A system and method for resource utilization of a deficit-adjusted high-salinity and high-alkalinity dredged material

Abstract

A system and method for resource utilization of high-salinity and alkaline dredged materials using a deficit-adjustment approach includes a salinity analysis module. The data output of the salinity analysis module is connected to the data inputs of a deficit-adjustment interval construction module, a deficit-adjustment feasibility determination module, and a collaborative scheduling module. The data output of the resource utilization scenario management module is connected to the data input of the deficit-adjustment interval construction module. The data output of the deficit-adjustment interval construction module is connected to the data inputs of the deficit-adjustment feasibility determination module and the collaborative scheduling module. The data output of the deficit-adjustment feasibility determination module is connected to the data input of a saline-alkali washing path generation module. The data input of the saline-alkali washing path generation module is bidirectionally connected to the data input of an execution and feedback module. The purpose of this invention is to overcome the over-treatment and resource waste caused by the "one-size-fits-all" saline-alkali washing method used in existing technologies for high-salinity and alkaline dredged materials.

Description

Technical Field

[0001] This invention relates to the fields of environmental protection and environmental engineering technology, and in particular to the management and treatment technology of high salinity and alkaline soil and solid waste, specifically to a deficit-type high salinity and alkaline dredging material resource utilization system and method. Background Technology

[0002] Dredging projects are widely implemented in port construction, waterway maintenance, and lake dredging. The large amounts of dredged material generated can lead to significant land occupation, groundwater salinization, and soil degradation if not properly disposed of. High-salinity dredged material, in particular—solids with high salt content and high soluble alkalinity—is often considered low-value or unusable "waste" in practical projects because its salinity is typically far higher than agricultural and ecological utilization standards. This makes it difficult to treat and limits its application scenarios.

[0003] Existing technologies for the resource utilization of high-salinity dredged materials mainly rely on strong washing and "pre-treatment followed by reuse," but these methods have limitations. For example, patent application publication number CN114793522A proposes a "soil wash brine recycling system and method combining electro-adsorption and condensation." This technology desalinates brine discharged through a hidden pipe by passing it through positively and negatively charged empty-roll electrode plates, utilizing the principle of electro-adsorption. After adsorption saturation, the power source is reversed to release salt ions, and the brine is then recycled through evaporation and condensation, solving the problem of water waste during soil washing. However, this technology only focuses on the recycling of soil wash brine and does not address the combination of pre-treatment and resource utilization of high-salinity dredged materials. Furthermore, electro-adsorption desalination relies on a fixed electrode structure, making it impossible to adjust the deficit according to the dynamic changes in salinity and alkalinity of the dredged materials. This limits its adaptability to high-salinity dredged materials and hinders the efficient resource conversion of dredged materials.

[0004] Patent application publication number CN113906853A proposes a "salt washing control system and method for high-salinity soil". This technology, through the synergistic action of a salt washing device, a salt return device, a residual water treatment device, and a transmission feedback control device, simulates a field salt washing scenario, achieving precise control over the number of salt washing cycles, water consumption, and soaking time for high-salinity soil. It can also simulate a salt return scenario, solving the problems of existing salt washing technologies such as inaccurate parameter control, water waste, and uneven salt washing. However, it has shortcomings, primarily targeting the improvement of high-salinity soil, without considering the differences between high-salinity dredged materials and natural soil in terms of moisture content, particle size distribution, and uniformity of salt and alkali distribution. It lacks an adaptation design for the resource utilization process of dredged materials and lacks a salinity deficit management concept, making it unable to dynamically adapt to salinity fluctuations during dredged material treatment, and thus failing to achieve efficient resource utilization of dredged materials.

[0005] Patent application publication number CN102577687A proposes a "comprehensive improvement method for coastal saline soil". This technology improves coastal saline soil through a comprehensive approach including underground pipe drainage, adding waste organic matter to improve soil physical structure, large-scale water washing of salt, application of microbial fertilizers, and three-dimensional plant configuration. It solves the problems of ecological damage, high cost, and slow greening progress of traditional topsoil planting. However, it has shortcomings such as a fixed improvement process, lack of dynamic scheduling mechanism, inability to adjust and control according to changes in salinity, long improvement cycle, and difficulty in adapting to the needs of large-scale and efficient resource utilization of high salinity dredged materials.

[0006] To address the aforementioned issues, this invention proposes a deficit-adjusting system and method for the resource utilization of high-salinity dredged materials. Through the concept of deficit-adjusting management and a dynamic scheduling mechanism, the effective utilization of high-salinity dredged materials is achieved. Summary of the Invention

[0007] The purpose of this invention is to overcome the technical problems of over-treatment and resource waste caused by the "one-size-fits-all" salt washing of high-salinity dredged materials in the prior art, and to propose a deficit-adjusting system and method for the resource utilization of high-salinity dredged materials. This invention introduces the concept of salinity deficit management, treating salinity as a management variable that can be managed under certain constraints to achieve phased surplus, spatial retention and scenario adaptation. Combined with a semi-centralized collaborative scheduling architecture and a trusted sharing mechanism of the consortium blockchain, this invention achieves efficient, low-consumption and controllable utilization of dredged materials in various resource utilization scenarios.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A deficit-adjusting high-salinity dredged material resource utilization system includes a salinity analysis module. The data output of the salinity analysis module is connected to the data inputs of a deficit adjustment interval construction module, a deficit adjustment feasibility determination module, and a collaborative scheduling module. The data output of the resource utilization scenario management module is connected to the data input of the deficit adjustment interval construction module. The data output of the deficit adjustment interval construction module is connected to the data inputs of the deficit adjustment feasibility determination module and the collaborative scheduling module. The data output of the deficit adjustment feasibility determination module is connected to the data input of a saline-alkali washing path generation module. The data of the saline-alkali washing path generation module is bidirectionally connected to the data of an execution and feedback module. The data output of the execution and feedback module is connected to the data inputs of the deficit adjustment interval construction module, the saline-alkali washing path generation module, and the collaborative scheduling module. The data of the collaborative scheduling module is bidirectionally connected to the data of the execution and feedback module.

[0009] It also includes a data acquisition module, whose data output terminal is connected to the data input terminal of the salinity analysis module; The data acquisition module is used to comprehensively and accurately perceive the basic properties of the dredged material to be treated, providing standardized and highly reliable raw data support for subsequent salinity analysis. The salinity analysis module analyzes the raw data acquired by the data acquisition module, performs spatial analysis of the salinity inside the dredged material, identifies the distribution characteristics and migration potential of salinity in different layers, and provides a basis for deficit determination.

[0010] The salinity analysis module includes a functional stratification unit. The data input of the functional stratification unit is connected to the data output of the data standardization unit through a standardized data interface. The data output of the functional stratification unit is connected to the data input of the salinity distribution calculation unit and the salinity migration simulation unit through a stratification parameter transmission link, providing parameters such as stratification boundaries and sensitivity weights. The data output of the salinity distribution calculation unit is connected to the data input of the salinity state vector generation unit through a distribution ratio transmission channel. The data output of the salinity migration simulation unit is connected to the data input of the salinity state vector generation unit through a migration potential transmission channel. The data output of the salinity state vector generation unit is connected to the data input of the deficit adjustment interval construction module, the deficit adjustment feasibility determination module, and the consortium blockchain data interaction unit.

[0011] The resource utilization scenario management module manages different dredged material resource utilization scenarios and their corresponding salinity tolerance conditions, providing a constraint basis for the construction of the deficit adjustment range. The resource utilization scenario management module includes a scenario identification unit. The data output end of the scenario identification unit is connected to the data input ends of the salinity tolerance range extraction unit and the scenario priority calculation unit through a scenario encoding transmission link, providing scenario type encoding. The data output end of the salinity tolerance range extraction unit is connected to the data input end of the deficit adjustment range construction module through a tolerance range transmission channel. The data output end of the scenario priority calculation unit is connected to the data input end of the deficit adjustment range construction module through a priority transmission channel.

[0012] The deficit adjustment range construction module is used to construct a scientific and reasonable salinity deficit adjustment range and quantify deficit adjustment requirements based on scenario requirements and the salinity and alkalinity characteristics of dredged materials. The deficit adjustment range construction module includes a deficit adjustment range generation unit, whose data input is connected to the data output of the salinity state vector generation unit, the salinity tolerance range extraction unit, the scenario priority calculation unit, and the feedback control unit, respectively. The data output of the deficit adjustment range generation unit is connected to the data input of the deficit amount calculation unit through the deficit adjustment range transmission channel. The data output of the deficit amount calculation unit is connected to the data input of the deficit parameter storage unit through the deficit amount transmission channel. The data output of the deficit parameter storage unit is connected to the data input of the deficit adjustment feasibility determination module and the consortium blockchain data interaction unit, respectively.

[0013] The feasibility assessment module for adjusting salinity is used to comprehensively evaluate whether the current salinity surplus can be managed spatially, temporally, or in a scenario-based manner, providing a decision-making basis for the generation of salinity washing pathways. This module includes a spatial adjustment assessment unit. The data input terminals of the spatial, temporal, and scenario adjustment assessment units are all connected to the data output terminals of the salinity state vector generation unit and the adjustment parameter storage unit, receiving the salinity state vector and adjustment parameters. The data output terminals of the spatial, temporal, and scenario adjustment assessment units are all connected to the data input terminal of the comprehensive assessment unit through an assessment index transmission channel. The data output terminal of the comprehensive assessment unit is connected to the data input terminal of the salinity washing pathway generation module through a feasibility result transmission link.

[0014] The aforementioned salt-alkali washing path generation module is used to generate a suitable salt-alkali washing scheme based on the feasibility determination result of the deficit adjustment, so as to achieve precise control of salinity and alkalinity. The salt-alkali washing path generation module includes a phased salt-alkali washing path construction unit. The data input terminals of the phased salt-alkali washing path construction unit and the enhanced salt-alkali washing path construction unit are connected to the data output terminal of the comprehensive judgment unit to receive the comprehensive judgment result. The data output terminals of the phased salt-alkali washing path construction unit and the enhanced salt-alkali washing path construction unit are connected to the data input terminal of the path optimization unit. The data terminal of the path optimization unit is bidirectionally connected to the data terminal of the execution and feedback module.

[0015] The aforementioned execution and feedback module is used to accurately execute the salinity washing path plan, monitor the salinity migration process in real time, and feed the monitoring data back to the front-end module. The execution and feedback module includes a salinity washing execution unit. The data input terminal of the salinity washing execution unit is connected to the data output terminals of the path optimization unit and the instruction issuance unit, respectively, to receive the optimal salinity washing path plan and execution instructions. The data output terminal of the salinity washing execution unit is connected to the data input terminal of the real-time monitoring unit. The data output terminal of the real-time monitoring unit is connected to the data input terminal of the data processing unit. The data output terminal of the data processing unit is connected to the data input terminal of the feedback control unit. The data output terminal of the feedback control unit is connected to the data input terminals of the resource output unit, the salinity washing execution unit, the deficit adjustment interval construction module, and the salinity washing path generation module, respectively. The data input terminal of the resource output unit is connected to the data output terminals of the feedback control unit and the scheduling effect evaluation unit, respectively, to receive output permission instructions and target scenario information. The data output terminal of the resource output unit is connected to the data input terminal of the consortium blockchain data interaction unit, synchronously outputting registration information.

[0016] The aforementioned collaborative scheduling module is used to share the salinity deficit adjustment status of multiple dredged material nodes based on a consortium blockchain, and to perform resource-based scheduling optimization at the system level to improve overall resource utilization efficiency. The collaborative scheduling module includes a consortium blockchain data interaction unit. The data input terminal of the consortium blockchain data interaction unit is connected to the data output terminals of the salinity state vector generation unit, deficit adjustment parameter storage unit, and resource utilization output unit of each dredged material node, respectively, to obtain a summary of the deficit adjustment status of each node through the consortium blockchain. The data output terminal of the consortium blockchain data interaction unit is connected to the data input terminal of the scheduling decision unit. The data output terminal of the scheduling decision unit is connected to the data input terminal of the instruction issuance unit. The data output terminal of the instruction issuance unit is connected to the salinity washing execution unit of each dredged material node. The data input terminal of the scheduling effect evaluation unit is connected to the feedback control unit of each node. The data output terminal of the scheduling effect evaluation unit is connected to the data input terminals of the scheduling decision unit and the resource utilization output unit of each node, respectively, to output the scenario capacity verification result and the permission instruction.

