Urban water system drainage transformation evaluation method, device, equipment and medium

Abstract

The invention relates to the technical field of urban water system reconstruction, and discloses an urban water system drainage reconstruction evaluation method, device and equipment and a medium. In the method disclosed by the invention, classification is carried out by utilizing technical types according to acquired multiple drainage reconstruction case data; by taking the transformation scale of each drainage transformation type data set as a core variable, optimizing parameters of the constructed cost estimation model, and then integrating the cost estimation models of different drainage transformation types as a cost estimation model library; and finally, predicting the cost of the to-be-evaluated drainage transformation scheme by using each cost estimation model in the cost estimation model library so as to improve the efficiency of cost comparison and selection of different drainage transformation schemes. The defect that a drainage system reconstruction project cost evaluation method disclosed in the prior art is difficult to be suitable for a scene of multi-scheme rapid comparison and selection at the initial stage of drainage system scheme design is overcome.

Description

Technical Field

[0001] This invention relates to the field of urban water system renovation technology, specifically to an assessment method, device, equipment, and medium for urban water system drainage renovation. Background Technology

[0002] Urban water environment problems are becoming increasingly prominent, and traditional combined sewer systems are struggling to meet the multiple demands of rainwater and sewage separation, flood control and drainage, and water pollution prevention and control in the new era. The serious problems of combined sewer systems and mixed connections in residential areas, municipal roads, and older districts severely hinder the continuous progress of urban water quality improvement and black and odorous water body treatment. Large-scale drainage system renovation projects are being carried out across various regions, with common technical approaches including rainwater and sewage separation in residential areas, separation of sewer systems on municipal roads, and separation of clean water and sewage.

[0003] The cost assessment methods for drainage system renovation projects disclosed in related technologies rely on professional cost estimators to conduct cost estimates based on complete construction drawings. However, these methods require a large amount of data and are heavily dependent on the efficiency of professional cost estimators, making them unsuitable for scenarios involving rapid comparison of multiple options in the early stages of drainage system design. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for evaluating urban water system drainage renovation, in order to solve the problem that the cost evaluation methods for drainage system renovation projects disclosed in related technologies are difficult to apply to scenarios involving rapid comparison of multiple schemes in the early stages of drainage system design.

[0005] In a first aspect, the present invention provides a method for evaluating the renovation of urban water system drainage, the method comprising: Based on the acquired drainage renovation case data, the data is classified according to technical type to obtain multiple drainage renovation type datasets; the drainage renovation case data includes renovation scale and actual construction cost, and renovation scale includes renovation area or pipeline renovation length. For each type of drainage renovation dataset, the parameters of the respective cost estimation model were optimized multiple times to obtain multiple optimized cost estimation models. By integrating multiple optimized cost estimation models, a cost estimation model library is obtained; Based on the current drainage renovation scheme to be evaluated, the optimized cost estimation model in the cost estimation model library is used for evaluation to obtain the corresponding cost prediction value of the drainage system renovation scheme.

[0006] Through the above implementation method, based on the acquired drainage renovation case data, the data is classified by technology type, and the renovation scale of each drainage renovation type dataset is used as the core variable. The parameters of the constructed cost estimation model are optimized, and then the cost estimation models of different drainage renovation types are integrated into a cost estimation model library. Finally, the cost of the drainage renovation scheme to be evaluated is predicted using the various cost estimation models in the cost estimation model library, so as to improve the efficiency of cost comparison of different drainage renovation schemes and overcome the shortcomings of the disclosed drainage system renovation project cost assessment methods in related technologies, which are difficult to apply to the scenario of rapid comparison of multiple schemes in the early stage of drainage system design.

[0007] In one optional implementation, for each drainage renovation type dataset, the parameters of the constructed cost estimation model are optimized multiple times to obtain multiple optimized cost estimation models, including: Based on each drainage renovation type dataset, the parameters of the respective cost estimation model are optimized multiple times until the optimized cost estimation model meets the set conditions. In each optimization process, outliers are removed using outlier removal methods based on drainage renovation case data in each drainage renovation type dataset. Based on the drainage renovation case data after outlier removal, the parameters of the corresponding cost estimation model are optimized using the least squares method.

[0008] Through the above implementation method, after removing outliers from the drainage renovation case data in each drainage renovation type dataset using the outlier removal method, the least squares method is used to optimize the parameters of the constructed cost estimation model. This allows the parameters of the cost estimation model for each drainage renovation type dataset to gradually approach the optimal solution, thereby ensuring that each cost estimation model more accurately describes the cost composition pattern of the corresponding drainage renovation type dataset and improving the accuracy of predicting the construction cost of the drainage renovation type dataset in their respective scenarios using the optimized cost estimation model.

[0009] In one optional implementation, the outlier removal method based on the drainage renovation case data in each drainage renovation type dataset includes: Based on the drainage renovation case data in each drainage renovation type dataset, the predicted construction cost of each drainage renovation case data is obtained using a nonlinear fitting method. Based on the predicted construction cost and actual construction cost of drainage renovation case data in each drainage renovation type dataset, the absolute error of drainage renovation case data in each drainage renovation type dataset is obtained using the absolute error calculation method. Based on the absolute error of the drainage renovation case data in each drainage renovation type dataset, outliers are filtered and removed using a pre-built outlier threshold for each drainage renovation type dataset, resulting in drainage renovation case data after outlier removal in each drainage renovation type dataset.

