A multi-objective parameter calibration method, system and device for a Xin'anjiang model based on multi-department collaboration and a medium

Abstract

This invention discloses a method, system, device, and medium for multi-objective parameter calibration of the Xin'anjiang model based on multi-departmental collaboration, belonging to the field of hydrology and water conservancy technology. The method includes obtaining the basin characteristic index values ​​of the target watershed; calculating the measured total outflow of the watershed based on the basin characteristic index values; constructing a multi-departmental multi-objective optimization model including a multi-departmental objective space based on the basin characteristic index values ​​of the target watershed, the Xin'anjiang model, and the measured total outflow of the watershed, wherein the multi-departmental objective space includes the multi-objective space corresponding to each decision-making department; using all parameters in the pre-obtained Xin'anjiang model as a common parameter space, and performing parameter search using an evolutionary optimization algorithm within the common parameter space based on the multi-departmental multi-objective optimization model until the maximum number of iterations is reached to obtain the optimal parameter solution set. This invention achieves the simultaneous acquisition of parameter solution sets that meet the differentiated needs of different decision-making departments in a single calibration process.

Description

Technical Field

[0001] This invention belongs to the field of hydrology and water conservancy technology, specifically relating to a method, system, device and medium for calibrating multi-objective parameters of the Xin'anjiang model based on multi-departmental collaboration. Background Technology

[0002] The Xin'anjiang model is a typical conceptual rainfall-runoff model. Due to its clear structure, well-defined physical meaning, and good applicability under various watershed conditions, it is widely used in operational scenarios such as watershed runoff simulation, water resource allocation analysis, and flood and drought disaster prediction. This model describes the rainfall-runoff-confluence process using a set of parameters with clear hydrological significance, and its simulation accuracy largely depends on the combination of model parameter values. Therefore, how to reasonably calibrate the Xin'anjiang model parameters is one of the key issues in improving the model's predictive accuracy and engineering application value.

[0003] The Xin'anjiang model involves a wide range of parameters, including evapotranspiration parameters K, UM, LM, and C; runoff parameters WM, B, and IM; water source delineation parameters SM, EX, KI, and KG; and runoff calculation parameters CI, CG, UH, KE, and XE. Among these, parameters such as K, WM, B, SM, KG, and KI are more sensitive to the model output. The model adjusts these parameters to adapt to the rainfall-runoff characteristics of different watersheds, thereby accurately simulating river flow processes.

[0004] Existing methods for calibrating the parameters of the Xin'anjiang model typically rely on historical hydrological data, adjusting parameters through manual experience or searching the parameter space using intelligent optimization algorithms to minimize the error between model simulation results and measured runoff. With the development of computational intelligence technology, multi-objective optimization methods have gradually been introduced into the parameter calibration process. These methods simultaneously consider multiple statistical indicators (such as Nash efficiency coefficient and root mean square error) to obtain parameter solutions with superior overall performance. These methods have improved the model's fitting accuracy and stability to some extent and have been applied in multiple watersheds and engineering practices.

[0005] However, in practical applications, different hydrological departments have varying focuses regarding the predictive performance of the Xin'anjiang model. For example, water resources management departments, primarily responsible for daily water resources monitoring and management, prioritize the model's overall predictive capability over longer timescales, requiring stable and balanced fit under normal hydrological conditions to support daily operations and planning decisions. In contrast, flood and drought disaster prevention departments, mainly serving the early warning and emergency response to extreme hydrological events such as floods and droughts, focus more on the model's predictive accuracy under extreme conditions, such as its ability to simulate peak flow, peak timing, or extreme low flow processes. These differences in departmental objectives lead to significant disagreements regarding the optimal values ​​for the Xin'anjiang model parameters. A parameter combination that performs well in the long-term overall fit may not provide sufficiently accurate predictions under extreme hydrological events; conversely, parameter combinations optimized for extreme conditions may sacrifice the model's overall stability under normal conditions. Therefore, finding effective parameter pairs for the search model to achieve a good balance among the differentiated needs of multiple business departments, and constructing a hydrological prediction model that takes into account both long-term forecasting performance and extreme event prediction capabilities, has become an important and complex technical problem in the application of the Xin'anjiang model project. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, system, device and medium for multi-objective parameter calibration of the Xin'anjiang model based on multi-departmental collaboration, so as to obtain parameter solution sets that meet the differentiated needs of different decision-making departments in a single calibration process.

