A sewage plant multi-energy utilization robust decision method and device, computer equipment and medium

Abstract

The sewage plant multi-energy utilization robust decision method, device, computer equipment and medium belong to the field of urban sewage plant comprehensive energy utilization and multi-energy complementary optimization. Among them, the method comprises the following steps: constructing a practical application-oriented sewage plant comprehensive energy utilization and multi-energy complementary optimization stochastic multi-criteria decision analysis framework for the robust decision of sewage plant comprehensive energy utilization and multi-energy complementary optimization under uncertain conditions; using game theory to aggregate and resolve the weight of each decision standard of sewage plant comprehensive energy utilization and multi-energy complementary optimization, and estimating the uncertainty of each decision standard of sewage plant comprehensive energy utilization and multi-energy complementary optimization; constructing a sewage plant comprehensive energy utilization and multi-energy complementary optimization stochastic multi-criteria decision acceptability analysis-gray correlation analysis model to quantitatively evaluate the uncertainty and decision-making risk of sewage plant comprehensive energy utilization and multi-energy complementary optimization multi-criteria decision; using significance analysis method to determine the influence of input parameter uncertainty of each decision standard of sewage plant comprehensive energy utilization and multi-energy complementary optimization on the decision scheme of sewage plant comprehensive energy utilization and multi-energy complementary optimization.

Description

Technical Field

[0001] This invention belongs to the field of comprehensive energy utilization and multi-energy complementarity technology for urban wastewater treatment plants, specifically involving a robust decision-making method, device, computer equipment, and medium for multi-energy utilization in urban wastewater treatment plants. Background Technology

[0002] Wastewater treatment is an energy-intensive process, consuming approximately 1% of the nation's total electricity and generating substantial carbon emissions. However, wastewater theoretically contains up to nine times the energy required for its treatment, representing a significant potential for energy self-sufficiency and carbon reduction. Therefore, integrating various energy sources such as municipal power, photovoltaic power, wind power, wastewater source heat pumps, and biogas cogeneration, along with electricity / heat / gas energy storage, to construct a comprehensive energy system for wastewater treatment plants has become an industry trend.

[0003] The optimization decision-making of this system involves multiple dimensions of objectives, including economic, environmental, social, and technological factors, and faces multiple uncertainties: on the one hand, the output of renewable energy sources such as solar and wind power is affected by weather and has strong randomness; on the other hand, parameters such as wastewater treatment biochemical processes, peak and off-peak electricity prices, and equipment operating status also fluctuate. In addition, the decision-making process usually involves multiple stakeholders, whose preferences for the weights of various evaluation criteria (such as investment costs, carbon emissions, and energy self-sufficiency rates) are subjective and conflicting. Traditional weight aggregation methods are prone to information loss and masking of true uncertainties.

[0004] Existing multi-criteria decision-making methods applied in this field are mostly limited to deterministic or fuzzy environments, failing to effectively handle the dual randomness of performance preference values ​​and weight vectors in the decision matrix. This neglect leads to insufficient robustness of decision results, making it impossible to quantify the risk of scheme ranking reversal and decision-making errors caused by input parameter uncertainty. Furthermore, the lack of effective sensitivity analysis tools to identify key influencing factors results in poor adaptability of the final scheme in complex and ever-changing real-world operating environments. Therefore, there is an urgent need for a decision-making method that integrates uncertainty quantification, robust scheme selection, and key factor identification. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a robust decision-making method, device, computer equipment and medium for multi-energy utilization in wastewater treatment plants, which can determine the optimal and most reasonable comprehensive energy utilization and multi-energy complementarity optimization scheme of wastewater treatment plants through multi-criteria decision analysis under uncertain conditions.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A robust decision-making process can be used in multiple wastewater treatment plants, with the following steps: A stochastic multi-criteria decision analysis framework for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is constructed for practical applications, so as to achieve robust decision-making for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants under uncertainty conditions. A feasible weight space for the decision criteria of comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is established. The game theory method is used to aggregate and resolve the weights of each decision criterion of comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants, and the uncertainty of each decision criterion of comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is estimated. Combining the theories of acceptability analysis of stochastic multi-criteria decision-making and grey relational analysis, a grey relational analysis model for acceptability analysis of stochastic multi-criteria decision-making in the optimization of integrated energy utilization and multi-energy complementarity in wastewater treatment plants is constructed. An uncertainty assessment index for the optimization of integrated energy utilization and multi-energy complementarity in wastewater treatment plants is proposed to quantify the uncertainty and decision error risk of multi-criteria decision-making in the optimization of integrated energy utilization and multi-energy complementarity in wastewater treatment plants. For each decision criterion of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants, significance analysis is used to determine the impact of the uncertainty of the input parameters of each decision criterion on the decision scheme of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants.

[0007] Preferably, the steps for constructing a stochastic multi-criteria decision analysis framework for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants, oriented towards practical applications, include: The key influencing factors of comprehensive energy utilization and multi-energy complementarity optimization in sewage treatment plants were identified. Based on the actual monitoring status of various production capacities, energy consumption, energy storage, and power distribution networks in the sewage treatment plant, long-term series monitoring data of sewage treatment energy consumption and renewable energy production capacity were collected. The monitoring data were checked and cleaned to ensure data quality and reliability. A multi-criteria decision analysis framework for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is constructed. Based on the actual needs and monitoring status of the wastewater treatment plant, the initial scheme set, decision criterion set, and decision criterion weight set for the comprehensive energy utilization and multi-energy complementarity optimization of the wastewater treatment plant are determined. The deterministic decision criterion preferences are transformed into random variables, constants, or a mixture of the two through probability distribution.

[0008] Preferably, the steps for constructing a multi-criteria decision analysis framework for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants also include: The selection and determination of the comprehensive energy utilization and multi-energy complementarity optimization scheme of the sewage treatment plant is taken as a multi-criteria decision analysis problem. Based on long-term series monitoring data, the access and shutdown of various renewable energy sources in the sewage treatment plant are determined. An initial alternative scheme set consisting of multiple comprehensive energy utilization and multi-energy complementarity optimization schemes of the sewage treatment plant is randomly generated. Each scheme includes the access status, access order and duration of various renewable energy sources in the sewage treatment plant. Key influencing factors of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants are selected as decision criteria for multi-criteria decision analysis. These criteria are used to evaluate and screen various alternative solutions and construct a decision matrix, where the elements of the decision matrix are the performance preference values ​​of each alternative solution under each decision criterion. The deterministic elements in the decision matrix are represented by random elements, and the preference values ​​of each decision criterion are transformed into random variables, constants, or a mixture of both using probability distribution methods to form a stochastic decision matrix.

[0009] Preferably, the steps for establishing the feasible space of the comprehensive energy utilization and multi-energy complementarity optimization decision-making criteria for wastewater treatment plants include: Based on the preferences of each decision-maker for different decision-making criteria, the basic weight vector for the comprehensive energy utilization and multi-energy complementarity optimization decision of wastewater treatment plants is obtained. Based on the basic decision weight vectors provided by different decision-makers, a linear combination method is used to obtain the decision standard combination weight vector; The decision standard coordination weight vector is obtained by minimizing the deviation between the combined weight vector of decision criteria and the basic weight vector of decision provided by each decision-maker. The decision criterion coordination weight vector is extended from a single point to the entire feasible space of decision criterion in order to quantitatively assess the uncertainty of the loss of preference information for each decision-maker.

[0010] Preferably, the steps for constructing a grey relational analysis model for the acceptability analysis of stochastic multi-criteria decision-making in the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants include: Grey system theory is used to aggregate the decision criterion weights and information preferences into quantified grey relational degree values, and a stochastic multi-criteria decision-grey relational analysis model is constructed to handle the uncertainty of decision criteria. A stochastic multi-criteria decision-making-grey relational analysis evaluation index system is established to assess and analyze the uncertainty of the decision results of the initial alternative schemes. The evaluation index system includes the risk of decision error and the uncertainty of the scheme ranking.

[0011] Preferably, the step of aggregating the decision criterion weights and information preferences into quantified grey relational values ​​using grey system theory includes: For each set of decision criteria, define a set of references for the decision criteria set; The decision matrix is ​​normalized, and the reference set is also normalized. Calculate the normalized grey relational coefficients for each initial alternative, where the identification coefficient is set to 0.5; Calculate the weighted sum of the grey relational coefficients of each initial alternative as the global evaluation value of each initial alternative with respect to all decision criteria.

[0012] Preferably, after running the stochastic multi-criteria decision-grey relational analysis model, it outputs five decision evaluation indicators, including: The decision alternative ranking acceptability index represents the expected volume of the favorable ranking weight set of each initial alternative and is used to measure the diversity of valuations that lead to different rankings of the initial alternatives. The overall acceptability index of decision options is used to examine the overall acceptability of each initial alternative and is defined as the weighted sum of the acceptability indices of all decision options at the level of acceptance. The decision scheme center weight vector is the expected centroid of the first-level weight space favorable to all initial alternatives, representing the preference information supporting the corresponding initial alternative; The decision confidence factor is the probability of the most favored initial alternative based on its own decision center weight vector, used to measure whether the standard data is accurate enough to identify the initial alternative. The decision alternative cross-confidence factor measures the probability that the decision alternative will achieve the best ranking when using the performance preference of the decision criteria weights of the target alternatives.

