Solid waste soil resource comprehensive utilization full life cycle management method

Abstract

The invention discloses a solid waste soil resource comprehensive utilization full life cycle management method. The method comprises the following steps: obtaining solid waste soil raw material data; selecting a proper pretreatment mode according to the raw material data to obtain an improved waste soil raw material; the improved waste soil raw materials are backfilled, environmental data in the backfilling process are collected in real time, and the backfilling mode is dynamically regulated and controlled according to the environmental data; and optimizing a backfilling process according to the environmental data change and the backfilling regulation and control mode, and generating a full-life-cycle management report. Preferably, the raw material pretreatment comprises particle crushing, chemical modification and activity correction; the environment data comprises temperature, underground water pressure and ground stress, and a regulation and control instruction is generated based on error judgment and an abnormal trigger factor; and calculating a process error through the performance data and the target vector, and performing optimization regulation and control according to a feedback formula. According to the method, whole-process perception, dynamic control and process optimization of solid waste soil recovery treatment are realized, and the resource utilization efficiency and the engineering safety are improved.

Description

Technical Field

[0001] The present invention relates to the field of solid waste soil resource recycling, and particularly to a full-life cycle management method for comprehensive utilization of solid waste soil resources. Background Art

[0002] At present, in the process of comprehensive utilization of solid waste soil, it is often difficult to standardize the production process due to the complex raw material composition and large fluctuations in physical and mechanical properties. Traditional processes mostly adopt fixed ratios and cannot adjust parameters according to the real-time changes of raw materials, resulting in unstable product quality, low resource utilization rate. At the same time, there is a lack of linkage with the Internet of Things and data analysis systems, and online monitoring and dynamic regulation cannot be achieved. In the process of traditional solid waste soil treatment, the following key technical pain points mainly exist: (1) The sampling link relies on manual experience to select points, which is difficult to cover the variable areas of the soil body, resulting in poor representativeness; (2) In the backfill and reuse stage, there is a lack of real-time perception and process monitoring of the underground environment, and it is difficult to respond in time to changes such as settlement, pressure, and water level, leaving hidden dangers of secondary environmental risks; (3) The detection, feedback, and management means after the process execution are scattered, lacking the ability of data traceability and full-life cycle management of the whole process.

[0003] Therefore, there is an urgent need for a full-life cycle management method that can realize data-driven, state perception, feedback regulation, and decision optimization from sampling to reuse, so as to improve the efficiency, reliability, and engineering adaptability of comprehensive utilization of solid waste soil resources. Summary of the Invention

[0004] The present invention provides a full-life cycle management method for comprehensive utilization of solid waste soil resources, which realizes the utilization of solid waste soil resources with data-driven throughout the process, real-time state perception, and optimized decision-making through feedback regulation.

[0005] In a first aspect, the present invention provides a full-life cycle management method for comprehensive utilization of solid waste soil resources, including: Obtaining solid waste soil raw material data; Selecting a suitable method to pre-treat the solid waste soil raw materials according to the solid waste soil raw material data to obtain improved waste soil raw materials; Backfilling the improved waste soil raw materials, obtaining environmental data during the backfilling process in real time, and dynamically regulating the backfilling method according to the environmental data; Optimizing the backfilling process and generating a full-life cycle management report for comprehensive utilization of waste soil resources according to the changes in the environmental data during the process of dynamically regulating the backfilling method.

[0006] Optionally, the step of selecting a suitable method to pre-treat the solid waste soil raw materials according to the solid waste soil raw material data to obtain improved waste soil raw materials further includes: crushing the solid waste soil raw material particles into target particle sizes and performing chemical modification and activity correction.

[0007] Optionally, for the backfilling of the improved waste soil raw materials, environmental data during the backfilling process is obtained in real time, and the backfilling method is dynamically adjusted according to the environmental data. It further includes: deploying an online monitoring system to collect the environmental data; obtaining real-time status data based on the environmental data and preprocessing data; dynamically adjusting the backfilling method according to the real-time status data; where the preprocessing data is the data of the improved waste soil raw materials.

[0008] Optionally, for the dynamically adjusting the backfilling method according to the real-time status data, it further includes: issuing a control instruction according to the error between the real-time status data and the target status data.

[0009] Optionally, for the backfilling of the improved waste soil raw materials, environmental data during the backfilling process is obtained in real time, and the backfilling method is dynamically adjusted according to the environmental data. It further includes: obtaining the current temperature, the current groundwater pressure, and the current ground stress in real time; obtaining an environmental anomaly trigger factor by comparing the differences between the current temperature, the current groundwater pressure, and the current ground stress and their respective thresholds; dynamically adjusting the backfilling method according to the value of the environmental anomaly trigger factor.

[0010] Optionally, during the process of dynamically adjusting the backfilling method according to the environmental data changes, optimizing the backfilling process and generating a full life cycle management report for the comprehensive utilization of waste soil resources, it further includes: obtaining the real-time process control instruction issued in the previous step, and at the same time, collecting the key performance data during the actual operation process to form a performance data vector; calculating the process error according to the performance data vector and the target vector; obtaining the process optimization adjustment range according to the process error and the feedback formula; integrating the data of the entire production cycle to obtain a full life cycle management report.

