Urban water system runoff pollution control evaluation method, device, equipment and medium

Abstract

The invention relates to the technical field of urban water system pollution control, and discloses an urban water system runoff pollution control evaluation method, device, equipment and medium, and the method comprises the steps: classifying the treatment engineering case data of a plurality of runoff pollution control facilities according to the technical types, and obtaining a subsample set corresponding to each technical type; cost estimation models of different sample types can be conveniently optimized; optimizing the function of the respective cost estimation model by using each sub-sample set, and constructing a cost estimation model library by integrating the optimized cost estimation models corresponding to different sample sets; and finally, evaluating the current to-be-evaluated runoff pollution control scheme according to the optimized cost estimation model in the cost estimation model library to obtain a cost prediction result of the runoff pollution control scheme, thereby greatly shortening the cost estimation time of a cost estimation method in related technologies. And the efficiency of comparing and selecting the cost of different runoff pollution control schemes in the initial stage of the project is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban water system pollution control, and particularly relates to a method, device, equipment and medium for evaluating urban water system runoff pollution control. BACKGROUND

[0002] With the acceleration of urbanization, the non-pervious area of the ground surface increases rapidly, which leads to an increase in rainfall runoff intensity and a faster flow speed, and problems such as urban waterlogging and runoff pollution are becoming increasingly serious. In this context, rainwater storage and source control engineering technologies are important components of urban runoff pollution control technologies. Common urban runoff pollution control schemes include technical types such as storage tanks, artificial wetlands, permeable pavement, green roofs, bioretention facilities, wet ponds, grassed swales, and sunken green spaces (Low Impact Development, LID facilities). The purpose of these schemes is to achieve near-natural management of runoff pollution through in-situ storage, infiltration, purification, and other means, to alleviate urban non-point source pollution and rainwater runoff pressure, to improve the water quality of receiving water bodies, and to enhance the environmental resilience of cities.

[0003] The cost estimation method of the runoff pollution control scheme in the related art is to manually calculate through construction drawing budget, quota analysis, and bill of quantities pricing. However, the cost estimation method of the urban runoff pollution control scheme in the related art is heavily dependent on the work efficiency of professional cost personnel, and is difficult to apply to the scenario of rapid comparison and selection of multiple schemes at the initial stage of runoff pollution control scheme design. SUMMARY

[0004] The present application provides a method, device, equipment and medium for evaluating urban water system runoff pollution control, to solve the problem that the cost estimation method of the runoff pollution control scheme in the related art is difficult to apply to the scenario of rapid comparison and selection of multiple schemes at the initial stage of runoff pollution control scheme design.

[0005] In a first aspect, the present application provides a method for evaluating urban water system runoff pollution control, comprising: Based on the obtained runoff pollution control facility management engineering case data, classify according to the technical type to obtain a plurality of sub-sample sets; the management engineering case data includes: technical type, construction scale parameter, total construction cost or unit cost; For each sub-sample set, respectively optimize the corresponding cost estimation model to obtain the optimized cost estimation model corresponding to each sub-sample set; the cost estimation model includes a power function cost model or a unit area cost model; Integrate the optimized cost estimation models of the plurality of sub-sample sets to obtain a cost estimation model library; Based on the current runoff pollution control scheme to be evaluated, the optimized cost estimation model in the cost estimation model library is used for evaluation, and the cost prediction result of the corresponding runoff pollution control scheme is obtained.

[0006] By adopting the above implementation, the treatment engineering case data of multiple runoff pollution control facilities is classified according to the technical type, the sub-sample set corresponding to each technical type is obtained, the cost estimation model of different sample types is optimized, and the function of each sub-sample set is optimized. The cost estimation model, and the optimized cost estimation model corresponding to different sample sets is integrated to construct a cost estimation model library. Finally, the current runoff pollution control scheme to be evaluated is evaluated based on the optimized cost estimation model in the cost estimation model library, and the cost prediction result of the runoff pollution control scheme is obtained. The time spent in the cost estimation method of the related technology is greatly shortened, and the efficiency of the cost comparison of different runoff pollution control schemes in the early stage of the project is improved.

[0007] In an optional implementation, the cost estimation model corresponding to each sub-sample set is optimized respectively, and the optimized cost estimation model corresponding to each sub-sample set is obtained, including: The parameters of the cost estimation model constructed for each sub-sample set are optimized respectively; If the cost estimation model is a power function cost model, the parameters of the corresponding power function cost model are optimized based on the treatment engineering case data in each sub-sample set by using a nonlinear least squares method; If the cost estimation model is a unit area cost model, the parameters of the corresponding unit area cost model are optimized based on the treatment engineering case data in each sub-sample set by using a statistical method.