[0013] Preferably, the decision error risk is used to measure the uncertainty of each decision option obtaining the highest ranking, and is defined as the weighted probability of a non-optimal alternative obtaining the highest ranking; the ranking uncertainty is used to measure the overall uncertainty of the ranking results of each decision option, and is the sum of the acceptability indices of the decision option ranking of each option obtaining all possible rankings other than the final ranking.

[0014] Preferably, the steps for determining the uncertainty of each decision criterion input parameter using significance analysis include: Considering the uncertainties of decision criteria and weights respectively, a significance analysis is conducted on the decision schemes to quantitatively evaluate the impact of input parameter uncertainty on the final decision scheme; The Spearman rank correlation coefficients corresponding to each decision criterion are calculated sequentially to assess the impact of the uncertainty of input parameters for different decision criters on the decision results.

[0015] A robust decision-making device for multi-use of wastewater treatment plants, employing the aforementioned robust decision-making method for multi-use of wastewater treatment plants, includes the following components: The module for constructing a stochastic multi-criteria decision analysis framework for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is used to build a stochastic multi-criteria decision analysis framework for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants oriented towards practical applications, so as to achieve robust decision-making for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants under uncertainty conditions. The module for estimating the uncertainty of decision criteria for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants is used to establish the feasible weight space of decision criteria for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. It uses game theory to aggregate and resolve the weights of each decision criterion for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants, and estimates the uncertainty of each decision criterion for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. The module for multi-criteria decision analysis of integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants combines the theory of acceptability analysis of stochastic multi-criteria decision-making and the theory of grey relational analysis to construct a stochastic multi-criteria decision-making acceptability analysis-grey relational analysis model for integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants. It also proposes an uncertainty assessment index for integrated energy utilization and multi-energy complementarity optimization decision-making in wastewater treatment plants to quantitatively assess the uncertainty and decision error risk of integrated energy utilization and multi-energy complementarity optimization decision-making in wastewater treatment plants. The uncertainty determination module for multi-criteria decision analysis of integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants is used to determine the impact of the uncertainty of the input parameters of each decision criterion on the decision scheme of integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants using significance analysis.

[0016] A computer device includes a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the steps of the robust decision-making method for multi-energy utilization in wastewater treatment plants.

[0017] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of a wastewater treatment plant can utilize a robust decision-making method are described.

[0018] The present invention can achieve the following beneficial effects: 1. This invention constructs a stochastic multi-criteria decision acceptability analysis-grey relational analysis model, which effectively handles the dual randomness of performance preference values ​​and weight vectors in the decision matrix. The model outputs evaluation indicators including the Decision Alternate Ranking Acceptability Index (RAI), the Overall Acceptability Index (HAI), and confidence factors, thereby quantitatively assessing the stability of the alternative ranking, the risk of decision error, and the uncertainty of the ranking, providing decision-makers with a risk-aware decision-making basis.

[0019] 2. This invention employs game theory to aggregate and resolve the decision criteria weights from different stakeholders, solving for the coordinated weight vector and extending it to the entire feasible weight space. This method effectively reduces the information loss caused by the traditional weight averaging method, objectively characterizes the weight uncertainty caused by preference conflicts and information loss, and enhances the robustness of the final decision scheme to weight perturbations.

[0020] 3. This invention introduces significance analysis methods (such as Spearman's rank correlation coefficient) to quantitatively assess the impact of the uncertainty of each decision-making criterion input parameter on the final scheme ranking. This helps decision-makers identify the key factors that play a dominant role in the decision outcome, thereby enabling targeted optimization in data collection, model building, or operational management, and improving the overall effectiveness of the decision-making system.

[0021] 4. The decision-making framework constructed in this invention directly integrates long-term on-site monitoring data from wastewater treatment plants, transforming deterministic performance preferences into random variables, thus truly reflecting the uncertainties in actual operation such as renewable energy output and wastewater treatment load. Therefore, the generated optimal solution is a robust solution that fully considers various random disturbances, significantly improving the adaptability and reliability of the decision-making results in complex and ever-changing actual operating conditions. Attached Figure Description

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 The flowchart shows a robust decision-making method for multi-energy utilization of wastewater treatment plants, proposed based on an example of multi-energy utilization in wastewater treatment plants. Figure 2 This is a schematic diagram of the feasible weight space for a robust decision-making method for multi-energy utilization of wastewater treatment plants, proposed based on an embodiment of multi-energy utilization of wastewater treatment plants. Figure 3 This is a schematic diagram of a robust decision-making device for multi-functional utilization of wastewater treatment plants, proposed according to an embodiment of multi-functional utilization of wastewater treatment plants. Figure 4 This is a schematic diagram of the hardware structure of a computer device proposed according to an embodiment of a wastewater treatment plant multi-functional utilization. Detailed Implementation

[0023] Preferred solutions include Figures 1 to 4 As shown, a robust decision-making method, device, computer equipment, and media for wastewater treatment plants are available. The specific method includes: 1. To address the optimization problem of integrated energy utilization and multi-energy complementarity in wastewater treatment plants under uncertain conditions, a stochastic multi-criteria decision analysis framework for integrated energy utilization and multi-energy complementarity in wastewater treatment plants is constructed for practical applications, enabling robust decision-making in the optimization of integrated energy utilization and multi-energy complementarity in wastewater treatment plants under uncertain conditions. 2. Establish the feasible weight space of the decision criteria for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. Use game theory to aggregate and resolve the weights of each decision criterion for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants, and estimate the uncertainty of each decision criterion for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. 3. Combining the theory of acceptability analysis of stochastic multi-criteria decision-making and the theory of grey relational analysis, we construct a stochastic multi-criteria decision-making acceptability analysis-grey relational analysis model for the optimization of integrated energy utilization and multi-energy complementarity in wastewater treatment plants. We also propose an uncertainty assessment index for the optimization of integrated energy utilization and multi-energy complementarity in wastewater treatment plants to quantitatively assess the uncertainty and decision error risk of multi-criteria decision-making in the optimization of integrated energy utilization and multi-energy complementarity in wastewater treatment plants. 4. For each decision criterion of the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants, significance analysis is used to determine the impact of the uncertainty of the input parameters of each decision criterion on the decision scheme of comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants.

[0024] Furthermore, step 1 specifically includes: 1.1 Identify the key influencing factors of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. Based on the actual monitoring status of various production capacities, energy consumption, energy storage, and power distribution networks within the wastewater treatment plant, collect long-term series monitoring data on the influencing factors of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants, such as wastewater treatment energy consumption and renewable energy production capacity. Check the quality of the monitoring data on the influencing factors of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants and clean the data to ensure the reliability of the long-term series monitoring data on comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. 1.2 Construct a multi-criteria decision analysis framework for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. Based on the actual needs of the wastewater treatment plant's comprehensive energy utilization and multi-energy complementarity optimization on-site and the actual monitoring status of the plant's capacity, energy consumption, energy storage, and power distribution network, determine the initial scheme set, decision criteria set, and weight set of the decision criteria for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. Considering the uncertainty of each decision criterion for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants, the deterministic preferences of the decision criteria for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants are transformed into random variables, constants, or a mixture of both through probability distribution.

[0025] Furthermore, step 2 specifically includes: 2.1 Based on the preferences of decision-makers regarding the comprehensive energy utilization and multi-energy complementarity optimization of various wastewater treatment plants for different optimization criteria, a basic weight vector for the comprehensive energy utilization and multi-energy complementarity optimization decision-making of wastewater treatment plants is obtained. ,in , This indicates the number of decision-makers involved in the comprehensive energy utilization and multi-energy complementarity optimization decision-making process of wastewater treatment plants; 2.2 Based on the basic weight vectors for the comprehensive energy utilization and multi-energy complementarity optimization decision-making of wastewater treatment plants provided by different decision-makers, a linear combination method is used to obtain the standard combination weight vector for the comprehensive energy utilization and multi-energy complementarity optimization decision-making of wastewater treatment plants, expressed as follows: ; 2.3 To obtain the optimal decision-making scheme for integrated energy utilization and multi-energy complementarity in wastewater treatment plants, the deviation between the weight vector of the standard combination of integrated energy utilization and multi-energy complementarity optimization decision-making scheme and the basic weight vector of integrated energy utilization and multi-energy complementarity optimization decision-making scheme provided by each decision-maker should be minimized, i.e. The coordination weight vector of the comprehensive energy utilization and multi-energy complementarity optimization decision-making criteria of the sewage treatment plant is obtained by solving the problem. 2.4 In the process of solving for the coordination weight vector of the comprehensive energy utilization and multi-energy complementarity optimization decision criteria of the wastewater treatment plant, the loss of preference information of each decision-maker is inevitable. In order to quantify and assess the uncertainty of the loss of preference information of each decision-maker, the coordination weight vector of the comprehensive energy utilization and multi-energy complementarity optimization decision criteria of the wastewater treatment plant is extended from a single point to the entire feasible weight space of the comprehensive energy utilization and multi-energy complementarity optimization decision criteria of the wastewater treatment plant.

