Solid waste soil resource comprehensive utilization full life cycle management method

Abstract

This invention discloses a method for the comprehensive utilization and full life-cycle management of solid waste soil resources, comprising: acquiring solid waste soil raw material data; selecting a suitable pretreatment method based on the raw material data to obtain improved waste soil raw materials; backfilling the improved waste soil raw materials, collecting environmental data in real time during the backfilling process, and dynamically adjusting the backfilling method based on the environmental data; optimizing the backfilling process based on changes in environmental data and the backfilling adjustment method, and generating a full life-cycle management report. Preferably, the raw material pretreatment includes particle crushing, chemical modification, and activity correction; the environmental data includes temperature, groundwater pressure, and geostress, and control instructions are generated based on error judgment and abnormal triggering factors; the process error is calculated through performance data and target vectors, and optimization and control are performed according to feedback formulas. This method realizes full-process perception, dynamic control, and process optimization of solid waste soil recycling and treatment, improving resource utilization efficiency and engineering safety.

Description

Technical Field

[0001] This invention relates to the field of solid waste soil resource reuse, and in particular to a method for the comprehensive utilization of solid waste soil resources throughout their entire life cycle. Background Technology

[0002] Currently, in the process of comprehensive utilization of solid waste soil, it is often difficult to standardize the production process due to the complex composition of raw materials and large fluctuations in physical and mechanical properties. Traditional processes mostly use fixed ratios, which cannot adjust parameters according to real-time changes in raw materials, resulting in unstable product quality and low resource utilization. At the same time, there is a lack of linkage with the Internet of Things and data analysis systems, making it impossible to achieve online monitoring and dynamic control. In the traditional solid waste soil treatment process, there are several key technical pain points: (1) The sampling process relies on manual experience to select points, which is difficult to cover areas with soil variability, resulting in poor representativeness; (2) In the backfilling and reuse stages, there is a lack of real-time perception and process monitoring of the underground environment, making it difficult to respond in time to changes in settlement, pressure, water level, etc., which may lead to secondary environmental risks; (3) The detection, feedback and management methods after the process are scattered, and there is a lack of data traceability and life cycle management capabilities.

[0003] Therefore, there is an urgent need for a full lifecycle management method that can achieve data-driven, state-aware, feedback-controlled, and decision-optimized management throughout the entire process from sampling to reuse, in order to improve the efficiency, reliability, and engineering adaptability of the comprehensive utilization of solid waste soil resources. Summary of the Invention

[0004] This invention provides a method for the comprehensive utilization of solid waste soil resources throughout their entire life cycle, which realizes the utilization of solid waste soil resources through data-driven processes, real-time status perception, and feedback regulation and optimization decision-making.

[0005] In a first aspect, the present invention provides a method for the comprehensive utilization of solid waste soil resources and its full life-cycle management, including:

[0006] Obtain solid waste soil raw material data;

[0007] Based on the solid waste soil raw material data, a suitable method is selected to pretreat the solid waste soil raw material to obtain improved waste soil raw material;

[0008] The improved waste soil material is backfilled, and environmental data during the backfilling process is acquired in real time. The backfilling method is dynamically adjusted based on the environmental data.

[0009] Based on the changes in environmental data during the dynamic adjustment of the backfilling method, the backfilling process is optimized and a full life cycle management report for the comprehensive utilization of waste soil resources is generated.

[0010] Optionally, the step of selecting an appropriate method to pretreat the solid waste soil raw material based on the solid waste soil raw material data to obtain improved waste soil raw material further includes: crushing the solid waste soil raw material particles into target particle size and performing chemical modification and activity correction.

[0011] Optionally, the backfilling of the improved waste soil material, acquiring environmental data in real time during the backfilling process, and dynamically adjusting the backfilling method based on the environmental data, further includes: deploying an online monitoring system to collect the environmental data; obtaining real-time status data based on the environmental data and preprocessed data; and dynamically adjusting the backfilling method based on the real-time status data; wherein the preprocessed data is the improved waste soil material data.

[0012] Optionally, the method of dynamically adjusting the backfilling based on the real-time status data further includes: issuing an adjustment command based on the error between the real-time status data and the target status data.

[0013] Optionally, the backfilling of the improved waste soil material, and the acquisition of environmental data during the backfilling process in real time, and the dynamic adjustment of the backfilling method based on the environmental data, further includes: acquiring the current temperature, current groundwater pressure, and current ground stress in real time; obtaining an environmental anomaly triggering factor by comparing the differences between the current temperature, the current groundwater pressure, and the current ground stress and their respective thresholds; and dynamically adjusting the backfilling method based on the value of the environmental anomaly triggering factor.

