Standardized construction management and control method and system for road passable steel bridge based on big data

Abstract

The present application belongs to the technical field of construction resource management, and discloses a road pass-through steel temporary bridge standardized construction management and control method and system based on big data; including: collecting project demand information, module combination record data, construction turnover information and module cost information of historical pass-through projects, and establishing a project-level correlation relationship; based on the project demand information, generating demand characteristic portraits corresponding to each demand category; according to the demand characteristic portraits, the project-level correlation relationship and the construction turnover information, generating a module system adaptation evaluation result set, and combining the module cost information to construct multiple candidate deployment strategies; obtaining project demand prediction information in the planning period, scenario deduction is performed on each candidate deployment strategy, and an implementation deployment strategy is selected; the present application can solve the dilemma of coexistence of non-adaptation module long-term idling and core module sudden break in steel temporary bridge pass-through asset scheduling, and improve the comprehensive management and control level of enterprises between capital efficiency and guarantee capacity.

Description

Technical Field

[0001] This invention relates to the field of construction resource management technology, and more specifically, to a standardized construction management and control method and system for temporary steel bridges for road maintenance based on big data. Background Technology

[0002] In view of this, the present invention proposes a standardized construction management and control method and system for temporary steel bridges for road maintenance based on big data to solve the above problems. Summary of the Invention

[0003] With the increasing density of urban underground space development and municipal pipeline renovation projects, the contradiction between road occupation construction and normal traffic flow in urban road networks is becoming increasingly prominent. As the core carrier for temporarily restoring existing traffic, steel temporary bridges are gradually evolving from traditional non-standard customization to prefabricated and modular assembly. Under this trend of intensification, construction companies usually rely on a set of statically predefined standard module systems, attempting to cover the traffic maintenance needs of different projects by providing several types of steel temporary bridge modules with general spans and widths. However, existing standardized management technologies for temporary bridges mostly remain at the level of passive selection for single projects, mainly relying on manual experience to piece together solutions from existing drawing libraries. This static configuration path cannot meet the highly heterogeneous requirements of municipal engineering in terms of shaft size, work opening retention, and traffic maintenance cycle. The selected general modules often have low compatibility when actually implemented, which leads to temporary cutting, welding, or excessive stacking on site. This not only increases the additional cost of unnecessary modifications and on-site construction time, but also makes it difficult for steel temporary bridge modules to achieve efficient and lossless circulation between different projects.

[0004] Furthermore, existing solutions for the scheduling and optimization of steel temporary bridge assets are mostly limited to inventory management from a single warehousing perspective. They lack the ability to cluster and deeply analyze massive historical demand profiles from a project group perspective, and they also lack an assessment mechanism to dynamically quantify system matching gaps and turnover delays by integrating real-time construction turnover information with predefined systems. Especially when facing complex traffic maintenance scenarios involving multiple projects in the future, existing technologies cannot reverse-engineer rigid modular systems based on the combined defects exposed in historical practice, nor can they integrate multi-dimensional constraints with future project demand forecasts to conduct scientific deployment simulations. This makes it easy for enterprises to fall into blind spots when allocating resources, making it difficult to achieve a balance between guarantee capacity and capital efficiency. They either face inventory capital tied up due to long-term idleness of incompatible modules, or emergency allocation crises caused by the shortage of core modules in the face of sudden projects. Therefore, there is an urgent need for a standardized construction management and control system for steel temporary bridges for road maintenance that can reverse-drive system optimization based on historical demand clustering and combine forecast information to conduct multi-strategy scenario simulations.

[0005] To overcome the aforementioned shortcomings of existing technologies and achieve the above objectives, this invention provides the following technical solution: a standardized construction management method for temporary steel bridges for road traffic maintenance based on big data, comprising: Collect project requirement information, module combination record data, construction turnover information and module cost information from historical traffic maintenance projects, and establish project-level correlation between project requirement information and module combination record data to form a basic dataset for construction management and control; Based on the project demand information in the construction management and control basic dataset, cluster analysis is performed according to the requirements of span length, traffic width, work opening retention, traffic maintenance duration, and road traffic guarantee level to generate demand feature profiles for each demand category. Based on the demand feature profile and project-level correlation, extract the historical module combination usage patterns corresponding to each demand category, and combine them with construction turnover information to calculate the matching gap value, modification additional cost, turnover retention value and combination complexity value of the predefined standard module system under each demand category, forming a module system adaptation evaluation result set. Based on the module system adaptation evaluation result set and module cost information, the predefined standard module system is back-optimized to determine the target standard module type set, target reserve quantity range, target combination priority rules and module retention and replacement rules. Obtain project demand forecast information during the planning period, construct multiple candidate deployment strategies based on the target standard module type set, target reserve quantity range, target combination priority rules, module retention and replacement rules, and construction turnover information, and conduct scenario simulations for each candidate deployment strategy based on the project demand forecast information during the planning period, and select the implementation deployment strategy based on the scenario simulation results.