[0026] Furthermore, step 3 specifically includes: 3.1 Using grey system theory, the weight values ​​and information preferences of the decision criteria for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants are aggregated into a quantitative grey relational degree value. A stochastic multi-criteria decision-making-grey relational analysis model for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is constructed to handle the uncertainty of the decision criteria for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. 3.2 An evaluation index system for stochastic multi-criteria decision-making and grey relational analysis of the comprehensive energy utilization and multi-energy complementarity optimization response of wastewater treatment plants was established to assess and analyze the uncertainty of the initial alternative decision-making results for comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. This evaluation index system mainly includes two indicators: ① the risk of decision-making error in comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants; ② the uncertainty of the ranking of decision-making schemes for comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants.

[0027] Furthermore, step 4 specifically involves: 4.1 Considering the uncertainties of the decision-making criteria and weights for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants, a significance analysis is conducted on the decision-making schemes for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants to quantitatively evaluate the impact of the uncertainty of input parameters on the final decision-making schemes for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. 4.2. Calculate the Pearson rank correlation coefficients corresponding to the comprehensive energy utilization and multi-energy complementarity optimization decision-making criteria for each wastewater treatment plant in turn, and evaluate the impact of the uncertainty of the input parameters of the comprehensive energy utilization and multi-energy complementarity optimization decision-making criteria for different wastewater treatment plants on the results of the comprehensive energy utilization and multi-energy complementarity optimization decision-making for wastewater treatment plants.

[0028] Furthermore, step 1.2 specifically includes: 1.2.1 The selection and determination of the comprehensive energy utilization and multi-energy complementarity optimization scheme for wastewater treatment plants is considered as a multi-criteria decision analysis problem. Before making the optimization decision, the access and shutdown of various renewable energy sources in the wastewater treatment plant are determined based on long-term series monitoring data of various influencing factors. A set of initial alternative schemes for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is randomly obtained. This set of schemes consists of… The system consists of an integrated energy utilization and multi-energy complementarity optimization scheme for each wastewater treatment plant. , Indicates the first Initial alternative schemes for comprehensive energy utilization and multi-energy complementarity optimization of each wastewater treatment plant, including plans for the connection and shutdown of renewable energy sources at each wastewater treatment plant; 1.2.2 Key influencing factors for the optimization of integrated energy utilization and multi-energy complementarity in wastewater treatment plants are selected as decision criteria for multi-criteria decision analysis in wastewater treatment plant integrated energy utilization and multi-energy complementarity optimization. These criteria are used to evaluate and screen alternative optimization schemes for various wastewater treatment plants. The decision criterion set for the optimization of integrated energy utilization and multi-energy complementarity in wastewater treatment plants is defined as follows: , contains Optimization decision-making criteria for comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants, using This represents the weight set of the decision-making criteria for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. Indicating the comprehensive energy utilization and multi-energy complementarity optimization decision-making criteria for wastewater treatment plants The corresponding weights. Let... The performance preference value is the decision-making standard for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. This indicates an optimized alternative scheme for comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants. , Indicating the comprehensive energy utilization and multi-energy complementarity optimization decision-making criteria for wastewater treatment plants The comprehensive energy utilization and multi-energy complementarity optimization decision matrix of wastewater treatment plants can be expressed as: The multi-criteria decision analysis of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants satisfies the following relationships:

[0029] In the formula, This is a function of the comprehensive energy utilization and multi-energy complementarity optimization decision-making model for the wastewater treatment plant. It is an optimized alternative scheme for the comprehensive energy utilization and multi-energy complementarity of wastewater treatment plants, obtained by measuring the comprehensive energy utilization and multi-energy complementarity optimization decision-making criteria of all wastewater treatment plants. The overall performance value. The ranking of integrated energy utilization and multi-energy complementary optimization alternatives for wastewater treatment plants can be determined based on the overall performance value of these alternatives, selecting the one with the highest value. The comprehensive energy utilization and multi-energy complementary optimization alternative scheme of the sewage treatment plant was used as the final decision-making scheme.

[0030] 1.2.3 Considering the uncertainty of the preferences of each decision-maker involved in the comprehensive energy utilization and multi-energy complementarity optimization decision of the wastewater treatment plant, the deterministic elements in the decision matrix are represented by random elements. A probability distribution method is used to transform the decision criteria preference values ​​of each decision into random variables, constants, or a mixture of both. The stochastic decision matrix for the comprehensive energy utilization and multi-energy complementarity optimization of the wastewater treatment plant is expressed as follows:

[0031] In the formula, This indicates the stochastic decision-making criteria preference for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. , .

[0032] Furthermore, step 3.1 specifically includes: 3.1.1 Grey relational analysis is used to solve the multi-criteria decision analysis problem of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. Through grey system theory, the weight values ​​of decision criteria and information preferences for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants are aggregated into a quantitative grey relational degree value. The weighted sum of the grey relational degree coefficients of each initial alternative scheme for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants is calculated, laying the foundation for the final decision of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. 3.1.2 Construct a stochastic multi-criteria decision-making model for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. Introduce the real-valued utility function for the stochastic multi-criteria decision-making analysis of the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants to obtain the ranking of the initial alternative schemes for the comprehensive energy utilization and multi-energy complementarity optimization of each wastewater treatment plant. Evaluate and analyze the advantages and disadvantages of the initial alternative schemes for the comprehensive energy utilization and multi-energy complementarity optimization of each wastewater treatment plant.

[0033] Furthermore, step 3.2 specifically includes: 3.2.1 Risk of Decision-Making Errors in Comprehensive Energy Utilization and Multi-Energy Complementarity Optimization of Wastewater Treatment Plants This represents the uncertainty in achieving the highest ranking for various wastewater treatment plant integrated energy utilization and multi-energy complementarity optimization decision-making schemes. When ranking and selecting these schemes, decision-makers are more concerned with higher-ranked schemes. Considering the uncertainty of the decision-making criteria and their weights for wastewater treatment plant integrated energy utilization and multi-energy complementarity optimization, it is possible for poorly performing schemes to achieve higher rankings, thus deviating from the optimal solution and negatively impacting the overall optimization process. The decision-making error risk for wastewater treatment plant integrated energy utilization and multi-energy complementarity optimization is defined as the weighted probability that a non-optimal alternative scheme achieves the highest ranking:

[0034] In the formula, To obtain the overall acceptability index of the comprehensive energy utilization and multi-energy complementarity optimization decision-making scheme of the wastewater treatment plant, the first... The first-level acceptability index of the comprehensive energy utilization and multi-energy complementary optimization alternative scheme of the wastewater treatment plant. Defined as risk weights, these are used to identify the contribution of each suboptimal wastewater treatment plant's comprehensive energy utilization and multi-energy complementarity optimization decision-making scheme to the decision risk. Represented as increments and sums dimensional vector: .

[0035] 3.2.2 Uncertainty of ranking the comprehensive energy utilization and multi-energy complementarity optimization schemes of wastewater treatment plants. This represents the overall uncertainty in ranking the comprehensive energy utilization and multi-energy complementarity optimization schemes for each wastewater treatment plant. The ranking uncertainty of the comprehensive energy utilization and multi-energy complementarity optimization schemes for each wastewater treatment plant is the sum of the acceptability indices of the comprehensive energy utilization and multi-energy complementarity optimization decision scheme levels obtained for each scheme, excluding the final ranking. The calculation formula is:

[0036] In the formula, It is an optimized alternative solution for comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants. Alternatives to the decision The final level.

[0037] Furthermore, step 3.1.1 specifically includes: 3.1.1.1 For each decision-making criterion set of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants, define the reference set of the decision-making criterion set for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants.

[0038]

[0039] 3.1.1.2 Optimization Decision Matrix for Comprehensive Energy Utilization and Multi-Energy Complementarity in Wastewater Treatment Plants Normalization Reference set Also normalized to ; 3.1.1.3. The initial alternative schemes and normalized grey relational coefficients for comprehensive energy utilization and multi-energy complementarity optimization of each wastewater treatment plant were calculated:

[0040] In the formula, Optimizing the initial alternative scheme for comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants The normalized decision criterion preference vector; The optimal identification coefficient for comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants is set at 0.5.

[0041] 3.1.1.4. Calculate the weighted sum of the grey relational coefficients of each initial alternative scheme for the comprehensive energy utilization and multi-energy complementarity optimization decision of the wastewater treatment plant, and use... express:

[0042] In the formula, Optimize initial alternative schemes for comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants A comprehensive assessment of all decision-making criteria. This provides a basis for the final decision-making scheme of comprehensive energy utilization and multi-energy complementarity optimization for each wastewater treatment plant, namely, the scheme with the largest grey relational degree is usually the more popular scheme.

[0043] Furthermore, step 3.1.2 specifically includes: 3.1.2.1 The merits of the comprehensive energy utilization and multi-energy complementarity optimization stochastic multi-criteria decision analysis schemes for wastewater treatment plants are evaluated using real-valued utility functions. The formula for calculating the real-valued utility function in stochastic multi-criteria decision analysis for comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is as follows:

[0044] In the formula, This indicates the initial alternative scheme for comprehensive energy utilization and multi-energy complementarity optimization decision-making in wastewater treatment plants. Performance preference vector of decision criteria.

[0045] In the grey relational analysis model of stochastic multi-criteria decision-making for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants, the grey relational algorithm is used to replace the real-valued utility function of the stochastic multi-criteria decision-making model for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants, resulting in:

[0046] In the formula, the function Grey relational analysis of each initial alternative scheme for comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants.