[0011] Optionally, after obtaining the full life cycle management report, it further includes: checking the continuity of each data through the full life cycle management report, and judging whether there are errors in the data according to the continuity.

[0012] Optionally, for obtaining the solid waste soil raw material data, it further includes: obtaining the solid waste soil raw material data by placing sensors at multiple locations in situ.

[0013] In a second aspect, the present invention provides an electronic device, including: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method in the present invention.

[0014] In a third aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method of the present invention.

[0015] The beneficial effects of the present invention are as follows. Compared with the prior art, the present invention has the following advantages: 1) Realize the closed-loop management of solid waste soil treatment driven by data throughout the whole process. This application runs through various stages such as sampling, pretreatment, backfilling, and feedback optimization. Based on the real-time acquisition and linkage regulation of raw material data and environmental data, a closed-loop system for the whole life cycle of solid waste soil resource utilization is constructed, improving the systematicness and intelligent level of management.

[0016] 2) Through the real-time monitoring and responsive regulation of environmental factors such as temperature, ground stress, and groundwater pressure during the backfilling process, the backfilling parameters can be dynamically adjusted according to the on-site geological change conditions. By collecting the deviation between the on-site performance data and the target parameters, generating regulatory feedback instructions in real time and optimizing the process execution strategy, the upgrade from "static process execution" to "dynamic adaptive optimization" is realized, improving the engineering quality control ability.

[0017] 3) Disclose the specific parameter design of the whole process and the available calculation methods, and based on the data of the whole life cycle management, through the coherent analysis of the data, judge whether there is any abnormality in the data, and reverse infer the sensor or system failure through the abnormal data.

[0018] Generally speaking, the present invention realizes the closed-loop management of solid waste soil treatment driven by data throughout the whole process, covering various stages such as sampling, pretreatment, backfilling, and feedback optimization, improving the systematic and intelligent levels; through the real-time monitoring and regulation of environmental factors such as temperature, ground stress, and groundwater pressure, combined with the deviation between the on-site performance data and the target parameters to generate feedback instructions, realizing the dynamic adaptive optimization of the backfilling process; at the same time, disclose the key parameters and calculation methods of the whole process, and construct a coherent analysis mechanism based on the life cycle data, which can be used to identify data abnormalities and reverse diagnose sensor or system failures, ensuring process stability and data reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present invention and, together with the specification, are used to explain the principles of the present invention.

[0020] Figure 1 It is a schematic diagram of a method for the whole life cycle management of comprehensive utilization of solid waste soil resources provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of a method for dynamically regulating the backfilling of solid waste soil provided by an embodiment of the present invention; Figure 3Schematic diagram of an optimized backfilling process method provided by an embodiment of the present invention; Through the above-mentioned drawings, specific embodiments of the present invention have been shown, and there will be more detailed descriptions hereinafter. These drawings and textual descriptions are not intended to limit the scope of the inventive concept in any way, but to illustrate the concept of the present invention to those skilled in the art by reference to specific embodiments. Specific Embodiments

[0021] The present invention will be further described below in conjunction with the drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and cannot be used to limit the protection scope of the present invention.

[0022] Figure 1 Schematic diagram of a full life cycle management method for comprehensive utilization of solid waste soil resources provided by an embodiment of the present invention. Now, specific discussions will be carried out for each step in combination with this embodiment.

[0023] S100. Obtain solid waste soil raw material data.

[0024] In this step, traditional sample collection methods can be used to obtain solid waste soil raw material data. In particular, the method of deploying sensors on-site can also be used to obtain solid waste soil raw material data. The overall goal of this step is to achieve on-site collection of solid waste soil samples and obtain real-time monitoring data of the underground environment, ensure that the collected data is comprehensive and accurate, and form a report on the basic physical and chemical parameters of the raw materials and the initial state data of the underground environment. The following details each sub-step of this step: S110. Sensor deployment and environmental monitoring equipment calibration. Select several monitoring points in the solid waste soil collection area. Each monitoring point should be equipped with a temperature sensor, a humidity sensor, and groundwater pressure and ground stress sensors. During installation, the positions (coordinates, depths) of each device should be recorded and the wireless data connection with the central control device should be ensured to be stable. At the same time, each sensor needs to be calibrated. Taking the temperature sensor as an example: Corrected temperature T_corrected = T_measured – ΔT; Wherein, T_measured is the temperature value initially measured by the sensor (unit: °C), and ΔT is the correction deviation (within the range given by the manufacturer, usually 0 to 1 °C); for example, if T_measured = 22.5 °C and the manufacturer stipulates a deviation ΔT = 0.5 °C, then T_corrected = 22.5 - 0.5 = 22.0 °C. For multi-parameter environmental monitoring, vector representation and matrix correction can be used: E_corrected = M × E_measured; Among them, \(E_{measured}\) is the environmental raw data vector \([T, H, P]\), specifically representing: \(T\): temperature (\(^{\circ}C\)); \(H\): relative humidity (\(\%\)); \(P\): groundwater pressure (\(MPa\)); \(M\) is a \(3\times3\) correction matrix, and each of its elements represents the correction coefficient of the corresponding sensor. The recommended range is \(0.95 - 1.05\), which is specifically determined according to the calibration experiment; \(E_{corrected}\) is the corrected environmental data vector. If \(E_{measured}=[22.5, 65, 0.85]\), and let \(M\) be the following matrix: \([0.98\ 0\ 0 / 0\ 1.02\ 0 / 0\ 0\ 1.00]\); then \(E_{corrected}=[22.05, 66.3, 0.85]\). That is, the temperature, humidity, and pressure sensors deployed at monitoring point A generate reference data after correction: temperature \(22.0^{\circ}C\), humidity \(66\%\), and groundwater pressure \(0.85 MPa\).