[0008] By adopting the above implementation, the parameters of the power function cost model are optimized by using a nonlinear least squares method, or the parameters of the unit area cost model are optimized by using a statistical method, so as to ensure the accuracy of the cost estimation of the optimized cost estimation model, and improve the accuracy of the cost comparison of different runoff pollution control schemes in the early stage of the project.

[0009] In an optional implementation, the method further includes: Based on each optimized cost estimation model, a preset model evaluation method is used to obtain an evaluation result of the corresponding cost estimation model; and the evaluation result is used to establish a cost estimation model library with the corresponding cost estimation model.

[0010] By adopting the above-mentioned implementation manner, the optimized cost estimation model is evaluated by using a model evaluation method, and the obtained evaluation result is constructed to a cost estimation model library, so that the fitting effect and prediction performance of the optimized cost estimation model can be conveniently judged.

[0011] In an optional implementation manner, when the cost estimation model is a power function cost model, the evaluation result of the corresponding cost estimation model is obtained based on each optimized cost estimation model by using a preset model evaluation method, and the evaluation result includes: The evaluation result of the cost estimation model is obtained by using a goodness-of-fit index, a root mean square error and a mean absolute error based on each optimized cost estimation model.

[0012] By adopting the above-mentioned implementation manner, the goodness-of-fit index is used to obtain the explanation ability of the optimized cost estimation model to data variation, the root mean square error is used to measure the difference degree between the predicted construction cost generated by the optimized cost estimation model and the actual construction cost, and the mean absolute error is used to reflect the overall error level of the optimized cost estimation model. By obtaining the evaluation result of the optimized cost estimation model, the estimation accuracy and error range of the optimized cost estimation model can be conveniently determined.

[0013] In an optional implementation manner, when the cost estimation model is a unit area cost model, the evaluation result of the corresponding cost estimation model is obtained based on each optimized cost estimation model by using a preset model evaluation method, and the evaluation result includes: The mean value, the median value, the quantile point and the outlier of the cost estimation model are obtained by using a box plot analysis method based on each optimized cost estimation model, and are used as the evaluation result of the cost estimation model.

[0014] By adopting the above-mentioned implementation manner, the mean value, the median value, the quantile point and the outlier of the cost estimation model are obtained by using the box plot analysis method, which can provide quantitative evaluation basis for the reliability and applicability of subsequent cost estimation by using the unit area cost model.

[0015] In an optional implementation manner, the method further includes: The original data of the obtained multiple runoff pollution control facility treatment engineering cases are processed by using a data processing method to obtain processed treatment engineering case data, the data processing method includes unifying dimension, eliminating missing values and extreme abnormal values, and the processed treatment engineering case data is used for classification according to technical types.

[0016] By adopting the above implementation method, the original data of multiple runoff pollution control facility treatment project cases obtained by data processing methods are processed to unify the data of different dimensions in the original data, and at the same time remove missing values ​​and extreme outliers in the original data, thereby improving the accuracy of subsequent optimization of the cost estimation model and ensuring the accuracy of the prediction results of the optimized cost estimation model.

[0017] In one optional implementation, the step of evaluating the current runoff pollution control scheme using an optimized cost estimation model from a cost estimation model library to obtain the corresponding cost prediction result for the runoff pollution control scheme includes: Based on the current runoff pollution control scheme to be evaluated, the technical type and construction scale parameters of the runoff pollution control scheme are obtained; Based on the technical type and construction scale parameters of the runoff pollution control scheme, the optimized cost estimation model in the cost estimation model library is used for evaluation to obtain the cost prediction results of the corresponding runoff pollution control scheme.

[0018] By adopting the above implementation method, based on obtaining the runoff pollution control scheme to be evaluated, the technical type and construction scale parameters of the corresponding runoff pollution control scheme are first obtained. The optimized cost estimation model in the cost estimation model library is determined by the technical type. The construction scale parameters are input into the optimized cost estimation model. The cost prediction result of the corresponding runoff pollution control scheme is obtained by using the optimized cost estimation model, thereby improving the efficiency of obtaining the cost required for each runoff pollution control scheme.

[0019] Secondly, the present invention provides an assessment device for urban water system runoff pollution control, the device comprising: The data classification module is used to classify the acquired case data of multiple runoff pollution control facilities according to the technology type, resulting in multiple sub-sample sets; the case data of the treatment projects includes: technology type, construction scale parameters, total construction cost or unit cost; The cost model optimization module is used to optimize the corresponding cost estimation model for each subsample set, so as to obtain the optimized cost estimation model for each subsample set; the cost estimation model includes a power function cost model or a unit area cost model. The model library building module is used to integrate optimized cost estimation models from multiple subsample sets to obtain a cost estimation model library. The cost assessment module is used to evaluate the current runoff pollution control schemes by using optimized cost estimation models from the cost estimation model library, and to obtain the cost prediction results of the corresponding runoff pollution control schemes.