[0047] 3.1.2.2. The ranking function for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is used to determine the initial ranking of the comprehensive energy utilization and multi-energy complementarity optimization schemes for each wastewater treatment plant. The initial ranking of the comprehensive energy utilization and multi-energy complementarity optimization schemes for each wastewater treatment plant is from best (ranked as 1) to worst (ranked as 2). The calculation formula for ranking the initial alternative schemes for comprehensive energy utilization and multi-energy complementarity optimization of each wastewater treatment plant is as follows:

[0048] In the formula, Optimized alternatives for comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants Stochastic multi-criteria decision analysis: decision criterion weights, performance, and preference vectors. , ; It is a utility function of type gra.

[0049] 3.1.2.3. A stochastic multi-criteria decision-making model for the integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants was run, and five evaluation indicators for the integrated energy utilization and multi-energy complementarity optimization decision-making were calculated and output, providing a basis for the final decision on the integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants. The evaluation indicators for the integrated energy utilization and multi-energy complementarity optimization decision-making of wastewater treatment plants include: ① Ranking Acceptability Index (RAI); ② Overall Acceptability Index (HAI); ③ Center Weight Vector (CWV); ④ Confidence Factor; and ⑤ Cross-Confidence Factor.

[0050] The Acceptability Index (RAI) for Integrated Energy Utilization and Multi-Energy Complementarity Optimization Decision-Making Schemes in Wastewater Treatment Plants is used to... express, This represents the expected volume of the favorable ranking weight set of each initial alternative scheme for the comprehensive energy utilization and multi-energy complementarity optimization decision of each wastewater treatment plant. It is mainly used to measure the impact of the initial alternative schemes on the comprehensive energy utilization and multi-energy complementarity optimization of each wastewater treatment plant. The diversity of valuations across different ranking levels. Performance preference distribution of the comprehensive energy utilization and multi-energy complementarity optimization decision criteria for wastewater treatment plants and Multidimensional integrals on:

[0051] Clearly, the acceptable range of the decision-making scheme level for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants is as follows: Where 0 indicates that the initial alternative for the integrated energy utilization and multi-energy complementarity optimization decision of wastewater treatment plants cannot obtain a given rank, and 1 indicates that any combination of standard weights for the integrated energy utilization and multi-energy complementarity optimization decision of wastewater treatment plants can always obtain a given rank. If an initial alternative for the integrated energy utilization and multi-energy complementarity optimization decision of wastewater treatment plants obtains the best ranking and has a large RAI, then the alternative is considered an acceptable alternative. However, alternatives with poor ranking and large RAI should be eliminated from the set of alternatives for the integrated energy utilization and multi-energy complementarity optimization decision of wastewater treatment plants.

[0052] The Overall Acceptability Index (HAI) of the comprehensive energy utilization and multi-energy complementarity optimization decision-making scheme for wastewater treatment plants is used. express, The overall acceptability of initial alternatives for integrated energy utilization and multi-energy complementarity optimization decisions at each wastewater treatment plant is used to examine the overall acceptability of these alternatives. It is defined as the weighted sum of the acceptability indices for all wastewater treatment plant integrated energy utilization and multi-energy complementarity optimization decision-making schemes. The overall acceptability index for integrated energy utilization and multi-energy complementarity optimization decision-making schemes at wastewater treatment plants is... The calculation formula is:

[0053] in, The meta-weights represent the decision-making criteria for integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants, reflecting the contribution of the acceptability index of each wastewater treatment plant's integrated energy utilization and multi-energy complementarity optimization decision-making scheme to the evaluation of initial alternative schemes. This method will... Defined as a monotonically decreasing vector The study simulates a scenario where the best ranking in the comprehensive energy utilization and multi-energy complementarity optimization decision-making of a wastewater treatment plant is better than the worst ranking.

[0054] The central weight vector (CWV) of the comprehensive energy utilization and multi-energy complementarity optimization decision-making scheme for wastewater treatment plants is used. express, Initial alternative solutions for comprehensive energy utilization and multi-energy complementarity optimization decision-making in all wastewater treatment plants The expected center of gravity of the favorable first-level weight space. Information indicating preferences for the initial alternative schemes for the integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants helps decision-makers understand how different weights are related to different decisions and facilitates the allocation of decision criteria weights for the integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants. Distribution of performance preference values ​​calculated as the decision-making criteria for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants and favorable first-level weights Multidimensional integrals:

[0055] Confidence factors for comprehensive energy utilization and multi-energy complementarity optimization decision-making schemes in wastewater treatment plants express, This represents the probability of the most favored initial alternative scheme for the integrated energy utilization and multi-energy complementarity optimization decision scheme of a wastewater treatment plant, based on its own central weight vector. It is a measure of whether the standard data is accurate enough to identify the initial alternative schemes for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. It can be regarded as the proportion of the stochastic criterion space that leads to the optimal alternative schemes for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. The multidimensional integral calculated as the performance preference distribution of the comprehensive energy utilization and multi-energy complementarity optimization decision criteria for wastewater treatment plants:

[0056] If the confidence factor for the integrated energy utilization and multi-energy complementarity optimization decision scheme of the wastewater treatment plant is small, it indicates that even using the performance preference value of this decision criterion, the initial alternative scheme for integrated energy utilization and multi-energy complementarity optimization is unlikely to be considered the most popular scheme. Conversely, if the confidence factor for the integrated energy utilization and multi-energy complementarity optimization decision scheme of the wastewater treatment plant is large, it is considered that the scheme has appropriate preference information, and the integrated energy utilization and multi-energy complementarity optimization decision scheme of the wastewater treatment plant is usually the most popular scheme.

[0057] The cross-confidence factor for the comprehensive energy utilization and multi-energy complementarity optimization decision-making scheme of wastewater treatment plants is mainly to improve the ability of the stochastic multi-criteria decision-making-grey relational analysis model for comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants to distinguish similar schemes. express. The decision criteria weight preference values ​​were calculated from the comprehensive energy utilization and multi-energy complementarity optimization decision-making schemes of other wastewater treatment plants. Among them, the initial alternative schemes for comprehensive energy utilization and multi-energy complementarity optimization decision-making of wastewater treatment plants were... Compared to the optimization objectives of comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants, the following alternative solutions are available. The cross-confidence coefficient is expressed as:

[0058] The cross-confidence factor for the integrated energy utilization and multi-energy complementarity optimization decision-making scheme of wastewater treatment plants mainly measures the probability that the alternative scheme will achieve the optimal ranking when using the performance preference of the decision criteria weights of the target integrated energy utilization and multi-energy complementarity optimization decision-making scheme. Clearly, if the cross-confidence factor... If the value is not 0, it indicates that the wastewater treatment plant's comprehensive energy utilization and multi-energy complementarity optimization decision-making alternative scheme is... This will be an alternative decision-making scheme for integrated energy utilization and multi-energy complementarity in wastewater treatment plants. Competing for the best ranking, non-zero cross-confidence factor This indicates the intensity of competition. Simultaneously, the cross-confidence factor... equal to confidence factor .

[0059] Secondly, the present invention also provides a robust decision-making device for multi-functional utilization of wastewater treatment plants, the device comprising: The module for constructing a stochastic multi-criteria decision analysis framework for integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants is used to build a practical application-oriented multi-criteria decision analysis framework for integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants, and to make robust decisions on integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants under uncertain conditions. The module for estimating the uncertainty of decision criteria for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants is used to establish the feasible weight space of decision criteria for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. It uses game theory to aggregate and resolve the weights of each decision criterion for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants, and estimates the uncertainty of each decision criterion for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. The module for multi-criteria decision analysis of integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants combines the theory of acceptability analysis of stochastic multi-criteria decision-making and the theory of grey relational analysis to construct a stochastic multi-criteria decision-making acceptability analysis-grey relational analysis model for integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants. It also proposes an uncertainty assessment index for integrated energy utilization and multi-energy complementarity optimization decision-making in wastewater treatment plants to quantitatively assess the uncertainty and decision error risk of integrated energy utilization and multi-energy complementarity optimization decision-making in wastewater treatment plants. The uncertainty determination module for multi-criteria decision analysis of integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants is used to determine the impact of the uncertainty of the input parameters of each decision criterion on the decision scheme of integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants using significance analysis.

[0060] Thirdly, the present invention also provides a computer device including a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the steps of the robust decision-making method for multi-utilization of wastewater treatment plants according to the first aspect or any embodiment of the first aspect.

[0061] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the robust decision-making method for multi-utilization of wastewater treatment plants according to the first aspect or any embodiment of the first aspect.

[0062] Example 1: The purpose of this invention is to propose a robust decision-making method, device, computer equipment, and medium for multi-utilization of wastewater treatment plants. It combines stochastic multi-criteria acceptability analysis theory with grey relational analysis, uses decision error risk and rank uncertainty to assess decision uncertainty, and uses significance analysis to examine the impact of input parameter uncertainty on the final evaluation of the scheme.

