Method and device for dynamically adjusting carbon emission in operation and maintenance period of railway bridge construction

Abstract

The invention provides a method and device for dynamically adjusting carbon emission in a railway bridge construction operation and maintenance period, and relates to the technical field of bridge construction operation and maintenance, and the method comprises the steps: constructing resource consumption, environmental influence and economic benefit index data, and forming a green construction evaluation model; determining each index weight for the green construction evaluation model based on network hierarchy analysis, performing similarity evaluation on each index weight through a weighted cloud model, and constructing a cloud similarity model; optimizing and solving a Pareto solution set for the green construction evaluation model according to chaotic mapping and a non-dominated sorting genetic algorithm, and calculating decision indexes for the Pareto solution set through a cloud similarity model to obtain a multi-target decision scheme; and performing hierarchical training on the multi-target decision scheme based on a clustering algorithm, constructing a green grade identification model, and dynamically adjusting the carbon emission in the construction operation and maintenance period of the railway bridge in combination with historical subjective evaluation. The problem that carbon emission cannot be effectively reduced in the construction operation and maintenance process is solved.

Description

Technical Field

[0001] This invention relates to the field of bridge construction and operation and maintenance technology, and more specifically, to a method and apparatus for dynamically adjusting carbon emissions during the construction and operation and maintenance period of railway bridges. Background Technology

[0002] In existing bridge construction and operation technologies, firstly, carbon emission assessments primarily rely on subjective expert evaluations, which are inherently uncertain and unsuitable for supporting multi-objective optimization decisions. Secondly, the identification of key influencing factors largely depends on empirical judgment, lacking a quantitative characterization of the coupling relationship between energy consumption, environment, and economic systems, thus failing to accurately assess the impact of each factor on carbon emissions. Finally, complex inherent trade-offs exist among multiple conflicting objectives such as resources, environment, and economy, and existing technologies cannot effectively coordinate these objectives. Consequently, carbon emissions throughout the entire railway bridge construction and operation process are difficult to adjust and optimize, leading to the problem of ineffective carbon emission reduction during construction and operation.

[0003] Therefore, there is an urgent need for a method and device for dynamically adjusting carbon emissions during the construction and maintenance of railway bridges, which would solve the problem of the inability to effectively reduce carbon emissions during construction and maintenance. Summary of the Invention

[0004] The purpose of this invention is to provide a method and apparatus for dynamically adjusting carbon emissions during the construction and maintenance period of railway bridges, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0005] Firstly, this application provides a method for dynamically adjusting carbon emissions during the construction and maintenance period of railway bridges, including:

[0006] To obtain data on the resource consumption, environmental impact, and economic benefits of bridges;

[0007] The data on resource consumption, environmental impact, and economic benefits are used to construct a green construction evaluation model.

[0008] The weight of each indicator in the green construction evaluation model is determined based on network hierarchy analysis, and a cloud similarity model is constructed by evaluating the similarity of each indicator weight through a weighted cloud model.

[0009] The Pareto solution set of the green construction evaluation model is optimized and solved using chaotic mapping and non-dominated sorting genetic algorithm. The decision index is calculated on the Pareto solution set using the cloud similarity model to obtain a multi-objective decision scheme.

[0010] The multi-objective decision-making scheme is trained hierarchically based on clustering algorithm to construct a green level identification model, and the carbon emissions during the construction and operation of railway bridges are dynamically adjusted by combining historical subjective evaluations.

[0011] Secondly, this application also provides a device for dynamically adjusting carbon emissions during the construction and maintenance period of railway bridges, comprising:

[0012] The acquisition module is used to acquire data on the bridge's resource consumption, environmental impact, and economic benefits.

[0013] The construction module is used to construct the data on resource consumption, environmental impact and economic benefits to form a green construction evaluation model;

[0014] The determination module is used to determine the weight of each indicator in the green construction evaluation model based on network hierarchy analysis, and to conduct a similarity evaluation of each indicator weight through a weighted cloud model to construct a cloud similarity model.

[0015] The optimization module is used to optimize the Pareto solution set of the green construction evaluation model based on chaotic mapping and non-dominated sorting genetic algorithm, and to calculate decision indicators on the Pareto solution set through the cloud similarity model to obtain a multi-objective decision scheme.

[0016] The training module is used to train the multi-objective decision-making scheme in a hierarchical manner based on the clustering algorithm, construct a green level identification model, and dynamically adjust the carbon emissions during the construction and operation of railway bridges by combining historical subjective evaluations.

