Customized Production Method and System for Soft Soil Stabilizing Agent Based on Regional Solid Waste Database

Abstract

This application relates to the technical field of solid waste treatment, and discloses a customized production method and system for soft soil solidification agents based on a regional solid waste database. The method first constructs a regional solid waste database using a data collection template, establishing a correlation between soil targets and corresponding solidification treatment schemes. It then acquires the soft soil targets to be treated in real time, filters matching and differing targets through component sub-item similarity calculations, retrieves the corresponding matching and differing schemes, and merges them to form an execution plan. Next, it classifies and disposes of the soil according to different execution instructions: if the instruction is "definite," a final plan is generated based on the execution information and implemented; if the instruction is "pending," supplementary information is added to form a pending plan for re-evaluation; if the instruction is "cancelled," the corresponding plan is deleted. This scheme relies on the database to achieve intelligent matching and customized configuration of soft soil solidification schemes, effectively improving the adaptability and preparation efficiency of solidification agent formulations under different soil conditions, reducing treatment costs, and facilitating the resource-based, harmless, and efficient disposal of engineering waste.

Description

Technical Field

[0001] This application relates to the technical field of solid waste treatment, and in particular to a customized production method and system for soft soil solidification agents based on a regional solid waste database. Background Technology

[0002] In the field of solid waste treatment, waste soil, as a type of solid waste with large volume and wide distribution, is widely generated in various scenarios such as building demolition, road construction, mining, and engineering construction. Its rational disposal and resource utilization are key links in practicing the concepts of solid waste reduction, resource recovery, and harmless treatment. Soft soil waste soil has characteristics such as high natural water content, large porosity, low shear strength, and significant thixotropy. If not disposed of properly, it will not only occupy a large amount of land resources but also damage the ecological environment.

[0003] Currently, the industry mainly adopts a targeted formulation adjustment model for the treatment of regional waste soil. The specific process is as follows: First, soil materials and soil waste generated from local solid waste dumps are collected as samples to be treated. The samples are sent to the laboratory for physicochemical property testing to determine the core indicators such as the moisture content, porosity, chemical composition, and mechanical properties of the waste soil. Then, based on the waste soil characteristic parameters obtained from the test, the basic components and content ratio of the soft soil solidification agent are preliminarily determined. Finally, the test data are manually sorted and analyzed, and the solidification agent formulation is repeatedly adjusted based on experience until a solidification agent formulation suitable for the waste soil in the region is obtained, thus completing the preliminary preparation work for waste soil solidification treatment.

[0004] However, in current waste soil treatment and soft soil solidification agent formulation debugging processes, core operations still rely heavily on manual labor, including the analysis of sample test data, the calculation of formulation parameters, and the control of the debugging process, resulting in extremely low automation. Furthermore, waste soil treatment work in different regions operates independently, and data on waste soil characteristics, solidification agent formulation parameters, and debugging experience from each region are not shared. This prevents data exchange and experience sharing between different regions, leading to a large amount of repetitive testing, analysis, and formulation debugging work, resulting in high labor and time costs for waste soil treatment. Summary of the Invention

[0005] To reduce the labor and time costs of waste soil treatment, this application provides a customized production method and system for soft soil solidification agents based on a regional solid waste database.

[0006] In the first aspect, this application provides a customized production method for soft soil stabilizers based on a regional solid waste database, employing the following technical solution: A customized production method for soft soil stabilizers based on a regional solid waste database includes the following steps: Soil targets and treatment plans are collected based on a preset collection template. The treatment plans correspond to and are related to the soil targets. A regional solid waste database is established based on the soil targets, treatment plans, and the relationship between the soil targets and treatment plans. Among them, soil targets have component sub-items, and treatment plans have component elements. The system acquires input soft soil targets in real time, matches them to obtain component items, and calculates the similarity between the component items of the soft soil targets and the component items of the soil targets. The higher the similarity, the smaller the difference between the soft soil targets and the soil targets; the lower the similarity, the greater the difference between the soft soil targets and the soil targets. The soil target corresponding to the component item with the highest similarity is selected as the matching target. Extract the differential components between the soft soil target and the matching target, calculate the similarity between the differential components and the component items of the soil target, and take the soil target corresponding to the component item with the highest similarity as the differential target; Based on the regional solid waste database, the corresponding treatment plan for the matching target is obtained as the matching plan, and the corresponding treatment plan for the different target is obtained as the difference plan. The matching plan and the difference plan are then merged to form the execution plan. Determine the execution plan, obtain the execution instructions and execution information corresponding to the determined execution plan. If the content of the execution instruction is "definite", then merge the execution information and the execution plan to obtain the final plan and execute the final plan. If the content of the execution instruction is "pending", then add the execution information to the execution plan to obtain the pending plan and re-determine the pending plan. If the content of the execution instruction is "cancel", then delete the execution plan and the pending plan.

[0007] By adopting the above technical solutions and relying on the regional solid waste database, intelligent customization of soft soil solidification agents is achieved through similarity matching, difference analysis and solution integration. This breaks the data silo problem of the traditional manual debugging mode, reduces repetitive testing, analysis and formula debugging work, significantly reduces the manpower and time costs of waste soil treatment, and improves the matching accuracy of solidification agent formula and the overall efficiency of waste soil resource utilization.

[0008] Furthermore, the method also includes the following steps: If the difference components or implementation information are not empty, and the final implementation plan is completed, the soft soil target and the final plan will be saved as new soil targets and treatment plans in the regional solid waste database.

[0009] By adopting the above technical solution, the regional solid waste database is dynamically iterated and continuously expanded by adding new soft soil targets and successfully implemented final solutions to the database.

[0010] Furthermore, the method also includes the following steps: The percentage content of the differential components in the soft soil target is calculated as the differential amount. If the differential amount is greater than the preset first reference amount, the difference between the final scheme and the matching scheme is extracted as the saved scheme. Differential components and preservation schemes will be stored in the regional solid waste database as new soil targets and treatment schemes.

[0011] By adopting the above technical solution, the percentage content of the differential components in the soft soil target is quantified and combined with the preset threshold screening. Only the significantly different soft soil components and the corresponding solidification schemes are updated to the regional solid waste database. This not only avoids invalid data redundancy, but also accurately accumulates effective formula experience under special working conditions, enabling the database to achieve high-quality iteration.

[0012] Furthermore, the method also includes the following steps: Get the most recent set number of differences, calculate the average of the multiple differences as the difference mean, and if the difference mean is greater than the second reference value, compare the currently calculated difference. If the difference is greater than the preset second reference value, then the difference content that differs from the difference target is extracted as the difference component, and the set of execution information corresponding to a single difference is extracted as the execution set; wherein, the second reference value is greater than the first reference value; The differentiated objectives and solutions will be saved as new soil objectives and treatment solutions in the regional solid waste database, and the differentiated components and implementation sets will also be saved as new soil objectives and treatment solutions in the regional solid waste database.