[0020] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the urban water system runoff pollution control assessment method described in the first aspect or any corresponding embodiment thereof.

[0021] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the urban water system runoff pollution control assessment method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of the urban water system runoff pollution control assessment method according to an embodiment of the present invention; Figure 3 This is a schematic diagram showing the fitting results of the optimized cost estimation model for storage tanks in the urban water system runoff pollution control assessment method according to an embodiment of the present invention. Figure 4 This is a schematic diagram showing the fitting results of the optimized cost estimation model for constructed wetlands in the urban water system runoff pollution control assessment method according to an embodiment of the present invention. Figure 5 This is a box plot of an optimized cost estimation model for permeable pavement based on the urban water system runoff pollution control assessment method according to an embodiment of the present invention. Figure 6 This is a box plot of the optimized cost estimation model for sunken green spaces in the urban water system runoff pollution control assessment method according to an embodiment of the present invention. Figure 7 This is a box plot of an optimized cost estimation model for bioretention facilities in the urban water system runoff pollution control assessment method according to an embodiment of the present invention. Figure 8 This is a box plot of an optimized cost estimation model for vegetated swales according to an embodiment of the urban water system runoff pollution control assessment method of the present invention. Figure 9This is a schematic diagram of the second process of the urban water system runoff pollution control assessment method according to an embodiment of the present invention; Figure 10 This is a structural block diagram of an urban water system runoff pollution control assessment device according to an embodiment of the present invention; Figure 11 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0026] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0027] As an optional application scenario of this invention, such as Figure 1 As shown, application 101 is installed in terminal device 110, and user 130 can interact with application 101 through terminal device 110 and / or access device of terminal device 110.

[0028] For example, application 101 can be any application that provides question-and-answer related services. For instance, application 101 could be a question-and-answer interactive application, such as a text-to-text application, an image-to-text application, etc. Figure 1 In the application scenario shown, if application 101 is active, the terminal device 110 can display the interface 102 of application 101. The interface 102 may include various pages that application 101 can provide, such as interactive pages, settings pages, query pages, etc.

[0029] In some embodiments, terminal device 110 is communicatively connected to server 120 to provide services to application 101. Terminal device 110 may be a mobile terminal, fixed terminal, or portable terminal, etc., including but not limited to mobile phones, desktop computers, laptop computers, multimedia tablets, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, terminal device 110 may also support any type of interface, and server 120 may be various types of computing systems or servers capable of providing computing power, including but not limited to mainframes, edge computing nodes, computing devices in cloud environments, etc.

[0030] It should be noted that, Figure 1 This is merely an example of an application scenario and does not limit the scope of protection of this invention.

[0031] The embodiments of the present invention will now be described with reference to the accompanying drawings. It should be understood that the pages shown in the drawings are merely examples, and various page designs are possible in practice. The various graphic elements on the page may have different arrangements and different visual representations; one or more elements may be omitted or replaced, and one or more other elements may also be present, without any limitation in the embodiments of the present invention. Furthermore, the embodiments described below primarily pertain to terminal device 110. It should be understood that the actions described relative to terminal device 110 can be performed by application 101 on terminal device 110, or can be performed by application 101 in conjunction with its server (e.g., server 120).

[0032] In related technologies, the cost estimation methods for urban water system runoff pollution control schemes mainly rely on traditional compilation processes. This involves detailed listing of each construction procedure, material specifications, and quantity of work through construction drawing budgets, quota analysis, and bill of quantities pricing, combined with manual calculations based on local cost standards. While this method offers high accuracy, it is cumbersome and time-consuming, making it difficult to meet the needs for "fast, concise, and comparable" investment estimations in early planning, scheme comparison, or large-scale system coordination.

[0033] Furthermore, the cost estimation methods for urban water system runoff pollution control schemes in related technologies still have the following shortcomings in application: 1. It relies heavily on the subjective judgment of designers or management units based on previous projects to select reference values, lacking quantitative basis and adaptive adjustment capabilities; 2. For low-impact development facilities, directly referencing the unit cost indicators provided in policy or industry documents and taking an intermediate or approximate value within the recommended range as the basis for estimation ignores the differences between different projects in terms of technology type, project scale, regional cost level and ancillary facilities, and makes it difficult to reflect the regular trend of cost changes with scale.

[0034] To overcome the aforementioned technical problems, this invention provides a method for assessing runoff pollution control in urban water systems. It categorizes case data of multiple runoff pollution control facilities according to their technology types, obtaining sub-sample sets corresponding to each technology type. This facilitates the optimization of cost estimation models for different sample types. Then, it optimizes the function of each cost estimation model using each sub-sample set, and constructs a cost estimation model library by integrating the optimized cost estimation models from different sample sets. Finally, it evaluates the current runoff pollution control scheme based on the optimized cost estimation models in the cost estimation model library, obtaining the cost prediction results of the runoff pollution control scheme. This significantly reduces the time spent on cost estimation using relevant technology cost estimation methods and improves the efficiency of cost comparison of different runoff pollution control schemes in the early stages of a project.