[0025] S120, On-site sample collection of solid waste soil. According to the distribution of monitoring points and the requirements of engineering design, use the on-site topographic map and sensor layout records to determine multiple sampling positions to ensure the representativeness of the covered area. Each sampling point should be marked with a number, geographical coordinates, and sampling depth (it is recommended to record data for two depth layers of \(0 - 1 m\) and \(1 - 3 m\)). Output solid waste soil samples with complete numbers and identifications. The sampling record form includes sampling positions, depths, and preliminary on-site descriptions. For example: Sampling near monitoring point A, numbered "Sample - A1", record the coordinates \((X = 120.123, Y = 30.456)\), depth \(0.8 m\), and the on-site observation shows that the sample is medium yellow and contains a small amount of fine sand.

[0026] S130, Preliminary data integration and generation of raw material reports.

[0027] Establish a data vector: \(D = [W, pH, D_{avg}, \sigma, T_{corrected}, H_{corrected}, P_{corrected}]\). Among them, \(W\) represents the moisture content (for example: \(0.1111\)); \(pH\) represents the soil sample acidity and alkalinity (for example: \(7.2\)); \(D_{avg}\) represents the average particle diameter (micrometers, example: \(150\)); \(\sigma\) represents the standard deviation of particle diameter distribution (micrometers, the value is obtained according to the instrument calculation); \(T_{corrected}\) is the corrected temperature (\(^{\circ}C\)); \(H_{corrected}\) is the corrected humidity (\(\%\)); \(P_{corrected}\) is the corrected groundwater pressure (\(MPa\)); each element in the \(D\) vector represents the basic physical or chemical parameters and environmental information of the solid waste soil.

[0028] Taking Sample-A1 as an example, a report is generated after integrating the data: moisture content W: 11.11%, pH value: 7.2, average particle diameter D_avg: 150 microns, standard deviation σ: 20 microns, environmental parameters: temperature 22.0 °C, humidity 66%, groundwater pressure 0.85 Mpa. The report is archived in tabular form and serves as the basis for raw material modification and subsequent processes.

[0029] S200. Select a suitable method to pretreat the solid waste soil raw material according to the solid waste soil raw material data to obtain an improved waste soil raw material.

[0030] The solid waste soil is subjected to preliminary physical treatment, including ultrafine pulverization, vibration screening, and water washing treatment, to remove harmful impurities and homogenize the particle size distribution; during this period, the underground environmental data is standardized according to a preset calibration formula. The "Report on Basic Physical and Chemical Parameters of Raw Materials and Initial State Data of Underground Environment" generated in step S100 is imported into a data processing terminal (such as a laptop or on-site data acquisition system), and the data content mainly includes moisture content W (unit: dimensionless or percentage, for example, 0.1111 represents 11.11%); pH value (for example, 7.2); average particle diameter D_avg (unit: micron, for example, 150); standard deviation of particle diameter σ (unit: micron, for example, 20); corrected temperature T_corrected (unit: °C, for example, 22.0); corrected humidity H_corrected (unit: %, for example, 66); corrected groundwater pressure P_corrected (unit: Mpa, for example, 0.85); Example: The imported sample data is Sample-A1, and its parameter record is [W = 0.1111, pH = 7.2, D_avg = 150, σ = 20, T_corrected = 22.0, H_corrected = 66, P_corrected = 0.85].

[0031] Construct a parameter vector. For each sample, construct a parameter vector V, which is defined as follows: V = [W, pH, D_avg, σ, T_corrected, H_corrected, P_corrected]. Where, W: moisture content; pH: acidity and alkalinity; D_avg: average particle diameter; σ: standard deviation of particle diameter distribution; T_corrected: ambient temperature; H_corrected: ambient humidity; P_corrected: groundwater pressure. This vector is used to describe the comprehensive characteristics of each batch of raw materials, facilitating subsequent unified processing and hierarchical modification. During operation, organize the data of each sample into the above vector and record it in the data table. For example, for Sample-A1, V = [0.1111, 7.2, 150, 20, 22.0, 66, 0.85].