[0013] By adopting the above technical solution, and by statistically analyzing the average of multiple recent differences and combining it with a higher second reference value for secondary verification, misjudgment caused by single data fluctuations can be effectively avoided, and special soft soil conditions with significant differences can be accurately identified. At the same time, key differentiating components and corresponding execution information sets are extracted, and the differentiating targets / plans and differentiating components / execution sets are synchronously stored in the regional solid waste database.

[0014] Furthermore, the method also includes the following steps: Based on the execution sets corresponding to multiple variance quantities, first calculate the ratio of the quantity of the execution set of a single variance quantity to the quantity of the corresponding final solution as the execution ratio, and then calculate the average of multiple execution ratios as the average execution ratio. The second reference value is adjusted based on the negative correlation between the execution ratio and the average value.

[0015] By adopting the above technical solution, the judgment threshold of the difference can be adaptively adjusted according to the proportion of actual solidification operation execution information, avoiding misjudgment or omission of fixed threshold under different working conditions, and making the database update strategy more in line with the actual construction characteristics.

[0016] Furthermore, the step of calculating the similarity between the component sub-items of the soft soil target and the component sub-items of the soil target includes the following sub-steps: The component items of soft soil target and soil target are dimensionless to obtain a standardized feature vector with consistent value range; wherein, the component items include water content, void ratio, organic matter content, shear strength and particle size distribution. The cosine similarity algorithm is used to calculate the cosine of the angle between two sets of standardized feature vectors to obtain the basic similarity of the component items; Based on the weights assigned to each component item according to its influence on the soft soil consolidation treatment, the basic similarity is weighted and corrected to obtain the component item similarity between the soft soil target and the soil target.

[0017] By adopting the above technical solution, the differences in dimensions and magnitudes of different component items are eliminated through dimensionless processing. The cosine similarity algorithm is used to stably quantify the similarity of the distribution ratio, and the engineering influence weight of each indicator is combined for correction, which effectively improves the accuracy of similarity calculation and engineering adaptability.

[0018] Furthermore, the step of fusing the matching scheme and the difference scheme as the execution scheme also includes the following sub-steps: The first temporary solution is obtained by superimposing the matching solution and the difference solution. The first temporary solution is checked for conflict based on a preset conflict element library. If a conflict element is detected, the conflict element in the first temporary solution is removed to obtain the second temporary solution. The execution plan is obtained by traversing the elements in the second temporary plan and removing duplicate elements.

[0019] By adopting the above technical solution, the advantages of two types of curing experience are integrated by superimposing matching and difference schemes. Then, conflicting components are detected and removed by conflict element library to avoid the incompatibility risk between curing materials. At the same time, duplicate elements are removed to simplify the formula, and finally a safe and compatible execution scheme without redundancy is obtained, which effectively ensures the stability and rationality of the curing formula and reduces construction and commissioning costs and failure risks.

[0020] Furthermore, the method also includes the following steps: The address corresponding to the soft soil target is obtained as the soft soil address, and the execution record corresponding to the address is obtained based on the soft soil address; Extract execution effect data from the execution record. If the execution effect data is less than the preset set effect data, if the content of the execution instruction in the first determination of the execution plan is determined, then the content of the execution instruction is modified to be pending. Then wait for execution information. After obtaining the new execution information, add it to the execution plan to obtain the pending plan, and re-determine the pending plan.

[0021] By adopting the above technical solution, corresponding historical execution records are obtained and effect data is extracted by associating with soft soil addresses. When the execution effect does not meet the preset standard, the original determined plan is changed to a pending state, and new execution information is added before the plan is re-determined. This allows for dynamic correction of working conditions caused by land history and subtle soil differences at the same address, effectively avoiding adaptation deviations in the solidification plan due to regional working condition differences, and improving the adaptation accuracy and construction success rate of soft soil solidification agent solutions.

[0022] Secondly, this application provides a customized production system for soft soil solidification agents based on a regional solid waste database, employing the following technical solution: A customized production system for soft soil stabilizers based on a regional solid waste database includes a processor that executes the steps of the customized production method for soft soil stabilizers based on a regional solid waste database as described in any of the preceding claims. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the steps of a customized production method for soft soil stabilizers based on a regional solid waste database. Detailed Implementation

[0024] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0025] This application discloses a customized production method for soft soil solidification agents based on a regional solid waste database. Targeting the resource utilization of soft soil waste generated from engineering construction and demolition, this method can adapt to the customized formulation of a series of soil solidification materials and lightweight fluidized solidified soil. It addresses the industry pain points of traditional solidification agent formulation adjustments, such as reliance on manual labor, low efficiency, and lack of data sharing. This method achieves precise adaptation of solidification materials and customized preparation of lightweight fluidized solidified soil. (Refer to...) Figure 1 The specific process is as follows: Step S1: Construct a regional solid waste database Based on a pre-set standardized data collection template, the data collection and correlation of soil targets and corresponding treatment solutions are completed, and a regional solid waste database is established.

[0026] The data collection template is divided into two main modules: Soil Target Acquisition Module: Input the component sub-items of soft soil samples, including key indicators such as moisture content, void ratio, organic matter content, liquid limit and plastic limit, shear strength, particle size distribution, geographical origin, and sampling conditions (such as roadbed, foundation pit backfill, river silt, and water treatment plant sludge). Each soil target is configured with a unique identifier ID. Processing solution acquisition module: Inputs the component elements of the solidification solution corresponding to the soil target, mainly divided into two categories: A series of soil stabilization material formulations: including parameters for various stabilization materials suitable for different soil types. Cement-based solidification materials (SHTSM-C series): ordinary Portland cement, blast furnace slag cement and cement-based materials with additives, suitable for solidification of sandy soil; Lime-based solidification materials (SHTSM-L series): quicklime, hydrated lime and modified lime-based materials, suitable for solidification of clayey soils; Mineral-based cementitious materials (SHTSM-M series): Made from blast furnace slag and fly ash as the main raw materials, and activated by activity, suitable for solidification of soft soil and silt with organic matter content ≤5%; Neutral curing materials (SHTSM-N series): with modified gypsum as the core component, suitable for sludge curing where the pH value after curing needs to be 6-8.5; Highly absorbent solidification material (SHTSM-HW series): sludge incineration ash-based water-absorbing material, suitable for the treatment of sludge with high moisture content; High organic matter solidification material (SHTSM-HO series): with CaO, activated Al2O3 and SiO2 as the main components, it can treat sludge with an organic matter content of more than 5%. Lightweight fluidized solidified soil preparation formula: Based on local clay, silt, engineering waste soil, silty soil and other raw materials, the formula is matched with corresponding solidification materials, water and pre-made air bubble groups. The type and amount of raw materials can be flexibly adjusted. At the same time, information such as construction parameters (mixing time, curing conditions, target strength) and curing effect test data (unconfined compressive strength, moisture content change rate) should be entered. Establish a one-to-one correlation between soil targets and treatment plans, and classify and store all data according to region and soil type to form a retrievable historical working condition data asset. For example, the silty soft soil generated from road construction in a certain area has a water content of 55% and an organic matter content of 2.5%. The corresponding formula for lightweight fluidized solidification soil using mineral-based cementitious materials is as follows: raw soil + slag-based solidification material + air bubble cluster, with a bulk density of 10 kN / m³. 3 The strength is 0.6 MPa. This related data is stored in the database as the basis for subsequent matching.