[0035] According to an embodiment of the present invention, an embodiment of an assessment method for runoff pollution control in urban water systems is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0036] This embodiment provides a method for assessing runoff pollution control in urban water systems, which can be used on the server terminal of the aforementioned urban water system. Figure 2 This is a flowchart of an urban water system runoff pollution control assessment method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: S201. Based on the acquired case data of multiple runoff pollution control facilities, the data is classified according to the technology type to obtain multiple sub-sample sets; the case data of the treatment projects includes: technology type, construction scale parameters, total construction cost or unit cost.

[0037] Case study data for pollution control projects is a collection of information gathered from actual completed or under-construction runoff pollution control facility projects, providing a basic data source for subsequent parameter optimization of cost estimation models.

[0038] The types of technologies include: water storage tanks, constructed wetlands, permeable paving, green roofs, bioretention facilities, wet ponds, vegetated swales, and sunken green spaces. These are categories classified according to the core treatment principles, technological structures, and functional positioning of runoff pollution control facilities.

[0039] Subsample sets represent collections of similar technical case data formed by grouping governance project case data according to technical type, and are used to optimize the parameters of their respective cost estimation models.

[0040] The construction scale parameter represents the renovation area of ​​each treatment project case data, and is used to describe the physical size, treatment capacity or coverage of runoff pollution control facilities.

[0041] Total construction cost refers to the total amount of expenses incurred in the construction of runoff pollution control facilities from project initiation to completion and acceptance. It is a core indicator reflecting the overall economic input of a project and is suitable for overall assessment of the cost scale of a single project.

[0042] The data of multiple runoff pollution control facilities were classified according to technology type to obtain sub-sample sets corresponding to each technology type, which facilitates the optimization of cost estimation models for different sample types.

[0043] For example, in order to reduce the probability of parameter inaccuracies in cost estimation models caused by using directly obtained case data from multiple runoff pollution control facilities, this embodiment of the invention further includes: Based on the original data of multiple runoff pollution control facility treatment project cases, data processing methods are used to process the data to obtain processed treatment project case data. The data processing methods include unifying dimensions, removing missing values ​​and extreme outliers, and the processed treatment project case data is used to classify according to technology type.

[0044] The raw data of multiple runoff pollution control facility treatment engineering cases obtained by data processing methods are processed to unify data of different dimensions in the raw data, and remove missing values ​​and extreme outliers in the raw data to improve the accuracy of subsequent cost estimation model optimization and ensure the accuracy of the prediction results of the optimized cost estimation model.

[0045] S202, for each subsample set, optimize the corresponding cost estimation model to obtain the optimized cost estimation model for each subsample set; the cost estimation model includes a power function cost model or a unit area cost model.

[0046] For example, the power function cost model is suitable for engineering types where the data sample has a large variability in the scale dimension and the sample size is sufficient, such as water storage tanks and constructed wetlands; while the unit area cost model is suitable for engineering types where the data sample size is uniform and nonlinear fitting is not possible, such as permeable pavement, green roofs, bioretention, wet ponds, grassed swales and sunken green spaces.

[0047] If the technology type of a certain subsample set is a water storage tank, then the parameters of the power function cost model are optimized using the governance project case data in that subsample set; and if the technology type of a certain subsample set is a green roof, then the parameters of the unit area cost model are optimized using the governance project case data in that subsample set.

[0048] S203, by integrating optimized cost estimation models from multiple subsample sets, yields a cost estimation model library.

[0049] By integrating optimized cost estimation models corresponding to different sample sets, a cost estimation model library is constructed to ensure that the cost estimation models in the library can cover the types of treatment engineering technologies for runoff pollution control facilities, thus facilitating cost prediction for treatment engineering projects of different technology types.

[0050] S204. Based on the current runoff pollution control scheme to be evaluated, the optimized cost estimation model in the cost estimation model library is used for evaluation to obtain the cost prediction results of the corresponding runoff pollution control scheme.

[0051] The runoff pollution control schemes currently being evaluated are those for which cost forecasting is required at the initial stage of the project. By comparing the costs required by multiple different runoff pollution control schemes, data references can be provided for users to select a runoff pollution control scheme that better meets their actual construction needs.