[0010] Through the above implementation method, the predicted construction cost of drainage renovation case data in each drainage renovation type dataset is calculated by using a nonlinear fitting method, which more accurately portrays the complex relationship between the cost and influencing factors of drainage renovation case data. Then, the absolute error calculation method is used to obtain the degree of deviation between the predicted construction cost and the actual construction cost of each drainage renovation case data. Finally, outlier values ​​in drainage renovation case data in each drainage renovation type dataset are screened and removed using an outlier threshold, providing a high-quality data foundation for the parameter optimization of the subsequent cost estimation model.

[0011] In one optional implementation, the construction of the outlier threshold includes: Based on the absolute error of each drainage renovation case data in each drainage renovation type dataset, the mean absolute error of each drainage renovation type dataset is obtained by using the absolute error mean calculation method. Based on the absolute error of each drainage renovation case data in each drainage renovation type dataset, the absolute error standard deviation of each drainage renovation type dataset is obtained using the absolute error standard deviation calculation method. Based on the mean and standard deviation of the absolute error of each drainage renovation type dataset, and combined with preset adjustable parameters, the outlier threshold of each drainage renovation type dataset is obtained using an outlier threshold calculation method.

[0012] The above implementation method uses the mean absolute error to reflect the general level of deviation between the predicted construction cost and the actual construction cost in each drainage renovation type dataset, and the standard deviation of absolute error to reflect the dispersion of the deviation. Combined with the outlier threshold calculation method, the outlier data threshold for each drainage renovation type dataset is obtained. This avoids the blindness of staff subjectively setting fixed thresholds and ensures that outlier values ​​in drainage renovation case data in each drainage renovation type dataset are removed reasonably and reliably using the outlier threshold.

[0013] In one optional implementation, the cost estimation model satisfies: , in, Indicates construction cost; This represents the scaling factor, which corresponds to the basic construction cost level per unit governance scale. Indicators representing the scale of governance, corresponding to the area or length of the remediation; This represents the scale elasticity coefficient, which corresponds to the marginal change trend of costs as the scale of governance changes. This refers to the fixed investment in a project, corresponding to basic expenses that are fixed expenditures regardless of the scale of the governance.

[0014] Through the above implementation methods, a cost estimation model is constructed by combining the scale coefficient, governance scale index, scale elasticity coefficient and project fixed input. This model accurately breaks down the cost composition of each type of drainage renovation, ensures the impact logic of project cost for each type of drainage renovation, comprehensively covers the cost composition of projects of different scales, and ensures the accuracy of estimation in all scenarios. It is especially suitable for a unified estimation standard for drainage renovation projects of multiple types and scales.

[0015] In one alternative implementation, it further includes: Based on each optimized cost estimation model, evaluation indicators are used to evaluate each optimized cost estimation model to obtain the evaluation results of each optimized cost estimation model; the evaluation indicators include the coefficient of determination, root mean square error and / or mean absolute error.

[0016] Through the above implementation methods, the coefficient of determination 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 degree of 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 using evaluation indicators to evaluate the optimized cost estimation model, it is convenient to clarify the estimation accuracy and error range of the optimized cost estimation model.

[0017] In one optional implementation, the step of evaluating the current drainage system renovation scheme using an optimized cost estimation model from a cost estimation model library to obtain the corresponding predicted cost value for the drainage system renovation scheme includes: Based on the current drainage renovation schemes to be evaluated, multiple drainage renovation types and corresponding renovation scales are obtained according to the technology type. Based on the various drainage renovation types and their corresponding renovation scales, the optimized cost estimation models in the cost estimation model library are used to evaluate and obtain the cost prediction values ​​for each drainage renovation type. By combining the cost forecasts of various drainage renovation types, the cost forecast of the corresponding drainage system renovation scheme is obtained.

[0018] Through the above implementation methods, the drainage renovation schemes to be evaluated are divided according to different technical types, so that different drainage renovation types are accurately matched with the corresponding optimized cost estimation models in the cost estimation model library. This facilitates the evaluation by using the optimized cost estimation models in combination with the corresponding renovation scale, giving full play to the impact of scale variables on costs in each drainage renovation scheme. Furthermore, by summarizing and splitting the cost prediction values ​​of each drainage renovation type, the system achieves complete coverage of multiple scenarios for the drainage renovation schemes to be evaluated, thereby improving the efficiency and flexibility of evaluating drainage renovation schemes.

[0019] Secondly, the present invention provides an assessment device for urban water system drainage renovation, the device comprising: The data classification module is used to classify multiple drainage renovation case data according to technical type to obtain multiple drainage renovation type datasets; the drainage renovation case data includes renovation scale and actual construction cost, and renovation scale includes renovation area or pipeline renovation length; The cost function optimization module is used to optimize the parameters of the cost estimation model constructed for each type of drainage renovation dataset multiple times, resulting in multiple optimized cost estimation models. The model library creation module is used to integrate multiple cost estimation models to obtain a cost estimation model library. The cost assessment module is used to evaluate the current drainage system renovation plan based on the optimized cost estimation model in the cost estimation model library, and obtain the corresponding cost prediction value of the drainage system renovation plan.