[0032] Conduct batch division according to the key indicators in the parameter vector V. Establish discrimination rules, such as grading based on D_avg and σ: If D_avg < 100 microns and σ < 15, it belongs to the "high-quality group"; If 100 ≤ D_avg ≤ 200 microns and 15 ≤ σ ≤ 30, it belongs to the "medium-quality group"; If D_avg > 200 microns or σ > 30, it belongs to the "low-quality group". To introduce multi-factor consideration, an auxiliary scoring formula is also designed to comprehensively evaluate the stability of the raw materials through moisture and pH. S = α × (1 - |W - W_target|) + β × (1 - |pH - pH_target| / pH_target); Where, S: comprehensive score; W_target: target moisture content, usually with a value range of 0.10 - 0.15; pH_target: target pH value, usually set at 7.0 (neutral) or adjusted according to process requirements (recommended range 7.0 - 8.0); α, β: weight coefficients, with reasonable values of 0.5 (adjustable range 0.3 - 0.7); Among them, the values of α and β can be set by senior engineers according to experience, or the values of α and β can be feedback modified according to the processing effect in the subsequent full-life cycle processing report. This formula is used to assist in judging whether the sample meets the uniformity requirements. The closer the S value is to 1, the higher the stability of the raw materials. According to D_avg, σ and S values, complete the batch division and output the batch division report and grouped data.

[0033] For example: For Sample-A1, it is known that W = 0.1111, pH = 7.2. Taking W_target = 0.125 and pH_target = 7.0, with α = β = 0.5, then S≈ 0.9788. Since D_avg = 150 microns (between 100 and 200) and σ = 20 (between 15 and 30), Sample-A1 is classified into the "medium quality group". With a relatively high S value, it is recorded in the batch report.

[0034] Based on the grouping results, select an appropriate crushing method. The goal is to reduce the particle diameter in the sample to within the predetermined target D_target, usually setting the range of D_target to be 80 - 100 microns. Set the crushing uniformity factor U to evaluate the crushing effect. The calculation formula is: U = D_avg / D_target. Where, D_avg: the current average particle diameter of the sample (unit: micron, obtained from the V vector), D_target: the target particle diameter (unit: micron, set according to engineering requirements), U: the uniformity factor. Ideally, U greater than 1 indicates that further crushing is required, and after crushing, U should be close to or less than 1. During the crushing process, continuously sample and detect D_avg, and adjust the rotation speed and feed rate of the crusher to ensure that the final D_avg reaches the D_target range. For example, for Sample-A1, its original D_avg = 150 microns. If D_target is set to 90 microns, then initially calculate U = 150 / 90 ≈ 1.67; if it is detected that D_avg drops to 85 microns after crushing, then update U_adjusted = 85 / 90 ≈ 0.94, which meets the requirements.

[0035] After crushing, the solid waste soil raw materials are fed into a vibrating screening device to remove large particles and impurities and achieve particle size homogenization. The aperture of the sieve mesh is selected to match D_target (generally, the sieve hole is about 1.1 times the target particle size) to ensure that most particles pass through. The screening passing rate E_screen represents the proportion of particles passing under the sieve mesh, and the formula is: E_screen = (N_passed / N_total) × 100%; where: N_total: the total number of particles entering the screening device (counted by the instrument); N_passed: the number of particles passing through the sieve mesh after screening; E_screen: the screening passing rate, and the target value is recommended to be greater than 85%. After the vibrating screening is completed, D_avg and σ are detected in real time to ensure that the particle distribution reaches a uniform state. For example, for Sample-A1, assuming that after crushing, N_total = 10,000 particles are counted, and among them, N_passed = 8,700, then E_screen = (8700 / 10000) × 100% = 87%, meeting the design requirements.

[0036] Based on the samples after physical pretreatment, the mineral active components in solid waste soil are detected, with the goal of adjusting the active component content to a predetermined target range (e.g., 40% - 60%). The original active content C_original (unit: percentage) is measured using a portable chemical detection instrument and then adjusted by adding chemical modifiers (such as alkaline activators, silicates, etc.). The adjustment formula is defined as follows: C_adjusted = C_original + ΔC; where, C_adjusted: the adjusted active content (percentage); C_original: the original active content measured after physical pretreatment (percentage); ΔC: the percentage of content to be increased, determined based on previous tests, and the recommended range is 0% - 10%. For Sample-A1, assuming C_original = 35% and the target C_adjusted = 50%, then theoretically ΔC = 15%; considering the reaction efficiency, the actual added modifier makes ΔC = 12%, and finally the measured C_adjusted is close to 47% - 50%. During the chemical modification process, the pH value must be regulated to facilitate the further activation of solid waste soil. The target pH value is generally set at 7.0 - 8.0, and a neutralizing reagent (such as lime or calcium carbonate) is used to adjust the pH. The adjustment amount can be calculated according to the following formula: pH_adjusted = pH_original + K × V_reagent; where, pH_adjusted: the adjusted pH value; pH_original: the pH value measured after physical pretreatment (e.g., 7.2, obtained from the data in step 1); K: the adjustment coefficient, indicating the adjustment effect of each unit volume of reagent on the pH (unit: pH unit / liter, the recommended range is 0.05 - 0.2, determined according to test data); V_reagent: the volume of the added neutralizing reagent (unit: liter, determined according to the sample volume, generally between 0.1 - 0.5 L); according to the actually measured adjusted target pH value, the addition amount is fine-tuned through experiments to ensure accurate pH adjustment. For example: if Sample-A1 measures pH_original = 7.2 and the target pH_adjusted = 7.5, taking K = 0.1, then V_reagent = (7.5 - 7.2) / 0.1 = 3.0 L; in actual operation, it is adjusted to 2.8 - 3.0 L according to the results of small-scale tests.