[0006] Furthermore, methods for generating demand feature profiles corresponding to each demand category include: In the construction management and control basic dataset, the project demand information corresponding to each historical traffic maintenance project is structured and encoded to form numerical feature vectors and categorical feature vectors for each historical traffic maintenance project. The numerical feature vectors and categorical feature vectors corresponding to the same historical traffic maintenance project are concatenated to form the demand feature vectors corresponding to each historical traffic maintenance project. Based on the demand feature vectors corresponding to each historical traffic maintenance project, demand clustering analysis is performed to obtain multiple demand categories. Each dimension in the demand feature vector is labeled as a feature dimension. For each demand category, the dimension feature value corresponding to each feature dimension is extracted from the demand feature vector of all historical maintenance projects. Based on the dimension feature value, the dominant demand value of each dimension feature corresponding to each demand category is determined. Based on the dimension feature value and the dominant demand value, the demand concentration of each dimension feature corresponding to each demand category is determined. The number of historical maintenance projects included in each demand category is counted to obtain the corresponding number of historical demands. The dominant demand value, demand concentration and number of historical demands of each feature dimension corresponding to each demand category are integrated to form the demand feature profile of the corresponding demand category.

[0007] Furthermore, methods for performing demand clustering analysis include: For each feature dimension in the demand feature vector, it is divided into demand skeleton dimension and elastic segmentation dimension; the dimension feature values ​​of each demand feature vector corresponding to each demand skeleton dimension are combined to form the corresponding skeleton code; historical projects with identical skeleton codes are grouped into the same initial skeleton group. Within each initial skeleton group, a secondary clustering subdivision is further performed based on the elastic subdivision dimension: the dimensional feature values ​​of each historical maintenance project corresponding to each elastic subdivision dimension are combined to form a corresponding joint encoding combination, and the subdivision center label is identified; the subdivision matching deviation between each historical maintenance project and its corresponding subdivision center label is calculated, and each historical maintenance project is assigned to the subdivision category corresponding to the subdivision center label with the smallest subdivision matching deviation; each subdivision category within each initial skeleton group is used as a candidate demand category; For all candidate requirement categories, further sparsity category trimming is performed: all candidate requirement categories are divided into sparse categories and non-sparse categories, and the category compatibility between each sparse category and each non-sparse category is calculated; each sparse category is merged into the non-sparse category with the highest corresponding category compatibility, and all non-sparse categories are used as requirement categories.

[0008] Furthermore, methods for extracting historical module combination usage patterns include: For each requirement category, the usage frequency of different module types in all corresponding module combination records is counted, and sorted from high to low usage frequency to generate a type sequence; for each sequence position in the type sequence, the corresponding cumulative coverage rate is calculated; for each requirement category, the sequence position in the corresponding type sequence where the cumulative coverage rate first exceeds or equals the preset backbone module coverage rate threshold is identified and marked as the backbone position; for each type sequence, the module type corresponding to the backbone position is summarized with all module types ranked before the backbone position to form a backbone module set; Based on the module combination record data of each historical maintenance project under each demand category, the module combination patterns corresponding to each historical maintenance project are extracted; the module combination patterns of any two historical maintenance projects corresponding to the same demand category are compared, and it is determined whether they are the same module combination pattern based on the comparison results; the same module combination patterns are merged to form a module combination pattern class; the frequency of occurrence of each module combination pattern class is counted, the multiple module combination pattern classes with the highest frequency of occurrence are extracted, and the corresponding module combination patterns are used as the typical combination configurations of the corresponding demand category. The core modules of each demand category are integrated with typical combination configurations to form historical module combination usage patterns for the corresponding demand category.