[0027] Step S2: Target matching and similarity calculation for soft soil The system acquires user-inputted soft soil targets in real time, extracts their component sub-items, performs similarity matching with soil targets in the database, and determines the matching targets.

[0028] The specific implementation process is as follows: Data Acquisition and Analysis: Users can input the component sub-item data of the soft soil to be treated through laboratory test report upload, on-site sampling data entry, and third-party testing system integration. The system will automatically analyze and extract key indicators such as moisture content and organic matter content to form a feature vector to be matched. Similarity calculation: A weighted cosine similarity algorithm is used to calculate the feature vectors of the soft soil target and each soil target in the database. The higher the similarity, the smaller the difference in soil characteristics and the stronger the adaptability of the solidification scheme. For example, the soft soil to be treated has a moisture content of 60% and an organic matter content of 3.2%, and its similarity with a certain soil target in the database (moisture content of 58% and organic matter content of 3.0%) can reach 94%, which is a highly matched condition. Matching target determination: Traverse the similarity results of all soil targets in the database, select the soil target with the maximum similarity as the matching target, and the processing scheme corresponding to the target provides a basic reference for subsequent formula fusion.

[0029] Step S3: Differential component extraction and differential target matching Extract the difference components between the soft soil target to be processed and the matching target, use the difference components as new feature vectors, and perform similarity matching with soil targets in the database again to determine the difference target.

[0030] The differential component refers to the sub-items of the soft soil to be treated and the matching target that exceed the preset deviation threshold, such as a moisture content deviation of ±5% or an organic matter content deviation of ±1%. These are key factors that prevent the matching scheme from being directly adapted. Taking moisture content as an example: if the moisture content of the soft soil to be treated is 65%, while the moisture content of the matching target is 55%, the deviation reaches 10%, exceeding the threshold. Then, the 10% difference in moisture content is taken as the core differential component. The feature vector corresponding to this differential component is matched with the database, and the soil target with the highest similarity is selected as the differential target. The scheme corresponding to this target is the adaptable formula for high moisture content conditions.

[0031] Step S4: Merge the matching scheme and the difference scheme to generate the execution plan. Based on the database, the matching scheme corresponding to the matching target and the difference scheme corresponding to the difference target are retrieved. The two schemes are merged to generate an execution scheme adapted to the soft soil to be treated. This scheme can be directly used as the formulation of soil stabilization materials or the preparation process parameters of lightweight fluidized solidified soil.

[0032] The specific process of solution integration: Scheme Overlay: The composition and proportion parameters of the matching scheme and the differential scheme are initially overlaid to obtain the first temporary scheme. For example: the matching scheme is a lightweight fluidized solidified soil base formula of "8% mineral-based solidification material + 90% raw soil", and the differential scheme is: adding 0.5% superabsorbent component to adapt to high moisture content working conditions. After overlay, the first temporary scheme is: 8% mineral-based solidification material + 90% raw soil + 0.5% superabsorbent component + pre-fabricated air bubble group; Conflict detection and deduplication: Based on a pre-set conflict element library, component conflict detection is performed on the first temporary scheme; for example, the incompatibility reaction between alkaline curing agent and acidic admixture; conflicting components are removed to obtain the second temporary scheme; the schemes are traversed to remove duplicate components and redundant parameters, and the bubble group admixture is optimized to adjust the bulk density according to the performance requirements of lightweight fluidized solidified soil; controlled at 6-12 kN / m³. 3 Within the scope, a streamlined, conflict-free execution plan is obtained; Execution Scheme Output: The output scheme includes the curing agent components, dosage, and construction process parameters. For lightweight fluidized solidified soil schemes, the raw material soil type, air bubble group addition ratio, target bulk density, and strength are also specified, which can be directly used as a guide for on-site preparation and construction. For example, for lightweight fluidized solidified soil schemes for bridge abutment load reduction scenarios, the strength can be controlled at 0.8 MPa and the bulk density reduced to 8 kN / m³ by adjusting the foam content. 3 This effectively reduces the load on bridge abutments; for riverbank protection projects, neutral curing material formulations can be selected to avoid impacting the aquatic environment.

[0033] Step S5: Execution Plan Determination and Closed-Loop Processing The generated execution plan is evaluated, and subsequent processing is completed according to different execution instructions, forming a closed-loop process for formula customization: The execution command is "Confirm": This indicates that the solution has been verified and confirmed to be suitable by the system or by manual review. The execution information, such as construction environment parameters and on-site adjustment information, is integrated with the execution plan to obtain a final solution that can be directly implemented. For example, adjusting the curing agent dosage by ±1% based on the on-site construction temperature, or optimizing the flowability parameters of lightweight fluidized solidified soil based on narrow construction space, this adjustment information is added to the plan to form the final production formula; The execution instruction is "pending": This means that the suitability of the solution is questionable. For example, if the differential components of the soft soil to be treated exceed the range of historical working conditions in the database, it is necessary to wait for new execution information such as supplementary field test data and laboratory test results. The new information will be added to the solution to obtain the pending solution, and the judgment process will be re-entered until the suitability is confirmed. The execution command is "Cancel": This indicates that the solution has compatibility risks, such as excessive cost or the curing effect failing to meet design requirements. The solution and its corresponding pending solutions are deleted to avoid ineffective construction investment. At the same time, users are guided to supplement more soft soil test data and re-match the formula.

[0034] After the final solution completes all execution processes and meets the preset acceptance criteria, the update mechanism of the regional solid waste database is activated to achieve closed-loop accumulation of operational data and the adapted solution. The specific execution logic is as follows: 1. Update trigger condition validation The system automatically verifies two core triggering conditions, and only triggers a database update when either condition is met and the final solution is executed: Condition 1: The difference component is not empty. That is, the differences between the component sub-items of the soft soil to be treated and the matching target extracted in step S3 (such as moisture content deviation of 8%, organic matter content exceeding the standard by 3%, abnormal porosity, etc.) have been specifically addressed by the final solution, and the difference component belongs to the working condition characteristics that are not fully covered in the database; Condition 2: The execution information is not empty. That is, the execution information integrated into the final solution in step S5 (such as on-site construction environment adjustment parameters, fine-tuning data of curing agent dosage, special working condition adaptation process, and supplementary data for curing effect testing) has practical engineering reference value and can provide experience support for similar soft soil treatment.