[0037] Mix the physically pre-treated solid waste soil and the chemical modifier in proportion according to a pre-determined ratio. The mixing amount is calculated using the following formula: Q = P_sample × R. Where, Q: total amount of chemical modifier required (unit: kg); P_sample: total mass of the solid waste soil sample (unit: kg, obtained by weighing on-site); R: modifier ratio (dimensionless, recommended range 0.10 - 0.20, i.e., add 10 - 20 kg of modifier per 100 kg of solid waste soil). Use a mechanical stirring device to stir the mixture evenly and let the container stand still for 30 - 60 minutes to promote the chemical reaction. For Batch Sample-A1, assume P_sample = 500 kg, take R = 0.15, then Q = 500 × 0.15 = 75 kg of modifier; let it stand still for 45 minutes after mixing, and measure the pH and C_adjusted values again to ensure that the set indicators are met.

[0038] Furthermore, the data can be standardized. V_processed = [W, pH_adjusted, D_avg_processed, σ_processed, C_adjusted, T_corrected, H_corrected, P_corrected]. Among them, the meanings of each component are the same as those described above, but D_avg_processed and σ_processed reflect the test results after physical pretreatment, and pH_adjusted and C_adjusted represent the indicators after chemical modification. V_normalized = (V_processed - V_min) / (V_max - V_min); where V_processed is the specific value of a certain parameter in the vector; V_min and V_max are respectively the minimum and maximum values detected for the same parameter in all batches of samples; V_normalized is the value after standardization. Normalization processing is performed on each parameter in V_processed respectively to generate the standardized data set D_standard. For example, for pH_adjusted, if the pH range of all samples is 7.0 - 8.0, then the pH_normalized of Sample-A1 = (7.5 - 7.0) / (8.0 - 7.0) = 0.5; similarly, if the range of D_avg_processed is set to 80 - 100 microns, then for Sample-A1 (D_avg_processed = 85 microns), the normalization result is (85 - 80) / (100 - 80) = 5 / 20 = 0.25. Store D_standard in the central database, and the file format is CSV or Excel, and the unit and parameter meaning are clearly marked for each field.

[0039] Step S300: Backfill the improved waste soil raw material, obtain the environmental data during the backfilling process in real time, and dynamically adjust the backfilling method according to the environmental data.

[0040] Deploy a multi-parameter online monitoring system in key construction processes such as solid waste soil mixing, pouring, and curing to collect real-time data on temperature, humidity, viscosity, rheological properties, as well as water pressure and stress changes in the underground environment. At the same time, introduce an "underground environment dynamic response system" which integrates micro mechanical actuators and active grouting devices. Using the monitored anomalies in the underground environment (such as local high water pressure, rapid stress changes, or abnormal temperature and humidity gradients), it automatically adjusts the local solid waste soil modification state, such as initiating local reinforcement, compaction, or grouting compensation to balance local environmental disturbances. The data management platform receives two parts of data: one is the standard process parameter data, and the other is the underground environment dynamic response feedback signal. The system automatically generates control instructions based on the preset physical and mechanical models and underground environment response models to achieve closed-loop regulation of process parameters and environmental response measures.

[0041] Figure 2 Schematic diagram of a dynamic regulation method for solid waste soil backfill provided by an embodiment of the present invention.

[0042] S310. Deploy an online monitoring system to collect the environmental data.

[0043] Install temperature, humidity, viscosity, rheological property sensors, as well as groundwater pressure and ground stress sensors at key process links (such as mixing, pouring, curing) and key underground environmental locations. Each monitoring point needs to be equipped with a multi-channel data acquisition module to ensure real-time data transmission to the central control terminal. Equipment parameter descriptions are as follows: Temperature sensor T_sensor: Measures the temperature of the process mixture (unit: °C); Humidity sensor H_sensor: Measures the relative humidity of the environment (unit: %); Viscosity sensor η_sensor: Measures the viscosity of the mixture (unit: Pa·s); Rheological sensor R_sensor: Measures the fluidity of the mixture (unit: m² / s); Groundwater pressure sensor P_sensor: Measures the groundwater pressure (unit: MPa); Ground stress sensor S_sensor: Measures the geotechnical stress (unit: MPa).

[0044] For each monitoring point, the real-time collection of sensor data constitutes an online data vector E_online, defined as: E_online = [T_m, H_m, η_m, R_m, P_m, S_m]; where, T_m: Currently measured temperature; H_m: Currently measured humidity; η_m: Currently measured viscosity of the mixture; R_m: Currently measured rheological index of the mixture; P_m: Currently measured groundwater pressure; S_m: Currently measured ground stress. Note: This vector reflects the process environment and underground environment states in real time, facilitating subsequent process regulation. For example: At a certain monitoring point at the outlet of the mixer, the data E_online = [23.0 °C, 68 %, 0.35 Pa·s, 1.2×10⁻ 5m² / s, 0.90 MPa, 1.0 MPa]。

[0045] S320. Obtain real-time status data based on the environmental data and the preprocessed data.