[0007] This invention provides the following technical solution:

[0008] Firstly, a multi-objective parameter calibration method for the Xin'anjiang model based on multi-departmental collaboration is provided, including: obtaining the watershed characteristic index values ​​of the target watershed;

[0009] Calculate the measured total outflow of the watershed based on the watershed characteristic index values;

[0010] Based on the watershed characteristic index values ​​of the target watershed, the Xin'anjiang model, and the measured total outflow of the watershed, a multi-sectoral multi-objective optimization model including a multi-sectoral objective space is constructed.

[0011] Using all parameters from the pre-acquired Xin'anjiang model as a common parameter space, and based on the multi-department multi-objective optimization model, an evolutionary optimization algorithm is used to search for parameters within the common parameter space until the maximum number of iterations is reached, thus obtaining the optimal parameter solution set.

[0012] As an optional technical solution of the present invention, the basin characteristic index values ​​of the target watershed include reservoir capacity data and downstream flow.

[0013] As an optional technical solution of the present invention, the downstream flow is pre-processed, and the pre-processing includes:

[0014] The missing values ​​of the downstream flow are interpolated and represented as follows:

[0015] ;

[0016] in, Indicates missing moments Downstream flow of the dam Indicates the missing moment Previous observation times, Indicates the missing moment Subsequent observation times, , They represent the observation times. and Downstream flow of the dam.

[0017] As an optional technical solution of the present invention, the measured total outflow of the watershed is calculated based on the watershed characteristic index value.

[0018] As an optional technical solution of the present invention, the construction of a multi-sectoral multi-objective optimization model, including a multi-sectoral objective space, based on the watershed characteristic index values ​​of the target watershed, the Xin'anjiang model, and the measured total outflow of the watershed, includes:

[0019] The multi-departmental target space includes the multi-target space corresponding to each decision-making department, and the decision-making departments include the water resources management department and the flood and drought disaster prevention department.

[0020] The objective functions corresponding to the water resources management department include the overall volume error function and the root mean square error function, which are expressed as follows:

[0021] ;

[0022] ;

[0023] The objective functions corresponding to the flood and drought disaster prevention departments include the average fourth-power error function and the mean logarithmic error function, which are expressed as follows:

[0024] ;

[0025] ;

[0026] in, This represents the overall volume error function. This represents the root mean square error function. This represents the average fourth-power error function. Let N represent the mean square logarithmic error function, and let N represent the total number of time steps. Represents individual parameters. Q represents the total simulated outflow of the Xin'anjiang River basin at time t, where the input parameter for the individual watershed characteristic index is x. obs,t Represents the measured total outflow of the watershed at time t;

[0027] The multi-objective space corresponding to the water resources management department Represented as:

[0028] ;

[0029] The multi-objective space corresponding to the flood and drought disaster prevention department Represented as:

[0030] ;

[0031] The multi-departmental target space , is represented as:

[0032] .

