluctuation during the step-by-step pressure test process are monitored in real time; when it is monitored that the data exceeds the preset temperature threshold and pressure fluctuation threshold, the fault detection unit sends a shutdown instruction to the equipment control system, and the control system controls the test equipment to stop running.

[0030] Preferably, the measured data value in the step S50 is input into a pre-designed resource utilization prediction model, and the model design step includes:

[0031] Physical parameter correlation construction: a relational expression between the compression displacement and the compressive strength, the pressure is constructed according to the basic principle of material mechanics; and an association relationship is established according to the particle size distribution and the compressive strength of the slag soil particles;

[0032] Multi-element linear regression solution: the compression displacement value, the compressive strength value under different pressures, and the corresponding pressure value and the physical parameters of the slag soil are substituted into the model to form a multi-element linear equation group, and the equation group is solved by multi-element linear regression algorithm iteration optimization to preliminarily determine the availability grade of the slag soil;

[0033] Optimal utilization mode determination: based on the obtained availability grade, the particle size distribution and the compressive strength of the slag soil are combined, and the optimal resource utilization mode is further calculated through a classification algorithm;

[0034] Model verification and optimization: standard slag soil samples of known resource utilization modes are obtained for model verification, test data are collected by a test equipment, and are input into a resource utilization prediction model, the output result of the model is compared with the known true value to calculate the accuracy rate, when the accuracy rate is less than 85%, the coefficients and function relationships in the model are adjusted, the model is retrained and optimized, and until the accuracy rate of the model to the calculated value of the resource utilization parameters of the shield slag soil is greater than or equal to 85%, the model is applied to actual tests.

[0035] Preferably, the present application also includes a system for predicting the resource utilization of shield slag soil, the system comprising:

[0036] Shield slag soil sample selection and pretreatment module: collecting a shield slag soil sample to be tested, visually checking whether impurities or abnormal substances exist on the appearance of the shield slag soil sample, replacing the shield slag soil sample to be tested when abnormalities exist, and pretreating the shield slag soil sample when no abnormalities exist to remove surface moisture and loose particles;

[0037] The shield muck sample fixing and measuring module: after the pretreatment is completed, the shield muck sample is placed in a test container, the sample mass is weighed by using an electronic balance, and the particle size distribution of the muck particles is measured by using a laser particle size analyzer;

[0038] The shield muck sample initial parameter acquisition module: the test equipment is started, the muck sample is compressed at a set initial pressure, and the initial compression displacement value D0 and the initial compression strength value S0 are measured by using a sensor installed on the equipment;

[0039] The shield muck sample test parameter acquisition module: a step-by-step pressure test is adopted, the test pressure is increased according to a set gradient, the compression displacement value Dn and the compression strength value Sn under each pressure value are measured after 60 seconds of stable keeping at each pressure value;

[0040] The shield muck resource utilization prediction module: the measured data values are input into a pre-designed resource utilization prediction model, whether the muck is suitable for resource utilization is judged in combination with the physical parameters of the muck, a data report is generated for the test results and the optimal utilization mode calculated, and the data report is stored in a database.

[0041] The test container in the shield muck sample fixing and measuring module is designed according to the volume of the shield muck and test requirements; the particle size distribution of the muck particles is measured by using a laser particle size analyzer, and the measurement error range is controlled to be ±0.1%.

[0042] Preferably, when there is no abnormality, the steps of removing surface moisture and loose particles in the shield muck sample selection and pretreatment module include:

[0043] Drying: the shield muck sample is placed in a constant-temperature drying box, the temperature is set to 105 DEG C, and the drying time is 2 hours;

[0044] Screening: after the drying is completed, the shield muck sample is screened by using a standard screen, and the loose particles with a particle size greater than 5 mm are removed;

[0045] Cleaning: after the screening is completed, the shield muck sample is cleaned by using a soft brush to remove the fine particles remaining on the surface of the shield muck sample;

[0046] Weighing: after the cleaning is completed, the net weight of the shield muck sample is weighed by using an electronic balance, and the initial mass data is recorded.

[0047] Preferably, the steps of compressing the shield muck sample at a set initial pressure and measuring the initial compression displacement value D0 and the initial compression strength value S0 by using a sensor installed on the equipment in the shield muck sample initial parameter acquisition module include:

[0048] Device self-checking: after checking the shield slag sample placement, start the test equipment, wait for the test equipment built-in self-checking program to complete the device self-checking; the self-checking content includes checking whether the control system, pressure loading unit and various sensors of the test equipment are running normally, when running abnormally, carry out abnormal troubleshooting and repair;

[0049] Parameter setting: set the initial pressure to 50kPa, control the pressure error within ±5kPa, and set the pressure loading rate to uniform speed mode;

[0050] Compression displacement measurement: after the slag sample is stably loaded at a pressure of 50kPa, the displacement sensor on the test equipment is used to collect the compression displacement value at a sampling frequency of 500 times per second by using the photoelectric encoding principle, the real-time collected compression displacement value is transmitted to the data processing unit of the equipment, after removing the noise interference, the processed compression displacement value is transmitted to the equipment control system to record the initial compression displacement value D0;

[0051] Compressive strength measurement: after the slag sample is stably loaded at a pressure of 50kPa, the pressure sensor on the test equipment is used to collect the compressive strength value based on the strain gauge principle at a sampling frequency of 500 times per second, the real-time collected compressive strength value is transmitted to the data processing unit of the equipment, after removing the zero drift error, it is transmitted to the equipment control system to record the initial compressive strength value S0;

[0052] Data review: after the equipment control system receives the initial compression displacement value D0 and the initial compressive strength value S0, the built-in data review program is automatically started, the initial parameter average value in the historical test data of the same type of shield slag is set in the data review program, when the initial compression displacement value D0 or the initial compressive strength value S0 exceeds the initial parameter average value ±15%, the control system issues an abnormal alarm, and the operator checks the slag sample for abnormalities; when the initial compression displacement value D0 and the initial compressive strength value S0 are within the range of the initial parameter average value ±15%, it is determined that the initial compression displacement value D0 and the initial compressive strength value S0 are effective, and they are stored as the basis data for subsequent resource utilization prediction.