[0014] Optionally, the step of optimizing the backfilling process and generating a full life cycle management report for comprehensive utilization of waste soil resources based on changes in environmental data during the process of dynamically adjusting the backfilling method further includes: obtaining real-time process control instructions issued in the previous step; simultaneously collecting key performance data during actual operation to form a performance data vector; calculating the process error based on the performance data vector and the target vector; obtaining the process optimization adjustment range based on the process error and feedback formula; and integrating the data of the entire production cycle to obtain a full life cycle management report.

[0015] Optionally, after obtaining the full lifecycle management report, the method further includes: checking the continuity of each data point through the full lifecycle management report, and determining whether there are any errors in the data based on the continuity.

[0016] Optionally, the acquisition of solid waste soil raw material data further includes: acquiring the solid waste soil raw material data by placing sensors at multiple locations in situ.

[0017] In a second aspect, the present invention provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0018] The memory stores the instructions that the computer executes;

[0019] The processor executes computer execution instructions stored in memory to implement the method of this invention.

[0020] Thirdly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method of the present invention.

[0021] The beneficial effects of the present invention are as follows: Compared with the prior art, the present invention has the following advantages:

[0022] 1) To achieve closed-loop management of solid waste soil treatment driven by data throughout the entire process, this application covers all stages of sampling, pretreatment, backfilling and feedback optimization. Based on the real-time acquisition and linkage control of raw material data and environmental data, a closed-loop system for the entire life cycle of solid waste soil resource utilization is constructed, which improves the systematicness and intelligence of management.

[0023] 2) By real-time monitoring and responsive control of environmental factors such as temperature, ground stress, and groundwater pressure during the backfilling process, backfilling parameters can be dynamically adjusted according to changes in on-site geological conditions. By collecting the deviation between on-site performance data and target parameters, control feedback instructions are generated in real time and the process execution strategy is optimized, achieving an upgrade from "static process execution" to "dynamic adaptive optimization," thereby improving the engineering quality control capability.

[0024] 3) The specific parameter design and calculation methods for the entire process are disclosed. Based on the data of the entire life cycle management, the data continuity analysis is used to determine whether there are any abnormalities in the data, and the sensor or system failure is deduced from the abnormal data.

[0025] In summary, this invention achieves closed-loop management of solid waste treatment driven by data throughout the entire process, covering all stages from sampling, pretreatment, backfilling, and feedback optimization, thus improving the level of systematization and intelligence. By real-time monitoring and control of environmental factors such as temperature, ground stress, and groundwater pressure, and by generating feedback instructions based on the deviation between on-site performance data and target parameters, dynamic adaptive optimization of the backfilling process is achieved. At the same time, the key parameters and calculation methods of the entire process are disclosed, and a coherent analysis mechanism is built on the basis of life cycle data, which can be used to identify data anomalies and reverse diagnose sensor or system faults, ensuring process stability and data reliability. Attached Figure Description

[0026] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0027] Figure 1 This is a schematic diagram of a method for the comprehensive utilization and full life-cycle management of solid waste soil resources provided in an embodiment of the present invention;

[0028] Figure 2 This is a schematic diagram of a method for dynamically controlling solid waste soil backfilling provided in an embodiment of the present invention;

[0029] Figure 3 This is a schematic diagram of an optimized backfilling process provided by an embodiment of the present invention;

[0030] The accompanying drawings have illustrated specific embodiments of the invention, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0031] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0032] Figure 1 This invention provides a schematic diagram of a method for the comprehensive utilization and full life-cycle management of solid waste soil resources. The specific steps of each step will now be discussed in conjunction with this embodiment.

[0033] S100, Obtain solid waste soil raw material data.

[0034] This step can employ traditional sample collection methods to obtain solid waste soil raw material data. Alternatively, it can utilize on-site sensor deployment to acquire solid waste soil raw material data. The overall goal of this step is to achieve on-site collection of solid waste soil samples and acquisition of real-time monitoring data of the underground environment, ensuring the comprehensiveness and accuracy of the collected data, and generating reports on the basic physical and chemical parameters of the raw materials and initial state data of the underground environment. The following details each sub-step of this step:

[0035] S110. Sensor Deployment and Environmental Monitoring Equipment Calibration. Several monitoring points should be selected 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 location (coordinates, depth) of each device must be recorded, and a stable wireless data connection with the central control unit must be ensured. Simultaneously, each sensor needs to be calibrated. Taking the temperature sensor as an example:

[0036] Corrected temperature T_corrected = T_measured – ΔT;

[0037] Where T_measured is the initial temperature value measured by the sensor (unit: °C), and ΔT is the correction deviation (usually 0–1 °C, depending on the manufacturer's specified range); for example, if T_measured = 22.5 °C, and the manufacturer specifies 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.