[0020] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the urban water system drainage renovation assessment method described in the first aspect or any corresponding embodiment.

[0021] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the urban water system drainage renovation 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 step in the evaluation method for urban water system drainage renovation schemes according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the fitting results of the optimized cost estimation model for the rainwater and sewage separation renovation technology in the urban water system drainage renovation scheme evaluation method according to an embodiment of the present invention. Figure 4 This is a schematic diagram of the fitting results of the optimized cost estimation model for the rainwater and sewage separation transformation technology of municipal roads in the urban water system drainage renovation scheme evaluation method according to an embodiment of the present invention. Figure 5 This is a schematic diagram of the fitting results of the optimized cost estimation model for the separation of clean water and sewage in the urban water system drainage renovation scheme evaluation method according to an embodiment of the present invention. Figure 6 This is a schematic diagram of the fitting results of the optimized cost estimation model for pipeline dredging technology in the urban water system drainage renovation scheme evaluation method according to an embodiment of the present invention. Figure 7 This is a schematic diagram of the fitting results of the optimized cost estimation model for the construction of diversion pipe networks in the urban water system drainage renovation scheme evaluation method according to an embodiment of the present invention. Figure 8 This is a schematic diagram of the second process of the urban water system drainage renovation scheme evaluation method according to an embodiment of the present invention; Figure 9 This is a structural block diagram of the urban water system drainage renovation scheme evaluation device according to an embodiment of the present invention; Figure 10 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] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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).

[0031] The disclosed methods for assessing urban water system drainage renovations in related technologies rely on professional cost estimators to estimate project costs based on complete construction drawings. This cost estimation method involves multiple components such as civil engineering, pipe materials, labor, and equipment, resulting in numerous items and a complex system. It requires detailed bills of quantities and regional quota data, which are voluminous and difficult to obtain, leading to a long estimation period for obtaining cost results. Furthermore, due to the differences in construction environment, topography, existing pipeline conditions, and supporting facilities among various renovation projects, it is difficult to compare the costs of different renovation schemes, making it unsuitable for scenarios requiring rapid comparison of multiple schemes in the early stages of urban water system drainage renovation design.

[0032] To overcome the aforementioned technical problems, this invention provides a method for evaluating urban water system drainage renovation schemes. Based on acquired drainage renovation case data, the method categorizes the data by technology type and uses the renovation scale of each drainage renovation type dataset as the core variable. The parameters of the constructed cost estimation model are optimized, and then a cost estimation model library is created by integrating cost estimation models from different drainage renovation types. Finally, the cost of the drainage renovation scheme to be evaluated is predicted using the various cost estimation models in the cost estimation model library. This improves the efficiency of cost comparison of different drainage renovation schemes and overcomes the shortcomings of existing drainage system renovation project cost evaluation methods, which are difficult to apply to scenarios requiring rapid comparison of multiple schemes in the early stages of drainage system design.

[0033] According to an embodiment of the present invention, an embodiment of an evaluation method for urban water system drainage renovation scheme 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.

[0034] This embodiment provides a method for evaluating urban water system drainage renovation schemes, which can be used in the aforementioned terminal server. Figure 2 This is a flowchart of an evaluation method for urban water system drainage renovation schemes 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 drainage renovation case data, the data is classified according to the technology type to obtain multiple drainage renovation type datasets; the drainage renovation case data includes the renovation scale and actual construction cost, and the renovation scale includes the renovation area or the pipeline renovation length.

[0035] The technologies include multiple drainage renovation types such as rainwater and sewage separation, clean water and sewage separation, pipe network dredging, pipe network repair, and new pipe construction, which are used to realize drainage renovation of urban water systems and ensure the normal sewage treatment tasks of urban water systems.

[0036] The drainage renovation case data consists of actual engineering data from typical drainage system renovation projects, covering typical governance scenarios such as residential communities, municipal roads, and districts. It can be obtained through the completion audit reports of completed projects, industry cost databases, local renovation cases, and literature reports. The drainage renovation case data includes the renovation scale and actual construction cost. The renovation scale includes the renovation area or the pipeline renovation length.

[0037] S202, for each type of drainage renovation dataset, the parameters of the cost estimation model constructed by each model are optimized multiple times to obtain multiple optimized cost estimation models.

[0038] The cost estimation model can be implemented as a power function constructed by taking the scale of the drainage renovation scheme as the independent variable and the construction cost as the dependent variable. It can characterize the "economies of scale" effect of typical urban water system drainage renovation schemes, that is, as the scale of treatment increases, the unit cost may decrease. The scale elasticity coefficient is used to judge the scale cost response law of different technology types. The fixed investment of the project ensures that the cost estimation model still has a relatively accurate fitting ability when the scale is small.

[0039] An exemplary cost estimation model satisfies: , in, Indicates construction cost; This represents the scaling factor, which corresponds to the basic construction cost level per unit governance scale. Indicators representing the scale of governance, corresponding to the area or length of the remediation; This represents the scale elasticity coefficient, which corresponds to the marginal change trend of costs as the scale of governance changes. This refers to the fixed investment in a project, corresponding to basic expenses that are fixed expenditures regardless of the scale of the governance.