[0052] This invention provides a method for assessing runoff pollution control in urban water systems. It categorizes case data of multiple runoff pollution control facilities according to their technology types, obtaining sub-sample sets corresponding to each technology type. This facilitates the optimization of cost estimation models for different sample types. Then, it optimizes the function of each cost estimation model using each sub-sample set. By integrating the optimized cost estimation models from different sample sets, a cost estimation model library is constructed. Finally, based on the optimized cost estimation models in the library, the current runoff pollution control scheme to be evaluated is assessed, yielding cost prediction results for the runoff pollution control scheme. This significantly reduces the time spent on cost estimation using relevant technology cost estimation methods and improves the efficiency of cost comparison of different runoff pollution control schemes in the early stages of a project.

[0053] This embodiment provides a method for assessing runoff pollution control in urban water systems, which can be used on the server terminal of the aforementioned urban water system. The process includes the following steps: S301, based on the acquired case data of multiple runoff pollution control facilities, the data is categorized according to technology type to obtain multiple sub-sample sets; the case data includes: technology type, construction scale parameters, total construction cost or unit cost. For details, please refer to [link to relevant documentation]. Figure 2 S201 of the illustrated embodiment will not be described again here.

[0054] S302, for each subsample set, optimize the corresponding cost estimation model to obtain the optimized cost estimation model for each subsample set; the cost estimation model includes a power function cost model or a unit area cost model.

[0055] Specifically, S302 above includes: S3021, for each subsample set, optimize the parameters of the cost estimation model constructed separately; Wherein, if the cost estimation model is a power function cost model, the parameters of the corresponding power function cost model are optimized using the nonlinear least squares method based on the governance engineering case data in each subsample set; If the cost estimation model is a unit area cost model, the parameters of the corresponding unit area cost model are optimized using statistical methods based on the governance project case data in each subsample set.

[0056] An exemplary power function cost model includes: , in, Indicates construction cost; This represents the scaling factor, which corresponds to the basic construction cost level of pollution control facilities per unit of runoff. This indicates the construction scale parameter, corresponding to the construction area of ​​runoff pollution control facilities; This represents the scale elasticity coefficient, which corresponds to the marginal change trend of costs as the scale of runoff pollution control facilities changes; This refers to the project's fixed investment, which corresponds to the basic costs of fixed expenditures that are independent of the size of the runoff pollution control facilities.

[0057] To simplify the model structure of the power function cost model and improve its generalization ability, the constant c can be omitted when the model fits well and the contribution of the constant term is not significant, thus forming a two-parameter model.

[0058] An exemplary unit area cost model includes: , in, Indicates construction cost; Indicates cost per unit area; This indicates the construction area of ​​runoff pollution control facilities.

[0059] Statistical methods were used to calculate the unit area cost of governance project case data in each subsample set, and the median unit area cost was used as the parameter of the unit area cost model.

[0060] S3022, Based on each optimized cost estimation model, the evaluation result of the corresponding cost estimation model is obtained using a preset model evaluation method; the evaluation result is used to establish a cost estimation model library with the corresponding cost estimation model.

[0061] The optimized cost estimation model is evaluated using model evaluation methods, and the evaluation results are built into a cost estimation model library to facilitate the assessment of the fitting effect and predictive performance of the optimized cost estimation model.

[0062] In an optional implementation, when the cost estimation model is a power function cost model, the above-mentioned S3022 includes: Based on each optimized cost estimation model, the goodness-of-fit index, root mean square error, and mean absolute error are used for evaluation to obtain the evaluation results of the cost estimation model.

[0063] The goodness-of-fit index is used to obtain the explanatory power of the optimized cost estimation model for data variation; the root mean square error is used to measure the difference between the predicted construction cost generated by the optimized cost estimation model and the actual construction cost; and the mean absolute error is used to reflect the overall error level of the optimized cost estimation model. By obtaining the evaluation results of the optimized cost estimation model, it is convenient to clarify the estimation accuracy and error range of the optimized cost estimation model.

[0064] In an optional implementation, when the cost estimation model is a unit area cost model, the above-mentioned S3022 includes: Based on each optimized cost estimation model, the mean, median, quantiles, and outliers of the cost estimation model are obtained using box plot analysis, which serve as the evaluation results of the cost estimation model.

[0065] By using box plot analysis, the mean, median, quantiles, and outliers of the cost estimation model can be obtained, providing a quantitative assessment basis for the reliability and applicability of subsequent cost estimation using the unit area cost model.

[0066] For example, such as Figure 3As shown, for a subset of data categorized as a water storage reservoir, the number of case studies in the corresponding treatment projects within that subset is 26. The optimized cost estimation model using the treatment project case studies data from that subset is as follows: , in, This indicates the construction cost, expressed in ten thousand yuan, corresponding to... Figure 3 The vertical coordinate in the figure; This represents the construction parameters of the governance project case data, in m³, corresponding to... Figure 3 The x-coordinate in the diagram.