[0017] The above system operates by following these steps: Step 1: Start the system. The data acquisition module performs full perception and standardized modeling of the basic properties of the dredged material to be treated, forming the raw data required for subsequent salinity analysis, and outputs the standardized basic property model as the unified input for subsequent calculations. Step 2: After obtaining the standardized basic attribute model output in Step 1, the system enters the salinity analysis stage, further resolving the salinity inside the dredged material from "total description" to a structured state of "layered distribution + sensitivity weight + migration potential". The salinity of the dredged material is profiled to obtain the distribution characteristics of salinity in different layers and identify its potential constraints on different resource utilization scenarios, forming a salinity state vector, thereby providing a calculable basis for the construction of deficit adjustment intervals and feasibility determination. Step 3: Based on the salinity state vector obtained in Step 2, the system introduces the dimension of "resource utilization scenario", transforming the "salt and alkalinity constraint" from a single threshold into a tolerance range Ω that varies with the scenario. j And calculate the comprehensive priority based on multi-objective decision-making. U j This is to determine the target boundary for constructing the deficit adjustment interval and the direction of subsequent scenario transfer decisions; Step 4: Obtain the salinity tolerance range Ω from Step 3. j With overall priority U j Afterwards, the system in Ω j Based on the introduction of controlled salinity surplus d j Construct the corresponding salinity deficit range Δ jFurthermore, the deficit range is transformed into a calculable deficit range parameter, allowing salinity to have a phased surplus, spatial retention, or scenario adaptation within a controllable boundary, thereby avoiding the excessive treatment of "one-size-fits-all" salinity washing. Step 5: After obtaining the salinity state vector generated in Step 2 and the deficit adjustment interval parameters formed in Step 4, the system conducts a deficit adjustment feasibility assessment on the salinity surplus to determine whether it can be managed through "spatial deficit adjustment, time deficit adjustment or scenario deficit adjustment", thereby deciding whether to adopt a phased salinity washing path or an enhanced salinity washing path. Step 6: After obtaining the feasibility assessment result of the deficit adjustment in Step 5, the system generates a salt washing path scheme that matches the assessment result, and solves the relationship between cost, time, resources and deficit adjustment constraints through the optimization model to obtain the optimal salt washing path parameters to guide the execution. Step 7: Implement the salt washing process according to the optimal salt washing path parameters output in Step 6, and realize the closed-loop control of "execution-monitoring-analysis-re-optimization" through PLC automatic control and sensor array monitoring, so that the salt washing intensity, stage switching and deficit adjustment range parameters can be dynamically corrected according to monitoring feedback. Step 8: When the system is in a scenario where multiple dredged material nodes are processed in parallel, the collaborative scheduling module realizes the sharing of the deficit status and resource recovery results of each node based on the consortium blockchain, and completes the closed-loop scheduling of "status sharing - decision optimization - instruction issuance - effect evaluation", thereby improving the overall resource recovery efficiency and reducing the total system cost.

[0018] This invention also includes a deficit-adjustment method for the resource utilization of high-salinity and alkaline dredged materials. Targeting high-salinity and alkaline dredged materials to be treated, this method constructs and determines the basic properties of the dredged material, salinity profile distribution, migration potential, resource utilization scenario tolerance range, and deficit adjustment range. It achieves path generation and closed-loop optimization of "staged salinity washing when deficit adjustment is feasible, and intensified salinity washing when deficit adjustment is not feasible." Furthermore, it achieves multi-node collaborative optimization of resource utilization in a multi-node parallel environment. This addresses the technical problems of existing dredged material resource utilization methods, which generally adopt a "one-size-fits-all" strong salinity washing strategy and lack a deficit determination mechanism based on salinity profile differences and scenario tolerance constraints, leading to over-treatment or insufficient compliance, high resource consumption, and difficulty in achieving multi-node collaborative optimization. The method includes the following steps: Step 1: Collect and record basic attribute data of the dredged material to be treated, so as to ensure that the subsequent salinity profile analysis, migration simulation and deficit interval construction have a unified data source and input boundary; the output of this step is the "basic attribute model of dredged material", which serves as the direct input for the subsequent salinity analysis stage in Step 2. Step 2: After obtaining the standardized basic attribute model, the salinity and alkalinity inside the dredged material are transformed from a general scalar description into a calculable structured state, forming the input variables required for subsequent deficit range construction and feasibility assessment. The core outputs of this step include: functional stratum division results S1 / S2 / S3, and stratum-sensitive weights. w i Salinity and alkalinity distribution ratio P i Migration potential indicators z peak , T stay , J back , Flag deficit And the structured salinity-alkalinity state vector; Step 3: Based on the structured salinity status, introduce the resource utilization scenario dimension, transforming the salinity constraint from a single threshold into a tolerance range Ω that varies with the scenario. j And through comprehensive priority U j This step determines the direction of constructing the deficit adjustment range and subsequent scenario transfer decisions. The core data output in this step includes: resource utilization scenario coding and the salinity tolerance range (Ω) for each scenario. j and overall priority U j ; Step 4: Obtain the salinity tolerance range Ω of the scene. j With overall priority U j Subsequently, a controlled salinity surplus was further introduced. d j Construct a salinity deficit range Δ j This allows for periodic surpluses, spatial retention, or scenario adaptation of salinity within controllable boundaries, thus avoiding excessive "one-size-fits-all" salinity washing. The core data output from this step includes: the deficit adjustment range Δ... j Adjustment loss indicators Q def and its indexed record information; Step 5: Obtain the structured salinity-alkalinity state vector V After setting the set of deficit adjustment parameters, a quantitative determination is made as to whether the current salinity surplus can be managed through "spatial deficit adjustment, time deficit adjustment, or scenario deficit adjustment" to determine whether to adopt phased or intensified salinity washing in the future. The output of this step is the comprehensive deficit adjustment feasibility determination result, which is the branch condition for entering step 6.1 or step 6.2. Step 6: After obtaining the feasibility assessment result of the deficit adjustment in Step 5, generate the corresponding salt washing path scheme, and solve the optimal salt washing path parameters among cost, time, resources and deficit adjustment constraints; the output of this step is the optimal salt washing path scheme, which includes the salt washing method, execution stage and time parameters and intensity parameters. Step 7: After obtaining the optimal salt-washing path, execute the salt-washing process according to the path parameters, and simultaneously monitor the salinity concentration at different depths. C ( z , t and conductivity EC ( z , t Continuous monitoring and data processing are performed, and path parameters are dynamically adjusted based on feedback to achieve a closed loop of "execution—monitoring—analysis—re-optimization"; the output of this step includes the stage removal rate. or remove Peak migration speed v move Final salinity concentration C final and remaining deficit adjustment demand Q remain And finally, a standard-compliant assessment will be conducted; Step 8: When processing multiple dredged material nodes in parallel, perform system-level resource recovery collaborative optimization based on the deficit status summary information of each node to improve overall resource recovery efficiency and reduce total processing cost. The inputs for this step are the identification code of the dredged material at each node and the current salinity level. C current , corresponding deficit interval Δ j and remaining deficit adjustment demand Q remain The summary information is used to output a cross-batch scene allocation and rhythm adjustment plan, and further optimization is triggered through effect evaluation.

[0019] Compared with the prior art, the present invention has the following technical effects: 1) This invention achieves salinity risk classification at the spatial stratigraphic scale through salinity profile analysis and functional stratification, enabling targeted regulation of salinity in different regions within high-salinity dredged materials, thereby avoiding unnecessary treatment of tolerable salinity strata. 2) By introducing the salinity tolerance range and deficit adjustment range corresponding to the resource utilization scenario, this invention can match different salinity constraints for different utilization purposes, thereby improving the flexibility and utilization rate of resource utilization. 3) This invention, through quantitative salinity migration simulation and comprehensive feasibility judgment mechanism, enables the system to determine whether deficit management is possible in three dimensions: space, time and scenario, thereby avoiding the risk of overtreatment caused by high-intensity one-time rinsing in most cases; 4) The phased deficit adjustment strategy and optimized path generation of this invention reduce the overall freshwater resource consumption and energy consumption, and significantly reduce the treatment cost and operating cycle compared with the traditional one-time strong washing method. 5) By leveraging the cross-node data sharing and optimized scheduling mechanism of the consortium blockchain, this invention can achieve collaborative resource scheduling of multiple dredged material batches, improve overall utilization efficiency, and reduce the total system processing cost. Attached Figure Description

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a diagram of the overall system architecture of the present invention; Figure 2 This is a schematic diagram of the various units in the system of the present invention; Figure 3 This is a flowchart of the system operation steps of the present invention; Figure 4 This is a flowchart of the method of the present invention; Figure 5 This is a flowchart illustrating the process of generating the salt and alkali washing pathway in this invention. Figure 6 This is a flowchart of the collaborative scheduling process of the present invention. Detailed Implementation

[0021] like Figure 1 As shown, a deficit-adjusting high-salinity dredged material resource utilization system includes: a data acquisition module 2, a salinity analysis module 1, a resource utilization scenario management module 6, a deficit adjustment interval construction module 3, a deficit adjustment feasibility determination module 4, a saline-alkali washing path generation module 7, an execution and feedback module 8, and a collaborative scheduling module 5. The data output terminal of the data acquisition module is connected to the data input terminal of the salinity analysis module. The data output terminal of the salinity analysis module is connected to the data input terminals of the deficit adjustment interval construction module, the deficit adjustment feasibility determination module, and the collaborative scheduling module, respectively. The data output terminal of the resource utilization scenario management module is connected to the data input terminal of the deficit adjustment interval construction module. The data output terminal of the deficit adjustment interval construction module is connected to the data input terminals of the deficit adjustment feasibility determination module and the collaborative scheduling module. The data output terminal of the deficit adjustment feasibility determination module is connected to the data input terminal of the saline-alkali washing path generation module. The data terminal of the saline-alkali washing path generation module is bidirectionally connected to the data terminal of the execution and feedback module. The data output terminal of the execution and feedback module is connected to the data input terminals of the deficit adjustment interval construction module, the saline-alkali washing path generation module, and the collaborative scheduling module. The data terminal of the collaborative scheduling module is bidirectionally connected to the data terminal of the execution and feedback module.

[0022] The data acquisition module is used to comprehensively and accurately perceive the basic properties of the dredged material to be treated, providing standardized and highly reliable raw data support for subsequent salinity analysis. The data acquisition module includes: a source identification unit, a physical structure parameter acquisition unit, a salinity detection unit, and a data standardization unit. The data output of the source identification unit is connected to the data inputs of the physical structure parameter acquisition unit, salinity detection unit, and data standardization unit via attribute association links, thus binding source information with subsequent acquisition parameters. The data output of the physical structure parameter acquisition unit is connected to the data input of the data standardization unit via a physical parameter transmission channel. The data output of the salinity detection unit is connected to the data input of the data standardization unit via a salinity data transmission channel. The data output of the data standardization unit is connected to the data input of the salinity analysis module via a standardized data interface.

[0023] The source identification unit is used to accurately identify the source type of dredged material, including but not limited to river dredged material, lake dredged material, harbor basin dredged material, or waterway dredged material. It records the environmental characteristics of the formation environment through a combination of sensor monitoring and historical data tracing, specifically including hydrodynamic conditions, sedimentary environment, and historical salinity background. At the same time, it assigns a unique identification code to each batch of dredged material for full-process data tracing. The physical structure parameter acquisition unit uses a multi-parameter synchronous acquisition device to collect the physical structure parameters of the dredged material, including water content, particle size distribution, initial compaction state, porosity, and equivalent permeability coefficient. The salinity detection unit adopts a "multi-point sampling + layered detection" mode to detect the salinity of the dredged material and obtain data on total salinity, conductivity, main soluble salinity types, and salinity distribution along the vertical direction. The data standardization unit is used to convert source information, physical parameters and salinity test results into a unified data model to form a basic attribute model of dredged material.

[0024] The salinity analysis module is used to spatially analyze the salinity within the dredged material, identify the distribution characteristics and migration potential of salinity in different strata, and provide a basis for deficit assessment. The salinity analysis module includes: a functional stratum division unit, a salinity distribution calculation unit, a salinity migration simulation unit, and a salinity state vector generation unit. The data input of the functional stratum division unit is connected to the data output of the data standardization unit via a standardized data interface. The data output of the functional stratum division unit is connected to the data inputs of the salinity distribution calculation unit and the salinity migration simulation unit via stratum parameter transmission links, providing parameters such as stratum boundaries and sensitivity weights. The data output of the salinity distribution calculation unit is connected to the data input of the salinity state vector generation unit via a distribution ratio transmission channel. The data output of the salinity migration simulation unit is connected to the data input of the salinity state vector generation unit via a migration potential transmission channel. The data output of the salinity state vector generation unit is connected to the data inputs of the deficit adjustment interval construction module, the deficit adjustment feasibility assessment module, and the consortium blockchain data interaction unit.