[0038] E_corrected = M × E_measured;

[0039] Wherein, E_measured is the original environmental data vector [T, H, P], specifically representing: T: temperature (°C); H: relative humidity (%); P: groundwater pressure (MPa); M is a 3×3 calibration matrix, where each element represents the calibration coefficient of the corresponding sensor, with a suggested range of 0.95 to 1.05, to be determined based on calibration experiments; E_corrected is the corrected environmental data vector. If E_measured = [22.5, 65, 0.85], and M is set as follows: [0.98 0 0 / 0 1.02 0 / 0 01.00]; then E_corrected = [22.05, 66.3, 0.85]. That is, the temperature, humidity, and pressure sensors deployed at monitoring point A generate the following baseline data after calibration: temperature 22.0 °C, humidity 66%, and groundwater pressure 0.85 MPa.

[0040] S120. Solid waste soil sample collection. Based on the distribution of monitoring points and engineering design requirements, multiple sampling locations are determined using a site topographic map and sensor placement records to ensure the representativeness of the coverage area. Each sampling point should be labeled with a number, geographic coordinates, and sampling depth (it is recommended to record data from two depth layers: 0–1 m and 1–3 m). Output numbered and fully labeled solid waste soil samples. The sampling record sheet includes the sampling location, depth, and preliminary site description. For example: a sample taken near monitoring point A is numbered "Sample-A1," with recorded coordinates (X=120.123, Y=30.456) and a depth of 0.8 m. The sample appears medium yellow and contains a small amount of fine sand.

[0041] S130, Preliminary data integration and raw material report generation.

[0042] Establish a data vector: D = [W, pH, D_avg, σ, T_corrected, H_corrected, P_corrected]. Where W represents moisture content (e.g., 0.1111); pH represents soil pH (e.g., 7.2); D_avg represents average particle diameter (micrometers, e.g., 150); σ represents the standard deviation of particle diameter distribution (micrometers, calculated based on instrument readings); T_corrected is the corrected temperature (°C); H_corrected is the corrected humidity (%); P_corrected is the corrected groundwater pressure (MPa); each element in the D vector represents basic physical or chemical parameters and environmental information of the solid waste soil.

[0043] For Sample-A1 as an example, the integrated data generates the following report: Moisture content W: 11.11%, pH value: 7.2, Average particle diameter D_avg: 150 micrometers, Standard deviation σ: 20 micrometers, Environmental parameters: Temperature 22.0 ℃, 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.

[0044] S200. Based on the solid waste soil raw material data, select an appropriate method to pre-treat the solid waste soil raw material to obtain improved waste soil raw material.

[0045] The solid waste soil undergoes preliminary physical treatment, including ultrafine crushing, vibrating screening, and water washing, to remove harmful impurities and homogenize the particle size distribution; during this process, the underground environmental data is standardized according to a preset calibration formula. Import the "Report on Basic Physical and Chemical Parameters of Raw Materials and Initial State Data of Underground Environment" generated in step S100 into a data processing terminal (e.g., a laptop or on-site data acquisition system). The data content mainly includes moisture content W (unit: dimensionless or percentage, e.g., 0.1111 represents 11.11%); pH value (e.g., 7.2); average particle diameter D_avg (unit: micrometers, e.g., 150); standard deviation of particle diameter σ (unit: micrometers, e.g., 20); corrected temperature T_corrected (unit: °C, e.g., 22.0); corrected humidity H_corrected (unit: %, e.g., 66); corrected groundwater pressure P_corrected (unit: MPa, e.g., 0.85). Example: The imported sample data is Sample-A1, and its parameters are recorded as [W = 0.1111, pH = 7.2, D_avg = 150, σ = 20, T_corrected = 22.0, H_corrected = 66, P_corrected = 0.85].

[0046] A parameter vector is constructed for each sample, defined as follows: V = [W, pH, D_avg, σ, T_corrected, H_corrected, P_corrected]. Where W: moisture content; pH: acidity / 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 describes the comprehensive characteristics of each batch of raw materials, facilitating subsequent unified processing and graded modification. During operation, the data for each sample is organized into the above vector and recorded in a data table. For example, for Sample-A1, V = [0.1111, 7.2, 150, 20, 22.0, 66, 0.85].