[0053] Compared with the prior art, the beneficial effects of the present application are:

[0054] In the sample processing stage, the sample is screened through a strict visual inspection mechanism, which can effectively eliminate slag containing impurities or abnormal substances, and ensure that the subsequent test is based on high-quality samples; the pretreatment link can accurately remove the surface moisture and loose particles of the sample through drying, screening, cleaning and weighing, etc., to build a reliable foundation for subsequent tests.

[0055] In the parameter measurement process, the mass and particle size distribution of the slag sample can be accurately obtained by using precision instruments, the measurement error is strictly controlled, and the high precision of the data is ensured. During the initial pressure compression test, the self-checking function of the equipment can ensure the normal operation of each part of the test equipment; the initial pressure and error range are strictly limited by parameter setting, high-frequency sampling is used for compression displacement and compressive strength measurement, and the initial parameters are compared with the average value of the initial parameters of the historical test data of similar slag through data review program, thereby effectively ensuring the reliability of the initial parameters and providing accurate basic data support for subsequent resource utilization prediction.

[0056] The step-by-step pressure test scientifically sets the pressure parameters, process and data collection time, and after each pressure increase, a certain time is stably maintained, the speed is controlled during pressure increase and a buffer time is reserved, high-frequency data collection is started at a specific time point during the stable maintenance stage, and the state is monitored through the sensor throughout the process, so that the compression displacement and compressive strength values of the slag under different pressures can be comprehensively and accurately obtained, thereby providing rich data support for in-depth analysis of the performance of the slag.

[0057] The construction of the resource utilization prediction model is based on the basic principles of material mechanics to establish a correlation formula, and the equation set is solved by iterative optimization through multivariate linear regression algorithm to preliminarily determine the availability grade of the slag, and then the optimal resource utilization mode is calculated by classification algorithm in combination with the particle size distribution and compressive strength. The model is verified by standard slag samples with known resource utilization modes, and when the accuracy is lower than the set value, the model is adjusted and optimized to ensure the reliability of the prediction results.

[0058] The modules of the system have clear division of labor and work together, from sample selection and pretreatment to final prediction and data report generation and storage, forming a complete workflow, which greatly improves the work efficiency and test accuracy. The matched equipment and computer program product realize the automatic data processing and prediction function of the method in practical application, and provide a scientific, efficient and convenient technical tool for resource utilization of shield slag. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 The working principle diagram of the method for predicting the resource utilization of shield slag is described in the present application.

[0060] Figure 2 The design diagram of the initial parameter measurement process is described in the present application.

[0061] Figure 3 The design diagram of the step-by-step pressure test process is described in the present application.

[0062] Figure 4 The design diagram of the resource utilization prediction model construction is described in the present application.

[0063] Figure 5 The architecture diagram of the shield slag resource utilization system is described in the present application. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0065] Please refer to Figures 1-5 The present application relates to a method for predicting the resource utilization of shield spoil, and the specific implementation steps are as follows:

[0066] Step S10: Collect the shield spoil sample to be tested, visually inspect whether there are impurities or abnormal substances on the appearance, replace the spoil sample to be tested when there are abnormalities, and pretreat the spoil sample when there are no abnormalities to remove surface moisture and loose particles.

[0067] Step S20: After the pretreatment is completed, place the shield spoil sample in a test container, weigh the sample mass using an electronic balance, and measure the particle size distribution of the spoil particles using a laser particle size analyzer. The test container is designed according to the volume of the shield spoil and the test requirements. The laser particle size analyzer is used to measure the particle size distribution of the spoil particles, and the measurement error range is controlled to be ±0.1%.

[0068] Step S30: Start the test equipment, compress the spoil sample at a set initial pressure, and measure the initial compression displacement value D0 and the initial compressive strength value S0 through the sensor installed on the equipment.

[0069] Step S40: Adopt step-by-step pressure test, increase the test pressure according to the set gradient, and measure the compression displacement value Dn and the compressive strength value Sn under each pressure value after a certain time is stably maintained.

[0070] Step S50: Input the measured data values into the pre-designed resource utilization prediction model, judge whether the spoil is suitable for resource utilization in combination with the physical parameters of the spoil, calculate the optimal resource utilization mode, generate a data report of the test results and the calculated optimal utilization mode, and store the data report in a database.

[0071] Embodiment 1:

[0072] This embodiment describes in detail the specific implementation mode of pretreating the shield spoil sample to remove surface moisture and loose particles when there are no impurities or abnormal substances on the appearance of the shield spoil sample in step S10.