[0009] Furthermore, methods for forming a modular system adaptation evaluation result set include: Obtain a predefined standard module system. For each requirement category, compare the standard module system with the dominant requirement value in the corresponding requirement feature profile dimension by dimension to determine whether there is a matching gap for the corresponding requirement category. For requirement categories with matching gaps, calculate the corresponding matching gap value respectively. Extract the corresponding on-site modification cost records from the module cost information of each historical maintenance project, and calculate the average modification cost per time for each demand category; calculate the procurement cost benchmark of typical solutions based on typical combination configurations, and calculate the additional value of modification for each demand category in combination with the average modification cost per time. Based on the on-site modification cost records, all historical maintenance projects are divided into historical modification projects and historical normal projects; based on the construction turnover information of each historical modification project and historical normal project, the turnover retention value corresponding to each demand category is calculated. Based on typical combinatorial configurations, calculate the category-level module type ratio for each requirement category; obtain assembly error records for each historical maintenance project and calculate the assembly error rate for each requirement category; based on the category-level module type ratio and the assembly error rate, calculate the combinatorial complexity value for each requirement category. The matching gap value, reconfiguration added value, turnover retention value and combination complexity value of each demand category are integrated to form a module system adaptation evaluation result set.

[0010] Furthermore, methods for determining the target standard module type set and the target reserve quantity range include: For each module type in the standard module system, a retention value assessment is performed to identify low-value candidate elimination types. For each low-value candidate elimination type, an alternative feasibility verification is performed, and the corresponding low-value candidate elimination type is removed from the standard module system based on the verification results. For the demand categories whose matching gap values ​​exceed the preset gap trigger threshold in the module system adaptation assessment results, a list of recommended module types for gap filling is generated. All module types not marked as low-value candidate elimination types in the standard module system, all low-value candidate elimination types that have not been deleted, and all recommended filling types in the list of recommended module types for gap filling are merged to form a target standard module type set. Each module type in the target standard module type set is taken as a target type; for each target type, the corresponding demand weighted quantity benchmark is calculated; based on construction turnover information, the turnover occupancy rate corresponding to each target type is calculated, and combined with the turnover retention value corresponding to each demand category, turnover compensation is performed on the demand weighted quantity benchmark corresponding to each target type to obtain the corresponding turnover compensation reserve benchmark; based on the turnover compensation reserve benchmark, the target reserve quantity range corresponding to each target type is determined.

[0011] Furthermore, the methods for determining the target combination priority rules and module retention / replacement rules include: Based on the demand characteristic profiles of each demand category, historical module combination usage patterns, and module system adaptation evaluation results, the executable combination configurations corresponding to each demand category are extracted; the module purchase unit price is obtained from the module cost information of each historical maintenance project; and the executable combination configurations under each demand category are prioritized according to the combination complexity value, turnover occupancy rate, turnover retention value, and module purchase unit price to form target combination priority rules. Obtain all steel temporary bridge modules currently in stock and mark them as existing modules; based on the module system adaptation evaluation result set, target standard module type set, module cost information, turnover retention value and combination complexity value, establish decision rules for retention, gradual withdrawal and replacement of all existing modules, forming module retention and replacement rules.

[0012] Furthermore, methods for obtaining project demand forecasting information during the planning period include: Obtain road maintenance projects that have been approved or are in the planning application stage within the planning period and mark them as predicted projects; for predicted projects that have been approved, obtain the corresponding basic project information and calculate the matching quantity of each demand category, and use the demand category with the largest matching quantity as the corresponding predicted demand category label; for predicted projects that are still in the planning application stage, calculate the occurrence probability of each demand category and form the corresponding probabilistic demand category label by summarizing; integrate the basic project information, predicted demand category labels and probabilistic demand category labels of all predicted projects within the planning period to form the project demand prediction information for the planning period. Methods for constructing multiple candidate deployment strategies include: Based on the target standard module type set, the target reserve quantity range, and the project demand forecast information during the planning period, multiple reserve plans under the reserve focus dimension and multiple turnover operation modes under the turnover efficiency dimension are generated respectively. The reserve plans and turnover operation modes are combined to form multiple candidate deployment strategies. At the same time, the module retention and replacement rules are incorporated into each candidate deployment strategy to form the initial module inventory structure of each candidate deployment strategy at the beginning of the planning period.

[0013] Furthermore, the methods for scenario simulation of each candidate deployment strategy include: Based on the project demand forecast information during the planning period and the initial module inventory structure corresponding to each candidate deployment strategy, the module allocation and module storage process of each candidate deployment strategy at different time steps is simulated step by step along the planning period time axis, and allocation gaps and guarantee failure events are identified; after completing the simulation of the entire planning period, the expected value of the cumulative guarantee failure number and the average inventory holding cost corresponding to each candidate deployment strategy are calculated; the expected value of the cumulative guarantee failure number and the average inventory holding cost corresponding to the same candidate deployment strategy are integrated to form the scenario extrapolation results of each candidate deployment strategy; Methods for selecting implementation and deployment strategies based on scenario simulation results include: From the scenario simulation results of each candidate deployment strategy, the expected value of the cumulative number of assurance failures and the average inventory cost are extracted. Based on the preset assurance failure tolerance threshold, candidate deployment strategies with the expected value of the cumulative number of assurance failures greater than the assurance failure tolerance threshold are eliminated, resulting in a set of feasible assurance candidate strategies. The candidate deployment strategy with the lowest average inventory cost in the set of feasible assurance candidate strategies is selected as the preliminary preferred strategy. Assurance safety margin verification is performed on the preliminary preferred strategy, and it is determined as the implementation deployment strategy after passing the verification.