[0047] Batch classification is based on key indicators in the parameter vector V. Judgment rules are established, such as grading based on D_avg and σ: if D_avg < 100 micrometers and σ < 15, it is classified as "high-quality group"; if 100 ≤ D_avg ≤ 200 micrometers and 15 ≤ σ ≤ 30, it is classified as "medium-quality group"; if D_avg > 200 micrometers or σ > 30, it is classified as "low-quality group". To incorporate multi-factor considerations, an auxiliary scoring formula is also designed to comprehensively evaluate the stability of raw materials through moisture and pH. S = α × (1 - |W - W_target|) + β × (1 - |pH - pH_target| / pH_target); where, S: overall score; W_target: target moisture content, typically ranging from 0.10 to 0.15; pH_target: target pH value, usually set to 7.0 (neutral) or adjusted according to process requirements (recommended range 7.0 to 8.0); α, β: weighting coefficients, with a reasonable value of 0.5 (adjustable range 0.3 to 0.7); the values ​​of α and β can be set by senior engineers based on experience, or modified based on feedback from subsequent lifecycle treatment reports. This formula is used to help determine whether the sample meets the homogeneity requirements; the closer the S value is to 1, the higher the raw material stability. Based on D_avg, σ, and S values, batch division is completed, and a batch division report and grouping data are output.

[0048] For example: For Sample-A1, given W = 0.1111, pH = 7.2, take W_target = 0.125, pH_target = 7.0, α = β = 0.5, then S≈ 0.9788. Since D_avg = 150 micrometers (between 100 and 200) and σ = 20 (between 15 and 30), Sample-A1 is classified as "medium quality group" with a high S value, which is recorded in the batch report.

[0049] Based on the grouping results, a suitable pulverization method is selected. The goal is to reduce the particle diameter in the sample to within a predetermined target D_target, typically set to 80–100 micrometers. A pulverization uniformity factor U is set to evaluate the pulverization effect, calculated as: U = D_avg / D_target. Where, D_avg: the current average particle diameter of the sample (unit: micrometers, obtained from the V vector), D_target: the target particle diameter (unit: micrometers, set according to engineering requirements), and U: the uniformity factor. Ideally, U greater than 1 indicates the need for further pulverization; after pulverization, U should be close to or less than 1. During the pulverization process, D_avg needs to be continuously sampled and monitored, and the pulverizer speed and feed rate adjusted to ensure that the final D_avg reaches the D_target range. For example, for Sample-A1, its original D_avg = 150 micrometers, if D_target = 90 micrometers is set, then the initial calculation U = 150 / 90 ≈ 1.67; if D_avg is detected to drop to 85 micrometers after pulverization, then U_adjusted = 85 / 90 ≈ 0.94 is updated, which meets the requirements.

[0050] After crushing, the solid waste soil raw material is fed into a vibrating screen to remove large particles and impurities, achieving particle size homogenization. The screen mesh size is selected to match D_target (generally, the mesh size is approximately 1.1 times the target particle size) to ensure that most particles pass through. The screening pass rate E_screen represents the proportion of particles passing through the screen, calculated as: E_screen = (N_passed / N_total) × 100%; where: N_total: the total number of particles entering the screening equipment (counted by the instrument); N_passed: the number of particles passing through the screen after screening; E_screen: the screening pass rate, with a target value recommended to be greater than 85%. After vibrating screening, D_avg and σ are monitored in real time to ensure uniform particle distribution. For example, for Sample-A1, assuming that after crushing, the total number of particles is N_total = 10,000, and N_passed = 8,700, then E_screen = (8700 / 10000) × 100% = 87%, which meets the design requirements.

[0051] Based on the physically pretreated samples, the active mineral components in the solid waste soil were tested, with the goal of adjusting the content of active components to a predetermined target range (e.g., 40%–60%). The original active content C_original (unit: percentage) was measured using a portable chemical testing 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: adjusted active content (percentage); C_original: original active content (percentage) measured after physical pretreatment; ΔC: the percentage increase required, determined based on previous experiments, with a recommended range of 0%–10%. For Sample-A1, assuming C_original = 35% and the target C_adjusted = 50%, theoretically ΔC = 15% is required; considering reaction efficiency, the actual addition of modifiers resulted in ΔC being 12%, leading to a final measured C_adjusted close to 47%–50%. During the chemical modification process, the pH value must be controlled to facilitate further activation of the solid waste soil. The target pH value is generally set between 7.0 and 8.0. Neutralizing agents (such as lime or calcium carbonate) are used to adjust the pH. The adjustment amount can be calculated using 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, representing the pH adjustment effect per unit volume of reagent (unit: pH units / L, recommended range 0.05–0.2, determined based on experimental data); V_reagent: the volume of neutralizing reagent added (unit: liters, determined based on sample volume, generally between 0.1 and 0.5 L); based on the actual measured target pH value, the amount added is fine-tuned experimentally to ensure accurate pH adjustment. For example: if Sample-A1 measures pH_original = 7.2 and the target pH_adjusted = 7.5, and K = 0.1, then V_reagent = (7.5 - 7.2) / 0.1 = 3.0 L; in actual operation, adjust to 2.8~3.0 L based on the results of small-batch tests.

