Drainage pipe network robust cleaning method and device, computer equipment and medium

Abstract

The invention discloses a stable cleaning method and device for a drainage pipe network, computer equipment and a medium, and belongs to the field of urban drainage pipe network sediment cleaning. The method comprises the following steps: constructing a random multi-criterion decision analysis framework for drainage pipe network sediment cleaning oriented to practical application, which is used for a robustness decision of drainage pipe network sediment cleaning under an uncertain condition; adopting a game theory method to aggregate and digest the weight of each decision-making standard for cleaning the sediment of the drainage pipe network, and estimating the uncertainty of each decision-making standard for cleaning the sediment of the drainage pipe network; constructing a drainage pipe network sediment cleaning random multi-criterion decision acceptability analysis-grey correlation analysis model, and quantitatively evaluating the uncertainty and decision error risk of the drainage pipe network sediment cleaning multi-criterion decision; and determining the influence of the uncertainty of the input parameters of each decision-making standard for cleaning the sediments of the drainage pipe network on the decision-making scheme for cleaning the sediments of the drainage pipe network by adopting a significance analysis method.

Description

Technical Field

[0001] The present invention belongs to the technical field of cleaning silt in urban drainage pipe networks, and in particular relates to a method, device, computer equipment and medium for robust cleaning of drainage pipe networks. Background Art

[0002] Urban drainage networks are one of the most important infrastructures in cities. Failures in drainage networks that affect their normal operation will have adverse effects on the environment and even endanger the health of urban residents. Obviously, the maintenance of urban drainage networks is crucial, and the daily operation and maintenance of drainage networks is also an important task for drainage companies. Sediment removal in drainage networks usually involves social, environmental, economic and even political impacts, as well as hydraulic and hydrological analysis. These interdependent effects between spatial and temporal relationships, especially the uncertainty of flow and velocity in different drainage pipes. Sediment removal decisions in drainage networks are usually guided by a large number of alternatives and decision criteria, and are measured in incommensurable units. In addition, decisions are often characterized by complex, interactive, uncertain outcomes and conflicts of interest among multiple stakeholders. Therefore, sediment removal in drainage networks requires the application of decision support tools that can consider stakeholders' views, trade-offs, goals, alternatives and numerous criteria.

[0003] Multi-criteria decision making is an umbrella term for a range of methods that can support management in problems involving multiple alternatives and criteria through a structured framework. The advantages of multi-criteria decision making are that it facilitates conflict resolution, the involvement of multiple stakeholders, group negotiation and collaborative decision making, making multi-criteria decision making a very suitable decision support tool for sewer network sediment removal.

[0004] Traditionally, multi-criteria decision-making methods applied to drainage network sediment removal are limited to deterministic or fuzzy environments, in which the relevant uncertainties are more or less ignored. In fact, drainage network sediment removal planners and managers always work in a changing and uncertain environment. For example, the flow rate and velocity of sewage in the drainage pipe are uncertain. Water demand and multi-purpose water supply may change over time and are difficult to accurately predict. In addition, the uncertainty of hydrological, environmental, economic, social and ecological data on which drainage network sediment removal decisions rely. All these uncertainties will eventually lead to uncertainty in the performance values ​​of decision criteria obtained by solving the optimization or simulation model of drainage network sediment removal. In addition, the weight of decision criteria is also considered to be a possible source of uncertainty in the multi-criteria decision-making process, mainly from the following three aspects. First, drainage network sediment removal decisions involve conflicts between multiple stakeholders with different backgrounds, views, responsibilities, interests and often competing goals. Second, in the weighting process, the subjective judgment and ambiguity of decision makers may lead to imprecise or uncertain decision criterion weight values. Finally, the aggregation or averaging of decision criterion weight values ​​from multiple decision makers is a source of additional uncertainty and leads to considerable information loss. Under this strategy, the true uncertainty of the decision criterion weight values ​​will be masked. In fact, in the multi-criteria decision-making modeling process, there is uncertainty in both the decision criterion performance preference value and the decision criterion weight value. The existence of uncertainty information does not make the decision-making of drainage network sediment removal easier, but ignoring this uncertain information will deviate from the actual situation. Including input parameter uncertainty (i.e., decision criterion performance preference value and decision criterion weight value) in the multi-criteria decision-making model for drainage network sediment removal helps to quantify the uncertainty generated in the model output and allows risk-informed drainage network sediment removal decisions to be made with higher reliability. Therefore, it is very important to track the sources of uncertainty in the multi-criteria decision-making analysis modeling process and evaluate the risks associated with the drainage network sediment removal decision.

[0005] However, the existing multi-criteria decision analysis methods for drainage network sediment removal cannot handle the impact of uncertainty in decision criterion performance preference values ​​and decision criterion weight values. Uncertain inputs of decision criterion performance preference values ​​and decision criterion weight values ​​will eventually lead to uncertainty in multi-criteria decision-making results, such as reversal of fixed ranks and decision risks. An important step to verify the stability of multi-criteria decision analysis results and determine the most critical input parameters is to perform sensitivity analysis. However, most of the existing multi-criteria decision analysis methods do not use any sensitivity analysis, thus ignoring the impact of changes in input parameters on model output.

[0006] In view of this, the present invention proposes a method, device, computer equipment and medium for robust cleaning of drainage pipe networks under uncertain conditions for cleaning silt in urban drainage pipe networks. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a method, device, computer equipment and medium for robust cleaning of a drainage network, which can determine the drainage pipes that need to be cleaned through multi-criteria decision analysis under uncertain conditions.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is: A method for robust cleaning of a drainage network, comprising the following steps: 1. Aiming at the problem of drainage network sediment cleaning under uncertain conditions, a stochastic multi-criteria decision analysis framework for drainage network sediment cleaning oriented to practical application is constructed to make robust decisions on drainage network sediment cleaning under uncertain conditions; 2. Establish the feasible rights space of the decision-making standards for the drainage network sediment cleaning, use the game theory method to aggregate and eliminate the weights of each decision-making standard for the drainage network sediment cleaning, and estimate the uncertainty of each decision-making standard for the drainage network sediment cleaning; 3. Combining the acceptability analysis theory of random multi-criteria decision-making and the grey correlation analysis theory, a random multi-criteria decision-making acceptability analysis-grey correlation analysis model for drainage network sediment cleaning is constructed, and an uncertainty evaluation index for drainage network sediment cleaning decision is proposed, which is used to quantitatively evaluate the uncertainty and decision-making error risk of multi-criteria decision-making for drainage network sediment cleaning; 4. For each decision-making standard for drainage network sediment cleaning, the significance analysis method is used to determine the impact of the uncertainty of the input parameters of each decision-making standard for drainage network sediment cleaning on the decision-making plan for drainage network sediment cleaning.

[0009] Furthermore, the step 1 is specifically as follows: Determine the key influencing factors of drainage network blockage and combine them with the actual monitoring status of the drainage network, collect long-term series monitoring data of various blockage influencing factors of the drainage network, check the quality of the monitoring data of various blockage influencing factors of the drainage network and clean the data to ensure the quality of the long-term series monitoring data of the drainage network is reliable; construct a multi-criteria decision analysis framework for drainage network sediment cleaning, and determine the initial solution set for drainage network sediment cleaning, the decision standard set for drainage network sediment cleaning, and the weight set of drainage network sediment cleaning decision standards according to the actual needs of the drainage network sediment cleaning site and the actual monitoring status of the drainage network. Considering the uncertainty of each decision standard for drainage network sediment cleaning, the deterministic drainage network sediment cleaning decision standard preference is converted into a random variable, a constant, or a mixture of the two through probability distribution. Further, the step 2 is specifically as follows: 2.1. According to the preferences of each drainage network sediment cleaning decision maker for different drainage network sediment cleaning decision criteria, the basic weight vector of drainage network sediment cleaning decision is obtained, and the weight vector is used ,in , Indicates the number of decision makers involved in making decisions about the removal of sediment from the drainage network; 2.2. Based on the basic weight vectors of drainage network sediment cleaning decision-making provided by different decision makers, the linear combination method is used to obtain the combined weight vector of the drainage network sediment cleaning decision-making standard, which is expressed as ; 2.3. In order to obtain the best drainage network sediment cleaning decision plan, the deviation between the drainage network sediment cleaning decision standard combination weight vector and the drainage network sediment cleaning decision basic weight vector provided by each decision maker should be minimized, that is, , and the coordinated weight vector of the decision-making criteria for clearing sediment in the drainage network is obtained; 2.4. In the process of solving the coordinated weight vector of the decision-making standard for clearing sediment in the drainage network, it is inevitable that the preference information of each decision maker will be lost. In order to quantitatively evaluate the uncertainty of the loss of preference information of each decision maker, the coordinated weight vector of the decision-making standard for clearing sediment in the drainage network is expanded from a single point to the entire feasible space of the decision-making standard for clearing sediment in the drainage network.