[0033] As an optional technical solution of the present invention, the step of using an evolutionary optimization algorithm to search for parameters in a common parameter space based on a multi-department, multi-objective optimization model until the maximum number of iterations is reached to obtain the optimal parameter solution set includes:

[0034] Within the common parameter space, an initial population consisting of several parameter individuals is randomly generated;

[0035] Offspring populations generated through crossover and mutation operations;

[0036] The current population, the offspring population, and the initial parameter sets of each preset decision-making department are merged to obtain the first candidate solution set for generating the next generation population. , is represented as:

[0037] ;

[0038] in, P Indicates the current population, Q Indicates the offspring population. This represents the initial parameter solution set of the water resources management department. This represents the initial parameter solution set for flood and drought disaster prevention departments;

[0039] Each parameter in the first candidate solution set is non-dominated and ranked within each decision-making department to obtain the first non-dominated level of the parameter in the water resources management department. And in the second non-dominant level of the flood and drought disaster prevention department ;

[0040] Each parameter is placed at the first non-dominant level of the water resources management department. And in the second non-dominant level of the flood and drought disaster prevention department Combine them into a new target space, and calculate the non-dominated level of each parameter individual in the first candidate solution set in the new target space;

[0041] The non-dominated hierarchy of each parameter individual in the first candidate solution set is sorted from low to high in the multi-sectoral target space. Parameter individuals are selected from the first candidate solution set layer by layer to be added to the next generation population until the population size is reached.

[0042] When the number of parameter individuals in any non-dominated level exceeds the remaining selectable capacity of the next generation population, calculate the crowding distance of parameter individuals within the non-dominated level, and select parameter individuals to join the next generation population in descending order of crowding distance.

[0043] The current population, the offspring population, and the initial parameter solution sets of each decision-making department are merged to obtain a candidate solution set for updating the next generation of parameter solution sets, denoted as:

[0044] ;

[0045] ;

[0046] in, This represents the initial parameter solution set used to update the water resources management department. The candidate solution set, This represents the initial parameter solution set used to update the flood and drought disaster prevention department. The candidate solution set;

[0047] Each parameter individual in the candidate solution set corresponding to each decision-making department is assigned to the reference vector with the smallest angle with its target vector. The angle penalty distance of the parameter individual associated with each reference vector is calculated. The parameter individual with the smallest angle penalty distance is selected from the parameter individuals associated with each reference vector to form the next generation of parameter solution set.

[0048] After reaching the maximum number of iterations, the final set of individual parameters obtained from the search is taken as the optimal parameter solution set.

[0049] Secondly, a multi-objective parameter calibration system for the Xin'anjiang model based on multi-departmental collaboration is provided, including: a data acquisition module for acquiring watershed characteristic index values ​​of the target watershed;

[0050] The calculation module is used to calculate the measured total outflow of the watershed based on the watershed characteristic index values;

[0051] The model building module is used to construct a multi-sectoral, multi-objective optimization model, including a multi-sectoral objective space, based on the watershed characteristic index values ​​of the target watershed, the Xin'anjiang model, and the measured total outflow of the watershed.

[0052] The solution module is used to take all the parameters in the pre-acquired Xin'anjiang model as a common parameter space, and use an evolutionary optimization algorithm to search for parameters in the common parameter space based on the multi-department multi-objective optimization model until the maximum number of iterations is reached to obtain the optimal parameter solution set.

[0053] Thirdly, a multi-objective parameter calibration device for the Xin'anjiang model based on multi-departmental collaboration is provided, comprising a processor and a medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method described in the first aspect.

[0054] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the steps of the method described in the first aspect.

[0055] Compared with the prior art, the beneficial effects of the present invention are:

[0056] This invention provides a multi-objective parameter calibration method for the Xin'anjiang model based on multi-departmental collaboration. By constructing a multi-departmental multi-objective optimization model and adopting a collaborative evolutionary search strategy, it can simultaneously meet the needs of water resource management departments for long-term forecast stability and flood and drought disaster prevention departments for extreme event prediction accuracy in a unified calibration process. This avoids the one-sidedness caused by the need for multiple calibrations or manual weight setting in traditional methods, and significantly improves the applicability, flexibility and engineering practical value of the Xin'anjiang model in multi-departmental collaborative business scenarios. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating a multi-objective parameter calibration method for the Xin'anjiang model based on multi-departmental collaboration, as described in an embodiment of the present invention. Detailed Implementation

[0058] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0059] Example 1

[0060] This embodiment provides a multi-objective parameter calibration method for the Xin'anjiang model based on multi-departmental collaboration, such as... Figure 1 As shown, the specific steps include the following:

[0061] Step 1: Obtain the watershed characteristic index values ​​of the target watershed.