[0052] Mix the physically pretreated solid waste soil with the chemical modifier according to the predetermined ratio. Calculate the mixing volume 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 from on-site weighing); R: modifier ratio (dimensionless, recommended range 0.10–0.20, i.e., 10–20 kg of modifier per 100 kg of solid waste soil). Use a mechanical mixer to uniformly stir the mixture and seal the container for 30–60 minutes to promote the chemical reaction. For Sample-A1 batch, assuming P_sample = 500 kg and R = 0.15, then Q = 500 × 0.15 = 75 kg of modifier; after mixing, let stand for 45 minutes and retest the pH and C_adjusted values ​​to ensure they meet the set targets.

[0053] Furthermore, the data can be standardized. V_processed = [W, pH_adjusted, D_avg_processed, σ_processed, C_adjusted, T_corrected, H_corrected, P_corrected]. The meanings of each component are the same as described above, but D_avg_processed and σ_processed reflect the detection results after physical preprocessing, while 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 parameter in the vector; V_min and V_max are the minimum and maximum values ​​detected for the same parameter in all batches of samples, respectively; and V_normalized is the standardized value. Normalization is then performed on each parameter in V_processed to generate the standardized dataset 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 D_avg_processed range is set to 80–100 micrometers, then for Sample-A1 (D_avg_processed = 85 micrometers), the normalized result is (85 - 80) / (100 - 80) = 5 / 20 = 0.25. D_standard is stored in a central database in CSV or Excel format, with each field clearly indicating the unit and parameter meaning.

[0054] Step S300: Backfill the improved waste soil material, acquire environmental data in real time during the backfilling process, and dynamically adjust the backfilling method based on the environmental data.

[0055] A multi-parameter online monitoring system is deployed in key construction processes such as solid waste soil mixing, pouring, and solidification to collect real-time data on temperature, humidity, viscosity, rheological properties, and changes in water pressure and stress in the underground environment. Simultaneously, an "underground environment dynamic response system" is introduced. This system integrates micro-mechanical actuators and active grouting devices. It automatically adjusts the local solid waste soil modification state based on monitored underground environmental anomalies (such as localized high water pressure, rapid stress changes, or abnormal temperature and humidity gradients), such as initiating local reinforcement, compaction, or grouting compensation to balance local environmental disturbances. The data management platform receives two types of data: standard process parameter data and underground environment dynamic response feedback signals. Based on preset physical and mechanical models and underground environment response models, the system automatically generates control commands to achieve closed-loop regulation of process parameters and environmental response measures.

[0056] Figure 2 This is a schematic diagram of a method for dynamically controlling solid waste soil backfilling provided in an embodiment of the present invention.

[0057] S310. Deploy an online monitoring system to collect the environmental data.

[0058] Temperature, humidity, viscosity, and rheological property sensors, as well as groundwater pressure and geostress sensors, are installed at key process stages (such as mixing, pouring, and curing) and critical underground environmental locations. Each monitoring point must be equipped with a multi-channel data acquisition module to ensure real-time data transmission to the central control terminal. Equipment parameters 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 property sensor R_sensor: measures the flowability of the mixture (unit: m² / s); Groundwater pressure sensor P_sensor: measures the groundwater pressure (unit: MPa); Geostress sensor S_sensor: measures the stress in the soil and rock (unit: MPa).

[0059] For each monitoring point, real-time sensor data is collected to form 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 properties of the mixture; P_m: currently measured groundwater pressure; S_m: currently measured ground stress. Explanation: This vector reflects the real-time state of the process environment and the underground environment, facilitating subsequent process control. For example: Data obtained at a monitoring point at the mixer outlet: E_online = [23.0 ℃, 68 %, 0.35 Pa·s, 1.2×10⁻ 5m² / s, 0.90 MPa, 1.0 MPa).

[0060] S320. Obtain real-time status data based on the environmental data and preprocessed data.