[0035] Acceptance criteria need to be clearly defined in conjunction with the engineering scenario. For example, the lightweight fluidized solidified soil solution needs to meet the target unit weight (6-12 kN / m³). 3 The soil stabilization material solution must meet the following requirements: strength (0.2-2MPa) and unconfined compressive strength, pH value and other indicators must be tested.

[0036] 2. Added structured data entry If the trigger condition verification passes, the system will complete the structured organization and storage of the newly added information according to the preset data template: Soil target dimension: The complete component sub-items of the soft soil to be treated (including original test data and details of differential components) are taken as new soil targets, assigned a unique identifier ID, and labeled with classification tags such as regional attributes and working conditions (e.g., roadbed, foundation pit backfill, high moisture content sludge treatment); Treatment scheme dimension: All core parameters of the final scheme are used as new treatment schemes, and a one-to-one correlation is established between them and the aforementioned new soil targets. Specifically, this includes: Selection of curing materials (such as SHTSM-HW superabsorbent curing material, mineral-based cementitious materials, etc.) and precise proportioning; The type of raw soil, the amount of pre-made air bubbles, and the fluidization treatment parameters of lightweight fluidized solidified soil; Additional adjustment information during execution, such as extended curing cycle parameters for environmental temperature adaptation and flowability optimization data for construction in confined spaces; Final curing effect test report, such as measured value of unconfined compressive strength, environmental protection index test results, etc.

[0037] 3. Database categorization, storage, and index optimization New data will be stored in the regional solid waste database according to the following rules to ensure efficient subsequent matching: The data is classified and archived in multiple dimensions according to soil type (soft soil, silt, high organic matter sludge, high water content mud, etc.), geographical region, and core differentiating characteristics (such as "high water content + low strength" and "neutral environmental requirements"). Establish feature indexes for the component sub-items of the new soil target, highlighting key indicators corresponding to the different components, such as "moisture content 65%" and "organic matter content 7%", to facilitate rapid matching during subsequent similarity calculations; By associating stored similar soil targets and treatment solutions, a complete data chain of "operating condition characteristics - adaptation solution - effect feedback" can be formed. For example, the combination of "high moisture content sludge (70% moisture content) + SHTSM-HW series solidification material + bubble cluster optimized formula" can be compared with the treatment solutions for low moisture content sludge in the database.

[0038] Step S7: Accumulation of directional working condition data based on difference threshold Based on the above embodiments, this method also establishes a targeted data accumulation mechanism for highly differentiated working conditions. Only key working condition features and adaptation schemes that exceed the conventional adaptation range are structured and stored to optimize the storage efficiency and special working condition adaptation capabilities of the regional solid waste database. The specific implementation process is as follows: The percentage content of the differential components in the target soft soil to be treated is calculated to obtain the differential quantity. The differential quantity can be quantified by weight percentage or component percentage according to the type of component sub-item: for indicators that can be characterized by mass ratio, such as water content and mud content, weight percentage is used; for component indicators such as organic matter content and mineral component ratio, component percentage is used.

[0039] The preset first reference value is a deviation threshold set for different component sub-items. It can be configured differently according to regional soil characteristics, solidification material compatibility rules and engineering experience. For example, for the moisture content index, the first reference value can be set to 10%, that is, when the deviation between the moisture content of the soft soil to be treated and the matching target moisture content exceeds 10%, it is judged as a high difference condition. For the organic matter content index, the first reference value can be set to 3%, that is, when the deviation of organic matter content exceeds 3%, it is judged as a high difference condition. Corresponding deviation thresholds can also be set for indices such as void ratio and particle size distribution.

[0040] When the calculated difference is greater than the corresponding first reference value, it indicates that the difference component has exceeded the adaptation range of the conventional solidification scheme, and the targeted data sedimentation process needs to be triggered; if the difference does not exceed the first reference value, it is determined to be a normal working condition, and no additional difference adaptation scheme needs to be sedimented to avoid the accumulation of redundant data in the database.

[0041] When the targeted sedimentation process is triggered, the system automatically extracts the differences between the final solution and the matching solution, and uses this as the storage solution for the high-difference working condition.

[0042] The differences mentioned here specifically refer to the key adjustments made to the matching scheme to accommodate this highly dissimilar component, rather than the complete final scheme. For example: For high-moisture sludge conditions with a moisture content deviation of 12% (exceeding the first reference value of 10%), the matching solution is "8% mineral-based cementitious material SHTSM-M series + 90% raw soil + pre-made air bubble clusters", and the final solution is "10% mineral-based cementitious material SHTSM-M series + 88% raw soil + 1% high-absorbency solidification material SHTSM-HW series + optimized air bubble cluster dosage". The difference between the two (2% increase in solidification material dosage, addition of SHTSM-HW high-absorbency component, and optimization of air bubble cluster dosage) is the retained solution. For high organic matter sludge conditions with an organic matter content deviation of 4% (exceeding the first reference amount of 3%), the matching solution is "12% of cement-based solidification material SHTSM-C series + 88% of raw soil". The final solution is "10% of cement-based solidification material SHTSM-C series + 3% of high organic matter solidification material SHTSM-HO series + 87% of raw soil". The difference (adjustment of solidification material selection and addition of SHTSM-HO series materials) is the preservation solution.

[0043] This extraction method retains only the core adaptation measures for the different components, and removes routine construction parameters that are irrelevant to the differences, ensuring that the preserved solutions focus on the adaptation rules for high-difference working conditions.

[0044] The extracted differential components and preservation plans will be used as new soil targets and treatment plans, linked and stored in the regional solid waste database, and specially tagged. Soil target dimension: Only store the feature data of the high-discrepancy component, such as "silty soft soil with a water content of 72% (12% higher than the normal working condition)" and "sludge from a water treatment plant with an organic matter content of 8% (4% higher than the normal working condition)," and label the corresponding difference amount and component sub-item type; Processing solution dimensions: Store the above-extracted preservation solutions, clarify key information such as the selection of solidification materials, dosage adjustment, and optimization of preparation parameters for lightweight fluidized solidified soil, and associate the solidification effect data corresponding to the solution, such as unconfined compressive strength, bulk density, and pH value test results; Classification and Indexing: Add special working condition labels such as "high moisture content deviation" and "high organic matter deviation" to new data, and establish a targeted index of differential components and preservation schemes to facilitate rapid matching of similar high-difference working conditions in the future.