[0067] The corresponding cost estimation model was evaluated with a goodness-of-fit index of 0.682, a root mean square error of 4330.90, and a mean absolute error of 2451.78.

[0068] For example, such as Figure 4 As shown, for a subset of data categorized as constructed wetlands, the number of case studies in the remediation projects within that subset is 29. The optimized cost estimation model using the case studies from the remediation projects in that subset is as follows: , in, This indicates the construction cost, expressed in ten thousand yuan, corresponding to... Figure 4 The vertical coordinate in the figure; This represents the construction parameters of the governance project case data, in units of 10. 6 ㎡, corresponding to Figure 4 The x-coordinate in the diagram.

[0069] Figure 4 The evaluation results of the cost estimation model are as follows: goodness of fit index is 0.909, root mean square error is 1684.31, and mean absolute error is 739.59.

[0070] like Figure 5 As shown, Figure 5 The vertical axis represents construction cost. For a specific technology type, the subsample set classified as permeable pavement has 43 sample data for treatment projects, with a mean of 357.66 yuan / m² and a median of 375.75 yuan / m².

[0071] like Figure 6 As shown, Figure 6 The vertical axis represents construction cost. For a specific technology type, the subsample set is classified as sunken green space. The sample size of the treatment project case data in the corresponding subsample set is 28, with a mean of 506.45 yuan / m² and a median of 422.99 yuan / m².

[0072] likeFigure 7 As shown, Figure 7 The vertical axis represents construction cost. For a subsample set classified as bioretention facilities for a certain technology type, the sample size of the treatment project case data in the corresponding subsample set is 17, with a mean of 499.99 yuan / m² and a median of 475.00 yuan / m².

[0073] like Figure 8 As shown, Figure 8 The vertical axis represents construction cost. For a specific technology type, the subsample set is classified as vegetated swales. The sample size of the treatment project case data in the corresponding subsample set is 14, with a mean of 219.10 yuan / m² and a median of 228.25 yuan / m².

[0074] S303, an optimized cost estimation model that integrates multiple subsets of samples, forms the cost estimation model library. For details, please refer to [link to relevant documentation]. Figure 2 S203 of the illustrated embodiment will not be described again here.

[0075] S304, based on the current runoff pollution control scheme to be evaluated, uses an optimized cost estimation model from the cost estimation model library for evaluation, and obtains the corresponding cost prediction results for the runoff pollution control scheme. For details, please refer to [link to relevant documentation]. Figure 2 S204 of the illustrated embodiment will not be described again here.

[0076] This invention provides a method for assessing runoff pollution control in urban water systems. It categorizes case data of multiple runoff pollution control facilities according to their technology types, obtaining sub-sample sets corresponding to each technology type. This facilitates the optimization of cost estimation models for different sample types. Then, it optimizes the function of each cost estimation model using each sub-sample set. By integrating the optimized cost estimation models from different sample sets, a cost estimation model library is constructed. Finally, based on the optimized cost estimation models in the library, the current runoff pollution control scheme to be evaluated is assessed, yielding cost prediction results for the runoff pollution control scheme. This significantly reduces the time spent on cost estimation using relevant technology cost estimation methods and improves the efficiency of cost comparison of different runoff pollution control schemes in the early stages of a project.

[0077] This embodiment provides a method for assessing runoff pollution control in urban water systems, which can be used on the server terminal of the aforementioned urban water system. Figure 9 This is a flowchart of an urban water system runoff pollution control assessment method according to an embodiment of the present invention, such as... Figure 9 As shown, the process includes the following steps: S901, based on the acquired case data of multiple runoff pollution control facilities, the data is categorized according to technology type to obtain multiple sub-sample sets; the case data includes: technology type, construction scale parameters, and total construction cost or unit cost. For details, please refer to [link to relevant documentation]. Figure 2 S201 of the illustrated embodiment will not be described again here.

[0078] S902, for each subset of samples, the corresponding cost estimation model is optimized to obtain the optimized cost estimation model for each subset; the cost estimation model includes a power function cost model or a unit area cost model. For details, please refer to [link to details]. Figure 2 S202 of the illustrated embodiment will not be described again here.

[0079] S903, an optimized cost estimation model that integrates multiple subsets of samples, forms the cost estimation model library. For details, please refer to [link to relevant documentation]. Figure 2 S203 of the illustrated embodiment will not be described again here.

[0080] S904, based on the current runoff pollution control scheme to be evaluated, uses the optimized cost estimation model in the cost estimation model library to evaluate it, and obtains the cost prediction results of the corresponding runoff pollution control scheme.