[0061] In the standardized dataset D_standard, key physicochemical parameters of solid waste soil, namely V_processed, are extracted. The online monitoring data E_online is combined with the preprocessed data vector V_processed to form the real-time state vector R_state, which is used for dynamic control. R_state = K1 × V_processed + K2 × E_online; where K1 is a weight coefficient vector describing the influence of preprocessed data on the state, in the form K1 = [k1,k2, k3, k4, k5, k6, k7, k8]; K2 is a weight coefficient vector describing the influence of online monitoring data on the state, in the form 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] (focusing on physical and chemical parameters), K2 = [1, 1, 0.5, 0.5, 1, 1] (focusing on environmental dynamic data), then Sample-A1's V_processed = [0.1111, 7.5, 85, 18, 50, 22.0, 66, 0.85], and E_online (taking data from the on-site mixing point) = [23.0, 68, 0.35, 1.2e-5, 0.90, 1.0]. Then R_state is calculated from two components: 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, finally R_state = 160.6111 + 93.0750 = 253.6861. The R_state value serves as an input indicator for dynamic control.

[0062] S330. Dynamically adjust the backfilling method based on the real-time status data.

[0063] The purpose of constructing a dynamic control feedback model is to issue control commands based on the error ΔR between R_state and the preset target state R_target. The control error is defined as: ΔR = R_target - R_state; where R_target is the preset ideal state value (determined according to process requirements, obtained through field experiments, with a range depending on specific engineering requirements, such as 250–260), R_state is the real-time state vector (obtained through step S320), and ΔR is the error used to determine whether process parameters need adjustment. 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. If the error exceeds the set threshold, control measures are initiated.

[0064] A simple proportional feedback control strategy is used to generate adjustment commands. The control command I_control is defined as I_control = K_p × ΔR; where I_control is the control command value used to adjust process parameters (such as stirring speed, feed ratio, etc.); K_p is the proportional control coefficient (a constant, recommended value range of 0.5 to 2.0, determined based on actual experiments); ΔR is the error, calculated from the above steps. The significance of this method is that the larger the error ΔR, the larger the control command I_control, prompting the process system to adjust with greater force, ensuring 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 amount of modifier added.

[0065] In another implementation, to ensure proactive response to anomalies in the underground environment (such as a sharp rise in groundwater pressure or a sudden change in ground stress), a "Dynamic Underground Environment Response System" (hereinafter referred to as DERS) is configured. DERS includes dedicated field execution modules, such as micromechanical 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 anomaly thresholds for temperature, groundwater pressure, and ground stress, respectively. For example, T_env_threshold is set to 30 ℃ (fluctuating by ±1 ℃), 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 an anomaly triggering factor F_env for the underground environment: 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: current on-site temperature; P_current: current groundwater pressure; S_current: current ground stress; T_env_threshold, P_env_threshold, S_env_threshold: preset thresholds; w_T, w_P, w_S are the weights of each parameter, with a recommended value range of 0.3 to 0.4, and a total sum of 1. Note: When any parameter exceeds the threshold, the value of max(0, …) becomes non-zero, F_env is incremented, and if F_env exceeds the set response threshold F_trigger (e.g., F_trigger = 0.5), an automatic response is triggered. For example: If the on-site monitoring data shows T_current = 31 ℃, P_current = 1.3 MPa, S_current = 1.6 MPa, and we take 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; if the data is higher, F_env will exceed 0.5, triggering automatic adjustment.

[0066] S400. Based on the changes in environmental data during the process of dynamically adjusting the backfilling method, optimize the backfilling process and generate a full life cycle management report for the comprehensive utilization of waste soil resources.

[0067] Based on the data analysis results, the system automatically generates process optimization plans and environmental response adjustment suggestions, and feeds them back to the remote monitoring center. Simultaneously, it periodically calibrates equipment and process parameters, such as adjusting the modifier dosage ratio and revising stirring and vibration parameters, to ensure that the solid waste soil modification process always matches changes in the underground environment. Finally, the optimization results are stored in the full lifecycle management database and linked with the project management, quality testing, and environmental monitoring systems to achieve dynamic monitoring and traceability management throughout the entire process.

[0068] Figure 3 This is a schematic diagram of an optimized backfilling process provided in this embodiment.

[0069] S410: Obtain the real-time process control instructions issued in the previous step, and at the same time, collect key performance data during actual operation to form a performance data vector.