[0010] Furthermore, the step 3 is specifically as follows: 3.1. The grey system theory is used to aggregate the weight value and information preference of the drainage network sediment cleaning decision-making criteria into a quantitative grey correlation value, and a random multi-criteria decision-making-grey correlation analysis model for drainage network sediment cleaning is constructed to deal with the uncertainty of the drainage network sediment cleaning decision-making criteria; 3.2. Establish a random multi-criteria decision-making-grey correlation analysis evaluation index system for drainage network sediment cleaning, and evaluate and analyze the uncertainty of the decision results of the initial alternative plans for drainage network sediment cleaning. The evaluation index system mainly includes two indicators: ① the risk of decision-making errors in drainage network sediment cleaning; ② the uncertainty of the ranking of drainage network sediment cleaning plans.

[0011] Furthermore, the step 4 is specifically as follows: 4.1. Consider the uncertainty of the decision criteria and weights of drainage network sediment removal, conduct significance analysis on the drainage network sediment removal decision plan, and quantitatively evaluate the impact of input parameter uncertainty on the final drainage network sediment removal decision plan; 4.2. Calculate the Spearman rank correlation coefficient corresponding to each drainage network sediment cleaning decision standard in turn, and evaluate the impact of the uncertainty of the input parameters of different drainage network sediment cleaning decision standards on the drainage network sediment cleaning decision results.

[0012] Furthermore, the step 1.2 is specifically as follows: 1.2.1. The selection and determination of drainage network sediment cleaning schemes is considered as a multi-criteria decision analysis problem. Before cleaning each sedimentation pipe in the drainage network, the cleaning order of each drainage pipe is determined based on the long-term monitoring data of various influencing factors of the drainage network. A group of initial alternative schemes for drainage network sediment cleaning are randomly obtained. The scheme consists of The drainage network sediment removal program consists of , Indicates The initial alternative plan for cleaning sediment in a drainage network, including whether each drainage pipe in a drainage network should be cleaned and the cleaning order of each drainage pipe; 1.2.2. Select the key influencing factors of drainage network blockage as the decision criteria for multi-criteria decision analysis of drainage network sedimentation, which is used to evaluate and screen the alternative solutions for drainage network sedimentation cleaning. Define the decision criteria set for drainage network sedimentation cleaning as , including Decision criteria for clearing sediment from drainage networks, using represents the weight set of decision criteria for clearing sediment from the drainage network, Indicates the decision criteria for clearing sediment from drainage networks The corresponding weight. is the performance preference value of the decision criterion for clearing sediment in the drainage network, where Indicates alternative solutions for clearing sediment from drainage networks , Indicates the decision criteria for clearing sediment from drainage networks , the decision matrix for clearing sediment in the drainage network can be expressed as The multi-criteria decision analysis for clearing sediment in the drainage network satisfies the following relationship:

[0013] In the formula, is a function of the adopted decision model for clearing sediment from the drainage network; It is an alternative solution for drainage network sediment removal based on all drainage network sediment removal decision criteria. The ranking of the drainage network sediment removal alternatives can be determined based on the total value of the overall performance of the drainage network sediment removal alternatives, and the one with the greatest The alternative plan for clearing silt from the drainage network will be used as the final decision-making plan.

[0014] 1.2.3. Considering the uncertainty of preferences of decision makers involved in the drainage network sediment cleaning decision, the deterministic elements in the drainage network sediment cleaning decision matrix are represented by random elements, and the probability distribution method is used to convert the preference values ​​of each decision criterion for drainage network sediment cleaning into random variables, constants, or a mixture of the two. The random decision matrix for drainage network sediment cleaning is expressed as:

[0015] In the formula, represents the standard preference for random decision-making on the cleaning of silt in the drainage network, , .

[0016] Furthermore, the step 3.1 is specifically as follows: 3.1.1. Grey correlation analysis is used to solve the multi-criteria decision analysis problem of drainage network sediment cleaning. The weight values ​​of the drainage network sediment cleaning decision criteria and information preferences are aggregated into a quantified grey correlation value through grey system theory. The weighted sum of the grey correlation coefficients of the initial alternatives for the drainage network sediment cleaning decision is calculated, laying the foundation for the final decision of the drainage network sediment cleaning. 3.1.2. Construct a random multi-criteria decision-making-grey correlation analysis model for drainage network sediment cleaning, introduce the real-valued utility function of random multi-criteria decision-making analysis for drainage network sediment cleaning, obtain the ranking of the initial alternative plans for drainage network sediment cleaning, and evaluate and analyze the advantages and disadvantages of each initial alternative plan for drainage network sediment cleaning.

[0017] Furthermore, the step 3.2 is specifically as follows: 3.2.1 Risk of wrong decision-making in clearing silt from drainage pipe networks It is used to measure the uncertainty of each drainage network sediment cleaning decision-making scheme to obtain the highest ranking. When ranking and screening the drainage network sediment cleaning decision-making schemes, the decision makers involved in the drainage network sediment cleaning scheme decision-making are more concerned about the schemes with higher rankings. Considering the influence of the drainage network sediment cleaning decision-making criteria and their weight uncertainty, it may happen that the scheme with poorer performance obtains a higher ranking result, that is, deviates from the optimal solution, thus bringing adverse effects to the drainage network sediment cleaning. The risk of drainage network sediment cleaning decision-making error is defined as the weighted probability that the non-optimal drainage network sediment cleaning alternative scheme obtains the highest ranking:

[0018] In the formula, To obtain the first The first level acceptability indicator for alternative options for removing sediment from the sewer network. Defined as a risk weight to identify the contribution of each non-optimal sewer network sediment removal decision to the decision risk, is expressed as an increment and Dimensional vector: .

[0019] 3.2.2. Uncertainty of the sorting scheme for drainage network sediment removal It is used to measure the overall uncertainty of the ranking results of each drainage network sediment cleaning decision plan. The ranking uncertainty of the drainage network sediment cleaning plan is the sum of the level acceptability index of all possible rankings of the drainage network sediment cleaning decision plans except the final ranking, and the calculation formula is:

[0020] In the formula, An alternative solution for cleaning silt from the drainage network Decision Alternatives final level.

[0021] Furthermore, the step 3.1.1 is specifically as follows: 3.1.1.1. Define the reference set of decision criteria for clearing silt in drainage network for each decision criteria set for clearing silt in drainage network

[0022]

[0023] 3.1.1.2. Decision Matrix for Cleaning Sediment from Drainage Network Normalized to , reference set Also normalized to ; 3.1.1.3. Calculate the initial alternative solutions and normalized grey correlation coefficients for the silt removal of each drainage network:

[0024] In the formula, Initial alternative solution for clearing silt from sewer networks The normalized decision criterion preference vector of ; It is the identification coefficient of sediment cleaning in the drainage network, and its value is 0.5.

[0025] 3.1.1.4. Calculate the weighted sum of the grey correlation coefficients of the initial alternatives for drainage network sediment removal decision making. express:

[0026] In the formula, Initial alternatives for clearing sediment from the sewer network Regarding the global evaluation of all decision criteria, It provides a basis for the final decision-making plan for the cleaning of sediment in each drainage network, that is, the plan with the largest grey correlation degree is usually the more popular plan.

[0027] Furthermore, the step 3.1.2 is specifically as follows: 3.1.2.1. The real-valued utility function is used to evaluate the advantages and disadvantages of the random multi-criteria decision-making analysis scheme for the removal of sediment in the drainage network. The formula for calculating the real-valued utility function of random multi-criteria decision analysis for clearing sediment in drainage networks is:

[0028] In the formula, Represents the initial alternative plan for the drainage network sediment removal decision Performance preference vector for decision criteria.

[0029] In the random multi-criteria decision-making-grey correlation analysis model for drainage network sediment cleaning, the grey correlation algorithm is used to replace the real-valued utility function of the random multi-criteria decision-making analysis for drainage network sediment cleaning, and the following results can be obtained:

[0030] In the formula, the function Grey correlation degree of each initial alternative plan for drainage network sediment removal decision.