[0025] The functional stratification unit is used to divide the dredged material based on its physical structural parameters and salinity-depth distribution function. C s ( z The dredged material was divided into three functional layers using a clustering analysis algorithm: a saline-sensitive layer (S1), a saline-insensitive layer (S2), and a potential saline-depleted layer (S3). Each layer was assigned a saline-sensitivity weight coefficient. w i ,satisfy ; The salinity distribution calculation unit is used to calculate the distribution ratio of salinity in different functional strata based on the functional strata division results, using the following formula: ; in, P i Indicates the first i Each functional layer, among which i =1, 2, 3 correspond to the proportions of salinity and alkalinity in S1, S2, and S3 to the total salinity and alkalinity, respectively. z i+1 , z i Indicates the first i The upper and lower boundary depths of each functional level H Indicates the total thickness of the dredged material profile. C s ( z () indicates that the salinity concentration varies with vertical depth. z The distribution function; The salinity migration simulation unit is used to numerically simulate the migration process of salinity under natural or leaching conditions based on a one-dimensional convection-dispersion equation and combined with the physical parameters of the dredged material. It predicts the trend, velocity, and residence time of salinity migration to different strata. The equation expression is as follows: ; in, C ( z,t )for t time, z Salinity concentration at depth t For time variables, z For vertical spatial coordinates, D The dispersion coefficient of salt and alkali. v This represents the convective migration rate of salt and alkali. Initial conditions: C ( z,t )= C s ( z )( t When the salinity concentration is 0, it represents the initial profile distribution. Boundary conditions: z =0 represents the Dirichlet boundary, under the conditions of salt-alkali washing. C (0 ,t )= C wash ( C wash (The salt and alkali content of the washing water) under natural conditions C (0 ,t )= C atm ( C atm (Atmospheric deposition salinity and alkali content). z = H For Neumann boundary, ; When the model calculation results meet at least one of the following conditions, the saline-alkali soil is deemed to have the potential for deficit management: 1) The peak salinity concentration is within the preset time scale. T pre The main migration occurs in the non-sensitive layer S2 or the potential saline-alkali layer S3, with a peak migration depth. z peak ≥ z 2 ( z 2 represents the lower boundary depth of layer S2). 2) Predicted residence time of salinity in the non-sensitive layer S2 or the potential saline-alkali layer S3 T stay ≥ Tthreshold ( T threshold (for the preset threshold time). T stay From the formula calculate; 3) Predicted back migration flux of salinity to sensitive layer S1 J back ≤ J safe ( J safe (for safe flux thresholds), migration flux ( z = z 1, z 1 represents the lower boundary depth of layer S1). The salinity state vector generation unit is used to generate the salinity distribution ratio. P i Does it have the potential for loss adjustment and the depth of peak migration? z peak Duration of stay T stay Migration flux J back and layer sensitivity weight w i The structured salinity-alkalinity state vector is integrated into the following expression: ; in, Flag deficit As an indicator of potential for loss adjustment, it possesses Flag deficit =1, does not have Flag deficit =0; The structured salinity state vector is written into the consortium blockchain as a deficit state summary and encrypted using the blockchain hash algorithm SHA-256 to ensure that the data is immutable and can be queried and verified during the collaborative scheduling of multiple dredging nodes.

[0026] The resource utilization scenario management module manages different dredged material resource utilization scenarios and their corresponding salinity tolerance conditions, providing constraints for the construction of deficit adjustment ranges. The module includes a scenario identification unit, a salinity tolerance range extraction unit, and a scenario priority calculation unit. The data output of the scenario identification unit is connected to the data inputs of the salinity tolerance range extraction unit and the scenario priority calculation unit via a scenario encoding transmission link, providing scenario type encoding. The data output of the salinity tolerance range extraction unit is connected to the data input of the deficit adjustment range construction module via a tolerance range transmission channel. The data output of the scenario priority calculation unit is connected to the data input of the deficit adjustment range construction module via a priority transmission channel. The scene recognition unit is used to determine the resource utilization scene to which the dredged material is intended to enter based on regional planning needs, engineering task book and market demand analysis. These scenes include, but are not limited to, agricultural soil improvement SC1, ecological restoration base SC2, engineering filling SC3 or greening matrix SC4. Each scene is assigned a unique code and additional information such as the service life and environmental requirements of the scene is recorded. The salinity tolerance range extraction unit, based on national standards and industry practice data, sets corresponding salinity tolerance ranges for different resource utilization scenarios, expressed as follows: ; Among them, Ω j Indicates the first j One resource utilization scenario, among which j =1,2,3,4, corresponding to the salinity tolerance range of resource utilization scenarios SC1~SC4; C j,min This indicates the minimum salinity level allowed in this resource utilization scenario, which is related to soil physicochemical properties or engineering stability; C j,max This indicates the maximum salinity level allowed in the resource utilization scenario, used to avoid adverse effects on crops, organisms, or engineering structures. The scenario priority calculation unit is used to construct a comprehensive benefit function based on multi-objective decision-making theory, incorporating indicators such as economic benefits, ecological benefits, and time costs, to rank different resource utilization scenarios. The function expression is as follows: ; in, U j Indicates the first j A comprehensive priority index for each resource utilization scenario, with a value range of [0,1]. U j The larger the value, the higher the priority. R j Indicators representing the economic benefits of resource utilization. R max To achieve the maximum economic benefit across all scenarios; E j Indicators representing ecological benefits E max For maximum ecological benefit; T j Indicators representing time cost T max To maximize time cost; α , β , c For the weighting coefficients, satisfying α + β + c=1, which can be adjusted according to regional development needs; The deficit adjustment range construction module is used to construct a scientific and reasonable salinity deficit adjustment range and quantify deficit adjustment requirements based on scenario requirements and the salinity and alkalinity characteristics of dredged materials. It is the core implementation module of the deficit adjustment concept of this invention. The deficit adjustment range construction module includes: a deficit adjustment range generation unit, a deficit adjustment amount calculation unit, and a deficit adjustment parameter storage unit. The data input end of the deficit adjustment range generation unit is connected to the data output end of the salinity state vector generation unit, the salinity tolerance range extraction unit, the scenario priority calculation unit, and the feedback control unit, respectively. The data output end of the deficit adjustment range generation unit is connected to the data input end of the deficit adjustment amount calculation unit through a deficit adjustment range transmission channel. The data output end of the deficit adjustment amount calculation unit is connected to the data input end of the deficit adjustment parameter storage unit through a deficit adjustment amount transmission channel. The data output end of the deficit adjustment parameter storage unit is connected to the data input end of the deficit adjustment feasibility determination module and the consortium blockchain data interaction unit, respectively.

[0027] The deficit adjustment range generation unit defines salinity as a management variable that can be controlled and configured within a controlled range. Based on the salinity tolerance range of the target resource utilization scenario, it introduces an allowable controlled salinity surplus to construct a salinity deficit adjustment range, expressed as: ; Where, Δ j Indicates the first j The salinity deficit range corresponding to each resource utilization scenario; d j This indicates the permissible controlled salinity surplus, determined based on the scenario's risk tolerance. At the same time, at least one deficit dimension should be introduced for the salinity deficit range, including: 1) Time dimension: Allows for periodic salinity surplus, that is, salinity exceeding Ω within a certain period of time. j But in Δ j Internally, subsequent reversion to Ω is achieved through natural rinsing or mild treatment. j ; 2) Spatial dimension: Allow for salinity surplus in non-sensitive layers, i.e., the salinity of sensitive layer S1 must satisfy Ω. j The salinity of the non-sensitive layer S2 and the potential salinity-alkali layer S3 can be Δ j Inside; 3) Utilize the scenario dimension: Allow for a surplus of salinity for specific purposes. That is, when a high-priority scenario cannot accommodate it temporarily, it can be transferred to a low-priority scenario with higher salinity tolerance, and then adjusted according to the needs of the scenario.

[0028] The deficit calculation unit is used to convert the salinity deficit range into a calculable salinity deficit index, which is used to quantify the cumulative degree to which salinity exceeds the resource utilization limit. The formula is: ; in, Q def This indicator represents the amount of salinity deficit, used to quantify the cumulative extent to which salinity exceeds the resource utilization limit; T window For time dimension adjustment window; x ) + Denotes the positive part function, when x Take when >0 x Otherwise, take 0; r The dry density of the dredged material; The deficit adjustment parameter storage unit is used to write deficit adjustment interval parameters, deficit adjustment amount indicators, and scene priorities into the consortium blockchain using an encryption algorithm. The storage structure includes dredged material identification code, target scene code, parameter generation time, and hash verification value to ensure parameter consistency and security when multiple nodes use it collaboratively. At the same time, a parameter index library is established to support fast query and retrieval.

[0029] The deficit adjustment feasibility determination module is used to comprehensively assess whether the current salinity surplus can be managed spatially, temporally, or in a scenario-based manner, providing a decision-making basis for the generation of salinity washing pathways. The module includes: a spatial deficit adjustment assessment unit, a temporal deficit adjustment assessment unit, a scenario-based deficit adjustment assessment unit, and a comprehensive determination unit. The data input terminals of the spatial, temporal, and scenario-based deficit adjustment assessment units are all connected to the data output terminals of the salinity state vector generation unit and the deficit parameter storage unit, receiving the salinity state vector and deficit parameters. The data output terminals of the spatial, temporal, and scenario-based deficit adjustment assessment units are all connected to the data input terminal of the comprehensive determination unit through an assessment index transmission channel. The data output terminal of the comprehensive determination unit is connected to the data input terminal of the salinity washing pathway generation module through a feasibility result transmission link.

[0030] The spatial deficit assessment unit is used to calculate the spatial schedulability index based on the salinity distribution ratio, layer sensitivity weight, and salinity migration simulation results. S spatial , S spatial Dimensionless, with values ​​ranging from [0,1], the calculation formula is: ; in, T stay,i For the first i The salinity residence time of each functional layer; T threshold,i For the first i Spatial dimension adjustment window for each functional level; R i For salt and alkali by the firsti The predicted proportion of functional layer migrations back to the sensitive layer S1 is calculated by the migration model. ( A The cross-sectional area of ​​the floor is [missing information]. M i For the first i (Total salinity and alkali in each functional layer). The spatial schedulability index is classified as follows: S spatial ≥0.6 indicates feasible space deficit adjustment; 0.3≤ S spatial <0.6 indicates partial feasibility; S spatial <0.3 is not feasible; The time deficit assessment unit calculates the time delay feasibility index based on the simulation results of salinity migration under natural conditions. S temporal , S temporal Dimensionless, with values ​​ranging from [0,1], the calculation formula is: ; in, T natural To reduce salinity to the deficit range Δ under natural conditions j The time required within the process is predicted by the migration model; T critical The maximum allowed waiting time for the target resource is determined based on the scenario requirements; The time delay feasibility index is classified as follows: S temporal ≥0.6 indicates that time deficit adjustment is feasible; 0.3≤ S temporal <0.6 indicates partial feasibility; S temporal <0.3 is not feasible; The scenario adjustment assessment unit calculates the scenario transfer feasibility index based on the scenario priority calculation results. S scene , S scene Dimensionless, with values ​​ranging from [0,1], the calculation formula is: ; in, I j For the scene j The fit index for the current salinity and alkalinity of the dredged material is expressed as: ; in, C currentThe overall salinity concentration of the dredged material is determined by... ( C i For the first i Calculation of average salinity and alkalinity concentration in each functional layer; The feasibility indexes for scenario transfer are categorized as follows: S scene ≥0.6 indicates feasible scene deficit adjustment; 0.3≤ S scene <0.6 indicates partial feasibility; S scene <0.3 is not feasible; The comprehensive judgment unit is used to perform weighted calculations on the above three indicators to obtain a comprehensive feasibility index for adjusting losses. S comprehensive The formula is: ; in, w s , w t , w u The weight coefficients are spatial, temporal, and scene weights, determined based on the analytic hierarchy process. Based on the risk tolerance, engineering constraints, and system operation strategies of the target resource utilization scenario, a comprehensive deficit adjustment feasibility threshold is set. l ; Comprehensive Judgment Rules: when S comprehensive When the salinity surplus is ≥λ, it can be managed through a deficit adjustment method, and the process can be entered into a phased process for generating the salinity washing pathway. when S comprehensive When the value is less than λ, it is determined that adjusting the deficit is not feasible, and the process of strengthening the washing salt and alkali production path is initiated.

[0031] The salt-alkali washing path generation module is used to generate a suitable salt-alkali washing scheme based on the feasibility assessment results, thereby achieving precise control of salinity and alkalinity. The salt-alkali washing path generation module includes: a phased salt-alkali washing path construction unit, an enhanced salt-alkali washing path construction unit, and a path optimization unit; the data input terminals of the phased and enhanced salt-alkali washing path construction units are connected to the data output terminal of the comprehensive assessment unit to receive the comprehensive assessment results; the data output terminals of the phased and enhanced salt-alkali washing path construction units are connected to the data input terminal of the path optimization unit; the data terminal of the path optimization unit is bidirectionally connected to the data terminal of the execution and feedback module.