[0073] After collecting the shield muck sample to be tested, the operator will visually inspect the sample, carefully observing its appearance to check for any obvious impurities such as wood blocks, metal fragments, plastic, or any abnormal substances in terms of color and texture. If any abnormalities are found, the operator will promptly replace the muck sample to be tested to ensure the accuracy and reliability of subsequent tests. When it is confirmed that there are no abnormalities in the appearance of the sample, the pre-treatment of the muck sample begins.

[0074] The first step of pre-treatment is drying. The operator carefully places the shield muck sample in a constant-temperature drying oven. To achieve good drying results, the temperature of the drying oven is set to 105°C. This temperature effectively removes the moisture on the surface of the sample without damaging its basic properties. The drying time is set to 2 hours, during which the drying oven continues to work, keeping the sample in a constant temperature environment to ensure that the moisture can evaporate fully. During the drying process, the operator will pay attention to maintaining air circulation in the drying oven to improve drying efficiency. After drying is complete, the moisture on the surface of the sample is basically removed, preparing for the subsequent screening step.

[0075] Screening is performed. After drying is complete, the operator uses a standard screen to screen the muck sample. The specifications of the standard screen are strictly designed to remove loose particles with a particle size greater than 5mm. During the screening process, the operator evenly spreads the muck sample on the screen and then screens it according to certain operation specifications to ensure that the sample can pass through the screen fully. For particles with a particle size greater than 5mm that remain on the screen, the operator will carefully clean them out. These particles are considered loose particles and need to be removed from the sample to avoid interfering with subsequent tests. The operator needs to have patience and care during the screening process to ensure the accuracy of the screening process and thus ensure that the particle size of the sample meets the testing requirements.

[0076] After screening is complete, the muck sample needs to be cleaned. At this time, there may be some small particles left on the surface of the sample, which may affect the subsequent measurement and test results if not cleaned. The operator uses a soft-bristled brush to clean the small particles left on the surface of the muck sample. The soft-bristled brush is soft and will not damage the sample, while effectively removing the small particles on the surface. During the cleaning process, the operator will gently brush the surface of the sample to ensure that every corner is cleaned and no small particles are left behind. After cleaning is complete, the surface of the sample becomes clean and tidy, laying a good foundation for the subsequent weighing step.

[0077] After the cleaning is completed, the operator uses an electronic balance to weigh the net weight of the slag sample. The electronic balance has high precision and can accurately measure the mass of the sample. Before weighing, the operator will first calibrate the electronic balance to ensure the accuracy of the measurement results. Then, the cleaned slag sample is carefully placed on the weighing pan of the electronic balance, and the balance is allowed to display a stable value. The operator will record the initial mass data at this time, which will serve as an important reference for subsequent tests. When recording the data, the operator will ensure the accuracy and completeness of the data to avoid recording errors.

[0078] Through the above four steps of pretreatment operation of drying, screening, cleaning and weighing, the water and loose particles on the surface of the shield slag sample can be effectively removed, ensuring that the quality and particle size of the sample meet the requirements of subsequent tests, and providing a reliable sample basis for the subsequent test steps of predicting the resource utilization of shield slag. During the entire pretreatment process, the operator needs to strictly follow the specified operation specifications for each step to ensure the accuracy and consistency of the operation, thereby ensuring the reliability and repeatability of the test results.

[0079] Example 2:

[0080] This embodiment describes in detail the specific implementation of compressing the slag sample at the set initial pressure in step S30 and measuring the initial compression displacement value D0 and the initial compressive strength value S0.

[0081] Before performing this step, the operator needs to first check whether the shield slag sample has been correctly placed in the test equipment, and start the test equipment after confirming that the sample is placed correctly. After the test equipment is started, it will automatically run the built-in self-checking program to comprehensively check each key part of the equipment. The self-checking content mainly includes the control system of the test equipment, checking whether it can normally receive and process instructions; the pressure loading unit, checking whether it can accurately apply pressure according to the set requirements; and various sensors such as displacement sensors, pressure sensors, etc., confirming whether they are running normally and can accurately collect data. During the self-checking process, if an abnormality is found in a certain part, for example, a fault prompt appears in the control system, the pressure loading unit cannot work normally, or the sensor data shows an abnormality, etc., the operator needs to immediately investigate the abnormality, find out the problem and repair it, until all equipment components pass the self-checking, ensuring that the test equipment is in good working condition, providing reliable hardware support for subsequent tests.

[0082] After the completion of the device self-test, enter the parameter setting link. The operator sets the initial pressure to 50 kPa in the control system of the test device. In order to ensure the accuracy of the pressure, the pressure error needs to be controlled within ±5 kPa, so that the initial pressure applied can meet the test requirements. At the same time, set the pressure loading rate to uniform mode, so that the pressure can be smoothly applied to the slag sample, avoiding sudden changes in pressure affecting the sample, thereby ensuring the stability and reliability of the test data. When setting the parameters, the operator will carefully check the input values to ensure that the parameter settings are correct and accurate, avoiding inaccurate test results due to incorrect parameter settings.

[0083] After the parameter setting is completed, the slag sample is compressed and the initial compression displacement value D0 is measured. When the slag sample is stably loaded at a pressure of 50 kPa, the displacement sensor on the test device starts to work. The displacement sensor uses the photoelectric encoding principle to collect the compression displacement value at a sampling frequency of 500 times per second. High sampling frequency can ensure that the collected data more accurately reflects the compression displacement change of the sample. The real-time collected compression displacement value is transmitted to the data processing unit of the device, and in the data processing unit, the data is processed to remove noise interference, removing noise caused by external environment or device itself and other factors, making the data more pure. The processed compression displacement value is transmitted to the device control system, and the initial compression displacement value D0 is recorded by the control system. In this process, the operator will closely monitor the data transmission and processing to ensure the integrity and accuracy of the data.