[0063] like Figure 1 As shown in the figure, this embodiment discloses a robust decision-making method for multi-energy utilization in wastewater treatment plants. The method includes the following steps: 1. To address the optimization problem of integrated energy utilization and multi-energy complementarity in wastewater treatment plants under uncertain conditions, a stochastic multi-criteria decision analysis framework for integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants is constructed for practical applications, enabling robust decision-making in this area under uncertain conditions. 1.1 Identify the key influencing factors of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. Based on the actual monitoring status of various production capacities, energy consumption, energy storage, and power distribution networks within the wastewater treatment plant, collect long-term series monitoring data on the influencing factors of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants, such as wastewater treatment energy consumption and renewable energy production capacity. Check the quality of the monitoring data on the influencing factors of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants and clean the data to ensure the reliability of the long-term series monitoring data on comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. 1.2 Construct a multi-criteria decision analysis framework for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. Based on the actual needs of the wastewater treatment plant's comprehensive energy utilization and multi-energy complementarity optimization on-site and the actual monitoring status of the plant's capacity, energy consumption, energy storage, and power distribution network, determine the initial scheme set, decision criteria set, and weight set of the decision criteria for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. Considering the uncertainty of each decision criterion for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants, the deterministic preferences of the decision criteria for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants are transformed into random variables, constants, or a mixture of both through probability distribution.

[0064] 1.2.1 The selection and determination of the comprehensive energy utilization and multi-energy complementarity optimization scheme of the wastewater treatment plant is regarded as a multi-criteria decision analysis problem. Before making the decision on the comprehensive energy utilization and multi-energy complementarity optimization of the wastewater treatment plant, the access and shutdown of various renewable energy sources of the wastewater treatment plant are determined based on the long-term series monitoring data of various influencing factors of the comprehensive energy utilization and multi-energy complementarity optimization of the wastewater treatment plant.

[0065] For the multi-energy utilization of wastewater treatment plants in the embodiment, a set of initial alternative schemes for comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants were randomly obtained. This set of schemes consists of... The system consists of an integrated energy utilization and multi-energy complementarity optimization scheme for each wastewater treatment plant. , Indicates the first The initial alternative scheme for the comprehensive energy utilization and multi-energy complementarity optimization of a wastewater treatment plant includes whether various renewable energy sources within the wastewater treatment plant are connected, as well as the order and duration of their connection. 1.2.2 Select key influencing factors of comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants as decision criteria for multi-criteria decision analysis of comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants, and use them to evaluate and screen alternative schemes for comprehensive energy utilization and multi-energy complementarity optimization of each wastewater treatment plant.

[0066] For the multi-energy utilization of wastewater treatment plants in the implementation example, the set of decision criteria for comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is defined as follows: , contains Optimization decision-making criteria for comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants, using This represents the weight set of the decision-making criteria for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. Indicating the comprehensive energy utilization and multi-energy complementarity optimization decision-making criteria for wastewater treatment plants The corresponding weights. Let... The performance preference value is the decision-making standard for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. This indicates an optimized alternative scheme for comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants. , Indicating the comprehensive energy utilization and multi-energy complementarity optimization decision-making criteria for wastewater treatment plants The comprehensive energy utilization and multi-energy complementarity optimization decision matrix of wastewater treatment plants can be expressed as: The multi-criteria decision analysis of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants satisfies the following relationships:

[0067] In the formula, This is a function of the comprehensive energy utilization and multi-energy complementarity optimization decision-making model for the wastewater treatment plant. It is an optimized alternative scheme for the comprehensive energy utilization and multi-energy complementarity of wastewater treatment plants, obtained by measuring the comprehensive energy utilization and multi-energy complementarity optimization decision-making criteria of all wastewater treatment plants. The overall performance value. The ranking of integrated energy utilization and multi-energy complementary optimization alternatives for wastewater treatment plants can be determined based on the overall performance value of these alternatives, selecting the one with the highest value. The comprehensive energy utilization and multi-energy complementary optimization alternative scheme of the sewage treatment plant was used as the final decision-making scheme.

[0068] 1.2.3 Considering the uncertainty of the preferences of each decision-maker involved in the comprehensive energy utilization and multi-energy complementarity optimization decision of the wastewater treatment plant, the deterministic elements in the decision matrix are represented by random elements. A probability distribution method is used to transform the decision criteria preference values ​​of each decision into random variables, constants, or a mixture of both. The stochastic decision matrix for the comprehensive energy utilization and multi-energy complementarity optimization of the wastewater treatment plant is expressed as follows:

[0069] In the formula, This indicates the stochastic decision-making criteria preference for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. , .

[0070] 2. For example Figure 2 As shown, a feasible weight space for the decision criteria of comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is established. The game theory method is used to aggregate and resolve the weights of each decision criterion of comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants, and the uncertainty of each decision criterion of comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is estimated. 2.1 Based on the preferences of decision-makers for comprehensive energy utilization and multi-energy complementarity optimization of various wastewater treatment plants regarding different decision-making standards, the basic weight vector for comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is obtained.

[0071] For the multi-energy utilization of the wastewater treatment plant in the example, it is assumed that the number of decision-makers involved in the comprehensive energy utilization and multi-energy complementarity optimization decision of the wastewater treatment plant is 5. ,in , This indicates the number of decision-makers involved in the comprehensive energy utilization and multi-energy complementarity optimization decision-making process of wastewater treatment plants; 2.2 Based on the basic weight vectors for the comprehensive energy utilization and multi-energy complementarity optimization decision-making of wastewater treatment plants provided by different decision-makers, a linear combination method is used to obtain the standard combination weight vector for the comprehensive energy utilization and multi-energy complementarity optimization decision-making of wastewater treatment plants, expressed as follows: ; 2.3 To obtain the optimal decision-making scheme for integrated energy utilization and multi-energy complementarity in wastewater treatment plants, the deviation between the weight vector of the standard combination of integrated energy utilization and multi-energy complementarity optimization decision-making scheme and the basic weight vector of integrated energy utilization and multi-energy complementarity optimization decision-making scheme provided by each decision-maker should be minimized, i.e. The coordination weight vector of the comprehensive energy utilization and multi-energy complementarity optimization decision-making criteria of the sewage treatment plant is obtained by solving the problem. 2.4 In the process of solving for the coordination weight vector of the comprehensive energy utilization and multi-energy complementarity optimization decision criteria of the wastewater treatment plant, the loss of preference information of each decision-maker is inevitable. In order to quantify and assess the uncertainty of the loss of preference information of each decision-maker, the coordination weight vector of the comprehensive energy utilization and multi-energy complementarity optimization decision criteria of the wastewater treatment plant is extended from a single point to the entire feasible weight space of the comprehensive energy utilization and multi-energy complementarity optimization decision criteria of the wastewater treatment plant.

[0072] 3. Combining the theory of acceptability analysis of stochastic multi-criteria decision-making and the theory of grey relational analysis, we construct a stochastic multi-criteria decision-making acceptability analysis-grey relational analysis model for the optimization of integrated energy utilization and multi-energy complementarity in wastewater treatment plants. We also propose an uncertainty assessment index for the optimization of integrated energy utilization and multi-energy complementarity in wastewater treatment plants to quantitatively assess the uncertainty and decision error risk of multi-criteria decision-making in the optimization of integrated energy utilization and multi-energy complementarity in wastewater treatment plants. 3.1 Using grey system theory, the weight values ​​and information preferences of the decision criteria for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants are aggregated into a quantitative grey relational degree value. A stochastic multi-criteria decision-making-grey relational analysis model for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is constructed to handle the uncertainty of the decision criteria for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. 3.1.1 Grey relational analysis is used to solve the multi-criteria decision analysis problem of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. Through grey system theory, the weight values ​​of decision criteria and information preferences for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants are aggregated into a quantitative grey relational degree value. The weighted sum of the grey relational degree coefficients of each initial alternative scheme for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants is calculated, laying the foundation for the final decision of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. 3.1.1.1 For each decision-making criterion set of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants, define the reference set of the decision-making criterion set for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants.

[0073]

[0074] 3.1.1.2 Optimization Decision Matrix for Comprehensive Energy Utilization and Multi-Energy Complementarity in Wastewater Treatment Plants Normalization Reference set Also normalized to ; 3.1.1.3. The initial alternative schemes and normalized grey relational coefficients for comprehensive energy utilization and multi-energy complementarity optimization of each wastewater treatment plant were calculated:

[0075] In the formula, Optimizing the initial alternative scheme for comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants The normalized decision criterion preference vector; The optimal identification coefficient for comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants is set at 0.5.

[0076] 3.1.1.4. Calculate the weighted sum of the grey relational coefficients of each initial alternative scheme for the comprehensive energy utilization and multi-energy complementarity optimization decision of the wastewater treatment plant, and use... express:

[0077] In the formula, Optimize initial alternative schemes for comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants A comprehensive assessment of all decision-making criteria. This provides a basis for the final decision-making scheme of comprehensive energy utilization and multi-energy complementarity optimization for each wastewater treatment plant, namely, the scheme with the largest grey relational degree is usually the more popular scheme.

[0078] 3.1.2 Construct a stochastic multi-criteria decision-making model for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. Introduce the real-valued utility function for the stochastic multi-criteria decision-making analysis of the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants to obtain the ranking of the initial alternative schemes for the comprehensive energy utilization and multi-energy complementarity optimization of each wastewater treatment plant. Evaluate and analyze the advantages and disadvantages of the initial alternative schemes for the comprehensive energy utilization and multi-energy complementarity optimization of each wastewater treatment plant.