[0032] The phased salt-alkali washing path construction unit is used to reconstruct the salt-alkali washing objective from "achieving salinity standards" to "meeting deficit adjustment range constraints." Based on the deficit adjustment dimension, the salt-alkali washing scheme is represented as a path sequence, expressed as: ; Among them, Π stage This is a set of phased salt-alkali washing pathway schemes. k For stage index, m k For the first k The salt and alkali control method adopted in each stage t k For the first k The execution time or duration of a phase; The principle for constructing the phased salt-leaching pathway is as follows: 1) Prioritize non-salt-washing methods, including but not limited to biological salt absorption, plow-layer salt retention, or mild salt-washing methods, including but not limited to natural leaching and drip irrigation. 2) Sensitive layer S1 is treated first, while non-sensitive layer S2 and potential salt-stagnant layer S3 are treated as needed; 3) The stage interval is determined based on the salinity migration monitoring results to ensure that salinity gradually migrates to the target layer; The enhanced salt and alkali washing pathway construction unit is used to accelerate salt and alkali removal by increasing the intensity of salt and alkali washing and optimizing the process when adjusting for salt deficit is not feasible. The pathway expression is as follows: ; Among them, Π strengthen To strengthen the set of salt-alkali washing pathway schemes, m high This is a high-intensity method for washing away salt and alkali. t high To enhance the duration of salt and alkali washing, p high The parameters for washing salt and alkali strength; During the enhanced salt and alkali washing process, when the salinity concentration drops to the target scenario's salinity tolerance range Ω... j median ( C j , min + C j , max When the temperature reaches 1 / 2, it automatically switches to mild salt and alkali washing to avoid overtreatment. The path optimization unit is used to construct a path optimization model with the objective of minimizing the cost of salt washing, while simultaneously satisfying constraints on deficit adjustment, time constraints, resource constraints, and intensity. The model expression is as follows: ; in,c w For unit water resource cost, q k For the first k Water consumption per unit time during a given period A For the area treated by washing away salt and alkali, t k For the first k Phase execution time, c e Cost per unit of energy consumption p k For the first k Operating power of staged salt and alkali washing equipment; The constraints include: Adjustment loss constraint: ,in, or ( m k ) is the first k The efficiency of this method in washing salt and alkali per unit time; Time constraints: ; Resource constraints: ( q max (to the maximum water supply capacity). Strength constraints: ( m max (for maximum leaching salt and alkali strength). The optimal salt-washing route scheme is obtained by solving the optimization model using a genetic algorithm.

[0033] The execution and feedback module is used to accurately execute the salinity washing path plan, monitor the salinity migration process in real time, and feed the monitoring data back to the front-end module. The execution and feedback module includes: a salinity washing execution unit, a real-time monitoring unit, a data processing unit, a feedback control unit, and a resource output unit. The data input terminal of the salinity washing execution unit is connected to the data output terminals of the path optimization unit and the instruction issuance unit, respectively, to receive the optimal salinity washing path plan and execution instructions. The data output terminal of the salinity washing execution unit is connected to the data input terminal of the real-time monitoring unit. The data output terminal of the real-time monitoring unit is connected to the data input terminal of the data processing unit. The data output terminal of the data processing unit is connected to the data input terminal of the feedback control unit. The data output terminal of the feedback control unit is connected to the data input terminals of the resource output unit, the salinity washing execution unit, the deficit adjustment interval construction module, and the salinity washing path generation module, respectively. The data input terminal of the resource output unit is connected to the data output terminals of the feedback control unit and the scheduling effect evaluation unit, respectively, to receive and output permission instructions and target scenario information. The data output terminal of the resource output unit is connected to the data input terminal of the consortium blockchain data interaction unit, synchronously outputting registration information.

[0034] The salt and alkali washing execution unit includes salt and alkali washing equipment, salt and alkali discharge facilities, and control valves. It accurately executes the salt and alkali washing operation according to the parameters in the optimal salt and alkali washing path scheme and the scheduling instructions. It is equipped with a PLC controller to realize the automation of salt and alkali washing mode switching, water consumption adjustment, and salt and alkali washing frequency control. The real-time monitoring unit is used to employ a distributed salinity sensor array, with sensors arranged at intervals, to monitor different depths in real time. z salinity concentration at the location C ( z , t and conductivity EC ( z , t Simultaneously monitor auxiliary parameters such as the moisture content and pore water pressure of the dredged material; The data processing unit is used to preprocess the monitoring data, using a moving average method to remove outliers and cubic spline interpolation to complete missing data; it then converts the preprocessed data into a standardized format and compares it with the initial salinity state vector and leaching path parameters to calculate the salinity removal rate and salinity migration rate. ,

[0035] in, C initial The initial salinity concentration, z peak ( t )for t Momentary peak salinity migration depth; The feedback control unit is used to, based on the data processing results, send salt and alkali washing parameter adjustment instructions to the salt and alkali washing execution unit via the PLC controller; and transmit the salinity removal rate to the path optimization unit. or remove Migration rate v move Data is collected to trigger a re-optimization of the salinity washing path; resource utilization results data such as node execution progress, execution cost, and final salinity concentration are uploaded to the scheduling effect evaluation unit to support system-level scheduling effect evaluation; based on the salinity washing effect data, the deficit interval parameters and time window of the deficit interval generation unit are dynamically adjusted; and the final salinity concentration of the dredged material is verified. C final Remaining deficit adjustment demand Q remain Does it meet the target scenario Ω? j Once the requirements are met, a start command should be sent to the resource output unit.

[0036] The resource recovery output unit receives the start command from the feedback control unit and the output permission command from the scheduling effect evaluation unit, and transports the qualified dredged material to the designated docking point according to the target scenario requirements; after the output is completed, it identifies the dredged material, sets the final salinity concentration, and so on. C final Resource information such as output volume, output time, and target scene encoding is synchronized to the consortium blockchain data interaction unit for filing.

[0037] The collaborative scheduling module is used to share the salinity deficit adjustment status of multiple dredged material nodes based on a consortium blockchain, and to perform resource-based scheduling optimization at the system level to improve overall resource utilization efficiency. The collaborative scheduling module includes: a consortium blockchain data interaction unit, a scheduling decision unit, an instruction issuance unit, and a scheduling effect evaluation unit. The data input terminal of the consortium blockchain data interaction unit is connected to the data output terminals of the salinity state vector generation unit, deficit adjustment parameter storage unit, and resource utilization output unit of each dredged material node, respectively, and obtains a summary of the deficit adjustment status of each node through the consortium blockchain. The data output terminal of the consortium blockchain data interaction unit is connected to the data input terminal of the scheduling decision unit. The data output terminal of the scheduling decision unit is connected to the data input terminal of the instruction issuance unit. The data output terminal of the instruction issuance unit is connected to the salinity washing execution unit of each dredged material node. The data input terminal of the scheduling effect evaluation unit is connected to the feedback control unit of each node. The data output terminal of the scheduling effect evaluation unit is connected to the data input terminals of the scheduling decision unit and the resource utilization output unit of each node, respectively, and outputs the scenario capacity verification result and the permission instruction.

[0038] The consortium blockchain data interaction unit is used to collect summary information on the salinity deficit status of multiple dredged material nodes after completing the deficit adjustment range construction and salinity washing through the consortium blockchain's P2P network. The summary information consists of the salinity state vector from the salinity state vector generation unit, the deficit adjustment parameters from the deficit parameter storage unit, and the resource information from the resource output unit, and includes at least: dredged material identification code and current salinity level. C current , corresponding deficit interval Δ j Remaining deficit adjustment demand Q remain ( , Q removed (This refers to the amount of salt and alkali removed). This unit uses smart contracts to achieve trusted sharing and access control of summary information. Only authorized nodes can query and modify data, ensuring data security and consistency. The scheduling decision unit is used to construct a system-level resource scheduling optimization model based on the deficit status summary information shared by the consortium blockchain, in order to maximize overall resource efficiency. ( M n For the firstn Total amount of dredged material at each node or n For the first n (Individual node resource utilization conversion rate) and minimize total system processing cost. To achieve this goal, we need to determine the priority order for different dredged material nodes to enter different resource utilization scenarios. The constraints of the scheduling optimization model include: 1) Scene capacity constraints: ( Cap j For the first j (Maximum capacity of each scene); 2) Salinity-alkalinity matching constraints: (No. n The current salinity of the dredged material at each node must meet the following requirement: j (Tolerance range for each scenario) 3) Transportation cost constraints: ( Cost trans,nj For the first n The node to the first j Transportation costs for each scenario Cost trans,max (to the maximum permissible transportation cost); The model is solved by particle swarm optimization algorithm to obtain a scheduling scheme, which includes target scenario allocation for each node, suggestions for adjusting the saline-alkali washing rhythm, and transportation route planning.

[0039] The instruction issuing unit is used to convert the scheduling plan into standardized scheduling instructions, including scene allocation instructions, salt washing rhythm adjustment instructions, and transportation scheduling instructions, and to issue them to the salt washing execution units of each dredged material node through an encrypted communication channel to ensure accurate and timely transmission of instructions. The scheduling effect evaluation unit is used to periodically collect resource utilization result data from the feedback control units of each node. This resource utilization result data includes actual utilization scenarios, final salinity concentration, processing costs, and resource utilization benefits; and calculates the overall resource utilization efficiency of the system. or system Total processing cost Cost system Compare this value with the expected value from the scheduling optimization model; when or system 10% lower than expected or Cost system When the value exceeds the expected value by 10%, the reasons for the non-compliance are analyzed and fed back to the scheduling decision unit for further optimization of the scheduling scheme; when the value is met, the output application submitted by the node is reviewed and an output permission instruction is issued to the resource output unit.

[0040] The core features of existing deficit irrigation technology are as follows:

[0041] Compared to deficit irrigation technology, this invention does not target the phased reduction of crop water supply, but rather focuses on the entire process of dredged material resource utilization, managing salinity as a system state variable. By introducing a salinity deficit range, the traditional constraint of eliminating salinity is transformed into a manageable object that can be configured across time, space, and utilization scenarios. Based on this, a salinity leaching path optimization and semi-centralized collaborative scheduling mechanism are constructed to improve the resource utilization efficiency of multiple dredged material nodes, demonstrating significant system innovation and engineering applicability. The specific differences between the two are as follows:

[0042] When the system is working, it includes the following steps: Step 1: Start the system. The data acquisition module performs full perception and standardized modeling of the basic properties of the dredged material to be treated, forming the raw data required for subsequent salinity analysis, and outputs the standardized basic property model as the unified input for subsequent calculations. Step 2: After obtaining the standardized basic attribute model output in Step 1, the system enters the salinity analysis stage, further resolving the salinity inside the dredged material from "total description" to a structured state of "layered distribution + sensitivity weight + migration potential". The salinity of the dredged material is profiled to obtain the distribution characteristics of salinity in different layers and identify its potential constraints on different resource utilization scenarios, forming a salinity state vector, thereby providing a calculable basis for the construction of deficit adjustment intervals and feasibility determination. Step 3: Based on the salinity state vector obtained in Step 2, the system introduces the dimension of "resource utilization scenario", transforming the "salt and alkalinity constraint" from a single threshold into a tolerance range Ω that varies with the scenario. j And calculate the comprehensive priority based on multi-objective decision-making. U j This is to determine the target boundary for constructing the deficit adjustment interval and the direction of subsequent scenario transfer decisions; Step 4: Obtain the salinity tolerance range Ω from Step 3. j With overall priority U j Afterwards, the system in Ω j Based on the introduction of controlled salinity surplus d j Construct the corresponding salinity deficit range Δ j Furthermore, the deficit range is transformed into a calculable deficit range parameter, allowing salinity to have a phased surplus, spatial retention, or scenario adaptation within a controllable boundary, thereby avoiding the excessive treatment of "one-size-fits-all" salinity washing. Step 5: After obtaining the salinity state vector generated in Step 2 and the deficit adjustment interval parameters formed in Step 4, the system conducts a deficit adjustment feasibility assessment on the salinity surplus to determine whether it can be managed through "spatial deficit adjustment, time deficit adjustment or scenario deficit adjustment", thereby deciding whether to adopt a phased salinity washing path or an enhanced salinity washing path. Step 6: After obtaining the feasibility assessment result of the deficit adjustment in Step 5, the system generates a salt washing path scheme that matches the assessment result, and solves the relationship between cost, time, resources and deficit adjustment constraints through the optimization model to obtain the optimal salt washing path parameters to guide the execution. Step 7: Implement the salt washing process according to the optimal salt washing path parameters output in Step 6, and realize the closed-loop control of "execution-monitoring-analysis-re-optimization" through PLC automatic control and sensor array monitoring, so that the salt washing intensity, stage switching and deficit adjustment range parameters can be dynamically corrected according to monitoring feedback. Step 8: When the system is in a scenario where multiple dredged material nodes are processed in parallel, the collaborative scheduling module realizes the sharing of the deficit status and resource recovery results of each node based on the consortium blockchain, and completes the closed-loop scheduling of "status sharing - decision optimization - instruction issuance - effect evaluation", thereby improving the overall resource recovery efficiency and reducing the total system cost.