[0031] 3.1.2.2. The drainage network sediment cleaning ranking function is used to determine the ranking of the initial alternatives for drainage network sediment cleaning. The initial alternatives for drainage network sediment cleaning are ranked from the best (ranked 1) to the worst (ranked ). The calculation formula for the initial alternative scheme ranking of silt removal in each drainage network is:

[0032] In the formula, Alternative solutions for clearing sediment from sewer networks Stochastic multi-criteria decision analysis decision criteria weight performance preference vector, , ; is a Gra-type utility function.

[0033] 3.1.2.3. Run the random multi-criteria decision-making-grey correlation analysis model for drainage network sediment cleaning, calculate and output 5 drainage network sediment cleaning decision evaluation indicators, and provide a basis for the final decision of drainage network sediment cleaning. The drainage network sediment cleaning decision evaluation indicators include: ① drainage network sediment cleaning decision scheme grade acceptability index (RAI); ② drainage network sediment cleaning decision scheme overall acceptability index (HAI); ③ drainage network sediment cleaning decision scheme center weight vector (CWV); ④ drainage network sediment cleaning decision scheme confidence factor; ⑤ drainage network sediment cleaning decision scheme cross confidence factor.

[0034] The acceptability index (RAI) of drainage network sediment removal decision options is used express, It represents the expected volume of the favorable ranking weight set of each initial alternative plan for the drainage network sediment cleaning decision, which is mainly used to measure the initial alternative plan that leads to the sediment cleaning of each drainage network. Diversity of valuations at different ranking levels. Calculated as the performance preference distribution of the decision criteria for drainage network sediment removal and Multidimensional integral on :

[0035] Obviously, the value range of the acceptability index of the drainage network sediment removal decision-making plan is , where 0 means that the initial alternatives for drainage network sediment cleaning decision cannot obtain a given rank, and 1 means that any combination of drainage network sediment cleaning decision criteria weights can always obtain a given rank. If a drainage network sediment cleaning decision initial alternative obtains the best ranking and has a large RAI, then the drainage network sediment cleaning is considered to be an acceptable solution, while the drainage network sediment cleaning alternatives with poor ranking and large RAI should be eliminated from the drainage network sediment cleaning alternatives set.

[0036] The overall acceptability index (HAI) of the decision-making plan for drainage network sediment removal is used express, It is used to check the overall acceptability of each initial alternative plan for drainage network sediment removal decision, which is defined as the weighted sum of the acceptability index of all drainage network sediment removal decision plans. The calculation formula is:

[0037] in, The meta-weights representing the decision-making criteria for drainage network sediment removal reflect the contribution of the grade acceptability index of each drainage network sediment removal decision-making scheme to the evaluation of the initial alternative schemes for drainage network sediment removal decision-making. Defined as a monotonically decreasing vector , to simulate the situation where the best ranking of drainage network sediment removal decisions is better than the worst ranking.

[0038] The central weight vector (CWV) of the drainage network sediment removal decision plan is used express, Decision making on initial alternatives for all sewer network silt removal The expected center of gravity of the favorable first-level weight space. The preference information indicating support for the corresponding initial alternatives for drainage network sediment removal helps decision makers understand how different weights are associated with different decisions and facilitates the weight assignment of drainage network sediment removal decision criteria. Calculated as the distribution of performance preference values ​​for the decision criteria for clearing sediment from the drainage network and favorable first-level weights Multidimensional integral of:

[0039] Confidence factor of drainage network sediment removal decision plan express, is the probability of the most favored initial alternative plan for drainage network sediment cleaning based on its own drainage network sediment cleaning decision plan center weight vector. It is a measure of whether the standard data is accurate enough to identify the initial alternatives for drainage network sediment removal and can be regarded as the proportion of the random criterion space that leads to the best drainage network sediment removal alternative. Calculated as the multidimensional integral of the performance preference distribution of the decision criteria for drainage network sediment removal:

[0040] If the confidence factor of the drainage network sediment cleaning decision plan is small, it means that even if the decision standard performance preference value is adopted, the drainage network sediment cleaning decision initial alternative is unlikely to be considered the most popular solution. On the contrary, if the confidence factor of the drainage network sediment cleaning decision plan is large, it is considered that the plan has appropriate preference information, and the drainage network sediment cleaning decision plan is usually the most popular solution.

[0041] The cross confidence factor of the drainage network sediment cleaning decision-making scheme is mainly used to improve the discrimination ability of the drainage network sediment cleaning random multi-criteria decision-making-grey correlation analysis model for similar schemes. express. It is calculated from the decision criteria weight preference values ​​of other drainage network sediment cleaning decision plans. Options relative to drainage network sediment removal targets The cross confidence coefficient is expressed as:

[0042] The cross confidence factor of the drainage network sediment cleaning decision plan is mainly to measure the probability of the drainage network sediment cleaning decision alternatives to obtain the best ranking when the decision criterion weight performance preference of the target drainage network sediment cleaning decision alternatives is used. Obviously, if the cross confidence factor If the value of is not 0, it means that the drainage network sediment cleaning decision alternative Will make alternative plans with the drainage network sediment removal decision Competing for best sorting, non-zero crossing confidence factor Indicates the intensity of competition. At the same time, the cross confidence factor Equal to the confidence factor .

[0043] The present invention also provides a device for robustly cleaning a drainage pipe network under uncertain conditions, the device comprising: The module for constructing a random multi-criteria decision analysis framework for drainage network sediment cleaning is used to build a random multi-criteria decision analysis framework for drainage network sediment cleaning for practical applications, and to make robust decisions on drainage network sediment cleaning under uncertain conditions; The module for estimating the uncertainty of the decision-making criteria for clearing sediment in the drainage network is used to establish the feasible space of the decision-making criteria for clearing sediment in the drainage network, aggregate and eliminate the weights of each decision-making criterion for clearing sediment in the drainage network by using the game theory method, and estimate the uncertainty of each decision-making criterion for clearing sediment in the drainage network; The multi-criteria decision analysis module for drainage network sediment cleaning is used to combine the random multi-criteria decision acceptability analysis theory and the grey correlation analysis theory to construct a random multi-criteria decision acceptability analysis-grey correlation analysis model for drainage network sediment cleaning, and proposes a drainage network sediment cleaning decision uncertainty assessment index, which is used to quantitatively assess the uncertainty and decision-making error risk of multi-criteria decision-making for drainage network sediment cleaning; The uncertainty determination module of the multi-criteria decision analysis for drainage network sediment cleaning is used to determine the impact of the uncertainty of the input parameters of each decision criterion for drainage network sediment cleaning on the decision plan for drainage network sediment cleaning by using the significance analysis method.

[0044] The present invention also provides a computer device, including a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the steps of the method for robust cleaning of a drainage network under uncertain conditions of the first aspect or any embodiment of the first aspect by executing the computer instructions.

[0045] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for robustly cleaning a drainage network under uncertain conditions of the first aspect or any embodiment of the first aspect are implemented.

[0046] The present invention can achieve the following beneficial effects: 1. By constructing a stochastic multi-criteria decision analysis framework for practical applications, the present invention can provide robust decision support for the cleaning of silt in drainage pipe networks under uncertain conditions, ensuring that reasonable decisions can still be made in the face of uncertain factors.

[0047] 2. The present invention adopts game theory methods to aggregate and eliminate the weights of various decision criteria and estimate the uncertainty of each decision criterion, thereby more scientifically dealing with the problems of uncertainty and loss of preference information in the decision-making process.

[0048] 3. The present invention combines the acceptability analysis theory of random multi-criteria decision-making and the grey correlation analysis theory to construct a new analysis model, proposes indicators for quantitatively evaluating decision uncertainty and error risk, and improves the accuracy and reliability of decision-making solutions.

[0049] 4. The present invention clarifies the impact of different decision criteria on the final solution through significance analysis of input parameter uncertainty, which helps to optimize resource allocation and ensure that key influencing factors are fully considered.

[0050] 5. The present invention makes the entire decision-making process more transparent and enhances the credibility and acceptance of decision-making results by calculating evaluation indicators such as decision-making error risk and ranking uncertainty.

[0051] 6. The computer device and storage medium provided by the present invention allow the above method steps to be automatically executed, reducing manual intervention, improving work efficiency, and can be promoted and used on a larger scale.