[0040] Specifically, the scale elasticity coefficient The scale elasticity coefficient is used to indicate whether there are economies of scale: a scale elasticity coefficient less than 1 indicates that there are significant economies of scale, which is the most common case; a scale elasticity coefficient of 1 indicates that cost and scale are linearly related, with no economies of scale; and a scale elasticity coefficient greater than 1 indicates that there are diseconomies of scale.

[0041] By optimizing the parameters of the constructed cost estimation model based on the dataset for each drainage renovation type, it is possible to ensure that renovation schemes of different technical types retain their own characteristics, thereby facilitating accurate prediction of the construction costs of renovation schemes of different technical types, ensuring the impact logic of project costs for each drainage renovation type, comprehensively covering the cost composition of projects of different scales, ensuring the accuracy of estimation in all scenarios, and being particularly suitable for a unified estimation standard for drainage renovation projects of multiple types and scales.

[0042] Furthermore, in practical applications, if statistical tests reveal that the project's fixed input... If not significant, the cost estimation model can be simplified to: , in, Indicates construction cost; This represents the scaling factor, which corresponds to the basic construction cost level per unit governance scale. Indicators representing the scale of governance, corresponding to the area or length of the remediation; This represents the scale elasticity coefficient, which corresponds to the marginal change trend of costs as the scale of governance changes.

[0043] S203 integrates multiple optimized cost estimation models to obtain a cost estimation model library.

[0044] The cost estimation model library is used to synthesize multiple optimized cost estimation models. The optimized cost estimation model is a cost estimation model that has been optimized separately for the scale coefficient, scale elasticity coefficient and fixed input of the project. It can ensure the accuracy of cost prediction for drainage renovation schemes of different technical types.

[0045] S204. Based on the current drainage renovation scheme to be evaluated, the optimized cost estimation model in the cost estimation model library is used for evaluation to obtain the corresponding predicted cost value of the drainage system renovation scheme.

[0046] By utilizing various cost estimation models in the cost estimation model library, the cost of the drainage renovation scheme to be evaluated is predicted, thereby improving the efficiency of cost comparison of different drainage renovation schemes and overcoming the shortcomings of the cost assessment methods for drainage system renovation projects disclosed in related technologies, which are difficult to apply to the scenario of rapid comparison of multiple schemes in the early stage of drainage system design.

[0047] This invention provides a method for evaluating urban water system drainage renovation schemes. Based on multiple drainage renovation case data, the method categorizes the data by technology type and uses the renovation scale of each drainage renovation type dataset as the core variable. The method optimizes the parameters of the constructed cost estimation model, then integrates cost estimation models of different drainage renovation types into a cost estimation model library. Finally, the method uses the cost estimation models in the cost estimation model library to predict the cost of the drainage renovation scheme to be evaluated, thereby improving the efficiency of cost comparison of different drainage renovation schemes. This overcomes the shortcomings of the cost evaluation methods for drainage system renovation projects disclosed in related technologies, which are difficult to apply to the scenario of rapid comparison of multiple schemes in the early stage of drainage system design.

[0048] This embodiment provides a method for evaluating urban water system drainage renovation schemes, which can be used in the aforementioned terminal server. The method includes the following steps: S301, based on the acquired drainage renovation case data, it is classified according to technical type to obtain multiple drainage renovation type datasets; the drainage renovation case data includes the renovation scale and actual construction cost, and the renovation scale includes the renovation area or the pipeline renovation length. For details, please refer to... Figure 2 S201 of the illustrated embodiment will not be described again here.

[0049] S302, for each drainage renovation type dataset, the parameters of the respective cost estimation model are optimized multiple times to obtain multiple optimized cost estimation models.

[0050] Specifically, the above-mentioned S302 is implemented as follows: S3021, Based on the dataset of each drainage renovation type, the parameters of the cost estimation model constructed by each model are optimized multiple times until the optimized cost estimation model meets the set conditions. In each optimization process, outliers are removed using outlier removal methods based on drainage renovation case data in each drainage renovation type dataset. Based on the drainage renovation case data after outlier removal, the parameters of the corresponding cost estimation model are optimized using the least squares method.

[0051] After removing outliers from the drainage renovation case data in each drainage renovation type dataset using outlier removal methods, the least squares method is used to optimize the parameters of the constructed cost estimation model. This allows the parameters of the cost estimation model for each drainage renovation type dataset to gradually approach the optimal solution, thereby ensuring that each cost estimation model more accurately describes the cost composition pattern of the corresponding drainage renovation type dataset and improving the accuracy of predicting the construction cost of the drainage renovation type dataset in their respective scenarios using the optimized cost estimation model.

[0052] The set conditions can be implemented to terminate the iteration early when the maximum number of iterations is reached or when no outliers are found in a certain round of iterations.

[0053] By using the maximum number of iterations or outliers to filter the drainage renovation case data in each drainage renovation type dataset, the final optimized cost estimation model can be accurately fitted to the drainage renovation case data in each drainage renovation type dataset.