[0017] The beneficial effects of this invention are as follows:

[0018] This invention constructs a green construction evaluation model by comprehensively considering factors such as resource consumption, environmental impact, and economic benefits. It utilizes hierarchical network analysis to determine indicator weights and constructs a cloud similarity model based on the similarity evaluation of a weighted cloud model for dynamic evaluation of the construction and operation process. Furthermore, it optimizes the Pareto solution set through chaotic mapping and a non-dominated sorting genetic algorithm, calculating decision indicators to obtain multi-objective decision schemes, providing a more accurate and reliable basis for decision-making in bridge construction and operation. The multi-objective decision schemes are trained hierarchically to construct a green level identification model, enabling refined management of the construction and operation process. It also dynamically adjusts carbon emissions during the railway bridge construction and operation period based on historical subjective evaluations, adjusting strategies promptly according to actual conditions to better adapt to constantly changing environments and requirements, ensuring that the construction and operation process remains in a good green state. In summary, this invention solves the problem of ineffective carbon emission reduction during construction and operation.

[0019] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the process for dynamically adjusting carbon emissions during the construction and maintenance period of railway bridges as described in this embodiment of the invention.

[0022] Figure 2 This is a schematic diagram of the carbon emission dynamic adjustment equipment during the construction and maintenance period of railway bridges as described in this embodiment of the invention.

[0023] The markings in the diagram are: 800, dynamic adjustment equipment for carbon emissions during the construction and maintenance of railway bridges; 801, processor; 802, memory; 803, multimedia components; 804, I / O interface; 805, communication components. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0025] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0026] Example 1:

[0027] This embodiment provides a method for dynamically adjusting carbon emissions during the construction and maintenance period of railway bridges.

[0028] See Figure 1 The figure shows that the method includes steps S1 to S5, including:

[0029] S1: Obtain data on the bridge's resource consumption, environmental impact, and economic benefits.

[0030] S2: Construct a green construction evaluation model by taking the data on resource consumption, environmental impact and economic benefits;

[0031] To clarify the specific method for obtaining the green construction evaluation model, step S2 includes S21 to S23, specifically:

[0032] S21: The data on resource consumption, environmental impact and economic benefits are screened through preset preliminary screening constraints to obtain a preliminary evaluation index system;

[0033] In this step, the preliminary screening constraints are set based on the actual situation during the construction and operation of railway bridges, as well as industry standards and specifications. By eliminating indicator data that is difficult to obtain, highly repetitive, or not closely related to green construction goals, the preliminary evaluation indicator system is designed to comprehensively and specifically reflect the resource consumption, environmental impact, and economic benefits during the construction and operation of railway bridges.

[0034] S22: Classify the preliminary evaluation index system based on the DPSIR model, and optimize the duplicate indicators through the classified index system to obtain the optimized evaluation index.

[0035] In this step, the DPSIR model is a five-dimensional model of driving force, pressure, state, influence, and response. Based on the DPSIR model, the preliminary evaluation index system is classified. After classification, the indicators under the same dimension are analyzed to find indicators that are duplicated or highly correlated. Duplicate indicators are merged or removed, and highly correlated indicators are screened according to the importance of green construction evaluation, retaining the most representative and key indicators.

[0036] S23: Based on the optimized evaluation indicators and the preset general indicator system, a green construction evaluation model is constructed to obtain the green construction evaluation model.

[0037] In this step, the general indicator system is formulated based on widely recognized green construction evaluation standards and specifications within the industry. The optimized indicators are compared with the general indicator system; optimized data is used for existing indicators, and new indicators are incorporated into the model according to their importance. The model structure and weight allocation are optimized to reflect resource consumption, environmental impact, and economic benefits, thus constructing a scientific and systematic green construction evaluation model.

[0038] S3: Based on network hierarchy analysis, determine the weight of each indicator in the green construction evaluation model, and conduct similarity evaluation of each indicator weight through a weighted cloud model to construct a cloud similarity model;

[0039] This step determines the indicator weights through network hierarchy analysis and constructs a cloud similarity model by combining the similarity evaluation of the weighted cloud model, which is used to dynamically evaluate the construction and operation process.