[0046] In the standardized data set D_standard, extract the key physicochemical parameters of the solid waste soil, namely V_processed. Combine the online monitoring data E_online with the preprocessed data vector V_processed to form a real-time status vector R_state for dynamic regulation. R_state = K1 × V_processed + K2 × E_online; where K1: weight coefficient vector, used to describe the influence of the preprocessed data on the status, and its form is K1 = [k1, k2, k3, k4, k5, k6, k7, k8]; K2: weight coefficient vector, used to describe the influence of the online monitoring data on the status, and its form is K2 = [k9, k10, k11, k12, k13, k14]; Note: V_processed is an 8-dimensional vector; E_online is a 6-dimensional vector. For example, let K1 = [1, 1, 1, 1, 1, 0, 0, 0] (emphasizing physical and chemical parameters), K2 = [1, 1, 0.5, 0.5, 1, 1] (emphasizing environmental dynamic data), then for Sample-A1, V_processed = [0.1111, 7.5, 85, 18, 50, 22.0, 66, 0.85], E_online (taking data at the on-site mixing point) = [23.0, 68, 0.35, 1.2e-5, 0.90, 1.0], then R_state is calculated by two components respectively: R_state_part1 = 1×0.1111 + 1×7.5 + 1×85 + 1×18 + 1×50 + 0×22.0 + 0×66 + 0×0.85 = 0.1111 + 7.5 + 85 + 18 + 50 = 160.6111, R_state_part2 = 1×23.0 + 1×68 + 0.5×0.35 + 0.5×1.2e-5 + 1×0.90 + 1×1.0 = 23.0 + 68 + 0.175 + 0.000006 + 0.90 + 1.0 ≈ 93.0750, and finally R_state = 160.6111 + 93.0750 = 253.6861. The value of R_state is used as the input index for dynamic regulation. S330. Dynamically adjust the backfilling method according to the real-time status data.

[0047] Build a dynamic adjustment feedback model, the purpose of which is to issue an adjustment instruction according to the error ΔR between R_state and the preset target state R_target. Define the adjustment error: ΔR = R_target - R_state; where, R_target: the preset ideal state value (determined according to the process requirements, obtained through on-site tests, and the range is based on the specific project requirements, such as 250 - 260), R_state: the real-time state vector (obtained through step S320), ΔR: the error, which is used to judge whether the process parameters need to be adjusted. For example, if the preset R_target = 255 and the calculated R_state = 253.69, then ΔR = 255 - 253.69 = 1.31; the error is small, and if the error exceeds the set threshold, the adjustment measure will be initiated.

[0048] Adopt a simple proportional feedback control strategy to generate an adjustment instruction. The adjustment instruction I_control is defined as I_control = K_p × ΔR; where, I_control: the numerical value of the control instruction, which is used to adjust the process parameters (such as stirring speed, feeding ratio, etc.); K_p: the proportional control coefficient (constant, the recommended value range is 0.5 - 2.0, determined according to actual tests); ΔR: the error, calculated from the above steps. The significance of this method is that the larger the error ΔR, the larger the adjustment instruction I_control, which prompts the process system to make adjustments with a higher intensity to ensure that the real-time state converges to R_target. For example, with K_p = 1.0, if ΔR = 1.31, then I_control = 1.0 × 1.31 = 1.31; the system will adjust the corresponding parameters according to this value, such as increasing the mixing speed or appropriately adjusting the dosage of the modifier.

[0049] In another embodiment, to ensure active response when anomalies occur in the underground environment (such as a sharp rise in groundwater pressure or a sudden change in in-situ stress), a "Dynamic Underground Environment Response System" (hereinafter referred to as DERS) is configured. DERS includes dedicated on-site execution modules, such as micro-mechanical actuators and active grouting devices. Each response unit is configured with monitoring threshold parameters T_env_threshold, P_env_threshold, and S_env_threshold, corresponding to the temperature, groundwater pressure, and in-situ stress anomaly thresholds respectively. For example: T_env_threshold is set to 30 °C (with a ±1 °C fluctuation), P_env_threshold is set to 1.2 MPa (±0.1 MPa), and S_env_threshold is set to 1.5 MPa (±0.1 MPa). Define the underground environment anomaly trigger factor F_env, where F_env = w_T × max(0, T_current - T_env_threshold) + w_P× max(0, P_current - P_env_threshold) + w_S × max(0, S_current - S_env_threshold); where: T_current: the current on-site detected temperature; P_current: the current groundwater pressure; S_current: the current in-situ stress; T_env_threshold, P_env_threshold, S_env_threshold: the preset thresholds; w_T, w_P, w_S are the weights for each item, and the recommended value range is 0.3 - 0.4, with a total sum of 1. Note: When any parameter exceeds the threshold, the value of max(0,...) is non-zero, and F_env accumulates. If F_env exceeds the set response threshold F_trigger (for example, F_trigger = 0.5), then an automatic response is triggered. For example: If the on-site monitoring shows T_current = 31 °C, P_current = 1.3 MPa, S_current = 1.6 MPa, and w_T = w_P = w_S = 0.33, then F_env = 0.33×(31 - 30) + 0.33×(1.3 - 1.2) + 0.33×(1.6 - 1.5) = 0.33×1 + 0.33×0.1 + 0.33×0.1 = 0.33 + 0.033 + 0.033 = 0.396. Since F_env < 0.5, no response is triggered at this time; if the data is higher, then F_env will exceed 0.5 and trigger an automatic adjustment.