[0079] 3.1.2.1 The merits of the comprehensive energy utilization and multi-energy complementarity optimization stochastic multi-criteria decision analysis schemes for wastewater treatment plants are evaluated using real-valued utility functions. The formula for calculating the real-valued utility function in stochastic multi-criteria decision analysis for comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is as follows:

[0080] In the formula, This indicates the initial alternative scheme for comprehensive energy utilization and multi-energy complementarity optimization decision-making in wastewater treatment plants. Performance preference vector of decision criteria.

[0081] In the grey relational analysis model of stochastic multi-criteria decision-making for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants, the grey relational algorithm is used to replace the real-valued utility function of the stochastic multi-criteria decision-making model for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants, resulting in:

[0082] In the formula, the function Grey relational analysis of initial alternative schemes for comprehensive energy utilization and multi-energy complementarity optimization decision-making in wastewater treatment plants.

[0083] 3.1.2.2. The ranking function for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is used to determine the initial ranking of the comprehensive energy utilization and multi-energy complementarity optimization schemes for each wastewater treatment plant. The initial ranking of the comprehensive energy utilization and multi-energy complementarity optimization schemes for each wastewater treatment plant is from best (ranked as 1) to worst (ranked as 2). The calculation formula for ranking the initial alternative schemes for comprehensive energy utilization and multi-energy complementarity optimization of each wastewater treatment plant is as follows:

[0084] In the formula, Optimized alternatives for comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants Stochastic multi-criteria decision analysis: decision criterion weights, performance, and preference vectors. , ; It is a utility function of type gra.

[0085] 3.1.2.3. A stochastic multi-criteria decision-making model for the integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants was run, and five evaluation indicators for the integrated energy utilization and multi-energy complementarity optimization decision-making were calculated and output, providing a basis for the final decision on the integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants. The evaluation indicators for the integrated energy utilization and multi-energy complementarity optimization decision-making of wastewater treatment plants include: ① Ranking Acceptability Index (RAI); ② Overall Acceptability Index (HAI); ③ Center Weight Vector (CWV); ④ Confidence Factor; and ⑤ Cross-Confidence Factor.

[0086] The Acceptability Index (RAI) for Integrated Energy Utilization and Multi-Energy Complementarity Optimization Decision-Making Schemes in Wastewater Treatment Plants is used to... express, This represents the expected volume of the favorable ranking weight set of each initial alternative scheme for the comprehensive energy utilization and multi-energy complementarity optimization decision of each wastewater treatment plant. It is mainly used to measure the impact of the initial alternative schemes on the comprehensive energy utilization and multi-energy complementarity optimization of each wastewater treatment plant. The diversity of valuations across different ranking levels. Performance preference distribution of the comprehensive energy utilization and multi-energy complementarity optimization decision criteria for wastewater treatment plants and Multidimensional integrals on:

[0087] Clearly, the acceptable range of the decision-making scheme level for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants is as follows: Where 0 indicates that the initial alternative for the integrated energy utilization and multi-energy complementarity optimization decision of a wastewater treatment plant cannot obtain a given rank, and 1 indicates that any combination of criterion weights for the integrated energy utilization and multi-energy complementarity optimization decision of a wastewater treatment plant can always obtain a given rank. If an initial alternative for the integrated energy utilization and multi-energy complementarity optimization decision of a wastewater treatment plant obtains the best ranking and has a large RAI, then the integrated energy utilization and multi-energy complementarity optimization of the wastewater treatment plant is considered an acceptable solution. However, for alternatives for the integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants with poor ranking and large RAI, they should be eliminated from the set of alternatives for the integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants.

[0088] The Overall Acceptability Index (HAI) of the comprehensive energy utilization and multi-energy complementarity optimization decision-making scheme for wastewater treatment plants is used. express, The overall acceptability of initial alternatives for integrated energy utilization and multi-energy complementarity optimization decisions at each wastewater treatment plant is used to examine the overall acceptability of these alternatives. It is defined as the weighted sum of the acceptability indices for all wastewater treatment plant integrated energy utilization and multi-energy complementarity optimization decision-making schemes. The overall acceptability index for integrated energy utilization and multi-energy complementarity optimization decision-making schemes at wastewater treatment plants is... The calculation formula is:

[0089] in, The meta-weights represent the decision-making criteria for integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants, reflecting the contribution of the acceptability index of each wastewater treatment plant's integrated energy utilization and multi-energy complementarity optimization decision-making scheme to the evaluation of initial alternative schemes. This method will... Defined as a monotonically decreasing vector The study simulates a scenario where the best ranking in the comprehensive energy utilization and multi-energy complementarity optimization decision-making of a wastewater treatment plant is better than the worst ranking.

[0090] The central weight vector (CWV) of the comprehensive energy utilization and multi-energy complementarity optimization decision-making scheme for wastewater treatment plants is used. express, Initial alternative solutions for comprehensive energy utilization and multi-energy complementarity optimization decision-making in all wastewater treatment plants The expected center of gravity of the favorable first-level weight space. Information indicating preferences for the initial alternative schemes for the integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants helps decision-makers understand how different weights are related to different decisions and facilitates the allocation of decision criteria weights for the integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants. Distribution of performance preference values ​​calculated as the decision-making criteria for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants and favorable first-level weights Multidimensional integrals:

[0091] Confidence factors for comprehensive energy utilization and multi-energy complementarity optimization decision-making schemes in wastewater treatment plants express, This represents the probability of the most favored initial alternative scheme for the integrated energy utilization and multi-energy complementarity optimization decision scheme of a wastewater treatment plant, based on its own central weight vector. It is a measure of whether the standard data is accurate enough to identify the initial alternative schemes for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. It can be regarded as the proportion of the stochastic criterion space that leads to the optimal alternative schemes for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. The multidimensional integral calculated as the performance preference distribution of the comprehensive energy utilization and multi-energy complementarity optimization decision criteria for wastewater treatment plants:

[0092] If the confidence factor for the integrated energy utilization and multi-energy complementarity optimization decision scheme of the wastewater treatment plant is small, it indicates that even using the performance preference value of this decision criterion, the initial alternative scheme for integrated energy utilization and multi-energy complementarity optimization is unlikely to be considered the most popular scheme. Conversely, if the confidence factor for the integrated energy utilization and multi-energy complementarity optimization decision scheme of the wastewater treatment plant is large, it is considered that the scheme has appropriate preference information, and the integrated energy utilization and multi-energy complementarity optimization decision scheme of the wastewater treatment plant is usually the most popular scheme.

[0093] The cross-confidence factor for the comprehensive energy utilization and multi-energy complementarity optimization decision-making scheme of wastewater treatment plants is mainly to improve the ability of the stochastic multi-criteria decision-making-grey relational analysis model for comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants to distinguish similar schemes. express. The decision criteria weight preference values ​​were calculated from the comprehensive energy utilization and multi-energy complementarity optimization decision-making schemes of other wastewater treatment plants. Among them, the initial alternative schemes for comprehensive energy utilization and multi-energy complementarity optimization decision-making of wastewater treatment plants were... Compared to the optimization objectives of comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants, the following alternative solutions are available. The cross-confidence coefficient is expressed as:

[0094] The cross-confidence factor for the integrated energy utilization and multi-energy complementarity optimization decision-making scheme of wastewater treatment plants mainly measures the probability that the alternative scheme will achieve the optimal ranking when using the performance preference of the decision criteria weights of the target integrated energy utilization and multi-energy complementarity optimization decision-making scheme. Clearly, if the cross-confidence factor... If the value is not 0, it indicates that the wastewater treatment plant's comprehensive energy utilization and multi-energy complementarity optimization decision-making alternative scheme is... This will be an alternative decision-making scheme for integrated energy utilization and multi-energy complementarity in wastewater treatment plants. Competing for the best ranking, non-zero cross-confidence factor This indicates the intensity of competition. Simultaneously, the cross-confidence factor... equal to confidence factor .

[0095] 3.2 An evaluation index system for stochastic multi-criteria decision-making and grey relational analysis of integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants was established to assess and analyze the uncertainty of the decision-making results of initial alternative schemes in integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants. This evaluation index system mainly includes two indicators: ① the risk of decision-making errors in integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants; ② the uncertainty of the ranking of integrated energy utilization and multi-energy complementarity optimization schemes in wastewater treatment plants.

[0096] 3.2.1 Risk of Decision-Making Errors in Comprehensive Energy Utilization and Multi-Energy Complementarity Optimization of Wastewater Treatment Plants This represents the uncertainty in achieving the highest ranking for various wastewater treatment plant integrated energy utilization and multi-energy complementarity optimization decision-making schemes. When ranking and selecting these schemes, decision-makers are more concerned with higher-ranked schemes. Considering the uncertainty of the decision-making criteria and their weights for wastewater treatment plant integrated energy utilization and multi-energy complementarity optimization, it is possible for poorly performing schemes to achieve higher rankings, thus deviating from the optimal solution and negatively impacting the overall optimization process. The decision-making error risk for wastewater treatment plant integrated energy utilization and multi-energy complementarity optimization is defined as the weighted probability that a non-optimal alternative scheme achieves the highest ranking:

[0097] In the formula, To obtain the overall acceptability index of the comprehensive energy utilization and multi-energy complementarity optimization decision-making scheme of the wastewater treatment plant, the first... The first-level acceptability index of the comprehensive energy utilization and multi-energy complementary optimization alternative scheme of the wastewater treatment plant. Defined as risk weights, these are used to identify the contribution of each suboptimal wastewater treatment plant's comprehensive energy utilization and multi-energy complementarity optimization decision-making scheme to the decision risk. Represented as increments and sums dimensional vector: .