[0084] Immediately after measuring the initial compressive strength value S0. Similarly, after the slag sample is stably loaded at a pressure of 50 kPa, the pressure sensor on the test device collects the compressive strength value based on the strain gauge principle at a sampling frequency of 500 times per second. The pressure sensor can sensitively sense the change in compressive strength of the sample during the compression process and convert these changes into electrical signals. After the real-time collected compressive strength value is transmitted to the data processing unit of the device, the zero drift error is removed, which may be caused by the characteristics of the sensor itself or environmental factors, removing this error can make the compressive strength value more accurate. The processed compressive strength value is transmitted to the device control system, and the initial compressive strength value S0 is recorded by the control system. During data collection and processing, the operator will regularly check the working state of the sensor and the data processing effect to ensure the accurate and reliable measurement of the compressive strength value.

[0085] After the device control system receives the initial compression displacement value D0 and the initial compressive strength value S0, it will automatically start the built-in data review program. In the data review program, the average value of the initial parameters in the historical test data of similar shield muck is pre-set. This average value is obtained by testing a large number of similar muck samples and has a certain reference value. When the initial compression displacement value D0 or the initial compressive strength value S0 received by the control system exceeds ±15% of the average value of the initial parameters, it indicates that the current test data deviates greatly from the historical data, and there may be abnormal conditions. At this time, the control system will issue an abnormal alarm. After hearing the alarm, the operator needs to investigate the muck sample for abnormalities, check whether the sample has problems during placement or whether there are other factors affecting the test results, and make appropriate treatment according to the investigation results. When the received initial compression displacement value D0 and the initial compressive strength value S0 are within ±15% of the average value of the initial parameters, it indicates that the test data meets the expectations, and the initial compression displacement value D0 and the initial compressive strength value S0 are determined to be valid, and the control system will store them as the basis data for subsequent resource utilization prediction. During the data review process, the operator will carefully investigate the cause of each alarm to ensure that the stored basis data is true and reliable, and to provide accurate basis for subsequent resource utilization prediction.

[0086] Through the above device self-checking, parameter setting, compression displacement measurement, compressive strength measurement and data review, the initial compression displacement value D0 and the initial compressive strength value S0 of the muck sample can be accurately obtained, laying a solid data foundation for subsequent step-by-step pressure test and resource utilization prediction. During the entire implementation process, each link is closely connected, and the operator needs to strictly follow the operation specification and carefully handle each detail to ensure the accuracy and reliability of the test process, so as to ensure that the finally obtained data can truly reflect the initial mechanical properties of the muck sample.

[0087] Example 3:

[0088] This embodiment describes in detail the specific implementation of step S40, which adopts step-by-step pressure test and increases the test pressure according to the set gradient.

[0089] Before performing the step-by-step pressure test, the operator needs to input the set pressure gradient value in the control system of the test device. According to the test requirements, the amplitude of each pressure increase is 100 kPa, and after the pressure increase is completed, it needs to be stably maintained for 60 seconds at the new pressure value. This parameter setting is determined based on the requirements of the shield muck mechanical property test, and the stable maintenance time can ensure that the muck sample reaches a relatively stable state under the pressure, providing reliable conditions for subsequent data collection. When inputting the parameters, the operator will carefully check the values to avoid affecting the test process and results due to input errors.

[0090] After the completion of the pressure parameter setting, the pressure process is entered. Each time the pressure is increased, the pressure is increased at a rate of 10 kPa / s, and the entire pressure process lasts 10 seconds, which is set as the buffer time. By increasing the pressure at such a speed and time, the pressure can be smoothly applied to the slag sample, avoiding the destruction of the sample structure caused by sudden large increases in pressure, thereby ensuring that the sample can exhibit a true mechanical response during the pressure process. During the pressure process, the operator will closely monitor the pressure display of the test equipment to ensure that the pressure increase rate and time meet the set requirements. If the pressure increase is found to be abnormal, the pressure will be paused in time and the equipment will be checked for faults.

[0091] During the 60-second phase of stable pressure, data collection is required. When the stable holding time reaches 50 seconds, the sensor starts collecting data at a sampling frequency of 500 times per second. The data collected in the last 10 seconds of the stable holding time is selected to ensure that the sample has fully stabilized at this pressure, and the data collected at this time can more accurately reflect the compression displacement and compressive strength characteristics of the sample at this pressure. The setting of high sampling frequency can ensure that the collected data has sufficient accuracy and can capture the subtle changes of the sample during the pressure process. The data collected by the sensor is transmitted to the data processing unit of the equipment in real time, and the operator will monitor the stability of data transmission during this process to prevent data loss or transmission errors.

[0092] During the entire step-by-step pressure test process, the fault detection unit on the test equipment will perform full-state monitoring. The fault detection unit mainly includes a temperature sensor and a pressure fluctuation sensor, wherein the temperature sensor is used to monitor the temperature changes during the step-by-step pressure test process in real time, and the pressure fluctuation sensor is used to monitor the pressure fluctuation. These two sensors can timely detect temperature abnormalities or pressure fluctuation abnormalities that may occur during the test process. When the monitored data exceeds the preset temperature threshold and pressure fluctuation threshold, it indicates that an abnormality has occurred during the test process, which may affect the accuracy of the test results or even cause equipment damage. At this time, the fault detection unit will send a shutdown instruction to the equipment control system, and the control system will immediately control the test equipment to stop running after receiving the instruction. After receiving the shutdown notice, the operator will quickly check the equipment, analyze the cause of the temperature or pressure abnormality, and take appropriate measures to handle it. After the fault is eliminated, it is considered whether to retest.