[0040] To clarify the specific method for obtaining the cloud similarity model, step S3 includes S31 to S35, specifically:

[0041] S31: Obtain historical survey data;

[0042] S32: Based on network hierarchy analysis, the green construction evaluation model is constructed to obtain a network hierarchy model, which includes a target layer, a standard layer, and a scheme layer.

[0043] In this step, the green construction evaluation model, which aims to achieve multi-objective optimization of the entire process of railway bridge construction and operation, is designated as the objective layer. The three criteria dimensions of resource consumption, environmental impact, and economic benefits in the green construction evaluation model are designated as the standard layer. The construction technology path, material selection scheme, and equipment usage scheme in the green construction evaluation model constitute the scheme layer. This hierarchical construction method clearly demonstrates the hierarchical relationship between the various indicators.

[0044] S33: Analyze the correlation between each indicator in the green construction evaluation model based on the historical survey data to obtain the indicator feedback relationship;

[0045] In this step, based on the historical survey data, the mutual influence and feedback mechanism between each indicator in the green construction evaluation model are analyzed to determine the dependence and feedback relationship between each indicator and to construct the indicator feedback relationship.

[0046] S34: Based on the feedback relationship of the indicators, determine the weight of each indicator in the green construction evaluation model to obtain the indicator weight value;

[0047] To clarify the specific method for obtaining the indicator weight values, step S3 includes S341 to S345, specifically:

[0048] S341: Based on the feedback relationship of the indicators, the weight of each indicator in the green construction evaluation model is determined to obtain the initial weight of the indicators;

[0049] In this step, the importance of the indicators in the green construction evaluation model is initially assessed based on the feedback relationship between the indicators, and an initial weight is assigned to each indicator. The initial weight reflects the relative importance of the indicators without considering the influence of other factors.

[0050] S342: Based on the network hierarchy model, compare the indicators with dependent feedback relationships, determine the relative importance by combining the initial weights of the indicators, and obtain the ranking vector;

[0051] In this step, the indicators with dependent feedback relationships are compared pairwise based on the network hierarchy model, and the relative importance between the indicators is determined by combining the initial weights of the indicators.

[0052] The ranking vector of each indicator is calculated by the eigenvalue method. The ranking vector is used to accurately reflect the relative importance of each indicator in the comprehensive evaluation.

[0053] S343: Analyze the interrelationships of the indicators in the network hierarchical model to obtain an unweighted hypermatrix;

[0054] In this step, the relationships between the indicators in the network hierarchy model are analyzed to determine the direct and indirect relationships, and then an unweighted supermatrix is ​​constructed.

[0055] S344: Normalize the sorting vector, construct a weighted matrix using the normalized sorting vector, and construct a weighted supermatrix by combining the unweighted supermatrix with the weighted matrix.

[0056] In this step, the ranking vector is normalized to ensure that the sum of the weights of each indicator is 1, thus obtaining a normalized ranking vector. Then, a weighted matrix is ​​constructed using the normalized ranking vector. The unweighted hypermatrix is ​​multiplied by the weighted matrix to obtain a weighted hypermatrix. This weighted hypermatrix reflects the interaction and weight influence between the indicators.

[0057] S345: Stabilize the weighted supermatrix and calculate the index weight values.

[0058] In this step, the weighted hypermatrix undergoes stabilization processing, and its stability is ensured through iterative calculation. Finally, the weight values ​​of each indicator are calculated and used as indicator weight values, which accurately reflect the importance of each indicator in the green construction evaluation.

[0059] S35: Based on the index weight values ​​and the reverse cloud generation model, the indicators in the green construction evaluation model are comprehensively calculated and constructed to obtain the cloud similarity model.

[0060] To clarify the further acquisition method of the cloud similarity model, step S35 includes S351 to S355, specifically:

[0061] S351: Obtain the cloud model for indicator evaluation;

[0062] S352: Based on the index weight values ​​and the reverse cloud generation model, the digital features in the evaluation cloud model are constructed to obtain the index standard cloud model;

[0063] In this step, based on the indicator weight values ​​and the reverse cloud generation model, the digital features in the evaluation cloud model are adjusted and optimized to construct the indicator standard cloud model. Specifically, the indicator weight values ​​are combined with the digital features in the evaluation cloud model, and the position, width, and thickness are weighted to construct the indicator standard cloud model.