[0043] Step 1 includes the following sub-steps: Step 1.1: The system first accurately identifies the source of the dredged material, including river dredging, lake dredging, harbor basin dredging, and waterway dredging. It then records the environmental characteristics of the formation process through a combination of sensor monitoring and historical data tracing, including at least hydrodynamic conditions, sedimentary environment, and historical salinity. Simultaneously, the system assigns a unique identification code to each batch of dredged material. This code serves as the primary key in the data structure of all subsequent modules throughout the entire process, used for on-chain traceability and cross-node collaboration. Step 1.2: With the source information already bound, the system synchronously collects multiple physical structural parameters of the dredged material, including moisture content, particle size distribution, initial compaction state, porosity, and equivalent permeability coefficient. These parameters will be directly used as the medium condition input for the one-dimensional convection-dispersion equation in the salinity migration simulation, determining the dispersion coefficient. D Convection velocity v The method of determining or calibrating these values ​​is therefore a key source of basic data for subsequent model calculations. Step 1.3: The system employs a "multi-point sampling + stratified detection" mode to detect the salinity of the dredged material, acquiring total salinity, conductivity, and main soluble salt types, with a focus on obtaining salinity distribution data along the vertical direction. This distribution data will be used to construct a salinity concentration-depth distribution function. C s (z This information is used as the direct input for calculating the initial conditions and distribution ratio in the salinity analysis module. Step 1.4: The system maps the source information, physical parameters and salinity test results into a standardized data model to form a basic attribute model of the dredged material. This model is then output to the salinity analysis module through a standardized data interface to ensure that there are no field ambiguities or unit inconsistencies when subsequent modules call the data.

[0044] Step 2 includes the following sub-steps: Step 2.1: The system is based on the physical structure parameters and salinity-depth distribution function obtained in Step 1. C s ( z Cluster analysis was used to functionally partition the dredged profile into three functional layers: a saline-alkali sensitive layer S1, a saline-alkali insensitive layer S2, and a potential saline-alkali retention layer S3. Each layer was assigned a saline-alkali sensitivity weight coefficient. w i ,in ;Should w i It will be used as a weighting factor in subsequent spatial deficit assessments to reflect the differentiated tolerance of different strata to salinity risks; Step 2.2: Obtain the layer boundary depth parameters z i , z i+1 With the total thickness of the cross section H Then, the distribution ratio of salinity in different functional strata was calculated. P i : ; in, P i Indicates the first i Each functional layer, among which i =1, 2, 3 correspond to the proportions of salinity and alkalinity in S1, S2, and S3 to the total salinity and alkalinity, respectively. z i+1 , z i Indicates the first i The upper and lower boundary depths of each functional level H Indicates the total thickness of the dredged material profile. C s ( z () indicates that the salinity concentration varies with vertical depth. z The distribution function; Step 2.3: The system further simulates the salinity migration process based on the one-dimensional convection-dispersion equation: ; in, C ( z,t )for t time, z Salinity concentration at depth t For time variables, z For vertical spatial coordinates, D The dispersion coefficient of salt and alkali. v The velocity is the convective migration rate of salt and alkali; the initial conditions of the model are set to... t =0 C ( z,t )= C s ( z ), Boundary conditions under saline-alkali washing conditions on the surface z =0 indicates the Dirichlet boundary. C (0 ,t )= C wash ( C wash (The salt and alkali content of the washing water) is [value] under natural conditions. C (0 ,t )= C atm ( C atm (Atmospheric deposition salinity and alkalinity), bottom layer z = H For Neumann boundary, ; Based on the simulation results, the system extracts key indicators of salinity migration potential, including peak salinity concentration migration depth. z peak Residence time in non-sensitive layers or potential saline-alkali layers T stay Flux returning to the sensitive layer J back And so on, and determine whether it has the potential for loss adjustment management based on the following conditions: 1) The peak salinity concentration is within the preset time scale. T pre The main migration occurs in the non-sensitive layer S2 or the potential saline-alkali layer S3, with a peak migration depth. z peak ≥ z 2 ( z 2 represents the lower boundary depth of layer S2). 2) Predicted residence time of salinity in the non-sensitive layer S2 or the potential saline-alkali layer S3 T stay ≥ T threshold( T threshold (for the preset threshold time). T stay From the formula calculate; 3) Predicted back migration flux of salinity to sensitive layer S1 J back ≤ J safe ( J safe (for safe flux thresholds), migration flux ( z = z 1, z 1 represents the lower boundary depth of layer S1). When at least one of the above conditions is met, the system determines that the batch of dredged material "has the potential for deficit management" and... Flag deficit Set to 1 otherwise set to 0; Step 2.4: The system will process the data from step 2.2. P i Migration potential indicators in step 2.3 z peak , T stay , J back , Flag deficit and the layer sensitivity weights in step 2.1 w i Integrate into a structured salinity-alkalinity state vector V The expression is: ; This vector is written into the consortium blockchain as a "deficit state digest" and stored using SHA-256 hash encryption to ensure that the data is immutable and can be queried and verified across nodes by the collaborative scheduling module.

[0045] Step 3 includes the following sub-steps: Step 3.1: Based on regional planning requirements, engineering task specifications, and market demand analysis, the system determines the intended resource utilization scenarios for the dredged material, including agricultural soil improvement (SC1), ecological restoration substrate (SC2), engineering fill (SC3), or greening matrix (SC4), etc., and assigns a unique code to each scenario. It also records additional information such as service life and environmental requirements as subsequent time constraints. T critical The basis for setting risk tolerance; Step 3.2: Based on national standards and industry practice data, the system sets salinity tolerance ranges for different scenarios: ; Among them, Ω j Indicates the first j One resource utilization scenario, among which j =1,2,3,4, corresponding to the salinity tolerance range of resource utilization scenarios SC1~SC4; C j,min This indicates the minimum salinity level allowed in this resource utilization scenario, which is related to soil physicochemical properties or engineering stability; C j,max This indicates the maximum salinity level allowed in the resource utilization scenario, used to avoid adverse effects on crops, organisms, or engineering structures. Step 3.3: The system constructs a comprehensive benefit function based on multi-objective decision-making theory and ranks different scenarios: ; in, U j Indicates the first j A comprehensive priority index for each resource utilization scenario, with a value range of [0,1]. U j The larger the value, the higher the priority. R j Indicators representing the economic benefits of resource utilization. R max To achieve the maximum economic benefit across all scenarios; E j Indicators representing ecological benefits E max For maximum ecological benefit; T j Indicators representing time cost T max To maximize time cost; α , β , c For the weighting coefficients, satisfying α + β + c =1, which can be adjusted according to the needs of regional development.

[0046] Step 4 includes the following sub-steps: Step 4.1: The system will use the scene salinity tolerance range Ω obtained in step 3.2. j As a hard constraint boundary, the allowable controlled salinity surplus is determined in conjunction with the scenario risk tolerance. d j Construct a salinity deficit range Δ j : ; Where, Δ j Indicates the first j The salinity deficit range corresponding to each resource utilization scenario; d j This represents the permissible controlled salinity surplus, determined based on the scenario's risk tolerance; when generating the deficit adjustment range, the system introduces at least one deficit adjustment dimension: 1) If a time dimension is introduced, then the salinity / alkalinity at a certain stage is allowed to be within Δ j Exceeding Ω j And subsequently regressed Ωj through natural rinsing or mild treatment; 2) If a spatial dimension is introduced, the sensitive layer S1 must satisfy Ω. j The non-sensitive layer S2 and the potential salt-alkali layer S3 allow for Δ j Internal surplus; 3) If the utilization scenario dimension is introduced, it is permissible to transfer to a lower priority scenario with higher salt and alkali tolerance when a high priority scenario cannot accommodate it temporarily, and then adjust it again when the conditions are met. This unit also receives outputs from the feedback control unit in the execution feedback module, such as the dynamic correction time window and the correction deficit interval parameters, therefore Δ j It can be adaptively updated within a closed loop; Step 4.2: The system converts the deficit adjustment range into a calculable deficit adjustment demand intensity and calculates the deficit adjustment amount index. Q def : ; in, Q def This indicator represents the amount of salinity deficit, used to quantify the cumulative extent to which salinity exceeds the resource utilization limit; T window For time dimension adjustment window; x ) + Denotes the positive part function, when x Take when >0 x Otherwise, take 0; r The dry density of the dredged material.

[0047] Step 4.3: The system will adjust the deficit interval parameter Δ j Adjustment loss indicators Q def Scenario Priority U j The dredging parameters are written into the consortium blockchain using an encryption algorithm. The storage structure includes the dredged material identification code, target scene code, parameter generation time, and hash verification value to ensure parameter consistency and security when multiple nodes work together. At the same time, a parameter index library is established to support quick access to the dredging feasibility determination module.

[0048] Step 5 includes the following sub-steps: Step 5.1: Based on the salinity distribution ratio, layer sensitivity weights, and salinity migration simulation results, the system calculates the spatial schedulability index. S spatial : ; in, T stay,i For the first i The salinity residence time of each functional layer; T threshold,i For the first i Spatial dimension adjustment window for each functional level; R i For salt and alkali by the first i The predicted proportion of functional layer migrations back to the sensitive layer S1 is calculated by the migration model. ( A The cross-sectional area of ​​the floor is [missing information]. M i For the first i (Total salinity and alkali in each functional layer). The system classifies the feasibility of spatial deficit adjustment accordingly: when S spatial A value ≥0.6 indicates that space deficit adjustment is feasible, while 0.3 ≤ S spatial <0.6 indicates partial feasibility. S spatial <0.3 is not feasible; Step 5.2: Based on the simulation results of salinity migration under natural conditions, the system calculates the feasibility index of time delay. S temporal : ; in, T natural To reduce salinity to the deficit range Δ under natural conditions j The time required within the process is predicted by the migration model; T critical The maximum allowed waiting time for the target resource is determined based on the scenario requirements; The system classifies the feasibility of time deficit adjustment accordingly: when S temporal ≥0.6 indicates that time deficit adjustment is feasible; 0.3≤ S temporal <0.6 indicates partial feasibility; S temporal <0.3 is not feasible; Step 5.3: Based on the scenario priority calculation results and the adaptability of the current salinity of the dredged material, the system calculates the scenario transfer feasibility index. S scene : ; in, I j For the scene j The fit index for the current salinity and alkalinity of the dredged material is expressed as: ; in, C current The overall salinity concentration of the dredged material is determined by... ( C i For the first i Calculation of average salinity and alkalinity concentration in each functional layer; The system classifies the feasibility of scene loss adjustment accordingly: when S scene ≥0.6 indicates feasible scene deficit adjustment; 0.3≤ S scene <0.6 indicates partial feasibility; S scene <0.3 is not feasible; Step 5.4: The system... S spatial , S temporal , S scene By performing weighted aggregation, a comprehensive feasibility index for adjusting deficits is obtained. S comprehensive : ; in, w s , w t , w u The weighting coefficients for space, time, and scenario are determined based on the analytic hierarchy process (AHP). A comprehensive deficit adjustment feasibility threshold is set according to the risk tolerance of the target resource utilization scenario, engineering constraints, and system operation strategies. l And perform a comprehensive system judgment: when S comprehensive When the salinity is ≥λ, it is determined that the salinity surplus can be managed through the deficit adjustment method, and the process proceeds to step 6.1 to generate a phased salinity washing path. when S comprehensive When the value is less than λ, it is determined that the deficit adjustment is not feasible, and the process proceeds to step 6.2 to generate an enhanced salt washing path.

[0049] Step 6 includes the following sub-steps: Step 6.1: When the feasibility assessment result for adjusting the deficit is deemed feasible, the system restructures the salinity washing target from "achieving the salinity standard in one go" to "meeting the deficit adjustment range constraints," and represents the salinity washing scheme as a phased path sequence: ; Among them, Π stage This is a set of phased salt-alkali washing pathway schemes. k For stage index, m k For the first k The salt and alkali control method adopted in each stage t k For the first k The execution time or duration of each stage; the phased path construction follows three principles: priority is given to non-salt-alkali washing methods or mild salt-alkali washing methods; sensitive layer S1 is treated first, while non-sensitive layer S2 and potential salt-alkali stagnant layer S3 are treated as needed; the stage interval is determined based on the salinity migration monitoring results, so that the salinity gradually migrates to the target layer, thus consistent with the spatial deficit adjustment concept; Step 6.2: When the feasibility assessment result for adjusting the deficit is deemed infeasible, the system generates an enhanced salt and alkali washing path: ; Among them, Π strengthen To strengthen the set of salt-alkali washing pathway schemes, m high This is a high-intensity method for washing away salt and alkali. t high To enhance the duration of salt and alkali washing, p high The system is configured with an automatic switching mechanism to enhance the salt and alkali washing intensity parameter; when the salt and alkali concentration drops to the target scenario's salt and alkali tolerance range (Ω), the system will switch to an automatic switching mechanism. j median ( C j , min + C j , max When the temperature reaches 1 / 2, it automatically switches to mild desalination to avoid overtreatment and waste of resources. Step 6.3: The system inputs the candidate paths from either Step 6.1 or Step 6.2 into the path optimization unit to construct an optimization model aimed at minimizing the cost of washing salt and alkali. ; in, c w For unit water resource cost, q k For the first k Water consumption per unit time during a given period A For the area treated by washing away salt and alkali, tk For the first k Phase execution time, c e Cost per unit of energy consumption p k For the first k The operating power of the staged salt-alkali washing equipment; the model is simultaneously subject to adjustment deficit, time, resource, and intensity constraints; the system solves the optimization model through a genetic algorithm to obtain the optimal salt-alkali washing path scheme.