[0050] S400. During the process of dynamically regulating the backfilling method, optimize the backfilling process according to the changes in the environmental data and generate a comprehensive life cycle management report for the comprehensive utilization of waste soil resources.

[0051] According to the data analysis results, the system automatically generates a process optimization plan and environmental response adjustment suggestions, and feeds them back to the remote monitoring center. At the same time, regularly calibrate the equipment and process parameters, such as adjusting the dosing ratio of the modifier, revising the mixing and vibration parameters, to ensure that the solid waste soil modification process is always matched with the changes in the underground environment. Finally, store the optimization results in the comprehensive life cycle management database, and link with the engineering project management, quality inspection and environmental monitoring systems to achieve full-process dynamic supervision and traceability management.

[0052] Figure 3 , which is a schematic diagram of a method for optimizing the backfilling process provided in this embodiment.

[0053] S410. Obtain the real-time process control instruction issued in the previous step. At the same time, collect the key performance data during the actual operation process to form a performance data vector.

[0054] Input: The real-time process control instructions and underground environment adaptive adjustment instructions issued in the previous step (collectively referred to as R_command). This instruction contains process parameter adjustment signals, such as stirring speed adjustment, feeding ratio change, environmental compensation amount, etc., and the numerical form can be represented by the vector I_total. At the same time, through the sensors of each on-site process equipment (such as mixers, pouring devices, temperature control systems), key performance data during the actual operation process is collected to form the performance data vector S_perf. I_total = [I_mix, I_feed, I_temp], where I_mix is the stirring speed adjustment instruction (unit: revolutions per minute), I_feed is the feeding ratio adjustment instruction (dimensionless ratio), and I_temp is the temperature control instruction (unit: °C increase or decrease); S_perf = [Q_1, Q_2, Q_3, …, Q_n], and Q_i respectively represent the key monitoring indicators in the process (for example, mixing uniformity, fluidity, setting time, product compressive strength, etc.), and the numerical values are obtained based on the real-time measurement results of on-site instruments. Using the data acquisition terminal, I_total and S_perf are uploaded to the central data management platform in real time and stored as a time series data file (such as CSV format), and each record is attached with a time stamp and the corresponding process step number. For example, assume that at a certain moment, the R_command instruction I_total = [0.65, 0.39, 0.26]; at the same time, the corresponding monitored S_perf =[Q1=95, Q2=0.82, Q3=1500], and the system will record the data "Time t1: I_total = [0.65, 0.39, 0.26]; S_perf = [95, 0.82, 1500]".

[0055] S420. Calculate the process error according to the performance data vector and the target vector.

[0056] According to the engineering design requirements and historical test data, the target values of each key performance index are determined in advance, and the target vector Q_target is constructed. Q_target = [Q1_target, Q2_target, Q3_target, …, Q_n_target], where Q1_target represents the target mixing uniformity (for example, the target value is 100, and the unit is based on the instrument setting), Q2_target represents the target fluidity (for example, 0.85), Q3_target represents the target compressive strength (unit: kPa, such as 1550 kPa), and the remaining Q_i_target are determined according to the specific process.

[0057] According to the performance vector S_perf and the target vector Q_target collected in real time, calculate the error for each metric. E = Q_target − S_perf, where E is the process error vector, and its components E_i = Q_i_target − Q_i_measured. The error E reflects the deviation between the current process and the expected target. The larger the value, the greater the room for adjustment and improvement. For example, if Q_target = [100, 0.85, 1550], and at a certain moment S_perf = [95, 0.82, 1500], then E = [100 − 95, 0.85 − 0.82, 1550 − 1500] = [5, 0.03, 50].

[0058] S430. Obtain the process optimization adjustment range according to the process error and the feedback formula.

[0059] I_opt = K_feedback × E, where I_opt is the optimization adjustment instruction vector for subsequent adjustment of equipment parameters; K_feedback is the feedback proportionality coefficient vector K_feedback = [k1, k2, k3, …, k_n], and each k_i is determined based on on-site tests. The recommended value range is 0.5 to 2.0; E is the aforementioned error vector. The significance of this formula is that the larger the error E, the larger the corresponding optimization adjustment range I_opt, prompting the system to automatically approach the target state. For example, if the feedback coefficient is taken as K_feedback = [1.0, 1.0, 1.0], then I_opt = [1.0×5, 1.0×0.03, 1.0×50] = [5, 0.03, 50]; this indicates that the relevant parameters need to be adjusted respectively to increase the mixing uniformity by 5 units, the fluidity by 0.03, and the compressive strength by 50 kPa.

[0060] S440. Integrate the data of the entire production cycle to obtain the full life cycle management report.