[0062] The watershed characteristic index values ​​of the target watershed include reservoir capacity data and downstream flow.

[0063] The downstream flow rate is pre-processed, and the pre-processing includes:

[0064] The missing values ​​of the downstream flow are interpolated and represented as follows:

[0065] ;

[0066] in, Indicates missing moments Downstream flow of the dam Indicates the missing moment Previous observation times, Indicates the missing moment Subsequent observation times, , They represent the observation times. and Downstream flow of the dam.

[0067] Step 2: Calculate the measured total outflow of the watershed based on the watershed characteristic index values.

[0068] The measured total outflow of the basin was calculated based on the reservoir capacity data and the downstream flow.

[0069] Step 3: Based on the watershed characteristic index values ​​of the target watershed, the Xin'anjiang model, and the measured total outflow of the watershed, construct a multi-sectoral and multi-objective optimization model that includes a multi-sectoral objective space.

[0070] The multi-department target space includes the multi-target space corresponding to each decision-making department.

[0071] The decision-making bodies include water resources management departments whose main tasks are daily water resources scheduling and operation management, and flood and drought disaster prevention departments whose main tasks are flood and drought disaster prevention.

[0072] The objective functions corresponding to the water resources management department include the overall volume error function and the root mean square error function, which are expressed as follows:

[0073] ;

[0074] ;

[0075] The objective functions corresponding to the flood and drought disaster prevention departments include the average fourth-power error function and the mean logarithmic error function, which are expressed as follows:

[0076] ;

[0077] ;

[0078] in, This represents the overall volume error function, used to evaluate the overall deviation between the model simulation results and the measured runoff from the perspective of absolute error. This represents the root mean square error function, used to evaluate the overall error magnitude between the model simulation results and the measured runoff from the perspective of root mean square error. This represents the average fourth-order error function, which uses high-order amplification of simulation errors to highlight the model's prediction accuracy requirements during high-flow and flood processes. represents the mean square logarithmic error function, which enhances the model's sensitivity to low flow and dry season predictions by measuring simulation errors on a logarithmic scale. N represents the total number of time steps. Represents individual parameters. Q represents the total simulated outflow of the Xin'anjiang River basin at time t, where the input parameter for the individual watershed characteristic index is x. obs,t This represents the measured total outflow of the watershed at time t.

[0079] The multi-objective space corresponding to the water resources management department Represented as:

[0080] ;

[0081] The multi-objective space corresponding to the flood and drought disaster prevention department Represented as:

[0082] ;

[0083] The multi-departmental target space , is represented as:

[0084] .

[0085] Step 4: Using all the parameters in the pre-acquired Xin'anjiang model as a common parameter space, and based on the multi-department multi-objective optimization model, perform parameter search in the common parameter space using an evolutionary optimization algorithm until the maximum number of iterations is reached, and obtain the optimal parameter solution set.

[0086] Within the common parameter space, an initial population comprising several parameter individuals is randomly generated. In this embodiment, each parameter individual includes 15 parameters of the Xin'anjiang model.

[0087] Offspring populations generated through crossover and mutation operations.

[0088] The current population, the offspring population, and the initial parameter sets of each preset decision-making department are merged to obtain the first candidate solution set for generating the next generation population. , is represented as:

[0089] ;

[0090] in, P Indicates the current population, Q Indicates the offspring population. This represents the initial parameter solution set of the water resources management department. This represents the initial parameter set for flood and drought disaster prevention departments.

[0091] Each parameter in the first candidate solution set is non-dominated and ranked within each decision-making department to obtain the first non-dominated level of the parameter in the water resources management department. And in the second non-dominant level of the flood and drought disaster prevention department .