[0052] 7. The present invention is particularly suitable for drainage network maintenance work in complex environments. It takes into account multiple influencing factors and different decision-maker preferences, and is therefore more adaptable to the diverse needs of the real world. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The present invention will be further described below in conjunction with the accompanying drawings and embodiments: Figure 1A flow chart of a method for robust cleaning of a drainage network proposed according to an embodiment of the drainage network; Figure 2 A schematic diagram of an executable space of a drainage network robust cleaning method proposed according to an embodiment of the drainage network; Figure 3 It is a structural schematic diagram of a robust cleaning device for a drainage network proposed according to an embodiment of the drainage network; Figure 4 The figure is a schematic diagram of the hardware structure of a computer device proposed according to a drainage network embodiment. DETAILED DESCRIPTION

[0054] The preferred solution is Figures 1 to 4 As shown in the figure, a method for robust cleaning of drainage pipe networks combines random multi-criteria acceptability analysis theory with grey correlation analysis, uses decision error risk and rank uncertainty to evaluate decision uncertainty, and uses significance analysis to test the impact of input parameter uncertainty on the final evaluation of the scheme. The specific steps are: 1. Aiming at the problem of drainage network sediment cleaning under uncertain conditions, a stochastic multi-criteria decision analysis framework for drainage network sediment cleaning oriented to practical application is constructed to make robust decisions on drainage network sediment cleaning under uncertain conditions; Determine the key influencing factors of drainage network blockage and combine them with the actual monitoring status of the drainage network, collect long-term monitoring data of various blockage influencing factors of the drainage network, check the quality of the monitoring data of various blockage influencing factors of the drainage network and clean the data to ensure the quality of the long-term monitoring data of the drainage network. Construct a multi-criteria decision analysis framework for drainage network sediment cleaning, determine the initial scheme set, drainage network sediment cleaning decision standard set and drainage network sediment cleaning decision standard weight set according to the actual needs of the drainage network sediment cleaning site and the actual monitoring status of the drainage network, consider the uncertainty of each decision standard for drainage network sediment cleaning, and convert the deterministic drainage network sediment cleaning decision standard preference into a random variable, a constant or a mixture of the two through probability distribution. The selection and determination of the drainage network sediment cleaning scheme is regarded as a multi-criteria decision analysis problem. Before each cleaning of the silted pipes in the drainage network, determine whether each drainage pipe should be cleaned and the cleaning order according to the long-term monitoring data of various determined influencing factors of the drainage network. For the drainage network in the embodiment, a group of initial alternative solutions for cleaning the silt in the drainage network is randomly obtained, and the group of solutions consists of The drainage network sediment removal program consists of , Indicates The initial alternative plan for cleaning sediment in a drainage network, including whether each drainage pipe in a drainage network should be cleaned and the cleaning order of each drainage pipe; The key influencing factors of drainage network blockage are selected as the decision criteria for the multi-criteria decision analysis of drainage network sedimentation, which is used to evaluate and screen the alternative solutions for drainage network sedimentation cleaning. For the drainage network in the embodiment, the decision criteria set for drainage network sedimentation cleaning is defined as , including Decision criteria for clearing sediment from drainage networks, using represents the weight set of decision criteria for clearing sediment from the drainage network, Indicates the decision criteria for clearing sediment from drainage networks The corresponding weight. is the performance preference value of the decision criterion for clearing sediment in the drainage network, where Indicates alternative solutions for clearing sediment from drainage networks , Indicates the decision criteria for clearing sediment from drainage networks , the decision matrix for clearing sediment in the drainage network can be expressed as The multi-criteria decision analysis for clearing sediment in the drainage network satisfies the following relationship:

[0055] In the formula, is a function of the adopted decision model for clearing sediment from the drainage network; It is an alternative solution for drainage network sediment removal based on all drainage network sediment removal decision criteria. The ranking of the drainage network sediment removal alternatives can be determined based on the total value of the overall performance of the drainage network sediment removal alternatives, and the one with the greatest The alternative plan for clearing silt from the drainage network will be used as the final decision-making plan.

[0056] 1.2.3. Considering the uncertainty of preferences of decision makers involved in the drainage network sediment cleaning decision, the deterministic elements in the drainage network sediment cleaning decision matrix are represented by random elements, and the probability distribution method is used to convert the preference values ​​of each decision criterion for drainage network sediment cleaning into random variables, constants, or a mixture of the two. The random decision matrix for drainage network sediment cleaning is expressed as:

[0057] In the formula, represents the standard preference for random decision-making on the cleaning of silt in the drainage network, , .

[0058] 2. If Figure 2As shown in the figure, the feasible space of decision-making standards for drainage network sediment cleaning is established, and the game theory method is used to aggregate and eliminate the weights of various decision-making standards for drainage network sediment cleaning, and the uncertainty of various decision-making standards for drainage network sediment cleaning is estimated; 2.1. According to the preferences of each drainage network sediment cleaning decision maker for different drainage network sediment cleaning decision criteria, the basic weight vector of drainage network sediment cleaning decision is obtained.

[0059] For the drainage network in the embodiment, it is assumed that the number of decision makers involved in the decision-making of drainage network sediment cleaning is 5. ,in , Indicates the number of decision makers involved in making decisions about the removal of sediment from the drainage network; 2.2. Based on the basic weight vectors of drainage network sediment cleaning decision-making provided by different decision makers, the linear combination method is used to obtain the combined weight vector of the drainage network sediment cleaning decision-making standard, which is expressed as ; 2.3. In order to obtain the best drainage network sediment cleaning decision plan, the deviation between the drainage network sediment cleaning decision standard combination weight vector and the drainage network sediment cleaning decision basic weight vector provided by each decision maker should be minimized, that is, , and the coordinated weight vector of the decision-making criteria for clearing sediment in the drainage network is obtained; 2.4. In the process of solving the coordinated weight vector of the decision-making standard for clearing sediment in the drainage network, it is inevitable that the preference information of each decision maker will be lost. In order to quantitatively evaluate the uncertainty of the loss of preference information of each decision maker, the coordinated weight vector of the decision-making standard for clearing sediment in the drainage network is expanded from a single point to the entire feasible space of the decision-making standard for clearing sediment in the drainage network.

[0060] 3. Combining the acceptability analysis theory of random multi-criteria decision-making and the grey correlation analysis theory, a random multi-criteria decision-making acceptability analysis-grey correlation analysis model for drainage network sediment cleaning is constructed, and an uncertainty evaluation index for drainage network sediment cleaning decision is proposed, which is used to quantitatively evaluate the uncertainty and decision-making error risk of multi-criteria decision-making for drainage network sediment cleaning; 3.1. The grey system theory is used to aggregate the weight value and information preference of the drainage network sediment cleaning decision-making criteria into a quantitative grey correlation value, and a random multi-criteria decision-making-grey correlation analysis model for drainage network sediment cleaning is constructed to deal with the uncertainty of the drainage network sediment cleaning decision-making criteria; 3.1.1. Grey correlation analysis is used to solve the multi-criteria decision analysis problem of drainage network sediment cleaning. The weight values ​​of the drainage network sediment cleaning decision criteria and information preferences are aggregated into a quantified grey correlation value through grey system theory. The weighted sum of the grey correlation coefficients of the initial alternatives for the drainage network sediment cleaning decision is calculated, laying the foundation for the final decision of the drainage network sediment cleaning. 3.1.1.1. Define the reference set of decision criteria for clearing silt in drainage network for each decision criteria set for clearing silt in drainage network

[0061]

[0062] 3.1.1.2. Decision Matrix for Cleaning Sediment from Drainage Network Normalized to , reference set Also normalized to ; 3.1.1.3. Calculate the initial alternative solutions and normalized grey correlation coefficients for the silt removal of each drainage network:

[0063] In the formula, Initial alternative solution for clearing silt from sewer networks The normalized decision criterion preference vector of ; It is the identification coefficient of sediment cleaning in the drainage network, and its value is 0.5.

[0064] 3.1.1.4. Calculate the weighted sum of the grey correlation coefficients of the initial alternatives for drainage network sediment removal decision making. express:

[0065] In the formula, Initial alternatives for clearing sediment from the sewer network Regarding the global evaluation of all decision criteria, It provides a basis for the final decision-making plan for the cleaning of sediment in each drainage network, that is, the plan with the largest grey correlation degree is usually the more popular plan.

[0066] 3.1.2. Construct a random multi-criteria decision-making-grey correlation analysis model for drainage network sediment cleaning, introduce the real-valued utility function of random multi-criteria decision-making analysis for drainage network sediment cleaning, obtain the ranking of the initial alternative plans for drainage network sediment cleaning, and evaluate and analyze the advantages and disadvantages of each initial alternative plan for drainage network sediment cleaning.