[0098] 3.2.2 Uncertainty of ranking the comprehensive energy utilization and multi-energy complementarity optimization schemes of wastewater treatment plants. This represents the overall uncertainty in ranking the comprehensive energy utilization and multi-energy complementarity optimization decision-making schemes for each wastewater treatment plant. The ranking uncertainty of the comprehensive energy utilization and multi-energy complementarity optimization schemes for each wastewater treatment plant is the sum of the acceptability indices of the comprehensive energy utilization and multi-energy complementarity optimization decision-making scheme levels obtained by each scheme, excluding the final ranking. The calculation formula is:

[0099] In the formula, It is an optimized alternative solution for comprehensive energy utilization and multi-energy complementarity in wastewater treatment plants. Alternatives to the decision The final level.

[0100] 4. For each decision criterion of the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants, significance analysis is used to determine the impact of the uncertainty of the input parameters of each decision criterion on the decision scheme of comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants.

[0101] 4.1 Considering the uncertainties of the decision-making criteria and weights for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants, a significance analysis is conducted on the decision-making schemes for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants to quantitatively evaluate the impact of the uncertainty of input parameters on the final decision-making schemes for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants. 4.2. Calculate the Pearson rank correlation coefficients corresponding to the comprehensive energy utilization and multi-energy complementarity optimization decision-making criteria for each wastewater treatment plant in turn, and evaluate the impact of the uncertainty of the input parameters of the comprehensive energy utilization and multi-energy complementarity optimization decision-making criteria for different wastewater treatment plants on the results of the comprehensive energy utilization and multi-energy complementarity optimization decision-making for wastewater treatment plants.

[0102] Based on the same inventive concept, the present invention also provides a robust decision-making device for multi-energy utilization in wastewater treatment plants, such as... Figure 3 As shown, the device includes: The module 201 for constructing a stochastic multi-criteria decision analysis framework for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is used to construct a stochastic multi-criteria decision analysis framework for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants oriented towards practical applications, and to make robust decisions on the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants under uncertain conditions; for details, please refer to the description of step 1 in the above embodiment, which will not be repeated here.

[0103] The uncertainty estimation module 202 for the decision criteria of integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants is used to establish the feasible weight space of the decision criteria for integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants. It uses game theory to aggregate and resolve the weights of each decision criterion for integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants, and estimates the uncertainty of each decision criterion for integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants. For details, please refer to the description of step 2 in the above embodiment, which will not be repeated here.

[0104] The wastewater treatment plant integrated energy utilization and multi-energy complementarity optimization multi-criteria decision analysis module 203 is used to combine the theory of acceptability analysis of stochastic multi-criteria decision-making and the theory of grey relational analysis to construct a stochastic multi-criteria decision-making acceptability analysis-grey relational analysis model for integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants. It also proposes an uncertainty assessment index for integrated energy utilization and multi-energy complementarity optimization decision-making of wastewater treatment plants to quantitatively assess the uncertainty and decision error risk of integrated energy utilization and multi-energy complementarity optimization multi-criteria decision-making of wastewater treatment plants. For details, please refer to the description of step 3 in the above embodiment, which will not be repeated here.

[0105] The uncertainty determination module 204 for multi-criteria decision analysis of integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants is used to determine the impact of the uncertainty of the input parameters of each decision criterion on the decision scheme of integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants using the significance analysis method. For details, please refer to the description of step 4 in the above embodiment, which will not be repeated here.

[0106] For the multi-energy utilization implementation of wastewater treatment plants, the stochastic multi-criteria decision analysis framework construction module 201 for comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants includes: The submodule for collecting and analyzing long-term series data of influencing factors of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants is used to acquire and analyze long-term series monitoring data of influencing factors of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants; for details, please refer to the description in the above embodiments, which will not be repeated here.

[0107] The sub-module of the comprehensive energy utilization and multi-energy complementarity optimization multi-criteria decision analysis framework for wastewater treatment plants is used to construct the comprehensive energy utilization and multi-energy complementarity optimization multi-criteria decision analysis framework for wastewater treatment plants; for details, please refer to the description in the above embodiments, and will not be repeated here.

[0108] For the multi-energy utilization implementation example of wastewater treatment plants, the uncertainty estimation module 202 for the comprehensive energy utilization and multi-energy complementarity optimization decision-making criteria of wastewater treatment plants includes: The submodule for setting preferences for comprehensive energy utilization and multi-energy complementarity optimization decisions in wastewater treatment plants is used to obtain the basic weight vector for comprehensive energy utilization and multi-energy complementarity optimization decisions based on the preferences of each decision-maker for different decision-making standards. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0109] The submodule for determining the weight vector combination of comprehensive energy utilization and multi-energy complementarity optimization decision criteria for wastewater treatment plants is used to obtain the weight vector combination of comprehensive energy utilization and multi-energy complementarity optimization decision criteria for wastewater treatment plants by using a linear combination method based on the basic weight vectors of comprehensive energy utilization and multi-energy complementarity optimization decision provided by different decision-makers; for details, please refer to the description in the above embodiments, which will not be repeated here.

[0110] The submodule for solving the coordination weight vector of the comprehensive energy utilization and multi-energy complementarity optimization decision-making of the wastewater treatment plant is used to solve for the coordination weight vector of the comprehensive energy utilization and multi-energy complementarity optimization decision-making standard of the wastewater treatment plant; for details, please refer to the description in the above embodiments, and will not be repeated here.

[0111] The submodule for extending the coordination weight vector of the comprehensive energy utilization and multi-energy complementarity optimization decision-making standard of the wastewater treatment plant is used to extend the coordination weight vector of the comprehensive energy utilization and multi-energy complementarity optimization decision-making standard of the wastewater treatment plant from a single point to the entire feasible weight space of the comprehensive energy utilization and multi-energy complementarity optimization decision-making standard of the wastewater treatment plant; for details, please refer to the description in the above embodiments, and will not be repeated here.

[0112] For the multi-energy utilization implementation of wastewater treatment plants, the multi-criteria decision analysis module 203 for comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants includes: The submodule for constructing a stochastic multi-criteria decision-making model for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants is used to construct such a model and address the uncertainty of the decision-making criteria. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0113] The submodule for uncertainty assessment and analysis of multi-criteria decision-making in integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants is used to assess and analyze the uncertainty of the decision-making results of the initial alternative schemes for integrated energy utilization and multi-energy complementarity optimization of wastewater treatment plants; for details, please refer to the description in the above embodiments, which will not be repeated here.

[0114] For the multi-energy utilization implementation of wastewater treatment plants, the uncertainty determination module 204 of the multi-criteria decision analysis for comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants includes: The saliency analysis submodule for the comprehensive energy utilization and multi-energy complementarity optimization decision-making scheme of wastewater treatment plant is used to perform saliency analysis on the comprehensive energy utilization and multi-energy complementarity optimization decision-making scheme of wastewater treatment plant, and to quantitatively evaluate the impact of input parameter uncertainty on the final scheme of comprehensive energy utilization and multi-energy complementarity optimization decision-making scheme of wastewater treatment plant; for details, please refer to the description in the above embodiments, and will not be repeated here.

[0115] The submodule for analyzing the uncertainty of input parameters for the comprehensive energy utilization and multi-energy complementarity optimization decision of wastewater treatment plants is used to calculate the Spearman rank correlation coefficient corresponding to the comprehensive energy utilization and multi-energy complementarity optimization decision criteria of each wastewater treatment plant, and to evaluate the impact of the uncertainty of input parameters of different comprehensive energy utilization and multi-energy complementarity optimization decision criteria on the results of comprehensive energy utilization and multi-energy complementarity optimization decision of wastewater treatment plants; for details, please refer to the description in the above embodiments, and will not be repeated here.

[0116] The specific limitations and beneficial effects of the aforementioned device can be found in the above description of the robust decision-making method for multi-utilization of wastewater treatment plants, and will not be repeated here. Each of the above modules can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0117] Figure 4 This is a schematic diagram of the hardware structure of a computer device proposed according to an embodiment of multi-functional utilization of a wastewater treatment plant. For example... Figure 4 As shown, the device includes one or more processors 310 and a memory 320, which includes persistent memory, volatile memory, and a hard disk. Figure 4 Taking a processor 310 as an example, the device may also include an input device 330 and an output device 340.

[0118] The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0119] Processor 310 can be a Central Processing Unit (CPU). Processor 310 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof. The general-purpose processor can be a microprocessor or any conventional processor.

[0120] The memory 320, as a non-transitory computer-readable storage medium, includes persistent memory, volatile memory, and a hard disk. It can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the robust multi-utilization decision-making method for wastewater treatment plants in this embodiment. The processor 310 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory 320, thereby implementing any of the aforementioned robust multi-utilization decision-making methods for wastewater treatment plants.

[0121] The memory 320 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data that is needed and required. Furthermore, the memory 320 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 320 may optionally include memory remotely located relative to the processor 310, and these remote memories can be connected to the data processing device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0122] Input device 330 can receive input digital or character information, and generate signal inputs related to user settings and function control. Output device 340 may include display devices such as a display screen.

[0123] One or more modules are stored in memory 320, and when executed by one or more processors 310, they perform actions such as... Figure 1 The method shown.