[0064] S353: Calculate and arrange the EPTCM cloud similarity between the indicator standard cloud model and the indicator evaluation cloud model to obtain a similarity matrix;

[0065] In this step, the differences between the indicator standard cloud model and the indicator evaluation cloud model in three numerical features—position, width, and thickness—are first calculated to obtain the similarity value for each feature. Then, these three similarity values ​​are combined to obtain the overall similarity between the indicator standard cloud model and the indicator evaluation cloud model. The similarity value is calculated for all indicators sequentially, and the similarity values ​​are arranged and combined by rows and columns to finally form a similarity matrix.

[0066] S354: Based on the index weight values ​​and the similarity matrix, a similarity judgment matrix is ​​constructed;

[0067] In this step, the weight value of each indicator is multiplied by its similarity value in the similarity matrix to obtain a weighted similarity value. Then, the weighted similarity values ​​of all indicators are arranged according to the structure of the original similarity matrix to form a similarity judgment matrix.

[0068] S355: Perform a comprehensive analysis and evaluation of the similarity in the similarity judgment matrix to construct a cloud similarity model.

[0069] In this step, the weighted similarity values ​​of each indicator are summarized and analyzed to determine the overall performance of each indicator in the green construction evaluation. Then, the entire green construction evaluation model is evaluated based on the overall performance to obtain a comprehensive evaluation result. Finally, a cloud similarity model is constructed based on the comprehensive evaluation result.

[0070] S4: The Pareto solution set of the green construction evaluation model is optimized and solved using the chaotic mapping and non-dominated sorting genetic algorithm. The decision index is calculated on the Pareto solution set using the cloud similarity model to obtain a multi-objective decision scheme.

[0071] In this step, the multi-objective decision-making scheme is used to provide a more accurate and reliable basis for decision-making in bridge construction and operation.

[0072] To clarify the specific methods for obtaining multi-objective decision-making solutions, step S4 includes S41 to S44, specifically:

[0073] S41: Based on the optimization of the population initialization process of the non-dominated sorting genetic algorithm using chaotic mapping, an optimized sorting genetic algorithm is obtained;

[0074] In this step, a series of chaotic sequences are generated based on the chaotic mapping function. These chaotic sequences are then mapped to the value range of the decision variables in the green construction evaluation model, thereby generating an initial population and obtaining the optimized sorting genetic algorithm. The initial population generated in this way more comprehensively covers the search space, improving the algorithm's search capability and optimization effect.

[0075] S42: The green construction evaluation model is solved by multi-objective optimization using the optimized sorting genetic algorithm to obtain the Pareto solution set;

[0076] In this step, the objective functions and constraints of the green construction evaluation model, including resource consumption, environmental impact, and economic benefits, are input into the optimized sorting genetic algorithm. An initial population is generated through chaotic mapping, and then iteratively evolves through genetic operations such as selection, crossover, and mutation. During each iteration, a non-dominated sorting mechanism is used to initially evaluate individuals in the population, selecting those that perform well across multiple objectives. Simultaneously, a crowding calculation mechanism further filters, retaining evenly distributed individuals and eliminating those with poor performance. Through this mechanism, the population is gradually guided towards the Pareto front, ultimately yielding the Pareto solution set.

[0077] S43: Based on the cloud similarity model, a decision index is calculated on the Pareto solution set through multi-criteria decision-making to obtain a compromise decision index value;

[0078] To clarify the specific method for obtaining the compromise decision indicator values, step S43 includes steps S431 to S433, specifically:

[0079] S431: Based on the cloud similarity model, normalize the similarity between the indicator evaluation cloud model and the indicator standard cloud model, and use the normalization result as the target weight coefficient.

[0080] In this step, the similarity between each indicator evaluation cloud model and the indicator standard cloud model is calculated based on the digital features of the cloud similarity model. Then, the similarity is normalized to a range between 0 and 1, and the normalized result is used as the target weight coefficient.

[0081] S432: Based on the target weight coefficient, the group and individual calculations are performed on different decision schemes in the Pareto solution set using the VIKOR multi-criteria decision model to obtain the group utility value and the individual regret value;

[0082] In this step, the ideal solution (optimal value) and the negative ideal solution (worst value) for each objective are determined based on the objective weight coefficients. Then, the weighted gaps for each decision scheme across all objectives are calculated and summed to obtain the group utility value. Furthermore, for each decision scheme, the weighted maximum gap across all objectives is calculated to obtain the individual regret value.

[0083] S433: Calculate the decision index based on the group utility value and the individual regret value to obtain the compromise decision index value.