[0045] Step S8: Systematic extreme operating condition feature accumulation based on batch difference mean verification Based on the above embodiments, this method also establishes a targeted feature deposition mechanism for systemic extreme difference working conditions. Through batch difference verification, progressive working condition feature extraction, and dual-dimensional data storage, it further improves the adaptability of the regional solid waste database to extreme soft soil working conditions and avoids invalid deposition caused by misjudgment of single abnormal data. The specific implementation process is as follows: Batch statistical analysis of the differences in similar recent operating conditions is conducted to identify whether there is a systematic trend of extreme operating conditions, rather than accidental differences caused by single data fluctuations: The system automatically obtains the most recent set number of differences. This set number can be configured according to the engineering application scenario. For example, it can take the differences recorded in the most recent 5 or 10 high-difference work condition processing. The time range can be limited to the same area and the same soil type work conditions within the last 30 days or the last 6 months to ensure that the data is regionally representative. The arithmetic mean of the multiple differences is calculated to obtain the mean difference value. This mean value reflects the overall difference level of soft soil in the same area in the recent period and can effectively filter out abnormal differences caused by accidental factors such as single detection error and construction fluctuation. A second reference value is preset, which is higher than the aforementioned first reference value. For example, for the moisture content index, if the first reference value has a deviation of 10%, the second reference value can be set to a deviation of 15%; for the organic matter content index, if the first reference value has a deviation of 3%, the second reference value can be set to a deviation of 5%. Only when the average difference is greater than the second reference value does it indicate that a batch of extreme difference conditions have occurred recently, triggering the subsequent processing procedure; if the average difference does not exceed the second reference value, the current difference is determined to be a single, accidental fluctuation, and there is no need to perform extreme condition sedimentation.

[0046] After the mean difference is verified, the difference in the current soft soil to be processed is judged a second time. Only when the current difference is also greater than the second reference value is the working condition confirmed as a systematic extreme difference working condition, and the feature extraction stage is entered, forming a dual anti-misjudgment mechanism of "batch trend verification + single working condition verification".

[0047] For extreme characteristics that exceed the adaptability range of the target differences, and simultaneously accumulate complete execution process experience: Differential component extraction: This involves extracting the differences between the differential components of the soft soil to be treated and the component sub-items of the differential target, i.e., the "incremental difference" between the current working condition and the existing differential targets in the database. For example, if the existing differential target in the database is silty soft soil with a moisture content deviation of 12%, while the current soft soil to be treated has a moisture content deviation of 17%, then the 5% moisture content deviation exceeding the differential target is the differential component. Similarly, if the organic matter content of the differential target is 6%, and the organic matter content of the current soft soil to be treated is 9%, then the 3% deviation exceeding the organic matter content is the differential component, accurately locating the new differential characteristics of extreme working conditions.

[0048] Execution Set Extraction: Extract all execution information corresponding to the current single difference and organize it into a structured execution set. This set is not a single execution instruction, but includes data from the entire process from trial mixing and commissioning to construction completion. For example: the amount of solidifying material adjusted to adapt to the different components (such as increasing the amount of SHTSM-HW super absorbent solidifying material from 1% to 2.5%), the optimized data of the air bubble group content of lightweight fluidized solidified soil, the correction parameters of on-site curing conditions, the comparison results of multiple sets of trial mixing tests, and emergency adjustment measures during construction, etc., to fully condense the adaptation experience for this extreme difference.

[0049] A progressive data chain for extreme operating conditions is constructed by adopting a dual data entry approach of "differentiated target scheme + extreme feature experience": First dimension: Store the difference targets and corresponding difference solutions as basic working condition data in the database, and retain the existing basic adaptation experience of extreme working conditions in the database; The second dimension is to store the extracted distinguishing components and execution sets as new soil targets and processing schemes in the database. That is, only the incremental distinguishing features that exceed the distinguishing targets and the corresponding full-process execution experience are stored, rather than repeatedly storing complete working condition data. This avoids data redundancy and establishes an association index of "distinguishing targets → distinguishing components → execution sets".

[0050] For example, the database already stores the difference target and solution for "silty soft soil with a moisture content deviation of 12% + SHTSM-M series solidification material + 1% super absorbent component". This step will add associated data for "the incremental moisture content deviation exceeding 12% + the execution set of increasing the super absorbent component dosage to 2.5%". When encountering the same type of soft soil with a moisture content deviation of 17% in the future, the execution set corresponding to the different components can be directly matched, and the complete adaptation and adjustment experience can be quickly called up without having to perform manual debugging and small-scale test verification again.

[0051] Step S9: Dynamically adaptively adjust the second reference value based on the average of the execution ratios. Based on the above embodiments, this method also sets up a dynamic optimization mechanism for the threshold of extreme working conditions, which solves the problem that a fixed second reference quantity cannot adapt to regional soil characteristics and dynamic changes in construction technology. By quantifying the proportion relationship between execution information and the final solution, the threshold is negatively correlated and adjusted, further optimizing the accuracy of extreme working condition precipitation and the system's adaptability. The specific implementation process is as follows: For each difference in the completed extreme condition determination, calculate its corresponding execution ratio to quantify the impact of supplementary execution information on the final solution under that condition: Clarify the definitions of the two core parameters: The quantity of the execution set: refers to the weighted total amount of valid execution information that has been verified by the engineering process within the execution set corresponding to the difference quantity. For example, the execution set includes information such as material dosage adjustment, construction process optimization, and trial mixing parameter correction. Each piece of information is assigned a weight according to its impact on the curing effect. For example, the weight of curing material dosage adjustment is 2, and the weight of construction and curing condition correction is 1. The quantity of the execution set is obtained by summing the weights of all valid information. The quantity of the final solution refers to the weighted total of all core control parameters in the final solidification solution (including soil solidification material formulation or lightweight fluidized solidified soil preparation parameters) corresponding to this difference. For example, key parameters such as the type of solidification material, the dosage of each component, the proportion of air bubbles, mixing time, and curing conditions are summed according to preset weights to obtain the quantity of the final solution. Calculation logic: Execution ratio = Quantity of execution set ÷ Quantity of final solution. This ratio reflects the proportion of parameters in the final solution that depend on supplementary execution information. The higher the ratio, the more difficult the adaptation to the working condition is, and the stronger the dependence on supplementary information.

[0052] For example, the execution set for a certain high water content sludge working condition contains 4 pieces of valid information (with weights of 2, 2, 1, and 1 respectively, and a weighted sum of 6). The final solution contains 8 core parameters (each with a weight of 1, and a weighted sum of 8). The execution ratio is 6 / 8 = 0.75, which means that 75% of the core parameters in this solution need to be determined by supplementary execution information, making the adaptation difficulty significantly higher than that of conventional working conditions.

[0053] To filter out occasional fluctuations in a single operating condition, the system selects the execution ratios corresponding to multiple differences within the same region, soil type, and recent set period, and calculates the average execution ratio to reflect the overall adaptation difficulty of similar operating conditions in the recent period. Data selection scope: For example, select the differences in extreme working condition treatments of silty soft soil in a certain area within the last 3 months to ensure that the data is representative of the region; Calculation logic: Average execution ratio = (sum of all selected execution ratios) ÷ number of selected differences.