[0093] For example, in a certain step-by-step pressure test, when the pressure increases to a certain set value and remains stable, the temperature sensor detects that the internal temperature of the device exceeds the preset temperature threshold, and the fault detection unit sends a shutdown instruction, and the device stops running. The operator checks and finds that the heat dissipation system of the device is malfunctioning, causing the temperature to rise. After repairing the heat dissipation system, the device is restarted and the test continues.

[0094] Through the above steps of pressure parameter setting, pressure process control, data acquisition time setting, and whole-process state monitoring, the step-by-step pressure test can be ensured to proceed smoothly, and accurate compression displacement values Dn and compressive strength values Sn can be obtained. During the entire implementation process, the operator needs to strictly control each link to ensure that the parameter setting is correct, the pressure process is smooth, the data acquisition is accurate, and the fault monitoring is timely, thereby providing reliable data support for subsequent data input into the resource utilization prediction model for analysis and judgment.

[0095] Example 4:

[0096] This embodiment describes in detail the specific implementation of inputting the measured data into the resource utilization prediction model and completing the model design in step S50. In actual operation, taking the shield muck sample excavated from a certain subway project as an example, the whole process from model construction to application is described.

[0097] According to the basic principles of material mechanics, a relationship between compression displacement and compressive strength, pressure is established. For example, when the subway muck sample produces a compression displacement of 0.3 mm under an initial pressure of 50 kPa, the compressive strength is 0.2 MPa, as the pressure gradually increases to 150 kPa, the compression displacement increases to 0.7 mm, and the compressive strength increases to 0.5 MPa. By analyzing the change rule of such measured data, the correlation between the three is established. At the same time, according to the particle size distribution and compressive strength of the muck particles, the correlation between the two is established. The proportion of particles with a particle size less than 0.075 mm in the subway muck sample is 40%, and the corresponding compressive strength under the initial pressure is 0.2 MPa. When the proportion of particles with a particle size less than 0.075 mm increases to 50%, the compressive strength under the same pressure may increase to 0.25 MPa. Based on this, the corresponding relationship between the two is established.

[0098] The multiple linear regression is performed to solve the equation, and the compression displacement values and the compressive strength values under different pressures, the corresponding pressure values and the physical parameters of the slag are substituted into the model. For example, the compression displacement value D0 of the subway slag sample under the pressure of 50 kPa is 0.3 mm, and the compressive strength value S0 is 0.2 MPa; the compression displacement value D1 under the pressure of 150 kPa is 0.7 mm, and the compressive strength value S1 is 0.5 MPa; the compression displacement value D2 under the pressure of 250 kPa is 1.0 mm, and the compressive strength value S2 is 0.7 MPa. At the same time, the physical parameters of the sample include that the proportion of particles with a particle size less than 0.075 mm is 40%, the water content is 8%, etc. These data are substituted into the model to form a multiple linear equation group, and the equation group is solved by the multiple linear regression algorithm. In the iteration process, the coefficients in the model are adjusted to gradually reduce the deviation between the calculated compression displacement value and the compressive strength value and the measured value, so as to preliminarily determine the availability grade of the slag. For example, after multiple iterations, it is determined that the availability grade of the subway slag sample is grade II, indicating that it has certain resource utilization potential.

[0099] On the basis of the obtained availability grade, the particle size distribution and the compressive strength of the slag are combined, and the optimal resource utilization mode is further calculated by the classification algorithm. The availability grade of the subway slag sample is grade II, the proportion of particles with a particle size less than 0.075 mm is 40%, and the compressive strength under the pressure of 250 kPa is 0.7 MPa. According to the rules of the classification algorithm, when the availability grade is grade II, the proportion of particles with a particle size less than 0.075 mm is between 30% and 50%, and the compressive strength is between 0.5 MPa and 1.0 MPa, it is suitable to be used as roadbed filler. Therefore, it is calculated that the optimal resource utilization mode of the subway slag sample is used for roadbed filler.

[0100] A standard slag sample with a known resource utilization mode is obtained to verify the model. For example, a standard slag sample with a known optimal utilization mode of brick making is selected, and its compression displacement value and compressive strength value under different pressures are collected by a testing device, such as a compression displacement of 0.25 mm and a compressive strength of 0.22 MPa under a pressure of 50 kPa, a compression displacement of 0.6 mm and a compressive strength of 0.45 MPa under a pressure of 150 kPa, and the like, and these test data are input into the resource utilization prediction model. The optimal utilization mode output by the model is brick making, which is consistent with the known true value, and the calculation accuracy is 100%. Another standard slag sample with a known optimal utilization mode of concrete admixture is selected for verification, and the optimal utilization mode output by the model is concrete admixture, and the accuracy is 90%. When the accuracy is less than 85%, the coefficients and functional relationships in the model are adjusted, the model is retrained and optimized. For example, if the accuracy of the model in a certain verification is 80%, whether the coefficients between the compression displacement and the compressive strength in the model are reasonable is analyzed, and after adjustment, the data is retrained until the accuracy of the model to the calculation value of the shield slag resource utilization parameters is greater than or equal to 85%, and then applied to actual testing.