[0070] Input: Real-time process control commands and underground environment adaptive adjustment commands (collectively referred to as R_command) issued in the previous step. These commands include process parameter adjustment signals, such as stirring speed adjustment, feed ratio change, and environmental compensation, and can be represented numerically by the vector I_total. Simultaneously, key performance data during actual operation are collected from sensors in various process equipment on-site (such as mixers, casting devices, and temperature control systems), forming a performance data vector S_perf. I_total = [I_mix, I_feed, I_temp], where I_mix is ​​the stirring speed adjustment command (unit: revolutions per minute), I_feed is the feed ratio adjustment command (dimensionless ratio), and I_temp is the temperature control command (unit: °C increment / decrement); S_perf = [Q_1, Q_2, Q_3, …, Q_n], where Qi represents key monitoring indicators in the process (e.g., mixing uniformity, flowability, setting time, product compressive strength, etc.), with values ​​obtained based on real-time measurements from on-site instruments. Using a data acquisition terminal, I_total and S_perf are uploaded to the central data management platform in real time and stored as time-series data files (such as CSV format). Each record is accompanied by a timestamp and the corresponding process step number. For example, suppose at a certain moment, the R_command instruction I_total = [0.65, 0.39, 0.26] is executed; at the same time, the corresponding monitoring results show S_perf = [Q1=95, Q2=0.82, Q3=1500]. The system will record the data "Time t1: I_total = [0.65, 0.39, 0.26]; S_perf = [95, 0.82, 1500]".

[0071] S420. Calculate the process error based on the performance data vector and the target vector.

[0072] Based on engineering design requirements and historical test data, target values ​​for each key performance indicator are predetermined, and a 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 (e.g., target value 100, unit based on instrument settings), Q2_target represents the target flowability (e.g., 0.85), Q3_target represents the target compressive strength (unit: kPa, e.g., 1550 kPa), and the remaining Q_i_targets are determined according to the specific process.

[0073] Based on the real-time acquired performance vector S_perf and target vector Q_target, the error for each index is calculated as follows: 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].

[0074] S430. Based on the process error and feedback formula, the process optimization adjustment range is obtained.

[0075] I_opt = K_feedback × E, where I_opt is the optimization adjustment command vector used for subsequent adjustment of equipment parameters; K_feedback is the feedback proportional coefficient vector K_feedback = [k1, k2, k3, …, k_n], where each k_i is determined based on field tests, and a suggested value range of 0.5 to 2.0 is recommended; E is the aforementioned error vector. The significance of this formula is that the larger the error E, the larger the corresponding optimization adjustment magnitude I_opt, prompting the system to automatically move closer to the target state. For example, if the feedback coefficient is 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 relevant parameters need to be adjusted separately to improve mixing uniformity by 5 units, fluidity by 0.03, and compressive strength by 50 kPa.

[0076] S440. Integrate the data from the entire production cycle to obtain a full lifecycle management report.

[0077] In addition to real-time feedback, data from the entire production cycle is accumulated and integrated to generate a full lifecycle management report. The report should include: raw data, preprocessed data, real-time monitoring data; errors (E) for each time period and feedback optimization records; and records and handling status of early warning events. The report can utilize statistical data, charts, and numerical analysis to facilitate subsequent process improvements and quality traceability. For example, after one day of operation, the system automatically generates a report "2025-04-08_A1_Lifecycle_Report.xlsx", recording data from initial equipment calibration, real-time process adjustments, to early warning handling.

[0078] It is particularly important to emphasize that, due to the extensive data processing involved in the aforementioned design, this system also incorporates verification of all generated data (including I_total, S_perf, E, etc.) to check data continuity, accuracy, and completeness. If abnormal data is detected, the original records are immediately retrieved for review, and the correction details are noted in the report. For example, if a sudden drop in flow rate is observed in the recorded data at a certain moment, the record is corrected after comparing it with the original sensor data to ensure accurate data transmission to the management system. Alternatively, if a sudden change is found in the data uploaded by the original sensor, further troubleshooting of the original sensor may be necessary. Through the analysis of data continuity, the entire system, including the data processing modules and the data acquisition modules (sensors), can be effectively monitored. In other words, by detecting abnormal data, potential system failures can be deduced.

[0079] According to embodiments of the present invention, an electronic device is also provided, which may include: a processor, a communications interface, a memory, and a communication bus, wherein the processor, the communications interface, and the memory communicate with each other through the communication bus. The processor can call logical instructions in the memory to execute a configuration software-based soft licensing implementation method.

[0080] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the 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 cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0081] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the configuration software-based software licensing implementation method provided by the above methods.