[0050] Step 7 includes the following sub-steps: Step 7.1: The salt-washing execution unit receives the optimal salt-washing path scheme output by the path optimization unit and the scheduling instructions issued by the collaborative scheduling module instruction issuing unit, controlling the salt-washing equipment, salt-discharge facilities, and valves to execute precisely. The PLC controller is used to realize the switching of salt-washing modes, water consumption adjustment, and salt-washing frequency control to ensure... m k , t k The strength parameters are executed according to the path scheme; Step 7.2: The system uses a distributed salinity sensor array arranged at intervals to monitor different depths in real time. z salinity concentration C ( z , t and conductivity EC ( z , t Simultaneously monitor auxiliary parameters such as moisture content and pore water pressure; Step 7.3: The system preprocesses the monitoring data, using the moving average method to remove outliers, cubic spline interpolation to complete missing data, and converts the preprocessed data into a standardized format. This data is then compared and analyzed with the initial salinity state vector and the salt-washing path parameters to calculate the salinity removal rate and migration rate. ,

[0051] in, C initial The initial salinity concentration, z peak ( t )for t Momentary peak salinity migration depth; Step 7.4: Based on the data processing results, the feedback control unit sends parameter adjustment commands to the salt and alkali washing execution unit and transmits them to the path optimization unit. or remove , v moveKey indicators such as these trigger a re-optimization of the salinity washing path; simultaneously, the system uploads resource-based results data such as node execution progress, execution cost, and final salinity concentration to the scheduling effect evaluation unit for system-level scheduling evaluation; based on the salinity washing effect data, the deficit interval parameters and time window in the deficit interval generation unit are dynamically adjusted to ensure that Δ j and T window Adaptive updates based on real-world engineering responses; Step 7.5: After the feedback control unit completes the phased monitoring, the system verifies the final salinity and alkalinity status of the dredged material. The verification indicators include at least the final salinity and alkalinity concentration. C final and remaining deficit adjustment demand Q remain and the target scenario's salt tolerance range Ω j After performing matching verification, the feedback control unit sends an application command to the resource output unit. Step 7.6: After receiving the application instruction and the output permission instruction from the scheduling effect evaluation unit, the resource recovery output unit transports the qualified dredged material to the designated docking point according to the target scenario requirements, and identifies and codes the dredged material. C final Resource information such as output volume, output time, and target scene encoding is synchronized to the consortium blockchain data interaction unit for filing, forming a traceable output record.

[0052] Step 8 includes the following sub-steps: Step 8.1: The consortium blockchain data interaction unit collects summary information of the deficit status of multiple dredged material nodes after completing the deficit adjustment interval construction and salinity washing through a P2P network. This summary is provided collaboratively by the salinity state vector generation unit, the deficit adjustment parameter storage unit, and the feedback control unit, and includes at least the dredged material identification code and the current salinity level. C current , corresponding deficit interval Δ j and remaining deficit adjustment demand Q remain Access control is implemented through smart contracts to ensure that only authorized nodes can query or modify data. Step 8.2: The scheduling decision unit constructs a system-level resource scheduling optimization model based on the on-chain shared digest to maximize overall resource efficiency. or system And minimize the total system processing cost. Cost system To achieve the goal while simultaneously satisfying the scenario capacity constraints. Cap jConstraints such as salinity matching and transportation cost are considered. The particle swarm optimization algorithm is used to solve the scheduling scheme, and the output includes the target scenario allocation of each node, suggestions for adjusting the salinity washing rhythm and transportation route planning. Step 8.3: The instruction issuing unit converts the scheduling plan into standardized scheduling instructions, including scenario allocation instructions, salt washing rhythm adjustment instructions, and transportation scheduling instructions, and issues them to the salt washing execution units at each node through an encrypted communication channel to ensure that the execution end can map the system-level optimization results into on-site control parameter adjustments; Step 8.4: The scheduling effect evaluation unit periodically collects the resource utilization result data uploaded by the feedback control unit of each node, calculates the overall resource utilization efficiency and total processing cost, and compares them with the expected value of the optimization model; when or system 10% lower than expected or Cost system When the performance exceeds the expected level by 10%, the system analyzes the cause and feeds it back to the scheduling decision unit to trigger re-optimization; when the target is met, the evaluation unit issues an output permission instruction to the resource output unit to complete the system-level closed-loop scheduling.

[0053] This invention also includes a deficit-adjustment method for the resource utilization of high-salinity and alkaline dredged materials. Targeting high-salinity and alkaline dredged materials to be treated, this method constructs and determines the basic properties of the dredged material, salinity profile distribution, migration potential, resource utilization scenario tolerance range, and deficit adjustment range. It achieves path generation and closed-loop optimization of "staged salinity washing when deficit adjustment is feasible, and intensified salinity washing when deficit adjustment is not feasible." Furthermore, it achieves multi-node collaborative optimization of resource utilization in a multi-node parallel environment. This addresses the technical problems of existing dredged material resource utilization methods, which generally adopt a "one-size-fits-all" strong salinity washing strategy and lack a deficit determination mechanism based on salinity profile differences and scenario tolerance constraints, leading to over-treatment or insufficient compliance, high resource consumption, and difficulty in achieving multi-node collaborative optimization. The method includes the following steps: Step 1: Collect and record basic attribute data of the dredged material to be treated, so as to ensure that the subsequent salinity profile analysis, migration simulation and deficit interval construction have a unified data source and input boundary; the output of this step is the "basic attribute model of dredged material", which serves as the direct input for the subsequent salinity analysis stage in Step 2. Step 2: After obtaining the standardized basic attribute model, the salinity and alkalinity inside the dredged material are transformed from a general scalar description into a calculable structured state, forming the input variables required for subsequent deficit range construction and feasibility assessment. The core outputs of this step include: functional stratum division results S1 / S2 / S3, and stratum-sensitive weights. w i Salinity and alkalinity distribution ratio P i Migration potential indicators zpeak , T stay , J back , Flag deficit And the structured salinity-alkalinity state vector; Step 3: Based on the structured salinity status, introduce the resource utilization scenario dimension, transforming the salinity constraint from a single threshold into a tolerance range Ω that varies with the scenario. j And through comprehensive priority U j This step determines the direction of constructing the deficit adjustment range and subsequent scenario transfer decisions. The core data output in this step includes: resource utilization scenario coding and the salinity tolerance range (Ω) for each scenario. j and overall priority U j ; Step 4: Obtain the salinity tolerance range Ω of the scene. j With overall priority U j Subsequently, a controlled salinity surplus was further introduced. d j Construct a salinity deficit range Δ j This allows for periodic surpluses, spatial retention, or scenario adaptation of salinity within controllable boundaries, thus avoiding excessive "one-size-fits-all" salinity washing. The core data output from this step includes: the deficit adjustment range Δ... j Adjustment loss indicators Q def and its indexed record information; Step 5: Obtain the structured salinity-alkalinity state vector V After setting the set of deficit adjustment parameters, a quantitative determination is made as to whether the current salinity surplus can be managed through "spatial deficit adjustment, time deficit adjustment, or scenario deficit adjustment" to determine whether to adopt phased or intensified salinity washing in the future. The output of this step is the comprehensive deficit adjustment feasibility determination result, which is the branch condition for entering step 6.1 or step 6.2. Step 6: After obtaining the feasibility assessment result of the deficit adjustment in Step 5, generate the corresponding salt washing path scheme, and solve the optimal salt washing path parameters among cost, time, resources and deficit adjustment constraints; the output of this step is the optimal salt washing path scheme, which includes the salt washing method, execution stage and time parameters and intensity parameters. Step 7: After obtaining the optimal salt-washing path, execute the salt-washing process according to the path parameters, and simultaneously monitor the salinity concentration at different depths. C ( z , t and conductivity EC ( z , tContinuous monitoring and data processing are performed, and path parameters are dynamically adjusted based on feedback to achieve a closed loop of "execution—monitoring—analysis—re-optimization"; the output of this step includes the stage removal rate. or remove Peak migration speed v move Final salinity concentration C final and remaining deficit adjustment demand Q remain And finally, a standard-compliant assessment will be conducted; Step 8: When processing multiple dredged material nodes in parallel, perform system-level resource recovery collaborative optimization based on the deficit status summary information of each node to improve overall resource recovery efficiency and reduce total processing cost. The inputs for this step are the identification code of the dredged material at each node and the current salinity level. C current , corresponding deficit interval Δ j and remaining deficit adjustment demand Q remain The summary information is used to output a cross-batch scene allocation and rhythm adjustment plan, and further optimization is triggered through effect evaluation.

[0054] Step 1 includes the following sub-steps: Step 1.1: Identify the source type of the dredged material. The source type should include at least river dredged material, lake dredged material, harbor basin dredged material, or waterway dredged material, and bind the source identification results with environmental characteristic information. The environmental characteristic information is formed by combining on-site monitoring information and historical data tracing information, and should include at least hydrodynamic conditions, sedimentary environment, and historical salinity background. Simultaneously, assign a unique identification code to this batch of dredged material. This identification code serves as the primary key throughout the entire process in all subsequent data structures, used for cross-step data indexing, tracing, and maintaining consistency between parallel batches. Step 1.2: With the source information already bound, synchronously collect the physical structural parameters of the dredged material. The collected parameters should include at least water content, particle size distribution, initial compaction state, void ratio, and equivalent permeability coefficient. These physical structural parameters will be used as the medium condition inputs for the one-dimensional convection-dispersion equation in the subsequent salinity migration simulation, determining the dispersion coefficient. D Convection velocity v The value or calibration method determines the key basic data source for the migration simulation in the subsequent step 2.3; Step 1.3: The salinity of the dredged material is measured using a "multi-point sampling + stratified detection" method to obtain the total salinity, conductivity, and main soluble salt types, with a focus on acquiring the vertical distribution data of salinity. This vertical distribution data will be used to construct a salinity concentration-depth distribution function.C s ( z This data serves as the direct input for subsequent steps 2: salinity profile analysis, stratigraphic division, distribution ratio calculation, and migration simulation. Step 1.4: After obtaining the source information, physical structure parameters, and salinity test results, these are uniformly mapped into a standard format data model to form a basic attribute model of the dredged material. This basic attribute model ensures that the meaning of fields, units, and scales are consistent when data is called in subsequent steps, avoiding structural errors in salinity status or deviations in adjustment judgment due to data ambiguity.