[0067] 3.1.2.1. The real-valued utility function is used to evaluate the advantages and disadvantages of the random multi-criteria decision-making analysis scheme for the removal of sediment in the drainage network. The formula for calculating the real-valued utility function of random multi-criteria decision analysis for clearing sediment in drainage networks is:

[0068] In the formula, Represents the initial alternative plan for the drainage network sediment removal decision Performance preference vector for decision criteria.

[0069] In the random multi-criteria decision-making-grey correlation analysis model for drainage network sediment cleaning, the grey correlation algorithm is used to replace the real-valued utility function of the random multi-criteria decision-making analysis for drainage network sediment cleaning, and the following results can be obtained:

[0070] In the formula, the function Grey correlation degree of each initial alternative plan for drainage network sediment removal decision.

[0071] 3.1.2.2. The drainage network sediment cleaning ranking function is used to determine the ranking of the initial alternatives for drainage network sediment cleaning. The initial alternatives for drainage network sediment cleaning are ranked from the best (ranked 1) to the worst (ranked ). The calculation formula for the initial alternative scheme ranking of silt removal in each drainage network is:

[0072] In the formula, Alternative solutions for clearing sediment from sewer networks Stochastic multi-criteria decision analysis decision criteria weight performance preference vector, , ; is a Gra-type utility function.

[0073] 3.1.2.3. Run the random multi-criteria decision-making-grey correlation analysis model for drainage network sediment cleaning, calculate and output 5 drainage network sediment cleaning decision evaluation indicators, and provide a basis for the final decision of drainage network sediment cleaning. The drainage network sediment cleaning decision evaluation indicators include: ① drainage network sediment cleaning decision scheme grade acceptability index (RAI); ② drainage network sediment cleaning decision scheme overall acceptability index (HAI); ③ drainage network sediment cleaning decision scheme center weight vector (CWV); ④ drainage network sediment cleaning decision scheme confidence factor; ⑤ drainage network sediment cleaning decision scheme cross confidence factor.

[0074] The acceptability index (RAI) of drainage network sediment removal decision options is used express, It represents the expected volume of the favorable ranking weight set of each initial alternative plan for the drainage network sediment cleaning decision, which is mainly used to measure the initial alternative plan that leads to the sediment cleaning of each drainage network. Diversity of valuations at different ranking levels. Calculated as the performance preference distribution of the decision criteria for drainage network sediment removal and Multidimensional integral on :

[0075] Obviously, the value range of the acceptability index of the drainage network sediment removal decision-making plan is , where 0 means that the initial alternatives for drainage network sediment cleaning decision cannot obtain a given rank, and 1 means that any combination of drainage network sediment cleaning decision criteria weights can always obtain a given rank. If a drainage network sediment cleaning decision initial alternative obtains the best ranking and has a large RAI, then the drainage network sediment cleaning is considered to be an acceptable solution, while the drainage network sediment cleaning alternatives with poor ranking and large RAI should be eliminated from the drainage network sediment cleaning alternatives set.

[0076] The overall acceptability index (HAI) of the decision-making plan for drainage network sediment removal is used express, It is used to check the overall acceptability of each initial alternative plan for drainage network sediment removal decision, which is defined as the weighted sum of the acceptability index of all drainage network sediment removal decision plans. The calculation formula is:

[0077] in, The meta-weights representing the decision-making criteria for drainage network sediment removal reflect the contribution of the grade acceptability index of each drainage network sediment removal decision-making scheme to the evaluation of the initial alternative schemes for drainage network sediment removal decision-making. Defined as a monotonically decreasing vector , to simulate the situation where the best ranking of drainage network sediment removal decisions is better than the worst ranking.

[0078] The central weight vector (CWV) of the drainage network sediment removal decision plan is used express, Decision making on initial alternatives for all sewer network silt removal The expected center of gravity of the favorable first-level weight space. The preference information indicating support for the corresponding initial alternatives for drainage network sediment removal helps decision makers understand how different weights are associated with different decisions and facilitates the weight assignment of drainage network sediment removal decision criteria. Calculated as the distribution of performance preference values ​​for the decision criteria for clearing sediment from the drainage network and favorable first-level weights Multidimensional integral of:

[0079] Confidence factor of drainage network sediment removal decision plan express, is the probability of the most favored initial alternative plan for drainage network sediment cleaning based on its own drainage network sediment cleaning decision plan center weight vector. It is a measure of whether the standard data is accurate enough to identify the initial alternatives for drainage network sediment removal and can be regarded as the proportion of the random criterion space that leads to the best drainage network sediment removal alternative. Calculated as the multidimensional integral of the performance preference distribution of the decision criteria for drainage network sediment removal:

[0080] If the confidence factor of the drainage network sediment cleaning decision plan is small, it means that even if the decision standard performance preference value is adopted, the drainage network sediment cleaning decision initial alternative is unlikely to be considered the most popular solution. On the contrary, if the confidence factor of the drainage network sediment cleaning decision plan is large, it is considered that the plan has appropriate preference information, and the drainage network sediment cleaning decision plan is usually the most popular solution.

[0081] The cross confidence factor of the drainage network sediment cleaning decision-making scheme is mainly used to improve the discrimination ability of the drainage network sediment cleaning random multi-criteria decision-making-grey correlation analysis model for similar schemes. express. It is calculated from the decision criteria weight preference values ​​of other drainage network sediment cleaning decision plans. Options relative to drainage network sediment removal targets The cross confidence coefficient is expressed as:

[0082] The cross confidence factor of the drainage network sediment cleaning decision plan is mainly to measure the probability of the drainage network sediment cleaning decision alternatives to obtain the best ranking when the decision criterion weight performance preference of the target drainage network sediment cleaning decision alternatives is used. Obviously, if the cross confidence factor If the value of is not 0, it means that the drainage network sediment cleaning decision alternative Will make alternative plans with the drainage network sediment removal decision Competing for best sorting, non-zero crossing confidence factor Indicates the intensity of competition. At the same time, the cross confidence factor Equal to the confidence factor .

[0083] 3.2. Establish a random multi-criteria decision-making-grey correlation analysis evaluation index system for drainage network sediment cleaning, and evaluate and analyze the uncertainty of the decision results of the initial alternative plans for drainage network sediment cleaning. The evaluation index system mainly includes two indicators: ① the risk of decision-making errors in drainage network sediment cleaning; ② the uncertainty of the ranking of drainage network sediment cleaning plans.

[0084] 3.2.1 Risk of wrong decision-making in clearing silt from drainage pipe networks It is used to measure the uncertainty of each drainage network sediment cleaning decision-making scheme to obtain the highest ranking. When ranking and screening the drainage network sediment cleaning decision-making schemes, the decision makers involved in the drainage network sediment cleaning scheme decision-making are more concerned about the schemes with higher rankings. Considering the influence of the drainage network sediment cleaning decision-making criteria and their weight uncertainty, it may happen that the scheme with poorer performance obtains a higher ranking result, that is, deviates from the optimal solution, thus bringing adverse effects to the drainage network sediment cleaning. The risk of drainage network sediment cleaning decision-making error is defined as the weighted probability that the non-optimal drainage network sediment cleaning alternative scheme obtains the highest ranking:

[0085] In the formula, To obtain the first The first level acceptability indicator for alternative options for removing sediment from the sewer network. Defined as a risk weight to identify the contribution of each non-optimal sewer network sediment removal decision to the decision risk, is expressed as an increment and Dimensional vector: .

[0086] 3.2.2. Uncertainty of the sorting scheme for drainage network sediment removal It is used to measure the overall uncertainty of the ranking results of each drainage network sediment cleaning decision plan. The ranking uncertainty of the drainage network sediment cleaning plan is the sum of the level acceptability index of all possible rankings of the drainage network sediment cleaning decision plans except the final ranking, and the calculation formula is:

[0087] In the formula, An alternative solution for cleaning silt from the drainage network Decision Alternatives final level.

[0088] 4. For each decision-making standard for drainage network sediment cleaning, the significance analysis method is used to determine the impact of the uncertainty of the input parameters of each decision-making standard for drainage network sediment cleaning on the decision-making plan for drainage network sediment cleaning.

[0089] 4.1. Consider the uncertainty of the decision criteria and weights of drainage network sediment removal, conduct significance analysis on the drainage network sediment removal decision plan, and quantitatively evaluate the impact of input parameter uncertainty on the final drainage network sediment removal decision plan; 4.2. Calculate the Spearman rank correlation coefficient corresponding to each drainage network sediment cleaning decision standard in turn, and evaluate the impact of the uncertainty of the input parameters of different drainage network sediment cleaning decision standards on the drainage network sediment cleaning decision results.