[0054] For example, the execution ratios of the most recent 5 times for silty soft soil in the same area were 0.6, 0.7, 0.75, 0.8, and 0.7, respectively. The sum of these ratios is 3.55, and the average execution ratio is 3.55 ÷ 5 = 0.71. This indicates that the overall difficulty in adapting to extreme working conditions for silty soft soil in this area has been relatively high recently.

[0055] Based on the negative correlation between the average execution ratio and the second reference value, the threshold for judging extreme operating conditions is dynamically adjusted to optimize the subsequent data accumulation strategy. The core logic is as follows: a higher average execution ratio indicates that the current second reference value is set too high, resulting in many discrepancies that should be classified as extreme conditions not being identified. This necessitates supplementing execution information for adaptation, leading to a higher execution ratio. Conversely, a lower average execution ratio indicates that the current second reference value is set too low, causing many non-extreme conditions to be misclassified as extreme, accumulating redundant data and resulting in a lower execution ratio. Therefore, the second reference value needs to be adjusted in reverse based on changes in the average execution ratio. Adjustment rules and boundary limits: Adjustment can be achieved using methods such as linear negative correlation and piecewise negative correlation, while setting upper and lower limits to avoid threshold failure. Linear adjustment example: The preset benchmark execution ratio threshold is 0.4, and the adjustment coefficient is "a deviation threshold change of ±1% corresponding to every 0.1 execution ratio unit". When the average execution ratio is higher than the benchmark value, the second reference value decreases by 1 unit for every 0.1 increase (e.g., the moisture content deviation threshold decreases by 1%); when the average execution ratio is lower than the benchmark value, the second reference value increases by 1 unit for every 0.1 decrease. Boundary control: The adjusted second reference value must always be greater than the first reference value. For example, if the first reference value of moisture content is 10%, the second reference value must be no less than 11% and must not exceed the preset upper limit value. For example, the moisture content deviation threshold must not exceed 20% to ensure that the judgment of extreme working conditions still has the ability to distinguish. Example: The original second reference value for moisture content was 15% deviation. The current average execution ratio is 0.71, which is 0.31 higher than the benchmark value of 0.4. According to the adjustment coefficient, the deviation needs to be reduced by 3%, and the adjusted second reference value is 12% deviation. If the average execution ratio subsequently drops to 0.3, which is 0.1 lower than the benchmark value, the second reference value will increase by 1%, becoming 13% deviation.

[0056] Step S10: Precise Calculation Method for Component Sub-item Similarity To address the similarity calculation bias caused by differences in dimensions and magnitudes among different component items and to ensure the matching accuracy between soft soil targets and soil targets in the database, this method employs a three-level calculation process: "dimensionless standardization → cosine similarity basic calculation → engineering weight correction." The specific implementation process is as follows: Because the components (moisture content, void ratio, organic matter content, shear strength, particle size distribution) of soft soil and soil targets have inconsistent dimensions and large differences in numerical magnitude, such as moisture content (in "%" with a range of 10%-80%), shear strength (in "kPa" with a range of 10-50 kPa), and void ratio (a dimensionless parameter with a range of 0.8-2.0), direct vector comparison can lead to an imbalance in weighting. For example, the numerical magnitude of shear strength may be too small and easily ignored. Therefore, it is necessary to first perform dimensionless processing, transforming all component components into standardized feature vectors in the [0,1] interval. The specific operation is as follows: The min-max standardization algorithm is adopted, and the core formula is: xstd = (x - xmin) / (xmax - xmin); where: xstd is the standardized value of the component item; x is the original detection value of the component item; xmin is the historical minimum value of the component item in the regional solid waste database, based on statistics of the same region and the same soil type, such as the minimum moisture content of 15% and the minimum shear strength of 12kPa; xmax is the historical maximum value of the component item in the regional solid waste database, such as the maximum moisture content of 75% and the maximum shear strength of 48kPa. Example of standardization of sub-indicators: Moisture content: The original moisture content of the soft soil to be treated is 55%. The historical moisture content range in the database is [15%, 75%]. Therefore, the standardized value is (55-15) / (75-15)=40 / 60≈0.67. Void ratio: The original void ratio of the soft soil to be treated is 1.2. The historical void ratio range in the database is [0.8, 2.0]. Therefore, the standardized value is (1.2-0.8) / (2.0-0.8)=0.4 / 1.2≈0.33. Shear strength: The original shear strength of the soft soil to be treated is 30 kPa. The historical shear strength range in the database is [12 kPa, 48 kPa]. Therefore, the standardized value is (30-12) / (48-12) = 18 / 36 = 0.5. The organic matter content and particle size distribution (taking the proportion of particles with a diameter ≤0.075mm as an example) are calculated in the same way according to the above formula, and finally the standardized feature vector V=[0.67,0.33,0.45,0.5,0.72] of the soft soil to be treated is formed, which corresponds to the moisture content, void ratio, organic matter content, shear strength and particle size distribution respectively.

[0057] The cosine similarity algorithm is used to calculate the cosine of the angle between the two standardized sets of feature vectors (the soft soil feature vector V to be processed and the target soil feature vector U in the database). This algorithm can effectively quantify the directional consistency between the two sets of vectors, is not affected by the absolute value of the values, and is suitable for multi-dimensional matching requirements of component items. The specific operation is as follows: The core formula for cosine similarity: ; Where: n=5 (corresponding to 5 component items); Vi is the standardized value of the i-th component item of the soft soil to be processed; Ui is the standardized value of the i-th component item of the soil target in the database; cosθ is the basic similarity, with a value range of [0,1]. The closer it is to 1, the more consistent the directions of the two sets of vectors are, and the smaller the difference in soil properties. Calculation example: Assume the standardized feature vector of a soil target in the database is U=[0.62,0.35,0.42,0.53,0.68], and the soft soil vector to be processed is V=[0.67,0.33,0.45,0.5,0.72]. Calculate: Numerator (vector dot product) ≈ 1.4745; Denominator (vector magnitude product) ≈ 1.440; The basic similarity is 1.4745 ÷ 1.440 ≈ 0.996, indicating that the soil characteristics of the two groups are highly matched.