[0092] Each parameter is placed at the first non-dominant level of the water resources management department. And in the second non-dominant level of the flood and drought disaster prevention department Combine them into a new target space, and calculate the non-dominated level of each parameter individual in the first candidate solution set within the new target space.

[0093] The parameter individuals in the first candidate solution set are sorted from low to high in the non-dominated hierarchy of the multi-sectoral target space. Parameter individuals are selected from the first candidate solution set layer by layer to be added to the next generation population until the population size is reached.

[0094] When the number of parameter individuals in any non-dominated level exceeds the remaining available capacity of the next generation population, the crowding distance of parameter individuals within the non-dominated level is calculated, and parameter individuals are selected to join the next generation population in descending order of crowding distance.

[0095] The current population, the offspring population, and the initial parameter solution sets of each decision-making department are merged to obtain a candidate solution set for updating the next generation of parameter solution sets, denoted as:

[0096] ;

[0097] ;

[0098] in, This represents the initial parameter solution set used to update the water resources management department. The candidate solution set, This represents the initial parameter solution set used to update the flood and drought disaster prevention department. The candidate solution set.

[0099] Each parameter individual in the candidate solution set corresponding to each decision-making department is assigned to the reference vector with the smallest angle with its target vector. The angle penalty distance of the parameter individual associated with each reference vector is calculated. The parameter individual with the smallest angle penalty distance is selected from the parameter individuals associated with each reference vector to form the next generation of parameter solution set.

[0100] After reaching the maximum number of iterations, the final set of individual parameters obtained from the search is taken as the optimal parameter solution set. This final set of individual parameters can serve as a parameter configuration scheme for different business departments in actual operation, allowing them to select or switch according to specific application scenarios.

[0101] In this embodiment, through the co-evolutionary mechanism between the population and the solution set, new potential optimal solutions can be continuously explored while inheriting historical excellent parameter solutions, thereby improving parameter search efficiency and reducing the risk of getting trapped in local optima.

[0102] Example 2

[0103] This embodiment provides a multi-objective parameter calibration system for the Xin'anjiang model based on multi-departmental collaboration, including:

[0104] The data acquisition module is used to acquire watershed characteristic index values ​​of the target watershed;

[0105] The calculation module is used to calculate the measured total outflow of the watershed based on the watershed characteristic index values;

[0106] The model building module is used to construct a multi-sectoral, multi-objective optimization model, including a multi-sectoral objective space, based on the watershed characteristic index values ​​of the target watershed, the Xin'anjiang model, and the measured total outflow of the watershed.

[0107] The solution module is used to take all the parameters in the pre-acquired Xin'anjiang model as a common parameter space, and use an evolutionary optimization algorithm to search for parameters in the common parameter space based on the multi-department multi-objective optimization model until the maximum number of iterations is reached to obtain the optimal parameter solution set.

[0108] Example 3

[0109] This embodiment provides a multi-objective parameter calibration device for the Xin'anjiang model based on multi-departmental collaboration, including a processor and a storage medium. The storage medium is used to store instructions. The processor is used to perform operations according to the instructions to execute the steps of the multi-objective parameter calibration method for the Xin'anjiang model based on multi-departmental collaboration described in Embodiment 1.

[0110] Example 4

[0111] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the multi-objective parameter calibration method for the Xin'anjiang model based on multi-departmental collaboration described in Embodiment 1.