[0124] The above-described product can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in [reference 1]. Figure 1 The relevant descriptions in the illustrated embodiments.

[0125] This invention also provides a non-transitory computer storage medium storing computer-executable instructions that can execute the robust decision-making method for multi-utilization of wastewater treatment plants in any of the above method embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above types of memory.

[0126] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0127] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A robust decision-making method for multi-utilization of wastewater treatment plants, characterized in that, Includes the following steps: A stochastic multi-criteria decision analysis framework for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is constructed for practical applications, so as to achieve robust decision-making for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants under uncertainty conditions. A feasible weight space for the decision criteria of comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is established. The game theory method is used to aggregate and resolve the weights of each decision criterion of comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants, and the uncertainty of each decision criterion of comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is estimated. Combining the theories of acceptability analysis of stochastic multi-criteria decision-making and grey relational analysis, a grey relational analysis model for acceptability analysis of stochastic multi-criteria decision-making in the optimization of integrated energy utilization and multi-energy complementarity in wastewater treatment plants is constructed. An uncertainty assessment index for the optimization of integrated energy utilization and multi-energy complementarity in wastewater treatment plants is proposed to quantify the uncertainty and decision error risk of multi-criteria decision-making in the optimization of integrated energy utilization and multi-energy complementarity in wastewater treatment plants. For each decision criterion of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants, significance analysis is used to determine the impact of the uncertainty of the input parameters of each decision criterion on the decision scheme of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants.

2. The robust decision-making method for multi-energy utilization of wastewater treatment plants according to claim 1, characterized in that, The steps for constructing a stochastic multi-criteria decision analysis framework for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants, oriented towards practical applications, include: The key influencing factors of comprehensive energy utilization and multi-energy complementarity optimization in sewage treatment plants were identified. Based on the actual monitoring status of various production capacities, energy consumption, energy storage, and power distribution networks in the sewage treatment plant, long-term series monitoring data of sewage treatment energy consumption and renewable energy production capacity were collected. The monitoring data were checked and cleaned to ensure data quality and reliability. A multi-criteria decision analysis framework for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is constructed. Based on the actual needs and monitoring status of the wastewater treatment plant, the initial scheme set, decision criterion set, and decision criterion weight set for the comprehensive energy utilization and multi-energy complementarity optimization of the wastewater treatment plant are determined. The deterministic decision criterion preferences are transformed into random variables, constants, or a mixture of the two through probability distribution.

3. The robust decision-making method for multi-energy utilization of wastewater treatment plants according to claim 2, characterized in that, The steps in constructing a multi-criteria decision analysis framework for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants also include: The selection and determination of the comprehensive energy utilization and multi-energy complementarity optimization scheme of the sewage treatment plant is taken as a multi-criteria decision analysis problem. Based on long-term series monitoring data, the access and shutdown of various renewable energy sources in the sewage treatment plant are determined. An initial alternative scheme set consisting of multiple comprehensive energy utilization and multi-energy complementarity optimization schemes of the sewage treatment plant is randomly generated. Each scheme includes the access status, access order and duration of various renewable energy sources in the sewage treatment plant. Key influencing factors of comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants are selected as decision criteria for multi-criteria decision analysis. These criteria are used to evaluate and screen various alternative solutions and construct a decision matrix, where the elements of the decision matrix are the performance preference values ​​of each alternative solution under each decision criterion. The deterministic elements in the decision matrix are represented by random elements, and the preference values ​​of each decision criterion are transformed into random variables, constants, or a mixture of both using probability distribution methods to form a stochastic decision matrix.

4. The robust decision-making method for multi-energy utilization of wastewater treatment plants according to claim 1, characterized in that, The steps to establish a feasible decision-making framework for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants include: Based on the preferences of each decision-maker for different decision-making criteria, the basic weight vector for the comprehensive energy utilization and multi-energy complementarity optimization decision of wastewater treatment plants is obtained. Based on the basic decision weight vectors provided by different decision-makers, a linear combination method is used to obtain the decision standard combination weight vector; The decision standard coordination weight vector is obtained by minimizing the deviation between the combined weight vector of decision criteria and the basic weight vector of decision provided by each decision-maker. The decision criterion coordination weight vector is extended from a single point to the entire feasible space of decision criterion in order to quantitatively assess the uncertainty of the loss of preference information for each decision-maker.

5. The robust decision-making method for multi-energy utilization of wastewater treatment plants according to claim 1, characterized in that, The steps involved in constructing a grey relational analysis model for the acceptability analysis of stochastic multi-criteria decision-making in wastewater treatment plant integrated energy utilization and multi-energy complementarity optimization include: Grey system theory is used to aggregate the decision criterion weights and information preferences into quantified grey relational degree values, and a stochastic multi-criteria decision-grey relational analysis model is constructed to handle the uncertainty of decision criteria. A stochastic multi-criteria decision-making-grey relational analysis evaluation index system is established to assess and analyze the uncertainty of the decision results of the initial alternative schemes. The evaluation index system includes the risk of decision error and the uncertainty of the scheme ranking.

6. The robust decision-making method for multi-energy utilization of wastewater treatment plants according to claim 5, characterized in that, The steps for aggregating decision criterion weights and information preferences into quantified grey relational degree values ​​using grey system theory include: For each set of decision criteria, define a set of references for the decision criteria set; The decision matrix is ​​normalized, and the reference set is also normalized. Calculate the normalized grey relational coefficients for each initial alternative, where the identification coefficient is set to 0.5; Calculate the weighted sum of the grey relational coefficients of each initial alternative as the global evaluation value of each initial alternative with respect to all decision criteria.

7. The robust decision-making method for multi-energy utilization of wastewater treatment plants according to claim 5, characterized in that, After running the stochastic multi-criteria decision-making-grey relational analysis model, it outputs five decision evaluation indicators, including: The decision alternative ranking acceptability index represents the expected volume of the favorable ranking weight set of each initial alternative and is used to measure the diversity of valuations that lead to different rankings of the initial alternatives. The overall acceptability index of decision options is used to examine the overall acceptability of each initial alternative and is defined as the weighted sum of the acceptability indices of all decision options at the level of acceptance. The decision scheme center weight vector is the expected centroid of the first-level weight space favorable to all initial alternatives, representing the preference information supporting the corresponding initial alternative; The decision confidence factor is the probability of the most favored initial alternative based on its own decision center weight vector, used to measure whether the standard data is accurate enough to identify the initial alternative. The decision alternative cross-confidence factor measures the probability that the decision alternative will achieve the best ranking when using the performance preference of the decision criteria weights of the target alternatives.

8. The robust decision-making method for multi-energy utilization of wastewater treatment plants according to claim 5, characterized in that, Decision error risk is used to measure the uncertainty of each decision option obtaining the highest ranking, and is defined as the weighted probability of a non-optimal alternative obtaining the highest ranking; the ranking uncertainty is used to measure the overall uncertainty of the ranking results of each decision option, and is the sum of the acceptability indices of the decision option rankings of each option obtaining all possible rankings other than the final ranking.

9. The robust decision-making method for multi-energy utilization of wastewater treatment plants according to claim 1, characterized in that, The steps for determining the uncertainty of each decision criterion input parameter using significance analysis include: Considering the uncertainties of decision criteria and weights respectively, a significance analysis is conducted on the decision schemes to quantitatively evaluate the impact of input parameter uncertainty on the final decision scheme; The Spearman rank correlation coefficients corresponding to each decision criterion are calculated sequentially to assess the impact of the uncertainty of input parameters for different decision criters on the decision results.

10. A robust decision-making device for multi-functional utilization in wastewater treatment plants, characterized in that, The system employs a robust decision-making method for multi-energy utilization in wastewater treatment plants according to any one of claims 1-9, and includes: The module for constructing a stochastic multi-criteria decision analysis framework for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants is used to build a stochastic multi-criteria decision analysis framework for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants oriented towards practical applications, so as to achieve robust decision-making for the comprehensive energy utilization and multi-energy complementarity optimization of wastewater treatment plants under uncertainty conditions. The module for estimating the uncertainty of decision criteria for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants is used to establish the feasible weight space of decision criteria for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. It uses game theory to aggregate and resolve the weights of each decision criterion for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants, and estimates the uncertainty of each decision criterion for comprehensive energy utilization and multi-energy complementarity optimization in wastewater treatment plants. The module for multi-criteria decision analysis of integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants combines the theory of acceptability analysis of stochastic multi-criteria decision-making and the theory of grey relational analysis to construct a stochastic multi-criteria decision-making acceptability analysis-grey relational analysis model for integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants. It also proposes an uncertainty assessment index for integrated energy utilization and multi-energy complementarity optimization decision-making in wastewater treatment plants to quantitatively assess the uncertainty and decision error risk of integrated energy utilization and multi-energy complementarity optimization decision-making in wastewater treatment plants. The uncertainty determination module for multi-criteria decision analysis of integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants is used to determine the impact of the uncertainty of the input parameters of each decision criterion on the decision scheme of integrated energy utilization and multi-energy complementarity optimization in wastewater treatment plants using significance analysis.

11. A computer device, characterized in that, The system includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the steps of the robust decision-making method for multi-utilization of wastewater treatment plants as described in any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the robust decision-making method for multi-utilization of wastewater treatment plants as described in any one of claims 1 to 9.

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