[0061] In addition to real-time feedback, accumulate and integrate the data of the entire production cycle to generate a full life cycle management report. The report content should cover: raw data, preprocessed data, real-time monitoring data; error E and feedback optimization records for each period; early warning event records and handling situations. The report can use statistical data, charts, and numerical analysis to facilitate subsequent process improvement and quality traceability. For example, after one day of operation, the system automatically generates a report "2025-04-08_A1_Lifecycle_Report.xlsx", recording the data of the whole process from equipment initial calibration, real-time process adjustment to early warning handling.

[0062] It is particularly emphasized that due to the large amount of data processing designed above, the system also adds verification of all the generated data (including data such as I_total, S_perf, E, etc.), checking the data continuity, accuracy and no omission. If abnormal data is found, the original record is immediately retrieved for review, and the correction situation is marked in the report. For example, if the liquidity suddenly drops abnormally in the recorded data at a certain moment, the record is corrected again after comparing with the original sensor data to ensure that the data is accurately transmitted to the management system. Or, if it is found that the data uploaded by the original sensor has a mutation, then it may be necessary to further troubleshoot the original sensor. Through the analysis of data continuity, the entire system, including the relevant modules for data processing in the system and the data acquisition module (sensor), can be effectively monitored. That is, in the case of finding abnormal data, the possible faults of the system can be deduced backwards.

[0063] According to an embodiment of the present invention, the present invention also provides an electronic device, which may include: a processor, a communications interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus. The processor can call the logical instructions in the memory to execute the implementation method based on the soft authorization of the configuration software.

[0064] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. And the aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical discs and other various media that can store program codes.

[0065] On the other hand, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the implementation method based on the soft authorization of the configuration software provided by the above-mentioned various methods.

[0066] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative labor.

[0067] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0068] It should be understood that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A comprehensive life cycle management method for the comprehensive utilization of solid waste soil resources, comprising: Obtaining solid waste soil raw material data; Selecting a suitable method to preprocess the solid waste soil raw materials according to the solid waste soil raw material data to obtain improved waste soil raw materials; Backfilling the improved waste soil raw materials, obtaining environmental data during the backfilling process in real time, and dynamically adjusting the backfilling method according to the environmental data; According to the changes in the environmental data during the process of dynamically adjusting the backfilling method, optimizing the backfilling process and generating a comprehensive life cycle management report for the comprehensive utilization of waste soil resources.

2. The full life cycle management method for comprehensive utilization of solid waste soil resources according to claim 1, characterized in that The step of selecting a suitable method to preprocess the solid waste soil raw materials according to the solid waste soil raw material data to obtain improved waste soil raw materials further comprises: Crushing the solid waste soil raw material particles into target particle sizes and performing chemical modification and activity correction.

3. The full life cycle management method for comprehensive utilization of solid waste soil resources according to claim 1, characterized in that, The step of backfilling the improved waste soil raw materials, obtaining environmental data during the backfilling process in real time, and dynamically adjusting the backfilling method according to the environmental data further comprises: Deploying an online monitoring system to collect the environmental data; Obtaining real-time status data according to the environmental data and the pretreatment data; Dynamically adjusting the backfilling method according to the real-time status data; Wherein, the pretreatment data is the improved waste soil raw material data.

4. A comprehensive utilization life cycle management method for solid waste soil resources according to claim 3, characterized in that, The step of dynamically adjusting the backfilling method according to the real-time status data further comprises: Issuing a control instruction according to the error between the real-time status data and the target status data.

5. A comprehensive utilization full life cycle management method for solid waste soil resources according to claim 1, characterized in that The step of backfilling the improved waste soil raw materials, obtaining environmental data during the backfilling process in real time, and dynamically adjusting the backfilling method according to the environmental data further comprises: Obtaining the current temperature, the current groundwater pressure and the current ground stress in real time; Obtaining an environmental anomaly trigger factor by comparing the differences between the current temperature, the current groundwater pressure and the current ground stress and their respective thresholds; Dynamically adjusting the backfilling method according to the value of the environmental anomaly trigger factor.

6. The comprehensive life cycle management method for the comprehensive utilization of solid waste soil according to claim 3, characterized in that, The step of optimizing the backfilling process and generating a comprehensive life cycle management report for the comprehensive utilization of waste soil resources according to the changes in the environmental data during the process of dynamically adjusting the backfilling method further comprises: Obtaining the real-time process control instruction issued in the previous step, and at the same time, collecting the key performance data during the actual operation process to form a performance data vector; Calculating the process error according to the performance data vector and the target vector; Obtaining the process optimization adjustment range according to the process error and the feedback formula; Integrating the data of the entire production cycle to obtain a comprehensive life cycle management report.

7. A comprehensive utilization full life cycle management method for solid waste soil resources according to claim 6, characterized in that, After obtaining the comprehensive life cycle management report, it further comprises: Checking the continuity of each data through the comprehensive life cycle management report, and judging whether there is an error in the data according to the continuity.

8. A comprehensive life cycle management method for the comprehensive utilization of solid waste soil resources according to claim 1, characterized in that, The step of obtaining the solid waste soil raw material data further comprises: Obtaining the solid waste soil raw material data by placing sensors at multiple locations in situ.

9. An electronic device, comprising a processor and a storage medium; characterized in that: The storage medium is used for storing instructions; The processor is used to operate according to the instructions to execute the steps of the method according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method according to any one of claims 1-8.

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