[0081] Specifically, the aforementioned S904 includes: S9041, Based on the current runoff pollution control scheme to be evaluated, obtain the technical type and construction scale parameters of the runoff pollution control scheme; S9042, based on the technical type and construction scale parameters of the runoff pollution control scheme, the optimized cost estimation model in the cost estimation model library is used for evaluation to obtain the cost prediction result of the corresponding runoff pollution control scheme.

[0082] Based on the runoff pollution control schemes to be evaluated, the technical type and construction scale parameters of the corresponding runoff pollution control schemes are first obtained. The optimized cost estimation model in the cost estimation model library is determined by the technical type. The construction scale parameters are input into the optimized cost estimation model. The cost prediction results of the corresponding runoff pollution control schemes are obtained by using the optimized cost estimation model, thereby improving the efficiency of obtaining the cost required for each runoff pollution control scheme.

[0083] This invention provides a method for assessing runoff pollution control in urban water systems. It categorizes case data of multiple runoff pollution control facilities according to their technology types, obtaining sub-sample sets corresponding to each technology type. This facilitates the optimization of cost estimation models for different sample types. Then, it optimizes the function of each cost estimation model using each sub-sample set. By integrating the optimized cost estimation models from different sample sets, a cost estimation model library is constructed. Finally, based on the optimized cost estimation models in the library, the current runoff pollution control scheme to be evaluated is assessed, yielding cost prediction results for the runoff pollution control scheme. This significantly reduces the time spent on cost estimation using relevant technology cost estimation methods and improves the efficiency of cost comparison of different runoff pollution control schemes in the early stages of a project.

[0084] Reference Figure 10 In this embodiment, an urban water system runoff pollution control assessment device is also provided. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0085] This embodiment provides a device for assessing runoff pollution control in urban water systems, the device comprising: The data classification module 1010 is used to classify the acquired case data of multiple runoff pollution control facilities according to the technology type to obtain multiple sub-sample sets; the case data of the treatment projects includes: technology type, construction scale parameters, total construction cost or unit cost; The cost model optimization module 1020 is used to optimize the corresponding cost estimation model for each subsample set to obtain the optimized cost estimation model for each subsample set; the cost estimation model includes a power function cost model or a unit area cost model. The model library construction module 1030 is used to integrate optimized cost estimation models from multiple subsample sets to obtain a cost estimation model library. The cost assessment module 1040 is used to evaluate the current runoff pollution control scheme based on the optimized cost estimation model in the cost estimation model library, and obtain the cost prediction result of the corresponding runoff pollution control scheme.

[0086] In some optional implementations, the cost model optimization module 1020 includes: The cost model optimization unit is used to optimize the parameters of the cost estimation model constructed for each subsample set. Wherein, if the cost estimation model is a power function cost model, the parameters of the corresponding power function cost model are optimized using the nonlinear least squares method based on the governance engineering case data in each subsample set; If the cost estimation model is a unit area cost model, the parameters of the corresponding unit area cost model are optimized using statistical methods based on the governance project case data in each subsample set.

[0087] In some optional implementations, the cost model optimization module 1020 further includes: The cost model evaluation unit is used to obtain the evaluation result of the corresponding cost estimation model based on each optimized cost estimation model using a preset model evaluation method; the evaluation result is used to establish a cost estimation model library with the corresponding cost estimation model.

[0088] In some optional implementations, when the cost estimation model is a power function cost model, the cost model evaluation unit is specifically used for: Based on each optimized cost estimation model, the goodness-of-fit index, root mean square error, and mean absolute error are used for evaluation to obtain the evaluation results of the cost estimation model.

[0089] In some optional implementations, when the cost estimation model is a unit area cost model, the cost model evaluation unit is specifically used for: Based on each optimized cost estimation model, the mean, median, quantiles, and outliers of the cost estimation model are obtained using box plot analysis, which serve as the evaluation results of the cost estimation model.

[0090] In some alternative implementations, it also includes: The data preprocessing module 1050 is used to process the raw data of multiple runoff pollution control facility treatment project cases using data processing methods to obtain processed treatment project case data. The data processing methods include unifying dimensions, removing missing values ​​and extreme outliers, and the processed treatment project case data is used for classification according to technology type.

[0091] In some alternative implementations, the cost assessment module 1040 includes: The parameter determination unit is used to obtain the technical type and construction scale parameters of the current runoff pollution control scheme to be evaluated. The cost assessment unit is used to evaluate the runoff pollution control scheme based on the technical type and construction scale parameters of the scheme, using an optimized cost estimation model from the cost estimation model library, and to obtain the cost prediction results of the corresponding runoff pollution control scheme.