[0055] Step 2 includes the following sub-steps: Step 2.1: Based on the physical structural parameters and salinity-depth distribution function obtained in Step 1 C s ( z The dredged material profile was functionally divided into three layers: a saline-alkali sensitive layer (S1), a saline-alkali insensitive layer (S2), and a potential saline-alkali retention layer (S3). Each layer was assigned a saline-alkali sensitivity weight coefficient. w i ,in ; Should w i This will be used as a weighting factor in subsequent spatial deficit assessments to reflect the differentiated tolerance of different strata to salinity risk. Therefore, the strata delineation boundaries output in this sub-step and w i This will serve as the input source for calculating the spatial schedulability index in step 5.1; Step 2.2: Obtain the layer boundary depth parameters z i , z i+1 With the total thickness of the cross section H Then, the distribution ratio of salinity in different functional strata was calculated. P i : ; in, P i Indicates the first i Each functional layer, among which i =1, 2, 3 correspond to the proportions of salinity and alkalinity in S1, S2, and S3 to the total salinity and alkalinity, respectively. z i+1 , z i Indicates the first i The upper and lower boundary depths of each functional level H Indicates the total thickness of the dredged material profile.C s ( z () indicates that the salinity concentration varies with vertical depth. z The distribution function; Should P i This will serve as the subsequent salinity / alkalinity state vector. V It is an important component and serves as the basic input for spatial deficit adjustment and scene adaptation calculations; Step 2.3: Obtain the medium conditions from Step 1.2 and Step 1.3. C s ( z Then, the salinity migration process was further simulated based on the one-dimensional convection-dispersion equation: ; in, C ( z,t )for t time, z Salinity concentration at depth t For time variables, z For vertical spatial coordinates, D The dispersion coefficient of salt and alkali. v The velocity is the convective migration rate of salt and alkali; the initial conditions of the model are set to... t =0 C ( z,t )= C s ( z ), Boundary conditions under saline-alkali washing conditions on the surface z =0 indicates the Dirichlet boundary. C (0 ,t )= C wash ( C wash (The salt and alkali content of the washing water) is [value] under natural conditions. C (0 ,t )= C atm ( C atm (Atmospheric deposition salinity and alkalinity), bottom layer z = H For Neumann boundary, ; After obtaining the simulation results, from C ( z,t Key indicators of salinity migration potential were extracted from spatiotemporal evolution, including the peak migration depth of salinity concentration. z peak Residence time in non-sensitive layers or potential saline-alkali layersT stay Flux returning to the sensitive layer J back And assess the potential for loss management based on the following conditions: 1) The peak salinity concentration is within the preset time scale. T pre The main migration occurs in the non-sensitive layer S2 or the potential saline-alkali layer S3, with a peak migration depth. z peak ≥ z 2 ( z 2 represents the lower boundary depth of layer S2). 2) Predicted residence time of salinity in the non-sensitive layer S2 or the potential saline-alkali layer S3 T stay ≥ T threshold ( T threshold (for the preset threshold time). T stay From the formula calculate; 3) Predicted back migration flux of salinity to sensitive layer S1 J back ≤ J safe ( J safe (for safe flux thresholds), migration flux ( z = z 1, z 1 represents the lower boundary depth of layer S1). The batch of dredged material was determined to have "potential for deficit management" and will Flag deficit Set to 1 otherwise set to 0; Flag deficit This will serve as an important source of prior information for the subsequent comprehensive feasibility assessment of deficit adjustment; Step 2.4: Calculate the result in step 2.2 P i Step 2.3 Extraction of migration potential indicators z peak , T stay , J back , Flag deficit and the layer sensitivity weights in step 2.1 w i Integrate to form a structured salinity-alkalinity state vector V Its expression is: ; Should V The four categories of information, namely "distribution, sensitivity, migration, and potential," are used to summarize the salinity status of dredged materials and serve as the unified input for the construction of the deficit adjustment range in step 4 and the determination of the feasibility of deficit adjustment in step 5. Step 3 includes the following sub-steps: Step 3.1: Based on regional planning needs, engineering task book and market demand analysis, determine the resource utilization scenarios in which the dredged material is to be used, including at least agricultural soil improvement SC1, ecological restoration base SC2, engineering filling SC3 or greening matrix SC4, and assign a unique code to each scenario, while recording additional information such as service life and environmental requirements. The additional information will serve as a subsequent time constraint. T critical The basis for setting the risk tolerance level is thus linked to the feasibility calculation for the time delay in step 5.2 and the adjustment of profit and loss in step 4.1. d j Establish a consistent input-output relationship; Step 3.2: After the scenario is determined, based on national standards and industry practice data, set the salinity tolerance range Ω for different scenarios. j : ; Among them, Ω j Indicates the first j The salinity tolerance range corresponding to each resource utilization scenario; C j,min This indicates the minimum salinity level allowed in this resource utilization scenario; C j,max This indicates the maximum salinity level allowed in the resource utilization scenario, used to avoid adverse effects on crops, organisms, or engineering structures. The Ω j This will be used as the adjustment range Δ in the subsequent step 4. j The constructed hard constraint boundary is also the criterion for judging whether the effect in step 7.5 meets the standard; Step 3.3: Obtain the tolerance range Ω for each scenario. j Subsequently, a comprehensive benefit function was constructed based on multi-objective decision-making theory to rank different scenarios: ; in, U j Indicates the first j A comprehensive priority index for each resource utilization scenario, with a value range of [0,1]. U j The larger the value, the higher the priority. R j Indicators representing the economic benefits of resource utilization. Rmax To achieve the maximum economic benefit across all scenarios; E j Indicators representing ecological benefits E max For maximum ecological benefit; T j Indicators representing time cost T max To maximize time cost; α , β , c For the weighting coefficients, satisfying α + β + c =1, which can be adjusted according to regional development needs; Should U j This will serve as an important input for the subsequent scenario transfer feasibility calculation in step 5.3 and the loss adjustment parameter recording in step 4.3.

[0056] Step 4 includes the following sub-steps: Step 4.1: Ω obtained in step 3.2 j As a hard constraint boundary, the allowable controlled salinity surplus is determined in conjunction with the scenario risk tolerance. d j Thus, a salinity deficit range Δ is constructed. j Its expression is: ; When generating the deficit adjustment range, at least one deficit adjustment dimension should be introduced: when introducing a time dimension, the salinity in a certain stage is allowed to be within Δ. j Exceeding Ω j And subsequently reverted to Ω through natural rinsing or mild treatment. j When a spatial dimension is introduced, the sensitive layer S1 must satisfy Ω. j The non-sensitive layer S2 and the potential salt-alkali layer S3 allow for Δ j Internal surplus; when the utilization scenario dimension is introduced, it is allowed to transfer to a lower priority scenario with higher salt tolerance when a high priority scenario cannot accommodate it temporarily, and then adjust it again when the conditions are met. The Δ j It allows for dynamic adjustments based on actual results during subsequent closed-loop processes, thus it is an updatable process variable; Step 4.2: To quantify the cumulative extent to which salinity exceeds the resource utilization limit, the deficit range Δj is further transformed into a calculable deficit amount index. Q def : ; in, Qdef This indicator represents the amount of salinity deficit, used to quantify the cumulative extent to which salinity exceeds the resource utilization limit; T window For time dimension adjustment window; x ) + Denotes the positive part function, when x Take when >0 x Otherwise, take 0; r The dry density of the dredged material; Should Q def This will be used as the input for the deficit adjustment constraint in the path optimization model of step 6, and also for calculating the remaining deficit adjustment demand in step 7.5. Q remain The benchmark for comparison; Step 4.3: Adjust the deficit interval parameter Δ j Adjustment loss indicators Q def Scenario Priority U j The parameters are then compiled into a set of adjustment parameters and established in correspondence with the dredged material identification code, target scene code, parameter generation time, etc. This ensures that the subsequent steps of adjustment feasibility determination (step 5) and path generation and optimization (step 6) can quickly call the same set of parameters, avoiding path deviation caused by inconsistent parameter versions.

[0057] Step 5 includes the following sub-steps: Step 5.1: Based on the salinity distribution ratio P i Layer-sensitive weights w i and the residence time of each layer in the salinity migration simulation results T stay,i Relocation ratio R i Computational space schedulability index S spatial : ; in, T stay,i For the first i The salinity residence time of each functional layer; T threshold,i For the first i Spatial dimension adjustment window for each functional level; R i For salt and alkali by the first i The predicted proportion of functional layer migrations back to the sensitive layer S1 is calculated by the migration model. ( A The cross-sectional area of ​​the floor is [missing information]. Mi For the first i (Total salinity and alkali in each functional layer). in accordance with S spatial The feasibility of spatial deficit adjustment is classified into levels: when S spatial A value ≥0.6 indicates that space deficit adjustment is feasible, while 0.3 ≤ S spatial <0.6 indicates partial feasibility. S spatial <0.3 is not feasible; Step 5.2: Calculate the feasibility index of time delay based on the simulation results of salinity migration under natural conditions. S temporal : ; in, T natural To reduce salinity to the deficit range Δ under natural conditions j The time required within the process is predicted by the migration model; T critical The maximum allowed waiting time for the target resource recorded in step 3.1 is determined based on the scenario requirements; in accordance with S temporal Feasibility classification for time deficit adjustment: when S temporal ≥0.6 indicates that time deficit adjustment is feasible; 0.3≤ S temporal <0.6 indicates partial feasibility; S temporal <0.3 is not feasible; Step 5.3: Based on scenario priority U j And the adaptability of the dredged material to its current salinity and alkalinity, to calculate the feasibility index of scenario transfer. S scene : ; in, I j For the scene j The fit index for the current salinity and alkalinity of the dredged material is expressed as: ; in, C current The overall salinity concentration of the dredged material is determined by... ( C i For the first i Calculation of average salinity and alkalinity concentration in each functional layer; in accordance withS scene Feasibility classification for scene loss adjustment: when S scene ≥0.6 indicates feasible scene deficit adjustment; 0.3≤ S scene <0.6 indicates partial feasibility; S scene <0.3 is not feasible; Step 5.4: For S spatial , S temporal , S scene By performing weighted aggregation, a comprehensive feasibility index for adjusting deficits is obtained. S comprehensive : ; in, w s , w t , w u The weighting coefficients are spatial, temporal, and scene-specific, and a comprehensive deficit adjustment feasibility threshold is set. l ; Then a comprehensive judgment is performed: when S comprehensive When ≥λ, the salinity surplus can be managed through a deficit adjustment method, proceeding to step 6.1 to generate a phased salinity washing path; when S comprehensive When the value is less than λ, it is determined that the deficit adjustment is not feasible, and the process proceeds to step 6.2 to generate the enhanced salt washing path; this determination result is the only triggering condition for the subsequent path generation branch.

[0058] Step 6 includes the following sub-steps: Step 6.1: When it is determined that adjusting the deficit is feasible, the goal of washing salt and alkali is restructured from "achieving the salinity standard in one go" to "meeting the deficit adjustment range constraint", and the washing salt and alkali scheme is represented as a stage path sequence Π. stage : ; Among them, Π stage This is a set of phased salt-alkali washing pathway schemes. k For stage index, m k For the first k The salt and alkali control method adopted in each stage t k For the first k The execution time or duration of a phase; The phased path construction follows three principles: priority is given to non-salt-alkali leaching methods or mild salt-alkali leaching methods; sensitive layer S1 is treated first, while non-sensitive layer S2 and potential salt-alkali stagnant layer S3 are treated as needed; the phase interval is determined based on the salinity migration monitoring results, so that salinity gradually migrates to the target layer, thus aligning with the concept of spatial deficit adjustment. Step 6.2: When it is determined that adjusting the deficit is not feasible, generate the enhanced salt and alkali washing path Π. strengthen : ; Among them, Π strengthen To strengthen the set of salt-alkali washing pathway schemes, m high This is a high-intensity method for washing away salt and alkali. t high To enhance the duration of salt and alkali washing, p high The parameters for washing salt and alkali strength; An automatic switching mechanism is set up during the enhanced salt and alkali washing process: when the salinity concentration drops to the target scenario's salinity tolerance range Ω... j median ( C j , min + C j , max When the temperature reaches 1 / 2, it automatically switches to mild desalination to avoid overtreatment and waste of resources. Step 6.3: Using the candidate paths formed in Step 6.1 or Step 6.2 as input, construct an optimization model with the objective of minimizing the cost of salt washing: ; in, c w For unit water resource cost, q k For the first k Water consumption per unit time during a given period A For the area treated by washing away salt and alkali, t k For the first k Phase execution time, c e Cost per unit of energy consumption p k For the first k Operating power of staged salt and alkali washing equipment; The model is simultaneously subject to deficit adjustment constraints, time constraints, resource constraints, and intensity constraints, and uses a genetic algorithm to solve for the optimal salt washing path scheme, providing clear path parameter inputs for the subsequent closed-loop execution in step 7.

[0059] Step 7 includes the following sub-steps: Step 7.1: Based on the optimal salt and alkali washing path scheme output in Step 6, index by stage. k Perform the corresponding salt and alkali washing method m k With duration t k Furthermore, under the enhanced salt-alkali washing path, the transition from high intensity to mild intensity is completed based on the median switching condition, thereby ensuring that the path parameters are consistent with the optimization solution results; Step 7.2: During execution, for different depths z salinity concentration C ( z , t and conductivity EC ( z , t This process involves real-time or near-real-time monitoring, while simultaneously collecting auxiliary parameters such as water content and pore water pressure to ensure data support for subsequent convection-dispersion migration interpretation and path correction. The output of this sub-step is a monitoring data sequence, including data from multiple time points and depths. C ( z , t ), EC ( z , t ) and auxiliary parameters; Step 7.3: Preprocess the monitoring data, using the moving average method to remove outliers, and using cubic spline interpolation to complete missing data. Compare and analyze the preprocessed data with the initial salinity state vector and path parameters to calculate the salinity removal rate and migration rate. ,

[0060] in, C initial The initial salinity concentration, z peak ( t )for t Peak salinity migration depth at any given time; or remove and v move This serves as an important basis for subsequent path parameter re-optimization and adaptive update of the deficit adjustment interval; Step 7.4: According to or remove , v move Key indicators such as these are used to adjust the execution parameters for washing salt and alkali, and the results of phased execution progress, execution cost and salinity changes are summarized to form feedback information, which is used to trigger path re-optimization; Simultaneously, the deficit adjustment range Δ is dynamically adjusted based on the data on the salt and alkali washing effect. j and time windowT window , so that Δ j and T window It adaptively updates with the actual engineering response, thereby ensuring that the deficit management strategy is consistent with the actual salt and alkali migration pattern; Step 7.5: After completing the phased monitoring and necessary parameter adjustments, verify the final salinity and alkalinity status of the dredged material. The verification indicators should include at least the final salinity and alkalinity concentration. C final and remaining deficit adjustment demand Q remain and will C final With respect to the salinity tolerance range Ω of the target resource utilization scenario j Perform matching verification: when C final Falling into Ω j Within the range, that is, satisfying C j,min ≤ C final ≤ C j,max and Q remain If the acceptable range is met, the resource utilization effect is deemed to have met the standard; otherwise, it is deemed not to have met the standard, and the process returns to step 6 to regenerate or optimize the salt and alkali washing path scheme until the target scenario requirements are met. Step 7.6: After step 7.5 determines that the standard has been met, transport the compliant dredged material to the designated transfer point according to the target scenario requirements, and record the dredged material identification code. C final Resource information such as output quantity, output time, and target scene encoding is used to form a traceable output record to support subsequent parallel batch scheduling or quality auditing.