[0054] The above steps disclose drainage renovation case data based on each drainage renovation type dataset, and outlier removal methods are used to remove outliers, including: Based on the drainage renovation case data in each drainage renovation type dataset, the predicted construction cost of each drainage renovation case data is obtained using a nonlinear fitting method. Based on the predicted construction cost and actual construction cost of drainage renovation case data in each drainage renovation type dataset, the absolute error of drainage renovation case data in each drainage renovation type dataset is obtained using the absolute error calculation method. Based on the absolute error of the drainage renovation case data in each drainage renovation type dataset, outliers are filtered and removed using a pre-built outlier threshold for each drainage renovation type dataset, resulting in drainage renovation case data after outlier removal in each drainage renovation type dataset.

[0055] By using nonlinear fitting methods to calculate the predicted construction cost of drainage renovation case data in each drainage renovation type dataset, the complex relationship between the cost and influencing factors of drainage renovation case data can be more accurately characterized. Then, by using the absolute error calculation method, the deviation between the predicted construction cost and the actual construction cost of each drainage renovation case data is obtained through the absolute error. Finally, outlier values ​​in drainage renovation case data in each drainage renovation type dataset are screened and removed using outlier thresholds, providing a high-quality data foundation for the parameter optimization of the subsequent cost estimation model.

[0056] The construction of the outlier threshold in the above steps includes: Based on the absolute error of each drainage renovation case data in each drainage renovation type dataset, the mean absolute error of each drainage renovation type dataset is obtained by using the absolute error mean calculation method. Based on the absolute error of each drainage renovation case data in each drainage renovation type dataset, the absolute error standard deviation of each drainage renovation type dataset is obtained using the absolute error standard deviation calculation method. Based on the mean and standard deviation of the absolute error of each drainage renovation type dataset, and combined with preset adjustable parameters, the outlier threshold of each drainage renovation type dataset is obtained using an outlier threshold calculation method.

[0057] The adjustable parameter can be implemented as a value between 2 and 3, which indicates the strictness of outlier removal.

[0058] The mean absolute error is used to reflect the general level of deviation between the predicted construction cost and the actual construction cost in each drainage renovation type dataset. The standard deviation of the absolute error is used to reflect the dispersion of the deviation. Combined with the outlier threshold calculation method, the outlier threshold of each drainage renovation type dataset is obtained. This avoids the blindness of staff subjectively setting fixed thresholds and ensures that outlier values ​​in drainage renovation case data in each drainage renovation type dataset are removed reasonably and reliably using outlier thresholds.

[0059] For example, such as Figure 3 As shown, for a drainage renovation dataset categorized as a residential community stormwater and sewage separation renovation technology, the dataset contains 322 drainage renovation case studies. After outlier removal, the remaining sample size is 314. The optimized cost estimation model using this drainage renovation case study data is as follows: , in This indicates the construction cost, expressed in ten thousand yuan, corresponding to... Figure 3 The vertical axis in the middle; and The area to be renovated is expressed in ha (ha), and is used to represent the scale of the remediation efforts. Figure 3 The x-coordinate in the diagram.

[0060] like Figure 4 As shown, for a drainage renovation type dataset categorized as municipal road stormwater and sewage separation renovation technology, the number of drainage renovation case data samples in the corresponding drainage renovation type dataset is 30. The cost estimation model optimized using the drainage renovation case data in the drainage renovation type dataset is as follows: , in This indicates the construction cost, expressed in ten thousand yuan, corresponding to... Figure 4 The vertical axis in the middle; and The length of the renovation is indicated in meters (m), and it is used to represent the scale of the remediation efforts, corresponding to... Figure 4 The x-coordinate in the diagram.

[0061] like Figure 5As shown, for a drainage renovation type dataset categorized as a clean water / sewage separation renovation technology, the number of drainage renovation case data samples in the corresponding drainage renovation type dataset is 65. The cost estimation model optimized using the drainage renovation case data in the drainage renovation type dataset is as follows: , in This indicates the construction cost, expressed in ten thousand yuan, corresponding to... Figure 5 The vertical axis in the middle; and The length of the renovation is indicated in meters (m), and it is used to represent the scale of the remediation efforts, corresponding to... Figure 5 The x-coordinate in the diagram.

[0062] like Figure 6 As shown, for a drainage renovation dataset categorized as pipeline dredging technology, the number of drainage renovation case data samples in the dataset is 156. The cost estimation model optimized using the drainage renovation case data from the dataset is as follows: , in This indicates the construction cost, expressed in ten thousand yuan, corresponding to... Figure 6 The vertical axis in the middle; and The length of the renovation is indicated in meters (m), and it is used to represent the scale of the remediation efforts, corresponding to... Figure 6 The x-coordinate in the diagram.

[0063] like Figure 7 As shown, for a drainage renovation type dataset categorized as a new diversion network construction technology, the number of drainage renovation case data samples in the corresponding drainage renovation type dataset is 204. The cost estimation model optimized using the drainage renovation case data in the drainage renovation type dataset is as follows: , in This indicates the construction cost, expressed in ten thousand yuan, corresponding to... Figure 7 The vertical axis in the middle; and The length of the renovation is indicated in meters (m), and it is used to represent the scale of the remediation efforts, corresponding to... Figure 7 The x-coordinate in the diagram.

[0064] Furthermore, the method provided by this invention also includes: Based on each optimized cost estimation model, evaluation indicators are used to evaluate each optimized cost estimation model to obtain the evaluation results of each optimized cost estimation model; the evaluation indicators include the coefficient of determination, root mean square error and / or mean absolute error.