[0084] In this step, the compromise decision index value comprehensively considers both group utility and individual regret, and is used to evaluate the overall performance of each alternative. The smaller the compromise decision index value, the better the balance between multiple objectives of the alternative, and the closer it is to the ideal optimal solution.

[0085] S44: Sort the candidate solutions in the Pareto solution set according to the compromise decision index value, and select the solution with the smallest compromise decision index value as the multi-objective decision solution.

[0086] S5: Based on the clustering algorithm, the multi-objective decision-making scheme is trained in a hierarchical manner to construct a green level identification model, and the carbon emissions during the construction and operation of railway bridges are dynamically adjusted in combination with historical subjective evaluations.

[0087] This step is used to adjust strategies in a timely manner according to the actual situation, to better adapt to the ever-changing environment and requirements, and to ensure that the construction and operation and maintenance process is always in a good green state.

[0088] To clarify the specific method for obtaining dynamic adjustments, step S5 includes S51 to S54, specifically:

[0089] S51: The multi-objective decision-making scheme is hierarchically divided based on a clustering algorithm, and multiple cluster centers are randomly generated through different levels to obtain the initial cluster centers;

[0090] In this step, the number of clusters K is pre-set based on the clustering algorithm, and the multi-objective decision scheme is classified into levels. In the feature space of the multi-objective decision scheme, K points are randomly selected as the initial cluster centers.

[0091] S52: Calculate the distance between the compromise decision index value in the multi-objective decision scheme and the initial cluster center, and assign each scheme in the multi-objective decision scheme to the nearest cluster based on the distance result to obtain the green level recognition model;

[0092] In this step, after being assigned to the nearest cluster, the average of all schemes within each cluster is calculated, and this average is used as the new cluster center. This process is repeated until the cluster centers no longer change or change only slightly, completing the cluster training. Based on the final cluster centers and the schemes within each cluster, threshold ranges for each green level are defined, and a green level recognition model is constructed.

[0093] S53: Based on the green level identification model and the preset green level warning interval, a warning model is constructed to obtain the warning model;

[0094] In this step, the green level warning range includes a good level, a moderate level, and a severe level, and the specific settings are as follows:

[0095] The warning range for a "good" rating is set to [0, 0.3].

[0096] The warning range for general levels is set to (0.3, 0.7].

[0097] The warning range for the severity level is set to (0.7, 1].

[0098] Based on the green level identification model, the compromise decision indicator values ​​are compared with the preset green level warning intervals to construct an early warning model. The specific rules are as follows:

[0099] A red warning signal is issued when the compromise decision indicator value falls within the warning range of the "serious" level; a yellow warning signal is issued when the compromise decision indicator value falls within the warning range of the "moderate" level; and a green warning signal is issued when the compromise decision indicator value falls within the warning range of the "good" level.

[0100] S54: Based on the aforementioned early warning model and historical subjective evaluation, dynamically adjust the carbon emissions during the construction and maintenance period of railway bridges.

[0101] Example 2:

[0102] This embodiment provides a device for dynamically adjusting carbon emissions during the construction and maintenance period of railway bridges. The device includes:

[0103] The acquisition module is used to acquire data on the bridge's resource consumption, environmental impact, and economic benefits.

[0104] The construction module is used to construct the data on resource consumption, environmental impact and economic benefits to form a green construction evaluation model;

[0105] The determination module is used to determine the weight of each indicator in the green construction evaluation model based on network hierarchy analysis, and to conduct a similarity evaluation of each indicator weight through a weighted cloud model to construct a cloud similarity model.

[0106] To clearly define the specific methods for obtaining the modules, the following are included:

[0107] The acquisition unit is used to acquire historical survey data;

[0108] A construction unit is used to construct a network hierarchy for the green construction evaluation model based on network hierarchy analysis, thereby obtaining a network hierarchy model, which includes a target layer, a standard layer, and a scheme layer.

[0109] The analysis unit is used to analyze the correlation between each indicator in the green construction evaluation model based on the historical survey data, and to obtain the indicator feedback relationship.

[0110] The determining unit is used to determine the weight of each indicator in the green construction evaluation model based on the indicator feedback relationship, and obtain the indicator weight value.

[0111] To clearly define the specific methods for obtaining the unit, the following are included:

[0112] The sub-unit is determined to determine the weight of each indicator in the green construction evaluation model based on the indicator feedback relationship, so as to obtain the initial weight of the indicator.