[0058] Basic similarity only reflects the overall matching degree of vectors and does not consider the differences in the influence of different component items on the solidification effect of soft soil. For example, moisture content directly determines the amount of air bubble group and the control of bulk density of lightweight fluidized solidified soil, and organic matter content affects the reaction efficiency of solidification materials. Its importance is higher than that of particle size distribution. Therefore, it needs to be corrected by weight allocation. The specific operation is as follows: Weight determination method: The weights of each component are determined using a combination of "engineering experience + analytic hierarchy process (AHP)" to ensure that the weight allocation aligns with actual solidification needs. A specific weight allocation example is provided (which can be adjusted according to regional soil characteristics): Weighted similarity calculation: The core formula is: Where: S is the final component item similarity; wi is the weight of the i-th component item; cosθi is the standardized basic similarity of the i-th component item, a single index similarity, which can be calculated by cosine similarity in a single dimension, or by directly using the corresponding contribution ratio in the vector cosine value; Corrected Example: Based on the basic similarity of 0.996, combined with the single-index similarity of each component (assuming they are 0.98, 1.0, 0.99, 0.99, and 1.0 respectively), the weighted similarity is calculated as follows: S = 0.30 × 0.98 + 0.25 × 1.0 + 0.20 × 0.99 + 0.15 × 0.99 + 0.10 × 1.0 ≈ 0.294 + 0.25 + 0.198 + 0.1485 + 0.1 = 0.9905. The final similarity is 0.9905, which is still considered a high match. The corresponding database soil target scheme can be directly used as the basis for the matching scheme.

[0059] Step S11: Conflict resolution and deduplication fusion process between matching and differing schemes To address potential issues such as component incompatibility and parameter duplication when combining matching and differential schemes, and to ensure the safety, rationality, and simplicity of the execution scheme, this method employs a three-level fusion mechanism: "scheme superposition → conflict detection and resolution → duplicate element removal." Combined with a pre-defined conflict element library and structured deduplication logic, it generates an optimal execution scheme adapted to the soft soil to be processed. The specific implementation process is as follows: The core parameters of the matching scheme and the difference scheme are fully superimposed to form a first temporary scheme containing all potential adaptable elements. The superposition process follows the principle of "full retention and dimension alignment" to ensure that the advantageous parameters of both types of schemes are not omitted. Overlaying dimensions and content: In terms of curing materials: retain the basic curing materials (such as SHTSM-M mineral-based cementitious materials) and proportions of the matching scheme, and add targeted curing materials (such as SHTSM-HW super absorbent curing materials) and dosage adjustment parameters of the different schemes; Specific parameters for lightweight fluidized solidified soil: superimposed parameters such as raw material soil type, pre-formed air bubble group dosage, fluidization treatment water consumption, and mixing time; Construction process dimension: superimposed matching scheme conventional construction process (such as pouring method, foundation curing cycle) and special adaptation process of different scheme, such as segmented pouring process under high moisture content conditions, and flowability optimization parameters in narrow space. Effect control dimension: superimposed target indicators of the two schemes (e.g., bulk density 6-12kN / m³) 3 (Including strength 0.2-2MPa, pH value 6-8.5, etc.) and testing requirements.

[0060] Overlay example: Matching solution (suitable for conventional silty soft soil): 8% SHTSM-M mineral-based solidification material + 90% raw soil + 2% pre-formed air bubble array + 3 min standard mixing time + 7 days curing period + target bulk density 10 kN / m³ 3 ; Differential solution (adapted to high moisture content components): SHTSM-HW super absorbent curing material 1.5% + pre-made air bubble group dosage adjusted to 1.5% + mixing time extended to 5 min + segmented casting process; First temporary solution (after overlay): 8% SHTSM-M mineral-based solidifying material + 1.5% SHTSM-HW superabsorbent solidifying material + 90% raw soil + 2% pre-fabricated air-filled aggregate + 1.5% pre-fabricated air-filled aggregate + conventional mixing time 3 min + mixing time 5 min + curing period 7 days + segmented casting process + target bulk density 10 kN / m³ 3 .

[0061] The pre-defined conflict element library is the core of ensuring the security of the solution. It stores incompatible combinations between curing materials and process parameters that have been verified by engineering. The conflict detection and resolution process is as follows: Conflicting element library construction logic: Material conflicts: Incompatible combinations of different series of curing materials, such as SHTSM-N neutral curing material and strongly alkaline SHTSM-L lime-based material, which cannot coexist and will lead to pH loss; acidic additives and SHTSM-C cement-based materials conflict and will inhibit the hydration reaction; Parameter conflict type: Stores contradictory process parameters, such as "rapid curing" and "segmented casting" being incompatible, and "low fluidity" and "construction in narrow space" being conflicting; Conflicting Effects: Parameter combinations that prevent the target performance from being achieved due to storage issues (e.g., pre-fabricated bubble cluster content exceeding 5% and bulk density ≤ 8kN / m³). 3 (Conflict: High organic matter content without added SHTSM-HO materials conflicts with strength ≥1MPa). The conflict element library supports dynamic updates and can be continuously supplemented by new conflict combinations discovered in engineering practice.

[0062] Conflict detection process: The system iterates through all elements of the first temporary solution, compares them with the conflict element database, and identifies conflicting combinations. For example, if the first temporary solution contains both "SHTSM-N neutral curing material" and "SHTSM-L lime-based material", the system will trigger a material conflict alarm; if it contains both "pre-fabricated bubble cluster content 4%" and "target bulk density 7kN / m³", the system will trigger a material conflict alarm. 3 If this is displayed, an effect conflict alert will be triggered.

[0063] Conflict resolution principles and procedures: The principle of "adaptation priority + engineering effectiveness" is adopted to remove conflicting elements: the elements of the differential scheme are designed for the core differential components of the current soft soil, and the adaptation priority is higher than the matching scheme; if there is a conflict between elements within the scheme, the elements that are more critical to the solidification effect are retained.

[0064] Example 1 (Material Conflict): The first temporary solution contains "SHTSM-M mineral-based material" and "acidic water-reducing agent" (the conflict library marks the two as incompatible). Since SHTSM-M material is the core basis of the matching solution and acidic water-reducing agent is only an optional optimization item in the differential solution, "acidic water-reducing agent" is removed and SHTSM-M material is retained. Example 2 (Parameter Conflict): The first temporary scheme includes "mixing time 3 min" and "mixing time 5 min". The 5 min mixing time in the differential scheme is an adaptation parameter for soil with high moisture content and has a higher priority. Therefore, "mixing time 3 min" is removed.

[0065] After digestion, the second temporary solution for the aforementioned example is: 8% SHTSM-M mineral-based solidifying material + 1.5% SHTSM-HW superabsorbent solidifying material + 90% raw soil + 1.5% pre-fabricated air bubble cluster + mixing time 5 min + curing period 7 days + segmented casting process + target bulk density 10 kN / m³ 3 .

[0066] The second temporary solution may contain duplicate elements of the same type, such as repeated curing material components and redundant process parameters. These need to be deduplicated through structured traversal to ensure the solution is concise and readily implementable. Duplicate element identification type: Completely duplicated elements: Completely consistent descriptions of the same curing material and the same process parameters, such as "SHTSM-HW high water absorption curing material 1.5%" appearing twice; Redundant elements of the same type: different expressions of the same parameter dimension, such as "maintenance cycle of 7 days" and "maintenance until the strength reaches the standard". The former is a specific executable parameter, while the latter is a target description, which is redundant of the same type. Derivative redundant elements: An element already contains the function of another element, such as "segmented casting process" already covering "narrow space adaptation casting", the latter is a derivative redundancy.