[0090] Based on the same inventive concept, the present invention also provides a robust cleaning device for a drainage network under uncertain conditions, such as Figure 3 As shown, the device comprises: The module 201 for constructing a random multi-criteria decision analysis framework for cleaning out sediment in drainage networks is used to construct a random multi-criteria decision analysis framework for cleaning out sediment in drainage networks for practical applications, and to make robust decisions on cleaning out sediment in drainage networks under uncertain conditions; for details, see the description of step 1 in the above embodiment.

[0091] The drainage network sediment cleaning decision criterion uncertainty estimation module 202 is used to establish the feasible weight space of the drainage network sediment cleaning decision criterion, adopt the game theory method to aggregate and eliminate the weights of each decision criterion for drainage network sediment cleaning, and estimate the uncertainty of each decision criterion for drainage network sediment cleaning; for details, please refer to the description of step 2 in the above embodiment.

[0092] The multi-criteria decision analysis module 203 for clearing sediment from drainage network is used to combine the random multi-criteria decision acceptability analysis theory and the grey correlation analysis theory to construct a random multi-criteria decision acceptability analysis-grey correlation analysis model for clearing sediment from drainage network, and propose an uncertainty assessment index for the decision on clearing sediment from drainage network, which is used to quantitatively assess the uncertainty and decision-making error risk of the multi-criteria decision on clearing sediment from drainage network; for details, see the description of step 3 in the above embodiment.

[0093] The uncertainty determination module 204 of the multi-criteria decision analysis for drainage network silt cleaning is used to determine the impact of the uncertainty of the input parameters of each decision criterion for drainage network silt cleaning on the decision plan for drainage network silt cleaning by using a significance analysis method for each decision criterion for drainage network silt cleaning; for details, please refer to the description of step 4 in the above embodiment.

[0094] For the drainage network embodiment, the drainage network sediment cleaning random multi-criteria decision analysis framework construction module 201 includes: The submodule for collecting and analyzing long-term series data of factors affecting blockage of drainage pipe network by sediment is used to obtain and analyze long-term series monitoring data of various blockage factors of drainage pipe network; for details, please refer to the description in the above embodiment.

[0095] The drainage network sediment cleaning multi-criteria decision analysis framework sub-module is used to construct a drainage network sediment cleaning multi-criteria decision analysis framework; for details, please refer to the description in the above embodiment.

[0096] For the drainage network embodiment, the drainage network sediment removal decision criterion uncertainty estimation module 202 includes: The drainage network silt cleaning decision preference setting submodule is used to obtain the drainage network silt cleaning decision basic weight vector according to the preferences of each drainage network silt cleaning decision maker for different drainage network silt cleaning decision criteria; for details, please refer to the description in the above embodiment.

[0097] The submodule for determining the combined weight vector of the decision criteria for clearing sediment from the drainage network is used to obtain the combined weight vector of the decision criteria for clearing sediment from the drainage network by using a linear combination method based on the basic weight vectors for the decision criteria for clearing sediment from the drainage network provided by different decision makers; for details, please refer to the description in the above embodiment.

[0098] The drainage network sediment cleaning decision coordination weight vector solving submodule is used to solve the drainage network sediment cleaning decision standard coordination weight vector; for details, please refer to the description in the above embodiment.

[0099] The drainage network silt cleaning decision coordination weight vector expansion submodule is used to expand the drainage network silt cleaning decision standard coordination weight vector from a single point to the entire drainage network silt cleaning decision standard feasible space; for details, please refer to the description in the above embodiment.

[0100] For the drainage network embodiment, the drainage network sediment cleaning multi-criteria decision analysis module 203 includes: The random multi-criteria decision-making-grey correlation analysis model construction submodule for drainage network silt cleaning is used to construct a random multi-criteria decision-making-grey correlation analysis model for drainage network silt cleaning, and to deal with the uncertainty of the decision-making criteria for drainage network silt cleaning; for details, please refer to the description in the above embodiment.

[0101] The uncertainty assessment and analysis submodule of the multi-criteria decision-making for clearing sediment from the drainage network is used to assess and analyze the uncertainty of the decision results of the initial alternative plans for clearing sediment from the drainage network; for details, please refer to the description in the above embodiment.

[0102] For the drainage network embodiment, the drainage network sediment cleaning multi-criteria decision analysis uncertainty determination module 204 includes: The drainage network sediment cleaning decision-making scheme significance analysis submodule is used to perform significance analysis on the drainage network sediment cleaning decision-making scheme, and quantitatively evaluate the impact of input parameter uncertainty on the final drainage network sediment cleaning decision-making scheme; for details, please refer to the description in the above embodiment.

[0103] The drainage network sediment cleaning decision input parameter uncertainty analysis submodule is used to calculate the Spearman rank correlation coefficient corresponding to each drainage network sediment cleaning decision standard, and evaluate the impact of different drainage network sediment cleaning decision standard input parameter uncertainty on the drainage network sediment cleaning decision result; for details, please refer to the description in the above embodiment.

[0104] The specific definition and beneficial effects of the above device can refer to the definition of the robust cleaning method of the drainage network under uncertain conditions above. The above modules can be implemented in whole or in part by software, hardware and their combination. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0105] Figure 4 This is a hardware structure diagram of a computer device proposed according to the drainage network embodiment. Figure 4 As shown, the device includes one or more processors 310 and a memory 320, and the memory 320 includes a persistent memory, a volatile memory, and a hard disk. Figure 4A processor 310 is taken as an example. The device may also include: an input device 330 and an output device 340.

[0106] The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0107] The processor 310 may be a central processing unit (CPU). The processor 310 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips. A general-purpose processor may be a microprocessor or the processor may be any conventional processor.

[0108] The memory 320 is a non-transient computer-readable storage medium, including persistent memory, volatile memory and hard disk, and can be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to the robust cleaning method of the drainage network under uncertainty conditions in the embodiment of the present application. The processor 310 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 320, that is, to implement any of the above-mentioned robust cleaning methods for drainage networks under uncertainty conditions.

[0109] The memory 320 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required by at least one function; the data storage area may store data required for use, etc. In addition, the memory 320 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 320 may optionally include a memory remotely arranged relative to the processor 310, and these remote memories may be connected to the data processing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0110] The input device 330 can receive input digital or character information and generate signal input related to user settings and function control. The output device 340 can include display devices such as display screens.

[0111] One or more modules are stored in the memory 320, and when executed by one or more processors 310, the execution is as follows: Figure 1 The method shown.

[0112] The above-mentioned product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to Figure 1 Related description of the illustrated embodiment.

[0113] The embodiment of the present invention also provides a non-transient computer storage medium, which stores computer executable instructions, and the computer executable instructions can execute the robust cleaning method of the drainage network under uncertainty conditions in any of the above method embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memory.

[0114] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for robust cleaning of a drainage network, characterized in that The following steps are involved: S1. Aiming at the problem of drainage network sediment cleaning under uncertainty conditions, a stochastic multi-criteria decision analysis framework for drainage network sediment cleaning for practical application is constructed to make robust decisions for drainage network sediment cleaning under uncertainty conditions. S2. Establish the feasible space of decision-making standards for drainage network sediment cleaning, use game theory methods to aggregate and eliminate the weights of various decision-making standards for drainage network sediment cleaning, and estimate the uncertainty of various decision-making standards for drainage network sediment cleaning; S3. Combining the acceptability analysis theory of random multi-criteria decision-making and the grey correlation analysis theory, a random multi-criteria decision-making acceptability analysis-grey correlation analysis model for drainage network sediment cleaning is constructed, and an uncertainty evaluation index for drainage network sediment cleaning decision is proposed, which is used to quantitatively evaluate the uncertainty and decision-making error risk of multi-criteria decision-making for drainage network sediment cleaning; S4. For each decision-making criterion for clearing sediment in the drainage network, a significance analysis method is used to determine the impact of the uncertainty of the input parameters of each decision-making criterion for clearing sediment in the drainage network on the decision-making plan for clearing sediment in the drainage network.

2. A method for robust cleaning of a drainage network according to claim 1, characterized in that: The sub-steps of S1 are: S1.

1. Determine the key factors affecting the blockage of the drainage network and collect the long-term monitoring data of various factors affecting the blockage of the drainage network in combination with the actual monitoring status of the drainage network. Check the quality of the monitoring data of various factors affecting the blockage of the drainage network and clean the data to ensure the quality of the long-term monitoring data of the drainage network is reliable. S1.