[0113] The comparison subunit is used to compare indicators with dependent feedback relationships based on the network hierarchical model, determine the relative importance by combining the initial weights of the indicators, and obtain the ranking vector.

[0114] The analysis subunit is used to analyze the interrelationships of various indicators in the network hierarchical model to obtain an unweighted hypermatrix.

[0115] The first construction subunit is used to normalize the sorting vector, construct a weighted matrix using the normalized sorting vector, and construct a weighted supermatrix by combining the unweighted supermatrix with the weighted matrix.

[0116] The calculation subunit is used to stabilize and calculate the weighted supermatrix to obtain the index weight values.

[0117] The calculation unit is used to comprehensively calculate and construct the indicators in the green construction evaluation model based on the indicator weight values ​​and the reverse cloud generation model to obtain the cloud similarity model.

[0118] To clarify the specific methods for obtaining computing units, the following are included:

[0119] Obtain sub-units for acquiring indicator evaluation cloud models;

[0120] The second construction subunit is used to construct the digital features in the evaluation cloud model based on the index weight values ​​and the reverse cloud generation model to obtain the index standard cloud model.

[0121] The arrangement sub-unit is used to calculate and arrange the EPTCM cloud similarity between the indicator standard cloud model and the indicator evaluation cloud model to obtain a similarity matrix;

[0122] The third construction subunit is used to construct a similarity judgment matrix based on the index weight values ​​and the similarity matrix.

[0123] The comprehensive analysis subunit is used to comprehensively analyze and evaluate the similarity in the similarity judgment matrix and construct a cloud similarity model.

[0124] The optimization module is used to optimize the Pareto solution set of the green construction evaluation model based on chaotic mapping and non-dominated sorting genetic algorithm, and to calculate decision indicators on the Pareto solution set through the cloud similarity model to obtain a multi-objective decision scheme.

[0125] The training module is used to train the multi-objective decision-making scheme in a hierarchical manner based on the clustering algorithm, construct a green level identification model, and dynamically adjust the carbon emissions during the construction and operation of railway bridges by combining historical subjective evaluations.

[0126] It should be noted that the specific manner in which each module performs its operation in the apparatus described in the above embodiments has been described in detail in the embodiments of the method, and will not be elaborated here.

[0127] Example 3:

[0128] Corresponding to the above method embodiments, this embodiment also provides a dynamic carbon emission adjustment device for the construction and maintenance period of railway bridges. The dynamic carbon emission adjustment device for the construction and maintenance period of railway bridges described below can be referred to in correspondence with the dynamic carbon emission adjustment method for the construction and maintenance period of railway bridges described above.

[0129] Figure 2 This is a block diagram illustrating a dynamic carbon emission adjustment device 800 for railway bridge construction and maintenance, according to an exemplary embodiment. Figure 2 As shown, the carbon emission dynamic adjustment device 800 during the construction and maintenance period of the railway bridge may include: a processor 801 and a memory 802. The carbon emission dynamic adjustment device 800 during the construction and maintenance period of the railway bridge may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0130] The processor 801 controls the overall operation of the carbon emission dynamic adjustment device 800 during the construction and maintenance period of the railway bridge to complete all or part of the steps in the aforementioned method for dynamic carbon emission adjustment during the construction and maintenance period of the railway bridge. The memory 802 stores various types of data to support the operation of the carbon emission dynamic adjustment device 800 during the construction and maintenance period of the railway bridge. This data may include, for example, instructions for any application or method operating on the carbon emission dynamic adjustment device 800 during the construction and maintenance period of the railway bridge, as well as application-related data, such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the carbon emission dynamic adjustment device 800 and other devices during the construction and maintenance of the railway bridge. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0131] In an exemplary embodiment, the carbon emission dynamic adjustment device 800 during the construction and maintenance period of a railway bridge can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the aforementioned method for dynamic adjustment of carbon emissions during the construction and maintenance period of a railway bridge.

[0132] Example 4:

[0133] Corresponding to the above method embodiments, this embodiment also provides a medium. The medium described below can be referred to in conjunction with the method for dynamic adjustment of carbon emissions during the construction and maintenance period of railway bridges described above.

[0134] A medium storing a computer program, which, when executed by a processor, implements the steps of the method for dynamically adjusting carbon emissions during the construction and maintenance period of railway bridges as described in the above method embodiments.