[0061] Step 8 includes the following sub-steps: Step 8.1: Collect summary information on the deficit status of multiple parallel batches after completing the deficit adjustment interval construction and salinity washing, including at least the dredged material identification code and the current salinity level. C current , corresponding deficit interval Δ j and remaining deficit adjustment demand Q remain This forms a shared dataset that can be invoked for scheduling decisions; Step 8.2: Construct a system-level resource scheduling optimization model based on the shared dataset to maximize overall resource efficiency. or system And minimize the total system processing cost. Cost system To achieve the goal while simultaneously satisfying the scenario capacity constraints. Cap j The scheduling scheme is obtained by solving the salinity and alkalinity matching constraints and transportation cost constraints using the particle swarm optimization algorithm. The scheme outputs the target scenario allocation for each batch, suggestions for adjusting the salinity washing rhythm, and transportation route planning. Step 8.3: Transform the scheduling scheme into standardized scheduling instructions, including scene allocation instructions, salt washing rhythm adjustment instructions and transportation scheduling instructions, and issue them to the execution process of the corresponding batches, so that the system-level optimization results can be mapped to the path parameter updates and scene transfer strategy adjustments of each batch. Step 8.4: Periodically collect resource recovery results data for each batch and calculate the overall resource recovery efficiency. or system Total processing cost Cost system And compare it with the expected value of the optimized model; when or system 10% lower than expected or Cost system If the result is 10% higher than expected, analyze the cause and trigger re-optimization; when the result meets expectations, complete the system-level closed-loop scheduling and allow the qualified batch to output resource-based products according to step 7.6.

Claims

1. A deficit-type high-salinity dredged material resource utilization system, characterized in that, The system includes a salinity analysis module (1), whose data output is connected to the data input of the deficit adjustment interval construction module (3), the deficit adjustment feasibility determination module (4), and the collaborative scheduling module (5); the data output of the resource utilization scenario management module (6) is connected to the data input of the deficit adjustment interval construction module (3); the data output of the deficit adjustment interval construction module (3) is connected to the data input of the deficit adjustment feasibility determination module (4) and the collaborative scheduling module (5); the data output of the deficit adjustment feasibility determination module (4) is connected to the data input of the salt-alkali washing path generation module (7); the data end of the salt-alkali washing path generation module (7) is bidirectionally connected to the data end of the execution and feedback module (8); the data output of the execution and feedback module (8) is connected to the data input of the deficit adjustment interval construction module (3), the salt-alkali washing path generation module (7), and the collaborative scheduling module (5); and the data end of the collaborative scheduling module (5) is bidirectionally connected to the data end of the execution and feedback module (8).

2. The system according to claim 1, characterized in that, It also includes a data acquisition module (2), whose data output terminal is connected to the data input terminal of the salinity analysis module (1); The data acquisition module (2) is used to comprehensively and accurately perceive the basic properties of the dredged material to be treated, and to provide standardized and highly reliable raw data support for subsequent salinity analysis. The salinity analysis module (1) analyzes the raw data obtained by the data acquisition module (2), performs spatial analysis of the salinity inside the dredged material, identifies the distribution characteristics and migration potential of salinity in different layers, and provides a basis for the determination of deficit.

3. The system according to claim 2, characterized in that, The salinity analysis module (1) includes a functional stratification unit. The data input end of the functional stratification unit is connected to the data output end of the data standardization unit through a standardized data interface. The data output end of the functional stratification unit is connected to the data input ends of the salinity distribution calculation unit and the salinity migration simulation unit through a stratification parameter transmission link, providing parameters such as stratification boundaries and sensitive weights. The data output end of the salinity distribution calculation unit is connected to the data input end of the salinity state vector generation unit through a distribution ratio transmission channel. The data output end of the salinity migration simulation unit is connected to the data input end of the salinity state vector generation unit through a migration potential transmission channel. The data output end of the salinity state vector generation unit is connected to the data input ends of the deficit adjustment interval construction module (3), the deficit adjustment feasibility judgment module (4), and the consortium blockchain data interaction unit, respectively.

4. The system according to claim 1, characterized in that, The resource utilization scenario management module (6) is used to manage different dredged material resource utilization scenarios and their corresponding salinity tolerance conditions, providing a constraint basis for the construction of the deficit range; the resource utilization scenario management module (6) includes a scenario identification unit, the data output end of the scenario identification unit is connected to the data input end of the salinity tolerance range extraction unit and the scenario priority calculation unit through the scenario coding transmission link, providing scenario type coding; The data output of the saline-alkali tolerance range extraction unit is connected to the data input of the deficit adjustment range construction module (3) through the tolerance range transmission channel; the data output of the scenario priority calculation unit is connected to the data input of the deficit adjustment range construction module (3) through the priority transmission channel.

5. The system according to any one of claims 1 to 4, characterized in that, The deficit adjustment interval construction module (3) is used to construct a scientific and reasonable salinity deficit adjustment interval and quantify the deficit adjustment requirement based on the scenario requirements and the salinity and alkalinity characteristics of the dredged material. The deficit adjustment interval construction module (3) includes a deficit adjustment interval generation unit. The data input end of the deficit adjustment interval generation unit is connected to the data output end of the salinity state vector generation unit, the salinity tolerance interval extraction unit, the scenario priority calculation unit, and the feedback control unit, respectively. The data output end of the deficit adjustment interval generation unit is connected to the data input end of the deficit amount calculation unit through the deficit adjustment interval transmission channel. The data output end of the deficit amount calculation unit is connected to the data input end of the deficit parameter storage unit through the deficit amount transmission channel. The data output end of the deficit parameter storage unit is connected to the data input end of the deficit adjustment feasibility judgment module (4) and the consortium blockchain data interaction unit, respectively.

6. The system according to claim 5, characterized in that, The feasibility assessment module (4) is used to comprehensively evaluate whether the current salinity surplus can be managed through spatial, temporal, or scenario-based methods, providing a decision-making basis for the generation of salinity washing pathways. The feasibility assessment module (4) includes a spatial deficit assessment unit. The data input terminals of the spatial deficit assessment unit, temporal deficit assessment unit, and scenario deficit assessment unit are all connected to the data output terminals of the salinity state vector generation unit and the deficit parameter storage unit, receiving the salinity state vector and deficit parameters. The data output terminals of the spatial deficit assessment unit, temporal deficit assessment unit, and scenario deficit assessment unit are all connected to the data input terminal of the comprehensive assessment unit through the assessment index transmission channel. The data output terminal of the comprehensive assessment unit is connected to the data input terminal of the salinity washing pathway generation module (7) through the feasibility result transmission link.

7. The system according to claim 1, 2, 3, 4, or 6, characterized in that, The salt-alkali washing path generation module (7) is used to generate a suitable salt-alkali washing scheme based on the feasibility judgment result of the deficit adjustment, so as to achieve precise control of salinity and alkalinity. The salt-alkali washing path generation module (7) includes a phased salt-alkali washing path construction unit. The data input end of the phased salt-alkali washing path construction unit and the enhanced salt-alkali washing path construction unit are connected to the data output end of the comprehensive judgment unit to receive the comprehensive judgment result. The data output end of the phased salt-alkali washing path construction unit and the enhanced salt-alkali washing path construction unit are connected to the data input end of the path optimization unit. The data end of the path optimization unit is bidirectionally connected to the data end of the execution and feedback module (8).

8. The system according to claim 7, characterized in that, The execution and feedback module (8) is used to accurately execute the salt-alkali washing path scheme, monitor the salinity migration process in real time, and feed back the monitoring data to the front-end module. The execution and feedback module (8) includes a salt-alkali washing execution unit. The data input end of the salt-alkali washing execution unit is connected to the data output end of the path optimization unit and the instruction issuance unit, respectively, to receive the optimal salt-alkali washing path scheme and execution instruction. The data output end of the salt-alkali washing execution unit is connected to the data input end of the real-time monitoring unit. The data output end of the real-time monitoring unit is connected to the data input end of the data processing unit. The data output end of the data processing unit is connected to the data input end of the feedback control unit. The data output end of the feedback control unit is connected to the data input ends of the resource output unit, the salt-alkali washing execution unit, the deficit adjustment interval construction module (3), and the salt-alkali washing path generation module (7), respectively. The data input end of the resource output unit is connected to the data output ends of the feedback control unit and the scheduling effect evaluation unit, respectively, to receive the output permission instruction and target scenario information. The data output end of the resource output unit is connected to the data input end of the consortium blockchain data interaction unit, and synchronously outputs the filing information.

9. The system according to claim 8, characterized in that, The collaborative scheduling module (5) is used to realize the sharing of salinity deficit adjustment status of multiple dredged material nodes based on the consortium blockchain, and to perform resource-based scheduling optimization at the system level to improve the overall resource utilization efficiency. The collaborative scheduling module (5) includes a consortium blockchain data interaction unit. The data input end of the consortium blockchain data interaction unit is connected to the data output end of the salinity state vector generation unit, deficit adjustment parameter storage unit and resource utilization output unit of each dredged material node, respectively, and obtains the deficit adjustment status summary of each node through the consortium blockchain. The data output end of the consortium blockchain data interaction unit is connected to the data input end of the scheduling decision unit. The data output end of the scheduling decision unit is connected to the data input end of the instruction issuance unit. The data output end of the instruction issuance unit is connected to the salinity washing execution unit of each dredged material node. The data input terminal of the scheduling effect evaluation unit is connected to the feedback control unit of each node; the data output terminal of the scheduling effect evaluation unit is connected to the data input terminals of the scheduling decision unit and the resource output unit of each node, respectively, and outputs the scenario capacity verification results and outputs the permission instructions.

10. The system according to claim 1, 2, 3, 4, 6, 8, or 9, characterized in that, The system operates by following these steps: Step 1: Start the system. The data acquisition module performs full perception and standardized modeling of the basic properties of the dredged material to be treated, forming the raw data required for subsequent salinity analysis, and outputs the standardized basic property model as the unified input for subsequent calculations. Step 2: After obtaining the standardized basic attribute model output in Step 1, the system enters the salinity analysis stage, further resolving the salinity inside the dredged material from "total description" to a structured state of "layered distribution + sensitivity weight + migration potential". The salinity of the dredged material is profiled to obtain the distribution characteristics of salinity in different layers and identify its potential constraints on different resource utilization scenarios, forming a salinity state vector, thereby providing a calculable basis for the construction of deficit adjustment intervals and feasibility determination. Step 3: Based on the salinity state vector obtained in Step 2, the system introduces the dimension of "resource utilization scenario", transforming the "salt and alkalinity constraint" from a single threshold into a tolerance range Ω that varies with the scenario. j And calculate the comprehensive priority based on multi-objective decision-making. U j This is to determine the target boundary for constructing the deficit adjustment interval and the direction of subsequent scenario transfer decisions; Step 4: Obtain the salinity tolerance range Ω from Step 3. j With overall priority U j Afterwards, the system in Ω j Based on the introduction of controlled salinity surplus δ j Construct the corresponding salinity deficit range Δ j Furthermore, the deficit range is transformed into a calculable deficit range parameter, allowing salinity to have a phased surplus, spatial retention, or scenario adaptation within a controllable boundary, thereby avoiding the excessive treatment of "one-size-fits-all" salinity washing. Step 5: After obtaining the salinity state vector generated in Step 2 and the deficit adjustment interval parameters formed in Step 4, the system conducts a deficit adjustment feasibility assessment on the salinity surplus to determine whether it can be managed through "spatial deficit adjustment, time deficit adjustment or scenario deficit adjustment", thereby deciding whether to adopt a phased salinity washing path or an enhanced salinity washing path. Step 6: After obtaining the feasibility assessment result of the deficit adjustment in Step 5, the system generates a salt washing path scheme that matches the assessment result, and solves the relationship between cost, time, resources and deficit adjustment constraints through the optimization model to obtain the optimal salt washing path parameters to guide the execution. Step 7: Implement the salt washing process according to the optimal salt washing path parameters output in Step 6, and realize the closed-loop control of "execution-monitoring-analysis-re-optimization" through PLC automatic control and sensor array monitoring, so that the salt washing intensity, stage switching and deficit adjustment range parameters can be dynamically corrected according to monitoring feedback; Step 8: When the system is in a scenario where multiple dredged material nodes are processed in parallel, the collaborative scheduling module realizes the sharing of the deficit status and resource recovery results of each node based on the consortium blockchain, and completes the closed-loop scheduling of "status sharing - decision optimization - instruction issuance - effect evaluation", thereby improving the overall resource recovery efficiency and reducing the total system cost.

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