[0112] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0114] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0115] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0116] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A multi-objective parameter calibration method for the Xin'anjiang model based on multi-departmental collaboration, characterized in that, include: Obtain the watershed characteristic index values ​​of the target watershed; Calculate the measured total outflow of the watershed based on the watershed characteristic index values; Based on the watershed characteristic index values ​​of the target watershed, the Xin'anjiang model, and the measured total outflow of the watershed, a multi-sectoral multi-objective optimization model including a multi-sectoral objective space is constructed. Using all the parameters in the pre-acquired Xin'anjiang model as a common parameter space, and based on the multi-department multi-objective optimization model, an evolutionary optimization algorithm is used to search for parameters in the common parameter space until the maximum number of iterations is reached, and the optimal parameter solution set is obtained. The multi-sectoral, multi-objective optimization model employs an evolutionary optimization algorithm to search for parameters within a common parameter space until the maximum number of iterations is reached, yielding the optimal parameter solution set, including: Within the common parameter space, an initial population consisting of several parameter individuals is randomly generated; Offspring populations generated through crossover and mutation operations; The current population, the offspring population, and the initial parameter sets of each preset decision-making department are merged to obtain the first candidate solution set for generating the next generation population. , is represented as: ； in, Indicates the current population, Indicates the offspring population. This represents the initial parameter solution set of the water resources management department. This represents the initial parameter solution set for flood and drought disaster prevention departments; Each parameter in the first candidate solution set is non-dominated and ranked within each decision-making department to obtain the first non-dominated level of the parameter in the water resources management department. And in the second non-dominant level of the flood and drought disaster prevention department ; Each parameter is placed at the first non-dominant level of the water resources management department. And in the second non-dominant level of the flood and drought disaster prevention department Combine them into a new target space, and calculate the non-dominated level of each parameter individual in the first candidate solution set in the new target space; The non-dominated hierarchy of each parameter individual in the first candidate solution set is sorted from low to high in the multi-sectoral target space. Parameter individuals are selected from the first candidate solution set layer by layer to be added to the next generation population until the population size is reached. When the number of parameter individuals in any non-dominated level exceeds the remaining selectable capacity of the next generation population, calculate the crowding distance of parameter individuals within the non-dominated level, and select parameter individuals to join the next generation population in descending order of crowding distance. The current population, the offspring population, and the initial parameter solution sets of each decision-making department are merged to obtain a candidate solution set for updating the next generation of parameter solution sets, denoted as: ； ； in, This represents the initial parameter solution set used to update the water resources management department. The candidate solution set, This represents the initial parameter solution set used to update the flood and drought disaster prevention department. The candidate solution set; Each parameter individual in the candidate solution set corresponding to each decision-making department is assigned to the reference vector with the smallest angle with its target vector. The angle penalty distance of the parameter individual associated with each reference vector is calculated. The parameter individual with the smallest angle penalty distance is selected from the parameter individuals associated with each reference vector to form the next generation of parameter solution set. After reaching the maximum number of iterations, the final set of individual parameters obtained from the search is taken as the optimal parameter solution set.

2. The multi-objective parameter calibration method of the Xin'anjiang model based on multi-department collaboration according to claim 1, characterized in that, The watershed characteristic index values ​​of the target watershed include reservoir capacity data and downstream flow.

3. The multi-objective parameter calibration method of the Xin'anjiang model based on multi-department collaboration according to claim 2, characterized in that, The downstream flow rate is pre-processed, and the pre-processing includes: The missing values ​​of the downstream flow are interpolated and represented as follows: ； in, Indicates missing moments Downstream flow of the dam Indicates the missing moment Previous observation times, Indicates the missing moment Subsequent observation times, , They represent the observation times. and Downstream flow of the dam.