[0135] The medium can specifically be any medium capable of storing program code, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0136] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0137] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A railway bridge construction operation and maintenance period carbon emission dynamic adjustment method, characterized in that, The method comprises the following steps: obtaining resource consumption, environmental impact and economic benefit index data of the bridge; constructing the resource consumption, environmental impact and economic benefit index data to form a green construction evaluation model; determining the weight of each index of the green construction evaluation model based on a network analytic hierarchy process, and evaluating the weight of each index based on a weighted cloud model to construct a cloud similarity model; optimizing and solving a Pareto solution set of the green construction evaluation model based on a chaotic mapping and a non-dominated sorting genetic algorithm, calculating a decision index of the Pareto solution set based on the cloud similarity model, and obtaining a multi-objective decision scheme; training the multi-objective decision scheme based on a clustering algorithm to construct a green grade recognition model, and dynamically adjusting carbon emissions of the railway bridge construction and operation period in combination with historical subjective evaluation.

2. The railway bridge construction operation and maintenance period carbon emission dynamic adjustment method according to claim 1, characterized in that, The method for determining the weight of each index of the green construction evaluation model based on a network analytic hierarchy process, and evaluating the weight of each index based on a weighted cloud model to construct a cloud similarity model comprises the following steps: obtaining historical research data; constructing a network hierarchy model of the green construction evaluation model based on a network analytic hierarchy process, wherein the network hierarchy model comprises a target layer, a standard layer and a scheme layer; analyzing the correlation between each index in the green construction evaluation model based on the historical research data to obtain an index feedback relationship; determining the weight of each index in the green construction evaluation model based on the index feedback relationship to obtain an index weight value; comprehensively calculating and constructing the index in the green construction evaluation model based on the index weight value and an inverse cloud generation model to obtain a cloud similarity model.

3. The railway bridge construction operation and maintenance period carbon emission dynamic adjustment method according to claim 2, characterized in that, The method for determining the weight of each index in the green construction evaluation model based on the index feedback relationship to obtain an index weight value comprises the following steps: determining the weight of each index in the green construction evaluation model based on the index feedback relationship to obtain an initial index weight; comparing the indexes with dependent feedback relationships based on the network hierarchy model, determining the relative importance in combination with the initial index weight, and obtaining a sorting vector; analyzing the mutual relationship of each index in the network hierarchy model to obtain an unweighted hypermatrix; normalizing the sorting vector, constructing a weighted matrix based on the normalized sorting vector, constructing the unweighted hypermatrix and the weighted matrix to obtain a weighted hypermatrix; stabilizing and calculating the weighted hypermatrix to obtain the index weight value.

4. The railway bridge construction operation and maintenance period carbon emission dynamic adjustment method according to claim 2, characterized in that, The method for comprehensively calculating and constructing the index in the green construction evaluation model based on the index weight value and an inverse cloud generation model to obtain a cloud similarity model comprises the following steps: obtaining an index evaluation cloud model; constructing the numerical characteristics in the evaluation cloud model based on the index weight value and the inverse cloud generation model to obtain an index standard cloud model; performing EPTCM cloud similarity calculation and arrangement on the index standard cloud model and the index evaluation cloud model to obtain a similarity matrix; constructing based on the index weight value and the similarity matrix to obtain a similarity judgment matrix; Comprehensive analysis and evaluation results of the similarity in the similarity judgment matrix are used to construct a cloud similarity model.

5. The railway bridge construction operation and maintenance period carbon emission dynamic adjustment method according to claim 1, characterized in that, According to the chaos mapping and the non-dominated sorting genetic algorithm, a Pareto solution set is obtained by optimizing and solving the green construction evaluation model, and a multi-objective decision scheme is obtained by calculating a decision index of the Pareto solution set through the cloud similarity model, including: According to the chaos mapping, a population initialization process of the non-dominated sorting genetic algorithm is optimized to obtain an optimized sorting genetic algorithm; According to the optimized sorting genetic algorithm, the green construction evaluation model is multi-objectively optimized and solved to obtain a Pareto solution set; Based on the cloud similarity model, a compromise decision index value is obtained by calculating a decision index of the Pareto solution set through multi-criteria decision making; According to the compromise decision index value, candidate schemes in the Pareto solution set are sorted, and a scheme with the smallest compromise decision index value is selected as a multi-objective decision scheme.