[0067] Deduplication logic: The system iterates through the second temporary scheme in the order of "curing materials → preparation parameters → construction process → effect indicators", and performs the "retain core, eliminate redundancy" operation on duplicate elements: For completely duplicate elements, keep only one instance. Redundant elements of the same type retain specific executable parameters, such as retaining "maintenance cycle 7 days" and removing "maintenance until strength meets the standard"; The derived repeating elements retain the core elements with more comprehensive functions, such as retaining "segmented casting process" and removing "narrow space adaptation casting".

[0068] Example of execution plan after deduplication: The final execution plan is as follows: 8% SHTSM-M mineral-based solidifying material + 1.5% SHTSM-HW superabsorbent solidifying material + 90% raw soil + 1.5% pre-fabricated air bubble clusters + mixing time 5 min + curing period 7 days + segmented pouring process + target bulk density 10 kN / m³ 3 This plan is conflict-free and free of redundancy, and can be directly used as a guide for on-site production and construction.

[0069] This application also discloses a customized production system for soft soil stabilizers based on a regional solid waste database, including a processor that executes the steps of the customized production method for soft soil stabilizers based on a regional solid waste database as described in any of the above embodiments.

[0070] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A customized production method for soft soil solidification agents based on a regional solid waste database, characterized in that, Includes the following steps: Soil targets and treatment plans are collected based on a preset collection template. The treatment plans correspond to and are related to the soil targets. A regional solid waste database is established based on the soil targets, treatment plans, and the relationship between the soil targets and treatment plans. Among them, soil targets have component sub-items, and treatment plans have component elements. The system acquires input soft soil targets in real time, matches them to obtain component items, and calculates the similarity between the component items of the soft soil targets and the component items of the soil targets. The higher the similarity, the smaller the difference between the soft soil targets and the soil targets; the lower the similarity, the greater the difference between the soft soil targets and the soil targets. The soil target corresponding to the component item with the highest similarity is selected as the matching target. Extract the differential components between the soft soil target and the matching target, calculate the similarity between the differential components and the component items of the soil target, and take the soil target corresponding to the component item with the highest similarity as the differential target; Based on the regional solid waste database, the corresponding treatment plan for the matching target is obtained as the matching plan, and the corresponding treatment plan for the different target is obtained as the difference plan. The matching plan and the difference plan are then merged to form the execution plan. Determine the execution plan, obtain the execution instructions and execution information corresponding to the determined execution plan. If the content of the execution instruction is "definite", then merge the execution information and the execution plan to obtain the final plan and execute the final plan. If the content of the execution instruction is "pending", then add the execution information to the execution plan to obtain the pending plan and re-determine the pending plan. If the content of the execution instruction is "cancel", then delete the execution plan and the pending plan.

2. The customized production method of soft soil solidification agent based on a regional solid waste database according to claim 1, characterized in that, The method also includes the following steps: If the difference components or implementation information are not empty, and the final implementation plan is completed, the soft soil target and the final plan will be saved as new soil targets and treatment plans in the regional solid waste database.

3. The customized production method of soft soil solidification agent based on a regional solid waste database according to claim 2, characterized in that, The method also includes the following steps: The percentage content of the differential components in the soft soil target is calculated as the differential amount. If the differential amount is greater than the preset first reference amount, the difference between the final scheme and the matching scheme is extracted as the saved scheme. Differential components and preservation schemes will be stored in the regional solid waste database as new soil targets and treatment schemes.

4. The customized production method of soft soil solidification agent based on a regional solid waste database according to claim 3, characterized in that, The method also includes the following steps: Get the most recent set number of differences, calculate the average of the multiple differences as the difference mean, and if the difference mean is greater than the second reference value, compare the currently calculated difference. If the difference is greater than the preset second reference value, then the difference content that differs from the difference target is extracted as the difference component, and the set of execution information corresponding to a single difference is extracted as the execution set; wherein, the second reference value is greater than the first reference value; The differentiated objectives and solutions will be saved as new soil objectives and treatment solutions in the regional solid waste database, and the differentiated components and implementation sets will also be saved as new soil objectives and treatment solutions in the regional solid waste database.

5. The customized production method of soft soil solidification agent based on a regional solid waste database according to claim 4, characterized in that, The method also includes the following steps: Based on the execution sets corresponding to multiple variance quantities, first calculate the ratio of the quantity of the execution set of a single variance quantity to the quantity of the corresponding final solution as the execution ratio, and then calculate the average of multiple execution ratios as the average execution ratio. The second reference value is adjusted based on the negative correlation between the execution ratio and the average value.

6. The customized production method of soft soil solidification agent based on a regional solid waste database according to claim 1, characterized in that, The step of calculating the similarity between the component sub-items of the soft soil target and the component sub-items of the soil target includes the following sub-steps: The component items of soft soil target and soil target are dimensionless to obtain a standardized feature vector with consistent value range; wherein, the component items include water content, void ratio, organic matter content, shear strength and particle size distribution. The cosine similarity algorithm is used to calculate the cosine of the angle between two sets of standardized feature vectors to obtain the basic similarity of the component items; Based on the weights assigned to each component item according to its influence on the soft soil consolidation treatment, the basic similarity is weighted and corrected to obtain the component item similarity between the soft soil target and the soil target.

7. The customized production method of soft soil solidification agent based on a regional solid waste database according to claim 1, characterized in that, The step of merging the matching scheme and the difference scheme as the execution scheme also includes the following sub-steps: The first temporary solution is obtained by superimposing the matching solution and the difference solution. The first temporary solution is checked for conflict based on a preset conflict element library. If a conflict element is detected, the conflict element in the first temporary solution is removed to obtain the second temporary solution. The execution plan is obtained by traversing the elements in the second temporary plan and removing duplicate elements.

8. The customized production method of soft soil solidification agent based on a regional solid waste database according to claim 1, characterized in that, The method also includes the following steps: The address corresponding to the soft soil target is obtained as the soft soil address, and the execution record corresponding to the address is obtained based on the soft soil address; Extract execution effect data from the execution record. If the execution effect data is less than the preset set effect data, if the content of the execution instruction in the first determination of the execution plan is determined, then the content of the execution instruction is modified to be pending. Then wait for execution information. After obtaining the new execution information, add it to the execution plan to obtain the pending plan, and re-determine the pending plan.

9. A customized production system for soft soil solidification agents based on a regional solid waste database, characterized in that, The system includes a processor that performs the steps of the customized production method for soft soil stabilizers based on a regional solid waste database as described in any one of claims 1-8.

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