[0065] The coefficient of determination 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 using evaluation indicators to evaluate the optimized cost estimation model, it is convenient to clarify the estimation accuracy and error range of the optimized cost estimation model.

[0066] For example, targeting Figure 3 The technology type classification in the text is an optimized cost estimation model for the rainwater and sewage separation renovation technology in residential areas. The evaluation results of the corresponding cost estimation model are: a determination coefficient of 0.746, a root mean square error of 285.04, and a mean absolute error of 196.42.

[0067] against Figure 4 The technology type classification in the text is an optimized cost estimation model for municipal road rainwater and sewage separation transformation technology. The evaluation results of the corresponding cost estimation model are: coefficient of determination is 0.8495, root mean square error is 384.05, and mean absolute error is 296.61.

[0068] against Figure 5 The optimized cost estimation model for the technology type classification of sewage diversion transformation technology has the following evaluation results: coefficient of determination is 0.728, root mean square error is 309.62, and mean absolute error is 187.79.

[0069] against Figure 6 The optimized cost estimation model for pipeline dredging technology is classified as a technology type. The evaluation results of the corresponding cost estimation model are: coefficient of determination is 0.922, root mean square error is 5.51, and mean absolute error is 1.62.

[0070] against Figure 7 The optimized cost estimation model for the technology type classification of the diversion network construction technology has the following evaluation results: coefficient of determination is 0.947, root mean square error is 74.43, and mean absolute error is 41.36.

[0071] S303 integrates multiple optimized cost estimation models to obtain a 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.

[0072] S304, based on the current drainage system renovation plan to be evaluated, uses an optimized cost estimation model from the cost estimation model library to conduct an evaluation, obtaining the predicted cost value of the corresponding drainage system renovation plan. For details, please refer to [link to relevant documentation]. Figure 2 S204 of the illustrated embodiment will not be described again here.

[0073] This invention provides a method for evaluating urban water system drainage renovation schemes. Based on multiple drainage renovation case data, the method categorizes the data by technology type and uses the renovation scale of each drainage renovation type dataset as the core variable. The method optimizes the parameters of the constructed cost estimation model, then integrates cost estimation models of different drainage renovation types into a cost estimation model library. Finally, the method uses the cost estimation models in the cost estimation model library to predict the cost of the drainage renovation scheme to be evaluated, thereby improving the efficiency of cost comparison of different drainage renovation schemes. This overcomes the shortcomings of the cost evaluation methods for drainage system renovation projects disclosed in related technologies, which are difficult to apply to the scenario of rapid comparison of multiple schemes in the early stage of drainage system design.

[0074] This embodiment provides a method for evaluating urban water system drainage renovation schemes, which can be used in the aforementioned terminal server. Figure 8 This is a flowchart of an evaluation method for urban water system drainage renovation schemes according to an embodiment of the present invention, such as... Figure 8 As shown, the process includes the following steps: S801, based on the acquired drainage renovation case data, it is classified according to technical type to obtain multiple drainage renovation type datasets; the drainage renovation case data includes the renovation scale and actual construction cost, and the renovation scale includes the renovation area or the pipeline renovation length. For details, please refer to... Figure 2 S201 of the illustrated embodiment will not be described again here.

[0075] S802 involves optimizing the parameters of the cost estimation model for each drainage renovation type dataset multiple times, resulting in several optimized cost estimation models. For details, please refer to [link to relevant documentation]. Figure 2 S202 of the illustrated embodiment will not be described again here.

[0076] S803 integrates multiple optimized cost estimation models to obtain a 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.

[0077] S804: Based on the current drainage renovation scheme to be evaluated, the optimized cost estimation model in the cost estimation model library is used for evaluation to obtain the corresponding predicted cost value of the drainage system renovation scheme.

[0078] Specifically, the aforementioned S804 includes: S8041, based on the current drainage renovation scheme to be evaluated, it is divided according to the technical type, resulting in multiple drainage renovation types and corresponding renovation scales; S8042, based on each type of drainage renovation and its corresponding scale, the optimized cost estimation model in the cost estimation model library is used to evaluate and obtain the cost prediction value of each type of drainage renovation. S8043, by combining the cost forecasts of various drainage renovation types, the cost forecast of the corresponding drainage system renovation scheme is obtained.

[0079] The drainage renovation schemes to be evaluated are divided into different technical types, so that different drainage renovation types can be accurately matched with the corresponding optimized cost estimation models in the cost estimation model library. This makes it convenient to use the optimized cost estimation models in combination with the corresponding renovation scale for evaluation, giving full play to the impact of scale variables on cost in each drainage renovation scheme. Then, by summarizing the cost prediction values ​​of each drainage renovation type after splitting, the full coverage of multiple scenarios of the drainage renovation schemes to be evaluated is achieved, improving the efficiency and flexibility of evaluating drainage renovation schemes.