4. The multi-objective parameter calibration method of the Xin'anjiang model based on multi-department collaboration according to claim 1, characterized in that, Based on the watershed characteristic index values ​​of the target watershed, the Xin'anjiang model, and the measured total outflow of the watershed, a multi-sectoral, multi-objective optimization model is constructed, including a multi-sectoral objective space, comprising: The multi-departmental target space includes the multi-target space corresponding to each decision-making department, and the decision-making departments include the water resources management department and the flood and drought disaster prevention department. The objective functions corresponding to the water resources management department include the overall volume error function and the root mean square error function, which are expressed as follows: ； ； The objective functions corresponding to the flood and drought disaster prevention departments include the average fourth-power error function and the mean logarithmic error function, which are expressed as follows: ； ； in, This represents the overall volume error function. This represents the root mean square error function. This represents the average fourth-power error function. Let N represent the mean square logarithmic error function, and let N represent the total number of time steps. Represents individual parameters. Q represents the total simulated outflow of the Xin'anjiang River basin at time t, where the input parameter for the individual watershed characteristic index is x. obs,t Represents the measured total outflow of the watershed at time t; The water resource management department corresponds to a multi-objective space is expressed as: ； The multi-objective space corresponding to the flood and drought disaster prevention department Represented as: ； The multi-departmental target space is represented as: 。 5. A multi-objective parameter calibration system based on the Xin'anjiang model with multi-department collaboration, characterized in that, include: The data acquisition module is used to acquire watershed characteristic index values ​​of the target watershed; The calculation module is used to calculate the measured total outflow of the watershed based on the watershed characteristic index values; The model building module is used to construct a multi-sectoral, multi-objective optimization model, including a multi-sectoral objective space, based on the watershed characteristic index values ​​of the target watershed, the Xin'anjiang model, and the measured total outflow of the watershed. The solution module is used to take all the parameters in the pre-acquired Xin'anjiang model as a common parameter space, and based on the multi-department multi-objective optimization model, to perform parameter search in the common parameter space using an evolutionary optimization algorithm until the maximum number of iterations is reached, and to obtain the optimal parameter solution set. The multi-sectoral, multi-objective optimization model employs an evolutionary optimization algorithm to search for parameters within a common parameter space until the maximum number of iterations is reached, yielding the optimal parameter solution set, including: Within the common parameter space, an initial population consisting of several parameter individuals is randomly generated; Offspring populations generated through crossover and mutation operations; merge the current population, the offspring population, and the preset initial parameter solution set of each decision-making department to obtain a first candidate solution set for generating a next generation population is represented as: ； in, Indicates the current population, Indicates the offspring population. This represents the initial parameter solution set of the water resources management department. This represents the initial parameter solution set for flood and drought disaster prevention departments; Each parameter in the first candidate solution set is non-dominated and ranked within each decision-making department to obtain the first non-dominated level of the parameter in the water resources management department. And in the second non-dominant level of the flood and drought disaster prevention department ; Each parameter is placed at the first non-dominant level of the water resources management department. And in the second non-dominant level of the flood and drought disaster prevention department Combine them into a new target space, and calculate the non-dominated level of each parameter individual in the first candidate solution set in the new target space; The non-dominated hierarchy of each parameter individual in the first candidate solution set is sorted from low to high in the multi-sectoral target space. Parameter individuals are selected from the first candidate solution set layer by layer to be added to the next generation population until the population size is reached. When the number of parameter individuals in any non-dominated level exceeds the remaining selectable capacity of the next generation population, calculate the crowding distance of parameter individuals within the non-dominated level, and select parameter individuals to join the next generation population in descending order of crowding distance. The current population, the offspring population, and the initial parameter solution sets of each decision-making department are merged to obtain a candidate solution set for updating the next generation of parameter solution sets, denoted as: ； ； in, This represents the initial parameter solution set used to update the water resources management department. The candidate solution set, This represents the initial parameter solution set used to update the flood and drought disaster prevention department. The candidate solution set; Each parameter individual in the candidate solution set corresponding to each decision-making department is assigned to the reference vector with the smallest angle with its target vector. The angle penalty distance of the parameter individual associated with each reference vector is calculated. The parameter individual with the smallest angle penalty distance is selected from the parameter individuals associated with each reference vector to form the next generation of parameter solution set. After reaching the maximum number of iterations, the final set of individual parameters obtained from the search is taken as the optimal parameter solution set.

6. A multi-objective parameter calibration device for the Xin'anjiang model based on multi-departmental collaboration, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the multi-objective parameter calibration method for the Xin'anjiang model based on multi-departmental collaboration as described in any one of claims 1 to 4.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that When the program is executed by the processor, it implements the steps of the multi-objective parameter calibration method for the Xin'anjiang model based on multi-departmental collaboration as described in any one of claims 1 to 4.

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