[0082] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0083] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0084] It should be understood that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for the comprehensive utilization of solid waste soil resources throughout its entire life cycle, comprising: Obtain solid waste soil raw material data, including moisture content, soil sample pH, average particle diameter, and standard deviation of particle diameter distribution. Based on the solid waste soil raw material data, select an appropriate method to pretreat the solid waste soil raw material, crush the solid waste soil raw material particles to the target particle diameter, and carry out chemical modification and activity correction to obtain improved waste soil raw material; The stability of the raw materials was comprehensively evaluated based on moisture content and pH, and a comprehensive score S was calculated. S=α×(1-|W-W_target|)+β×(1-|pH- H_target| / pH_target); Where W is the moisture content; pH is the acidity / alkalinity; W_target is the target moisture content; pH_target is the target pH value; and α and β are weighting coefficients. Backfill with improved waste soil raw materials, collect underground environmental data in real time during the backfilling process, including temperature, groundwater pressure and ground stress; calculate environmental anomaly triggering factors based on underground environmental data, and when the environmental anomaly triggering factors exceed the preset threshold, activate the underground environment dynamic response system to carry out local reinforcement or grouting compensation, and dynamically adjust the backfilling method. A performance data vector is constructed based on the key performance data collected during the dynamic control backfilling process. The process error is calculated based on the performance data vector and the target vector. The process optimization adjustment range is obtained based on the process error and the feedback formula. The backfilling process is optimized and a full life cycle management report is generated.

2. The method for comprehensive utilization and full life-cycle management of solid waste soil resources according to claim 1, characterized in that, The solid waste soil raw material particles are crushed to a target particle diameter D_target of 80-100 micrometers. The crushing uniformity factor U=D_avg / D_target is controlled, where D_avg is the average particle diameter of the current sample. The screening pass rate E_screen=(N_passed / N_total)×100% is greater than 85%, where N_total is the total number of particles entering the screening equipment and N_passed is the number of particles passing through the screen after screening. Chemical modification and activity correction are then performed to achieve an activity content C_adjusted of 40%-60% and a pH_adjusted of 7.0-8.0, thus obtaining improved waste soil raw material.

3. The method for comprehensive utilization of solid waste soil resources and full life-cycle management according to claim 2, characterized in that, Real-time acquisition of underground environmental data during the backfilling process, including current temperature T_current, current groundwater pressure P_current, and current ground stress S_current; calculation of environmental anomaly triggering factor 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_env_threshold, P_env_threshold, and S_env_threshold are preset thresholds; w_T, w_P, and w_S are the weights of each factor; When F_env exceeds the preset response threshold F_trigger, the underground environment dynamic response system is activated to perform local reinforcement or grouting compensation and dynamically adjust the backfilling method.

4. The method for comprehensive utilization of solid waste soil resources and full life-cycle management according to claim 3, characterized in that, The key performance data collected during the dynamic control backfilling process constitutes a performance data vector S_perf. The process error E = Q_target - S_perf is calculated based on the performance data vector S_perf and the target vector Q_target. The process optimization adjustment range is obtained based on the process error E and the feedback formula I_opt = K_feedback × E. The backfilling process is optimized and a full life cycle management report is generated, where I_opt is the optimization adjustment instruction vector and K_feedback is the feedback proportional coefficient vector.

5. The method for comprehensive utilization of solid waste soil resources and full life-cycle management according to claim 1, characterized in that, The real-time acquisition of underground environmental data during the backfilling process includes: deploying temperature sensor T_sensor, humidity sensor H_sensor, viscosity sensor η_sensor, rheology sensor R_sensor, groundwater pressure sensor P_sensor, and ground stress sensor S_sensor in the key process stages of mixing, pouring, and solidification. The online data vector E_online=[T_m, H_m, η_m, R_m, P_m, S_m] is constructed, where T_m is the currently measured temperature, H_m is the currently measured humidity, η_m is the currently measured viscosity of the mixture, R_m is the currently measured rheological index of the mixture, P_m is the currently measured groundwater pressure, and S_m is the currently measured ground stress.

6. The method for comprehensive utilization of solid waste soil resources and full life-cycle management according to claim 5, characterized in that, The preprocessed data vector V_processed in the standardized dataset D_standard is fused with the online data vector E_online to form the real-time state vector R_state=K1×V_processed+K2×E_online, where K1 is the weight coefficient vector describing the impact of the preprocessed data and K2 is the weight coefficient vector describing the impact of the online monitoring data. The control error ΔR = R_target - R_state is calculated based on the real-time state vector R_state and the preset target state R_target. When the control error ΔR exceeds the set threshold, the control command I_control = K_p × ΔR is generated, where K_p is the proportional control coefficient and its value ranges from 0.5 to 2.

0.

7. The method for comprehensive utilization of solid waste soil resources and full life-cycle management according to claim 1, characterized in that, By deploying micro mechanical actuators and active grouting devices in the backfill area, the local solid waste soil modification state is automatically adjusted based on the detected underground environmental anomalies, and local reinforcement, compaction or grouting compensation is initiated to balance the local environmental disturbance.

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