[0080] This invention provides a method for evaluating urban water system drainage renovation schemes. Based on multiple drainage renovation case data, the method categorizes the data by technology type and uses the renovation scale of each drainage renovation type dataset as the core variable. The method optimizes the parameters of the constructed cost estimation model, then integrates cost estimation models of different drainage renovation types into a cost estimation model library. Finally, the method uses the cost estimation models in the cost estimation model library to predict the cost of the drainage renovation scheme to be evaluated, thereby improving the efficiency of cost comparison of different drainage renovation schemes. This overcomes the shortcomings of the cost evaluation methods for drainage system renovation projects disclosed in related technologies, which are difficult to apply to the scenario of rapid comparison of multiple schemes in the early stage of drainage system design.

[0081] This embodiment also provides an urban water system drainage renovation assessment device, which is used to implement the above embodiments and preferred embodiments; 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.

[0082] This embodiment provides an assessment device for urban water system drainage renovation, such as... Figure 9 As shown, it includes: The data classification module 910 is used to classify multiple drainage renovation case data according to technical type to obtain multiple drainage renovation type datasets; the drainage renovation case data includes renovation scale and actual construction cost, and the renovation scale includes renovation area or pipeline renovation length. The cost function optimization module 920 is used to optimize the parameters of the cost estimation model constructed for each drainage renovation type dataset multiple times, so as to obtain multiple optimized cost estimation models. The model library creation module 930 is used to integrate multiple cost estimation models to obtain a cost estimation model library; The cost assessment module 940 is used to evaluate the current drainage renovation scheme based on the optimized cost estimation model in the cost estimation model library, and obtain the corresponding cost prediction value of the drainage system renovation scheme.

[0083] In some optional implementations, the cost function optimization module 920 is specifically used for: Based on each drainage renovation type dataset, the parameters of the respective cost estimation model are optimized multiple times until the optimized cost estimation model meets the set conditions. In each optimization process, outliers are removed using outlier removal methods based on drainage renovation case data in each drainage renovation type dataset. Based on the drainage renovation case data after outlier removal, the parameters of the corresponding cost estimation model are optimized using the least squares method.

[0084] In some optional implementations, the outlier removal unit 9201 in the cost function optimization module 920 is specifically used for: Based on the drainage renovation case data in each drainage renovation type dataset, the predicted construction cost of each drainage renovation case data is obtained using a nonlinear fitting method. Based on the predicted construction cost and actual construction cost of drainage renovation case data in each drainage renovation type dataset, the absolute error of drainage renovation case data in each drainage renovation type dataset is obtained using the absolute error calculation method. Based on the absolute error of the drainage renovation case data in each drainage renovation type dataset, outliers are filtered and removed using a pre-built outlier threshold for each drainage renovation type dataset, resulting in drainage renovation case data after outlier removal in each drainage renovation type dataset.

[0085] In some optional implementations, the construction of the outlier threshold in the outlier removal unit 9201 of the cost function optimization module 920 includes: Based on the absolute error of each drainage renovation case data in each drainage renovation type dataset, the mean absolute error of each drainage renovation type dataset is obtained by using the absolute error mean calculation method. Based on the absolute error of each drainage renovation case data in each drainage renovation type dataset, the absolute error standard deviation of each drainage renovation type dataset is obtained using the absolute error standard deviation calculation method. Based on the mean and standard deviation of the absolute error of each drainage renovation type dataset, and combined with preset adjustable parameters, the outlier threshold of each drainage renovation type dataset is obtained using an outlier threshold calculation method.

[0086] In some optional implementations, the cost estimation model constructed by the cost function optimization module 920 satisfies: , in, Indicates construction cost; This represents the scaling factor, which corresponds to the basic construction cost level per unit governance scale. Indicators representing the scale of governance, corresponding to the area or length of the remediation; This represents the scale elasticity coefficient, which corresponds to the marginal change trend of costs as the scale of governance changes. This refers to the fixed investment in a project, corresponding to basic expenses that are fixed expenditures regardless of the scale of the governance.

[0087] In some alternative implementations, it also includes: The cost model evaluation module 950 is used to evaluate each optimized cost estimation model using evaluation indicators to obtain the evaluation results of each optimized cost estimation model; the evaluation indicators include the coefficient of determination, root mean square error and / or mean absolute error.

[0088] In some optional implementations, the cost evaluation module 940 provided in this embodiment of the invention includes: Scheme division unit 9401 is used to divide the drainage renovation schemes to be evaluated according to the technology type, so as to obtain multiple drainage renovation types and corresponding renovation scales. The cost prediction unit 9402 is used to evaluate each type of drainage renovation and its corresponding renovation scale using the optimized cost estimation model in the cost estimation model library, and to obtain the cost prediction value for each type of drainage renovation. Cost output unit 9403 is used to synthesize the cost prediction values ​​of various drainage renovation types to obtain the cost prediction value of the corresponding drainage system renovation scheme.

[0089] The urban water system drainage renovation assessment device provided in this embodiment of the invention can execute the urban water system drainage renovation 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.

[0090] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0091] The following is a detailed reference. Figure 10 This diagram illustrates a suitable structural schematic 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, graphics processor, etc.) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from memory 1008 into random access memory (RAM) 1003. The RAM 1003 also stores various programs and data required for the operation of the electronic device. The processor 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0092] Typically, the following devices can be connected to the I / O interface 1005: input devices 1006 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 1007 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; memory devices 1008 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows electronic devices to exchange data via wireless or wired communication with other devices. Although Figure 10 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.