[0092] The urban water system runoff pollution control assessment device provided in this embodiment of the invention can execute the urban water system runoff pollution control assessment method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0093] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0094] The following is a detailed reference. Figure 11 The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 1101, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1102 or a program loaded from memory 1108 into random access memory (RAM) 1103. The RAM 1103 also stores various programs and data required for the operation of the electronic device. The processor 1101, ROM 1102, and RAM 1103 are interconnected via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0095] Typically, the following devices can be connected to I / O interface 1105: input devices 1106 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 1107 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 1108 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1109. Communication device 1109 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 11 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0096] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 1109, or installed from a memory 1108, or installed from a ROM 1102. When the computer program is executed by the processor 1101, it performs the functions defined in the urban water system runoff pollution control assessment method of the embodiments of the present invention.

[0097] Figure 11 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0098] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the urban water system runoff pollution control assessment method shown in the above embodiments is implemented.

[0099] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0100] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for assessing runoff pollution control in urban water systems, characterized in that, The method includes: Based on the acquired case data of multiple runoff pollution control facilities, the data is classified according to technology type to obtain multiple sub-sample sets; the case data of the control projects includes: technology type, construction scale parameters, total construction cost or unit cost; For each subsample set, the corresponding cost estimation model is optimized to obtain the optimized cost estimation model for each subsample set; the cost estimation model includes a power function cost model or a unit area cost model. By integrating optimized cost estimation models from multiple subsets, a cost estimation model library is obtained. Based on the current runoff pollution control scheme to be evaluated, the optimized cost estimation model in the cost estimation model library is used for evaluation to obtain the cost prediction results of the corresponding runoff pollution control scheme.

2. The method according to claim 1, characterized in that, The step involves optimizing the corresponding cost estimation model for each subsample set to obtain an optimized cost estimation model for each subsample set, including: For each subset of samples, the parameters of the respective cost estimation model are optimized. Wherein, if the cost estimation model is a power function cost model, the parameters of the corresponding power function cost model are optimized using the nonlinear least squares method based on the governance engineering case data in each subsample set; If the cost estimation model is a unit area cost model, the parameters of the corresponding unit area cost model are optimized using statistical methods based on the governance project case data in each subsample set.

3. The method according to claim 1, characterized in that, Also includes: Based on each optimized cost estimation model, the evaluation results of the corresponding cost estimation model are obtained using a preset model evaluation method; the evaluation results are used to establish a cost estimation model library with the corresponding cost estimation model.

4. The method according to claim 3, characterized in that, When the cost estimation model is a power function cost model, the step of obtaining the evaluation result of the corresponding cost estimation model based on each optimized cost estimation model using a preset model evaluation method includes: Based on each optimized cost estimation model, the goodness-of-fit index, root mean square error, and mean absolute error are used for evaluation to obtain the evaluation results of the cost estimation model.

5. The method according to claim 3, characterized in that, When the cost estimation model is a unit area cost model, the step of obtaining the evaluation result of the corresponding cost estimation model based on each optimized cost estimation model using a preset model evaluation method includes: Based on each optimized cost estimation model, the mean, median, quantiles, and outliers of the cost estimation model are obtained using box plot analysis, which serve as the evaluation results of the cost estimation model.

6. The method according to claim 1, characterized in that, Also includes: Based on the original data of multiple runoff pollution control facility treatment project cases, data processing methods are used to process the data to obtain processed treatment project case data. The data processing methods include unifying dimensions, removing missing values ​​and extreme outliers, and the processed treatment project case data is used to classify according to technology type.

7. The method according to any one of claims 1 to 6, characterized in that, The method involves evaluating the current runoff pollution control scheme using an optimized cost estimation model from a cost estimation model library, thereby obtaining the corresponding cost prediction results for the runoff pollution control scheme, including: Based on the current runoff pollution control scheme to be evaluated, the technical type and construction scale parameters of the runoff pollution control scheme are obtained; Based on the technical type and construction scale parameters of the runoff pollution control scheme, the optimized cost estimation model in the cost estimation model library is used for evaluation to obtain the cost prediction results of the corresponding runoff pollution control scheme.

8. A device for assessing runoff pollution control in urban water systems, characterized in that, The device includes: The data classification module is used to classify the acquired case data of multiple runoff pollution control facilities according to the technology type, resulting in multiple sub-sample sets; the case data of the treatment projects includes: technology type, construction scale parameters, total construction cost or unit cost; The cost model optimization module is used to optimize the corresponding cost estimation model for each subsample set, so as to obtain the optimized cost estimation model for each subsample set; the cost estimation model includes a power function cost model or a unit area cost model. The model library building module is used to integrate optimized cost estimation models from multiple subsample sets to obtain a cost estimation model library. The cost assessment module is used to evaluate the current runoff pollution control schemes by using optimized cost estimation models from the cost estimation model library, and to obtain the cost prediction results of the corresponding runoff pollution control schemes.

9. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the urban water system runoff pollution control assessment method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the urban water system runoff pollution control assessment method according to any one of claims 1 to 7.

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