[0101] In the whole model design process, each step is closely connected, and through the analysis of specific subway slag samples, the whole process from physical parameter correlation construction to model verification and optimization is detailed, which ensures that the model can accurately judge whether the slag is suitable for resource utilization and calculate the optimal resource utilization mode, and provides a scientific decision basis for the resource utilization of shield slag.

[0102] Example 5:

[0103] This embodiment describes in detail the specific implementation of the shield slag resource utilization prediction system, taking the shield slag generated by a certain city subway tunnel construction project as an example, and describes the cooperative work flow of each module of the system.

[0104] In the shield slag sample selection and pretreatment module, the operator collects the slag sample to be tested from the unearthing point of the subway project, visually inspects the sample, and observes that there are no obvious wood blocks, metal fragments and other impurities in the sample, and the color and texture are normal. Then the sample is pretreated: the sample is placed in a constant temperature drying box, the temperature is set to 105°C, and dried for 2 hours to remove surface moisture; use a standard screen to remove loose particles with a particle size greater than 5 mm; use a soft brush to clean the surface of the remaining fine particles; finally, use an electronic balance to weigh the net weight of the sample, and record the initial mass data as 2.5 kg. This module ensures the consistency of the sample through standardized operation, and provides a reliable basis for subsequent testing.

[0105] In the shield muck sample fixing and measuring module, a cylindrical container with a diameter of 10 cm and a height of 25 cm is designed according to the sample volume (about 2000 cm³) and test requirements. The pretreated sample is poured into the container and compacted. The sample mass is weighed again using an electronic balance with an accuracy of 0.1 mg to confirm that it is consistent with the initial mass. The particle size distribution is measured by a laser particle size analyzer. The instrument uses the Mie scattering principle. The sample is uniformly dispersed in the carrier liquid during measurement. The error control standard is ±0.1%. The proportion of particles with a particle size less than 0.075 mm in the muck is 38%, and the proportion of particles with a particle size of 0.075-5 mm is 62%. The data is transmitted to the system database in real time.

[0106] The shield muck sample initial parameter acquisition module starts the test equipment and performs self-checking: checks the control system display, the pressure loading unit for oil leakage, and the displacement sensor and pressure sensor readings. The initial pressure is set to 50 kPa (error ±5 kPa) and loaded at a constant speed. When the pressure is stable, the displacement sensor (optoelectronic encoding principle, sampling frequency 500 times / s) collects the compression displacement value. After noise removal by the data processing unit, D0 is recorded as 0.28 mm. The pressure sensor (strain gauge principle, sampling frequency 500 times / s) removes the zero drift error and records S0 as 0.19 MPa. The system automatically calls historical data of similar muck (average initial parameters D0=0.3 mm, S0=0.2 MPa). The current data is within ±15% and is determined to be valid and stored.

[0107] The shield muck sample test parameter acquisition module performs step-by-step pressure testing: the control system input pressure gradient is 100 kPa, and each pressure increase is stable for 60 seconds. When the pressure increases by 10 kPa / s for 10 seconds, the buffer enters the stable stage. When the pressure rises to 150 kPa and remains for 50 seconds, the sensor collects data at a frequency of 500 times / s, and D1=0.65 mm, S1=0.48 MPa are measured. When the pressure rises to 250 kPa and remains for 50 seconds, D2=1.02 mm, S2=0.71 MPa are measured. The temperature sensor (monitoring range 0-100°C) and pressure fluctuation sensor (accuracy ±1 kPa) are used to monitor the whole process. The temperature is stable at 25°C, and the pressure fluctuation is within ±3 kPa, without triggering the stop command.

[0108] After receiving the measured data, the shield muck resource utilization prediction module starts model operation: according to the principle of material mechanics, the correlation between compression displacement and pressure, and compressive strength is established, such as the linear positive correlation between compression displacement and pressure of the muck under 50-250kPa pressure, and the increase of compressive strength with the increase of pressure. Put D0, D1, D2 and corresponding pressure value, particle size distribution (38% less than 0.075mm) and other parameters into the multiple linear regression model, and determine the availability level as level II after iterative solution. Combined with the classification algorithm, the optimal utilization mode is calculated as roadbed filler under the applicable conditions of particle size distribution and compressive strength matching roadbed filler in this level. The system automatically generates data reports containing sample information, test parameters, model output results, etc., and stores them in the database for traceability.

[0109] During system operation, each module interacts in real time through the data interface: the sample quality data of the pretreatment module is synchronized to the measurement module, the D0, S0 data of the initial parameter module is transmitted to the test parameter module as the basis for subsequent pressure, and the prediction module calls all measured data to complete model calculation. For example, when the sample pretreated quality data is abnormal (such as deviation from the initial collection value more than 5%), the system will automatically prompt to resample to ensure the reliability of the whole process data. Through modular design and standardized process, the system realizes the whole process automation from sample collection to resource utilization scheme output, and provides efficient decision support for the resource utilization of shield muck in subway projects.