[0014] The system for standardized construction management and control of temporary steel bridges for road maintenance based on big data, and the implementation of the aforementioned method for standardized construction management and control of temporary steel bridges for road maintenance based on big data, include: The data collection module is used to collect project requirement information, module combination record data, construction turnover information and module cost information of historical traffic maintenance projects, and establish project-level correlation between project requirement information and module combination record data to form a basic dataset for construction management and control. The demand clustering module is used to perform clustering analysis based on project demand information in the construction management and control basic dataset, according to the requirements of span length, passage width, work opening retention, traffic maintenance duration, and road traffic guarantee level, and generate demand feature profiles corresponding to each demand category. The adaptation assessment module is used to extract the historical module combination usage patterns corresponding to each demand category based on the demand feature profile and project-level correlation, and combined with construction turnover information, calculate the matching gap value, modification additional cost, turnover retention value and combination complexity value of the predefined standard module system under each demand category, forming a module system adaptation assessment result set. The system optimization module is used to perform reverse optimization on a predefined standard module system based on the module system adaptation evaluation result set and module cost information, and to determine the target standard module type set, target reserve quantity range, target combination priority rules, and module retention and replacement rules. The simulation and deployment module is used to obtain project demand forecast information during the planning period. Based on the target standard module type set, target reserve quantity range, target combination priority rules, module retention and replacement rules and construction turnover information, it constructs multiple candidate deployment strategies, performs scenario simulations on each candidate deployment strategy based on the project demand forecast information during the planning period, and selects the implementation deployment strategy based on the scenario simulation results.

[0015] The technical effects and advantages of this invention, based on a big data-driven standardized construction management and control method and system for road maintenance steel temporary bridges, are as follows: By constructing a basic dataset for construction management and control, including project demand information, module combination record data, construction turnover information, and module cost information, the information barrier between on-site demand data and warehouse asset data in the traditional standardized management and control system for temporary steel bridges was broken down. This achieved a fundamental shift from passive selection for single projects to proactive evolution of the standard system driven by multi-project demand profiles. Through multi-dimensional structured coding and cluster analysis of project demand information from historical road maintenance projects, a demand characteristic profile system was established with cross-span length, traffic width, work access retention, maintenance duration, and road traffic guarantee level as core dimensions. This revealed the heterogeneous patterns of demand under different urban road maintenance scenarios, effectively overcoming the fundamental defects of relying on manual experience to piece together solutions from atlases and failing to systematically identify demand classification characteristics. By integrating and analyzing the predefined standard module system with historical module combination usage patterns and construction turnover information for each demand category, a four-dimensional adaptation evaluation index was calculated, including matching gap value, additional cost of modification, turnover retention value, and combination complexity value. This achieved a comprehensive quantitative diagnosis of the original system in terms of demand coverage, cost burden, turnover efficiency, and operational complexity, thus addressing shortcomings. The lack of a system-level adaptive dynamic evaluation mechanism was addressed. By using the adaptation evaluation results to drive the retention and elimination of module types, the calculation of reserve quantity ranges, the generation of combination priority rules, and the establishment of rules for retaining and replacing existing modules, the historical practical-driven evolution and optimization of the standard module system was achieved. This ensured that the system configuration results no longer relied on statically predefined subjective experience, but rather on objective reverse solutions derived from historical real demand and turnover efficiency data. By introducing project demand forecast information during the planning period and constructing three reserve focus strategies (conservative, balanced, and ample) and two combination candidate deployment strategies (standard and aggressive turnover efficiency modes), a time-step-driven planning period scenario simulation mechanism was used to gradually simulate each candidate strategy. Combined with a three-level screening logic of first ensuring feasibility screening, then minimizing costs and verifying the safety margin pressure, the implementation deployment strategy was determined. This enabled the use of scientifically quantified forward-looking simulation to replace blind resource allocation in highly uncertain multi-project concurrent maintenance scenarios. This systematically solved the dilemma of long-term idleness of non-adaptive modules and sudden shortage of core modules in the asset scheduling of steel temporary bridge maintenance, comprehensively improving the company's overall management level between capital efficiency and maintenance capabilities. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the standardized construction management and control system for road maintenance steel temporary bridges based on big data, according to Embodiment 1 of the present invention. Figure 2 This is a flowchart of the standardized construction management method for road maintenance steel temporary bridges based on big data, as described in Embodiment 2 of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1: Please refer to Figure 1 As shown in this embodiment, the standardized construction management and control system for temporary steel bridges for road traffic maintenance based on big data includes a data collection module, a demand clustering module, an adaptation evaluation module, a system optimization module, and a simulation and deployment module. The modules are connected by wired and / or wireless means to realize data transmission between modules.