2. Construct a multi-criteria decision analysis framework for drainage network sediment cleaning. According to the actual needs of the drainage network sediment cleaning site and the actual monitoring status of the drainage network, determine the initial scheme set, the decision standard set and the weight set of the decision standard for drainage network sediment cleaning. Taking into account the uncertainty of each decision standard for drainage network sediment cleaning, the deterministic decision standard preference for drainage network sediment cleaning is converted into a random variable, a constant or a mixture of the two through probability distribution.

3. A method for robust cleaning of a drainage network according to claim 1, characterized in that: The sub-steps of S2 are: S2.

1. Obtain the basic weight vector of drainage network sediment cleaning decision-making according to the preferences of each drainage network sediment cleaning decision-maker for different drainage network sediment cleaning decision-making criteria : ， in , Indicates the number of decision makers involved in making decisions about the removal of sediment from the drainage network; S2.

2. Based on the basic weight vectors of drainage network sediment cleaning decision-making provided by different decision makers, a linear combination method is used to obtain the standard combination weight vector of drainage network sediment cleaning decision-making , expressed as: ； S2.3, in order to obtain the best drainage network sediment cleaning decision-making plan, the deviation between the drainage network sediment cleaning decision-making standard combination weight vector and the drainage network sediment cleaning decision-making basic weight vector provided by each decision maker is minimized, that is, , and the coordinated weight vector of the decision-making criteria for clearing sediment in the drainage network is obtained; S2.

4. In the process of solving the coordinated weight vector of the decision-making standard for clearing sediment from the drainage network, the preference information of each decision maker will be lost. In order to quantitatively evaluate the uncertainty of the loss of preference information of each decision maker, the coordinated weight vector of the decision-making standard for clearing sediment from the drainage network is expanded from a single point to the entire feasible space of the decision-making standard for clearing sediment from the drainage network.

4. A method for robust cleaning of a drainage network according to claim 1, characterized in that: The sub-steps of step S3 are: S3.

1. Use grey system theory to aggregate the weight values ​​and information preferences of the drainage network sediment cleaning decision criteria into a quantified grey correlation value, and construct a random multi-criteria decision-making-grey correlation analysis model for drainage network sediment cleaning to deal with the uncertainty of the drainage network sediment cleaning decision criteria; S3.

2. Establish a random multi-criteria decision-making-grey correlation analysis evaluation index system for drainage network sediment cleaning, and evaluate and analyze the uncertainty of the decision-making results of the initial alternative plans for drainage network sediment cleaning; The random multi-criteria decision-making-grey correlation analysis evaluation index system for drainage network sediment cleaning mainly includes two indicators: ① The risk of decision-making errors in drainage network sediment cleaning; ② The uncertainty of the ranking of drainage network sediment cleaning plans.

5. A method for robust cleaning of a drainage network according to claim 1, characterized in that: The sub-steps of S4 are: S4.

1. Consider the uncertainty of the decision criteria and weights for drainage network sediment removal, conduct significance analysis on the drainage network sediment removal decision plan, and quantitatively evaluate the impact of input parameter uncertainty on the final drainage network sediment removal decision plan; S4.

2. Calculate the Spearman rank correlation coefficient corresponding to each drainage network sediment cleaning decision standard in turn, and evaluate the impact of the uncertainty of input parameters of different drainage network sediment cleaning decision standards on the drainage network sediment cleaning decision results.

6. A method for robust cleaning of a drainage network according to claim 2, characterized in that: The sub-steps of S1.2 are: S1.2.

1. The selection and determination of drainage network sediment cleaning schemes is considered as a multi-criteria decision analysis problem. Before cleaning each sedimentation pipe in the drainage network, the cleaning order of each drainage pipe is determined based on the long-term monitoring data of various influencing factors of the drainage network. A group of initial alternative schemes for drainage network sediment cleaning are randomly obtained. The scheme consists of The drainage network sediment removal program consists of , Indicates An initial alternative plan for cleaning sediment in a drainage network, including whether to clean each drainage pipe in a drainage network and the cleaning order of each drainage pipe; S1.2.

2. Select the key influencing factors of drainage network blockage as the decision criteria for multi-criteria decision analysis of drainage network sedimentation, and use them to evaluate and screen alternative solutions for clearing sedimentation in each drainage network; The decision criteria set for clearing sediment in the drainage network is defined as , including Decision criteria for clearing sediment from drainage networks, using represents the weight set of decision criteria for clearing sediment from the drainage network, Indicates the decision criteria for clearing sediment from drainage networks The corresponding weights; set up is the performance preference value of the decision criterion for clearing sediment in the drainage network, where Indicates alternative solutions for clearing sediment from drainage networks , Indicates the decision criteria for clearing sediment from drainage networks , the decision matrix for clearing sediment in the drainage network is expressed as: ; The multi-criteria decision analysis for clearing sediment in the drainage network satisfies the following relationship:

7. In the formula, is a function of the adopted decision model for clearing sediment from the drainage network; It is an alternative solution for drainage network sediment removal based on all drainage network sediment removal decision criteria. the total value of overall performance; The ranking of the drainage network sediment removal alternatives is determined based on the total value of the overall performance of the drainage network sediment removal alternatives. The alternative plan for clearing sediment from the drainage network is used as the final decision-making plan; S1.2.

3. Considering the uncertainty of preferences of decision makers involved in the drainage network sediment cleaning decision, the deterministic elements in the drainage network sediment cleaning decision matrix are represented by random elements, and the probability distribution method is used to convert the preference values ​​of each decision criterion for drainage network sediment cleaning into random variables, constants, or a mixture of the two; the random decision matrix for drainage network sediment cleaning is represented as: ； In the formula, represents the standard preference for random decision-making on the cleaning of silt in the drainage network, , .

8. A method for robust cleaning of a drainage network according to claim 4, characterized in that: The sub-steps of S3.1 are: S3.1.

1. Grey correlation analysis is used to solve the multi-criteria decision analysis problem of drainage network sediment cleaning. The weight values ​​of the drainage network sediment cleaning decision criteria and information preferences are aggregated into a quantified grey correlation value through grey system theory, and the weighted sum of the grey correlation coefficients of the initial alternatives for the drainage network sediment cleaning decision is calculated; S3.1.

2. Construct a random multi-criteria decision-making-grey correlation analysis model for drainage network sediment cleaning, introduce the real-valued utility function of random multi-criteria decision-making analysis for drainage network sediment cleaning, obtain the ranking of the initial alternative plans for drainage network sediment cleaning, and evaluate and analyze the advantages and disadvantages of each initial alternative plan for drainage network sediment cleaning.

9. A method for robust cleaning of a drainage network according to claim 4, characterized in that: The sub-steps of S3.2 are: S3.2.1 Risk of wrong decision-making in clearing sediment in drainage network It is used to measure the uncertainty of each drainage network sediment cleaning decision-making scheme to obtain the highest ranking; when ranking and screening the drainage network sediment cleaning decision-making schemes, the risk of drainage network sediment cleaning decision-making error is defined as the weighted probability that the non-optimal drainage network sediment cleaning alternative scheme obtains the highest ranking: ； In the formula, To obtain the first A first-level acceptability indicator for alternative options for clearing sediment from the sewer network at the location; Will Defined as a risk weight to identify the contribution of each non-optimal sewer network sediment removal decision to the decision risk, is expressed as an increment and Dimensional vector: ; S3.2.

2. Uncertainty in the sequencing of drainage network sediment removal programs It is used to measure the overall uncertainty of the ranking results of each drainage network sediment cleaning decision-making scheme; the ranking uncertainty of the drainage network sediment cleaning scheme is the sum of the level acceptability index of all possible rankings of the drainage network sediment cleaning decision-making schemes except the final ranking, and the calculation formula is: ； In the formula, An alternative solution for cleaning silt from the drainage network Decision Alternatives final level.

10. A method for robust cleaning of a drainage network according to claim 8, characterized in that: The sub-steps of S3.1.1 are: S3.1.1.

1. For each decision standard set for clearing sediment in the drainage network, define the reference set of decision standard sets for clearing sediment in the drainage network: ； ； In the formula, represents the reference set of decision criteria for clearing sediment from the drainage network, Indicates the first decision criteria, Indicates Decision-making criteria for clearing sediment from drainage networks and Comparative values ​​of decision criteria for sediment removal in drainage networks. 11.S3.1.1.