[0093] 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 1009, or installed from a memory 1008, or installed from a ROM 1002. When the computer program is executed by the processor 1001, it performs the functions defined in the urban water system drainage renovation assessment method of the embodiments of the present invention.

[0094] Figure 10 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.

[0095] 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 drainage renovation assessment method shown in the above embodiments is implemented.

[0096] 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.

[0097] 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 evaluating the renovation of urban water system drainage, characterized in that, The method includes: Based on the acquired drainage renovation case data, the data is classified according to technical type to obtain multiple drainage renovation type datasets; the drainage renovation case data includes renovation scale and actual construction cost, and renovation scale includes renovation area or pipeline renovation length. For each type of drainage renovation dataset, the parameters of the respective cost estimation model were optimized multiple times to obtain multiple optimized cost estimation models. By integrating multiple optimized cost estimation models, a cost estimation model library is obtained; Based on the current drainage renovation scheme to be evaluated, the optimized cost estimation model in the cost estimation model library is used for evaluation to obtain the corresponding cost prediction value of the drainage system renovation scheme. The cost estimation model satisfies: ， in, Indicates construction cost; This represents the scaling factor, which corresponds to the basic construction cost level per unit governance scale. Indicators representing the scale of governance, corresponding to the area or length of the remediation; This represents the scale elasticity coefficient, which corresponds to the marginal change trend of costs as the scale of governance changes. This refers to the fixed investment in a project, corresponding to basic expenses that are fixed expenditures regardless of the scale of the governance.

2. The method according to claim 1, characterized in that, For each type of drainage renovation dataset, the parameters of the constructed cost estimation model are optimized multiple times to obtain multiple optimized cost estimation models, including: Based on each drainage renovation type dataset, the parameters of the respective cost estimation model are optimized multiple times until the optimized cost estimation model meets the set conditions. In each optimization process, outliers are removed using outlier removal methods based on drainage renovation case data in each drainage renovation type dataset. Based on the drainage renovation case data after outlier removal, the parameters of the corresponding cost estimation model are optimized using the least squares method.

3. The method according to claim 2, characterized in that, The outlier removal method is used to remove outliers from the drainage renovation case data in each drainage renovation type dataset, including: Based on the drainage renovation case data in each drainage renovation type dataset, the predicted construction cost of each drainage renovation case data is obtained using a nonlinear fitting method. Based on the predicted construction cost and actual construction cost of drainage renovation case data in each drainage renovation type dataset, the absolute error of drainage renovation case data in each drainage renovation type dataset is obtained using the absolute error calculation method. Based on the absolute error of the drainage renovation case data in each drainage renovation type dataset, outliers are filtered and removed using a pre-built outlier threshold for each drainage renovation type dataset, resulting in drainage renovation case data after outlier removal in each drainage renovation type dataset.

4. The method according to claim 3, characterized in that, The construction of the outlier threshold includes: Based on the absolute error of each drainage renovation case data in each drainage renovation type dataset, the mean absolute error of each drainage renovation type dataset is obtained by using the absolute error mean calculation method. Based on the absolute error of each drainage renovation case data in each drainage renovation type dataset, the absolute error standard deviation of each drainage renovation type dataset is obtained using the absolute error standard deviation calculation method. Based on the mean and standard deviation of the absolute error of each drainage renovation type dataset, and combined with preset adjustable parameters, the outlier threshold of each drainage renovation type dataset is obtained using an outlier threshold calculation method.

5. The method according to claim 1, characterized in that, Also includes: Based on each optimized cost estimation model, evaluation indicators are used to evaluate them, and the evaluation results of each optimized cost estimation model are obtained. The evaluation metrics include the coefficient of determination, root mean square error, and / or mean absolute error.

6. The method according to claim 1, characterized in that, The process involves evaluating the current drainage system renovation scheme using an optimized cost estimation model from a cost estimation model library to obtain the predicted cost value of the corresponding scheme, including: Based on the current drainage renovation schemes to be evaluated, multiple drainage renovation types and corresponding renovation scales are obtained according to the technology type. Based on the various drainage renovation types and their corresponding renovation scales, the optimized cost estimation models in the cost estimation model library are used to evaluate and obtain the cost prediction values ​​for each drainage renovation type. By combining the cost forecasts of various drainage renovation types, the cost forecast of the corresponding drainage system renovation scheme is obtained.

7. A device for evaluating the renovation of urban water system drainage, characterized in that, The device includes: The data classification module is used to classify multiple drainage renovation case data according to technical type to obtain multiple drainage renovation type datasets; the drainage renovation case data includes renovation scale and actual construction cost, and renovation scale includes renovation area or pipeline renovation length; The cost function optimization module is used to optimize the parameters of the cost estimation model constructed for each type of drainage renovation dataset multiple times, resulting in multiple optimized cost estimation models. The model library creation module is used to integrate multiple cost estimation models to obtain a cost estimation model library. The cost assessment module is used to evaluate the current drainage system renovation plan based on the optimized cost estimation model in the cost estimation model library, and obtain the corresponding cost prediction value of the drainage system renovation plan.

8. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the urban water system drainage renovation assessment method according to any one of claims 1 to 6 by executing the computer instructions.

9. 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 drainage renovation assessment method according to any one of claims 1 to 6.

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