[0110] It should be noted that in this text, relationship terms such as first and second are only used to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the term "includes" "contains" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0111] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method of predicting the resource utilisation of shield muck, characterised by, The method comprises the following steps: Step S10: Collect the shield muck sample to be tested, visually check whether there are impurities or abnormal substances in the appearance, replace the muck sample to be tested when there are abnormalities, and pretreat the muck sample when there are no abnormalities to remove surface moisture and loose particles; Step S20: After the pretreatment is completed, the shield muck sample is placed in a test container, the sample mass is weighed by using an electronic balance, and the particle size distribution of the muck particles is measured by using a laser particle size analyzer; Step S30: The test equipment is started, the muck sample is compressed at a set initial pressure, and the initial compression displacement value D0 and the initial compressive strength value S0 are measured by using a sensor installed on the equipment; Step S40: A step-by-step pressure test is adopted, the test pressure is increased according to a set gradient, the compression displacement value Dn and the compressive strength value Sn under each pressure value are measured after 60 seconds of stable keeping when each pressure value is reached; Step S50: The measured data values are input into a pre-designed resource utilization prediction model, whether the muck is suitable for resource utilization is judged in combination with the physical parameters of the muck, the optimal resource utilization mode is calculated, a data report is generated from the test result and the calculated optimal utilization mode, and the data report is stored in a database, and the physical parameters of the muck include the mass and the particle size distribution of the muck sample; The test container in the step S20 is designed according to the volume of the shield muck and test requirements; the particle size distribution of the muck particles is measured by using a laser particle size analyzer, and the measurement error range is controlled to be ±0.1%; In the step S50, the measured data values are input into a pre-designed resource utilization prediction model, and the model design steps include: Physical parameter correlation construction: a relational expression among the compression displacement, the compressive strength and the pressure is constructed according to the basic principle of material mechanics; and a correlation relationship is established according to the particle size distribution of the muck particles and the compressive strength; Multiple linear regression solution: the compression displacement values, the compressive strength values under different pressures, the corresponding pressure values and the physical parameters of the muck are substituted into the model to form a multiple linear equation set, the equation set is solved by using a multiple linear regression algorithm for iterative optimization, and the availability grade of the muck is preliminarily determined; Optimal utilization mode determination: based on the obtained availability grade, the particle size distribution and the compressive strength of the muck are combined, and the optimal resource utilization mode is further calculated by using a classification algorithm; Model verification and optimization: standard muck samples with known resource utilization modes are obtained to verify the model, test data are collected by using a test equipment, are input into the resource utilization prediction model, and the calculation accuracy is compared between the output result of the model and the known true value; when the accuracy is lower than 85%, the coefficients and the functional relationship in the model are adjusted, the model is retrained and optimized, and the model is applied to actual tests until the accuracy of the model to the resource utilization parameter calculation value of the shield muck is greater than or equal to 85%.

2. The method of claim 1, wherein, In the step S10, when there are no abnormalities, the muck sample is pretreated to remove surface moisture and loose particles, and the pretreatment steps include: Drying: the shield muck sample is placed in a constant-temperature drying box, the temperature is set to 105 DEG C, and the drying time is 2 hours; Screening: After drying, the sample is screened using standard sieves to remove loose particles larger than 5 mm in size; Cleaning: After screening, the sample is cleaned using a soft brush to remove any fine particles remaining on the surface; Weighing: After cleaning, the sample is weighed using an electronic balance to determine the net weight, and the initial mass data is recorded.

3. The method of claim 1, wherein, The step S30 of compressing the sample at a set initial pressure includes the steps of measuring the initial compression displacement value D0 and the initial compressive strength value S0 by the sensors installed on the device: Self-checking: After the shield sample is placed correctly, the testing device is started, and the built-in self-checking program is completed. The self-checking includes checking the control system, pressure loading unit, and various sensors of the testing device. When an abnormality is found, the abnormality is checked and repaired. Parameter setting: The initial pressure is set to 50 kPa, the control pressure error is within ±5 kPa, and the pressure loading rate is set to uniform speed mode. Compression displacement measurement: After the sample is stably loaded at a pressure of 50 kPa, the displacement sensor on the testing device is used to collect the compression displacement value at a sampling frequency of 500 times per second based on the photoelectric encoding principle. The real-time collected compression displacement value is transmitted to the data processing unit of the device, and after removing noise interference, the processed compression displacement value is transmitted to the device control system to record the initial compression displacement value D0. Compressive strength measurement: After the sample is stably loaded at a pressure of 50 kPa, the pressure sensor on the testing device is used to collect the compressive strength value at a sampling frequency of 500 times per second based on the strain gauge principle. The real-time collected compressive strength value is transmitted to the data processing unit of the device, and after removing the error of zero drift, it is transmitted to the device control system to record the initial compressive strength value S0. Data review: After the device control system receives the initial compression displacement value D0 and the initial compressive strength value S0, the built-in data review program is automatically started. In the data review program, the average value of the initial parameters in the historical test data of similar shield samples is pre-set. When the initial compression displacement value D0 or the initial compressive strength value S0 exceeds the average value of the initial parameters by ±15%, the control system issues an abnormal alarm, and the operator checks the sample for abnormalities. When the initial compression displacement value D0 and the initial compressive strength value S0 are within the range of ±15% of the average value of the initial parameters, it is determined that the initial compression displacement value D0 and the initial compressive strength value S0 are valid, and they are stored as the basis data for subsequent resource utilization prediction.