6. The railway bridge construction operation and maintenance period carbon emission dynamic adjustment method according to claim 1, characterized in that, Based on the clustering algorithm, the multi-objective decision scheme is hierarchically trained to construct a green level recognition model, and the carbon emission of the railway bridge construction and operation period is dynamically adjusted in combination with historical subjective evaluation, including: Based on the clustering algorithm, the multi-objective decision scheme is hierarchically trained to construct a green level recognition model, and the carbon emission of the railway bridge construction and operation period is dynamically adjusted in combination with historical subjective evaluation, including: According to the compromise decision index value, candidate schemes in the Pareto solution set are sorted, and a scheme with the smallest compromise decision index value is selected as a multi-objective decision scheme. Based on the clustering algorithm, the multi-objective decision scheme is hierarchically trained to construct a green level recognition model, and the carbon emission of the railway bridge construction and operation period is dynamically adjusted in combination with historical subjective evaluation, including: The method comprises the following steps:

7. A railway bridge construction operation and maintenance period carbon emission dynamic adjustment device, characterized in that, An acquisition module is configured to acquire resource consumption, environmental impact and economic benefit index data of a bridge; A construction module is configured to construct the resource consumption, environmental impact and economic benefit index data to form a green construction evaluation model; A determination module is configured to determine a weight of each index of the green construction evaluation model based on a network analytic hierarchy process, and to construct a cloud similarity model by performing similarity evaluation on the weight of each index through a weighted cloud model; An optimization module is configured to obtain a Pareto solution set by optimizing and solving the green construction evaluation model based on the chaos mapping and the non-dominated sorting genetic algorithm, and to obtain a multi-objective decision scheme by calculating a decision index of the Pareto solution set through the cloud similarity model; A training module is configured to hierarchically train the multi-objective decision scheme based on the clustering algorithm, to construct a green level recognition model, and to dynamically adjust the carbon emission of the railway bridge construction and operation period in combination with historical subjective evaluation. The determination module comprises: 8.The railway bridge construction operation period carbon emission dynamic adjustment device according to claim 7, characterized in that, An acquisition unit is configured to acquire historical research data; A construction unit is configured to construct a network hierarchy model of the green construction evaluation model based on a network analytic hierarchy process, the network hierarchy model comprising a target layer, a standard layer and a scheme layer; An analysis unit is configured to analyze a correlation between each index in the green construction evaluation model based on the historical research data to obtain an index feedback relationship. ​ A determining unit is configured to determine the weight of each index in the green construction evaluation model based on the index feedback relationship, to obtain an index weight value; A calculating unit is configured to comprehensively calculate and construct the index in the green construction evaluation model based on the index weight value and an inverse cloud generation model, to obtain a cloud similarity model.

9. The railway bridge construction operation and maintenance period carbon emission dynamic adjustment device according to claim 8, characterized in that, The determining unit comprises: A determining sub-unit is configured to determine the weight of each index in the green construction evaluation model based on the index feedback relationship, to obtain an initial index weight; A comparing sub-unit is configured to compare the indexes with the dependent feedback relationship based on the network hierarchy model, to determine the relative importance in combination with the initial index weight, to obtain a sorting vector; An analyzing sub-unit is configured to analyze the mutual relationship of the indexes in the network hierarchy model, to obtain an unweighted hypermatrix; A first constructing sub-unit is configured to normalize the sorting vector, to construct a weighted matrix through the normalized sorting vector, to construct the unweighted hypermatrix and the weighted matrix, to obtain a weighted hypermatrix; A calculating sub-unit is configured to stably process and calculate the weighted hypermatrix, to obtain the index weight value.

10. The railway bridge construction operation and maintenance period carbon emission dynamic adjustment device according to claim 8, characterized in that, The calculating unit comprises: An acquiring sub-unit is configured to acquire an index evaluation cloud model; A second constructing sub-unit is configured to construct the numerical features in the evaluation cloud model based on the index weight value and an inverse cloud generation model, to obtain an index standard cloud model; An arranging sub-unit is configured to perform EPTCM cloud similarity calculation and arrangement on the index standard cloud model and the index evaluation cloud model, to obtain a similarity matrix; A third constructing sub-unit is configured to construct based on the index weight value and the similarity matrix, to obtain a similarity judgment matrix; A comprehensive analyzing sub-unit is configured to comprehensively analyze and judge the similarity in the similarity judgment matrix, to construct a cloud similarity model.

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