2. Decision Matrix for Cleaning Sediment from Drainage Network Normalized to , reference set Also normalized to ; S3.1.1.

3. Calculate the initial alternatives for clearing sediment from each drainage network and the normalized grey correlation coefficient: ； In the formula, Initial alternative solution for clearing silt from sewer networks The normalized decision criterion preference vector of ; Identification coefficient for silt removal in drainage network; S3.1.1.

4. Calculate the weighted sum of the grey correlation coefficients of the initial alternatives for drainage network sediment removal decisions. express: ； In the formula, Initial alternatives for clearing sediment from the sewer network Regarding the global evaluation of all decision criteria, It provides a basis for the final decision-making plan for the cleaning of sediment in each drainage network, that is, the plan with the largest grey correlation degree is usually the more popular plan.

12. A method for robust cleaning of a drainage network according to claim 7, characterized in that: The step 3.1.2 is specifically as follows: S3.1.2.

1. Use a real-valued utility function to evaluate the pros and cons of the random multi-criteria decision-making analysis scheme for clearing sediment from the drainage network. The real-valued utility function is used to The formula for calculating the real-valued utility function of random multi-criteria decision analysis for clearing sediment in drainage networks is: ； In the formula, Represents the initial alternative plan for the drainage network sediment removal decision performance preference vector of decision criteria; In the random multi-criteria decision-making-grey correlation analysis model for drainage network sediment cleaning, the grey correlation algorithm is used to replace the real-valued utility function of the random multi-criteria decision-making analysis for drainage network sediment cleaning, and the following is obtained: ； In the formula, the function Grey correlation degree of each initial alternative plan for drainage network sediment removal decision; S3.1.2.

2. Use the drainage network sediment cleaning ranking function to determine the ranking of the initial alternatives for drainage network sediment cleaning. The initial alternatives for drainage network sediment cleaning are ranked from best to worst, that is, ranked from 1 to the worst. ; The calculation formula for the initial alternative scheme ranking of silt removal in each drainage network is: ； In the formula, Alternative solutions for clearing sediment from sewer networks Stochastic multi-criteria decision analysis decision criteria weight performance preference vector, , ; is a gra-type utility function; S3.1.2.

3. Run the random multi-criteria decision-making-grey correlation analysis model for drainage network sediment cleaning, calculate and output 5 drainage network sediment cleaning decision-making evaluation indicators, and provide a basis for the final decision on drainage network sediment cleaning; The evaluation indicators of drainage network sediment cleaning decision-making include: ① drainage network sediment cleaning decision-making scheme level acceptability index, namely RAI; ② drainage network sediment cleaning decision-making scheme overall acceptability index, namely HAI; ③ drainage network sediment cleaning decision-making scheme center weight vector, namely CWV; ④ drainage network sediment cleaning decision-making scheme confidence factor; ⑤ drainage network sediment cleaning decision-making scheme cross confidence factor; The acceptability index of decision-making scheme for drainage network sediment removal is used express, The expected volume of the favorable ranking weight set of each initial alternative plan for the drainage network sediment cleaning decision is used to measure the initial alternative plan that leads to the sediment cleaning of each drainage network. Diversity of valuations at different ranking levels; Calculated as the performance preference distribution of the decision criteria for drainage network sediment removal and Multidimensional integral on : ； Obviously, the value range of the acceptability index of the drainage network sediment removal decision-making plan is , where 0 means that the initial alternatives for the drainage network sediment cleaning decision cannot obtain a given rank, and 1 means that any combination of drainage network sediment cleaning decision criteria weights can always obtain a given rank; if a drainage network sediment cleaning decision initial alternative obtains the best ranking and has a larger grade acceptability index, then the drainage network sediment cleaning is considered to be an acceptable solution, and for the drainage network sediment cleaning alternatives with poor ranking and a larger grade acceptability index, they should be eliminated from the drainage network sediment cleaning alternatives set; The overall acceptability index of the decision-making plan for drainage network sediment removal is used express, It is used to check the overall acceptability of each initial alternative plan for drainage network sediment removal decision, which is defined as the weighted sum of the grade acceptability index of all drainage network sediment removal decision plans; the overall acceptability index of the drainage network sediment removal decision plan The calculation formula is: ； in, The meta-weights representing the decision-making criteria for drainage network sediment removal reflect the contribution of the grade acceptability index of each drainage network sediment removal decision-making scheme to the evaluation of the initial alternative schemes for drainage network sediment removal decision; Will Defined as a monotonically decreasing vector , to simulate the situation where the best ranking of drainage network sediment removal decisions is better than the worst ranking; The central weight vector of the decision-making scheme for clearing sediment in the drainage network is used express, Decision making on initial alternatives for all sewer network silt removal The expected center of gravity of the favorable first-level weight space; The information indicating preferences in favor of the corresponding initial alternatives for drainage network sediment removal helps decision makers understand how different weights are associated with different decisions and helps in weighting the decision criteria for drainage network sediment removal; Calculated as the distribution of performance preference values ​​for the decision criteria for clearing sediment from the drainage network and favorable first-level weights Multidimensional integral of: ； Confidence factor of drainage network sediment removal decision plan express, is the probability of the most favored initial alternative plan for drainage network sediment cleaning based on the central weight vector of its own drainage network sediment cleaning decision plan; is a measure of whether the standard data are accurate enough to identify the initial alternatives for the drainage network sediment removal, which can be regarded as the proportion of the random criterion space that leads to the best drainage network sediment removal alternatives; Calculated as the multidimensional integral of the performance preference distribution of the decision criteria for drainage network sediment removal: ； If the confidence factor of the drainage network sediment cleaning decision plan is small, it means that even if the decision standard performance preference value is adopted, the drainage network sediment cleaning decision initial alternative is unlikely to be considered the most popular plan; on the contrary, if the confidence factor of the drainage network sediment cleaning decision plan is large, it is believed that the plan has appropriate preference information, and the drainage network sediment cleaning decision plan is usually the most popular plan; The cross confidence factor of the decision-making scheme for drainage network sediment cleaning is used to improve the discrimination ability of the random multi-criteria decision-making-grey correlation analysis model for drainage network sediment cleaning on similar schemes. express; It is calculated by the decision criteria weight preference value of other drainage network sediment cleaning decision plans; among them, the initial alternative plan for drainage network sediment cleaning decision Options relative to drainage network sediment removal targets The cross confidence coefficient is expressed as: ； The cross confidence factor of the drainage network sediment cleaning decision-making scheme is mainly to measure the probability of the drainage network sediment cleaning decision alternatives to obtain the best ranking when the decision criterion weight performance preference of the target drainage network sediment cleaning decision alternatives is used; obviously, if the cross confidence factor If the value of is not 0, it means that the drainage network sediment cleaning decision alternative Will make alternative plans with drainage network sediment removal decisions Competing for best sorting, non-zero crossing confidence factor Indicates the intensity of competition; at the same time, the cross confidence factor Equal to the confidence factor .

13. A robust cleaning device for a drainage network under uncertain conditions, the device comprising: The module for constructing a random multi-criteria decision analysis framework for drainage network sediment cleaning is used to build a random multi-criteria decision analysis framework for drainage network sediment cleaning for practical applications, and to make robust decisions on drainage network sediment cleaning under uncertain conditions; The module for estimating the uncertainty of the decision-making criteria for clearing sediment in the drainage network is used to establish the feasible space of the decision-making criteria for clearing sediment in the drainage network, aggregate and eliminate the weights of each decision-making criterion for clearing sediment in the drainage network by using the game theory method, and estimate the uncertainty of each decision-making criterion for clearing sediment in the drainage network; The multi-criteria decision analysis module for drainage network sediment cleaning is used to combine the random multi-criteria decision acceptability analysis theory and the grey correlation analysis theory to construct a random multi-criteria decision acceptability analysis-grey correlation analysis model for drainage network sediment cleaning, and proposes a drainage network sediment cleaning decision uncertainty assessment index, which is used to quantitatively assess the uncertainty and decision-making error risk of multi-criteria decision-making for drainage network sediment cleaning; The uncertainty determination module of the multi-criteria decision analysis for drainage network sediment cleaning is used to determine the impact of the uncertainty of the input parameters of each decision criterion for drainage network sediment cleaning on the decision plan for drainage network sediment cleaning by using the significance analysis method.

14. A computer device, characterized in that: include: one or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, a robust cleaning method for a drainage network as described in any one of claims 1 to 10 is implemented.

15. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, a robust cleaning method for a drainage network as described in any one of claims 1-10 is implemented.

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