4. The method of claim 1, wherein, The step S40 of adopting step-by-step pressure testing includes the step of increasing the test pressure according to the set gradient: Pressure parameter setting: The set pressure gradient value is input into the control system of the testing device, and the pressure is increased by 100 kPa each time. After the pressure is increased, it is stably maintained at the new pressure value for 60 seconds. Pressure process: When the pressure is increased each time, the pressure is increased at a speed of 10 kPa / s, and the pressure time is 10 seconds, which is the buffer time. Data acquisition time setting: During each 60-second stable maintenance phase, the sensor collects data at a sampling frequency of 500 times per second when the maintenance time reaches 50 seconds. Whole process state monitoring: during the step-by-step pressure test, a fault detection unit on the test equipment performs whole process state monitoring, the fault detection unit includes a temperature sensor and a pressure fluctuation sensor, and real-time monitoring of the temperature and pressure fluctuation during the step-by-step pressure test is performed; when it is monitored that the data exceeds preset temperature threshold values and pressure fluctuation threshold values, the fault detection unit sends a shutdown instruction to a device control system, and the control system controls the test equipment to stop running.

5. A system for predicting the resource utilization of shield muck, applied to a method for predicting the resource utilization of shield muck according to any one of claims 1 to 4, characterized in that, The system comprises: Shield muck sample selection and pretreatment module: collect a shield muck sample to be tested, visually check whether there is impurity or abnormal substance in the appearance of the shield muck sample, replace the shield muck sample to be tested when there is abnormality, and pretreat the shield muck sample when there is no abnormality to remove surface moisture and loose particles; Shield muck sample fixing and measuring module: after the pretreatment is completed, place the shield muck sample in a test container, weigh the sample mass by using an electronic balance, and measure the particle size distribution of the shield muck particles by using a laser particle size analyzer; Shield muck sample initial parameter acquisition module: start the test equipment, compress the shield muck sample at a set initial pressure, and measure an initial compression displacement value D0 and an initial compression strength value S0 by using a sensor installed on the equipment; Shield muck sample test parameter acquisition module: adopt step-by-step pressure test, increase the test pressure according to a set gradient, and measure a compression displacement value Dn and a compression strength value Sn under each pressure value after 60 seconds of stable keeping at each pressure value; Shield muck resource utilization prediction module: input the measured data values into a pre-designed resource utilization prediction model, judge whether the shield muck is suitable for resource utilization in combination with physical parameters of the shield muck, generate a data report of the test result and an optimal utilization mode calculated, and store the data report in a database; The test container in the shield muck sample fixing and measuring module is designed according to the volume of the shield muck and test requirements; the particle size distribution of the shield muck particles is measured by using the laser particle size analyzer, and the measurement error range is ±0.1%.

6. A system for predicting the resource recovery potential of shield muck according to claim 5, wherein, The step of pretreating the shield muck sample to remove surface moisture and loose particles when there is no abnormality in the shield muck sample selection and pretreatment module comprises the following steps: Drying: place the shield muck sample in a constant-temperature drying box, set the temperature to 105 DEG C, and dry for 2 hours; Screening: after the drying is completed, screen the shield muck sample by using a standard screen to remove loose particles with a particle size greater than 5 mm; Cleaning: after the screening is completed, clean the shield muck sample by using a soft brush to remove fine particles remaining on the surface of the shield muck sample; Weighing: after the cleaning is completed, weigh the net weight of the shield muck sample by using the electronic balance, and record the initial mass data.

7. The system for predicting the recyclability of shield muck according to claim 5, wherein, The step of compressing the shield muck sample at a set initial pressure and measuring an initial compression displacement value D0 and an initial compression strength value S0 by using a sensor installed on the equipment in the shield muck sample initial parameter acquisition module comprises the following steps: Device self-checking: after the shield muck sample is placed correctly, start the test equipment, and wait for the built-in self-checking program of the test equipment to complete the device self-checking; the self-checking content comprises checking whether the control system, the pressure loading unit and various sensors of the test equipment are normally operated, performing abnormality troubleshooting and repairing when there is abnormal operation, and the like. Parameter setting: set the initial pressure to 50 kPa, control the pressure error within ± 5 kPa, and set the pressure loading rate to uniform mode; Compression displacement measurement: after the slag sample is stably loaded at a pressure of 50 kPa, the displacement sensor on the test equipment is used to collect the compression displacement value at a sampling frequency of 500 times per second based on the photoelectric encoding principle. The real-time collected compression displacement value is transmitted to the data processing unit of the equipment, after removing noise interference, the processed compression displacement value is transmitted to the equipment control system to record the initial compression displacement value D0; Compressive strength measurement: after the slag sample is stably loaded at a pressure of 50 kPa, the pressure sensor on the test equipment is used to collect the compressive strength value at a sampling frequency of 500 times per second based on the strain gauge principle. The real-time collected compressive strength value is transmitted to the data processing unit of the equipment, after removing the error of zero drift, it is transmitted to the equipment control system to record the initial compressive strength value S0; Data review: after the equipment control system receives the initial compression displacement value D0 and the initial compressive strength value S0, it automatically starts the built-in data review program. In the data review program, the average value of the initial parameters in the historical test data of similar shield slag is set in advance. When the initial compression displacement value D0 or the initial compressive strength value S0 exceeds the average value of the initial parameters by ± 15%, the control system issues an abnormal alarm, and the operator checks the slag sample for abnormalities. When the initial compression displacement value D0 and the initial compressive strength value S0 are within the range of ± 15% of the average value of the initial parameters, it is determined that the initial compression displacement value D0 and the initial compressive strength value S0 are valid, and they are stored as the basis data for subsequent resource utilization prediction.

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