[0019] The data collection module is used to collect project requirement information, module combination record data, construction turnover information and module cost information of historical traffic maintenance projects, and establish project-level correlation between project requirement information and module combination record data to form a basic dataset for construction management and control. Methods for collecting project requirement information from historical traffic maintenance projects include: The project management system (an engineering project comprehensive management platform used to record and manage road maintenance project initiation information, site survey data, design parameters, and completion acceptance information) is used to obtain the basic project information and site requirements parameters for each historical road maintenance project. Historical road maintenance projects refer to road maintenance steel temporary bridge projects that have been completed and passed final acceptance. Basic project information includes a unique project code, project location, project type, project scale level, project start date, and project completion date. Site requirements parameters include span length requirements, traffic width requirements, work access retention requirements, road maintenance duration requirements, and road traffic guarantee level requirements. Among them, the project unique code is a predefined unique identifier for the historical traffic maintenance project, which is used as the primary key for data association in the construction management basic dataset; the span length requirement refers to the clearance length that the steel temporary bridge needs to span in the historical traffic maintenance project, which is measured by the site survey personnel according to the actual dimensions of the excavated trench or working pit and then entered into the project management system; the passage width requirement refers to the effective passage clearance that the steel temporary bridge deck needs to ensure in the historical traffic maintenance project, which is determined by the site survey personnel according to the road grade and the parallel needs of pedestrians and vehicles and then entered into the project management system. The requirement for retaining work openings refers to the location and size of construction openings that need to be retained during the construction of the temporary steel bridge. These requirements are confirmed by the construction designers based on the requirements of the underground pipeline construction work surface and then entered into the project management system. The location of the work opening refers to its position or arrangement within the horizontal plane of the temporary steel bridge, reflecting whether it is located in the middle, side, end, or other relative positions of the bridge deck. The opening size refers to the length and width of the corresponding work opening, reflecting the effective working space required for the entry and exit of construction equipment, personnel, and materials. The requirement for the continuous period of traffic maintenance refers to the minimum number of days that the temporary steel bridge needs to maintain full traffic function in its original location. This requirement is confirmed by the construction unit based on the underground engineering construction plan and then entered into the project management system. The requirement for the level of road traffic guarantee refers to the level requirements of the transportation authority for temporary traffic capacity in historical traffic maintenance projects. This includes four levels: pedestrian priority, non-motorized vehicle traffic level, one-way motorized vehicle traffic level, and two-way motorized vehicle traffic level. This requirement is specified by the transportation authority in the project approval documents and then entered into the project management system. The basic project information and corresponding field requirement parameters of each historical road maintenance project are linked and integrated using the project's unique code as the primary key to form the project requirement information for each historical road maintenance project.

[0020] Methods for collecting module combination record data from historical connectivity maintenance projects include: Obtain the module allocation list and on-site assembly record for each historical road maintenance project from the material management system (i.e., the warehousing and logistics management platform used to record and manage the outbound allocation, on-site assembly plan and return verification information of steel temporary bridge modules); integrate the module allocation list and on-site assembly record for each historical road maintenance project to form the module combination record data for each historical road maintenance project; Among them, the steel temporary bridge module refers to the smallest functional unit of the steel temporary bridge that has been prefabricated and assembled in the factory and can be independently coded, allocated, transported, hoisted, spliced, dismantled, stored and rotated; each steel temporary bridge module is prefabricated and assembled in the factory from components such as main beams, secondary beams and bridge decks, and is hoisted and spliced ​​as a whole at the construction site for traffic maintenance; the steel temporary bridge modules are divided into various module types according to different specifications of span and width, including standardized combinations of different span levels and different width levels; The module allocation list records the module code, module type, module specifications, and allocation quantity of all steel temporary bridge modules allocated from the module warehouse to the construction site for the corresponding historical traffic maintenance projects. The module code is a unique material number for each steel temporary bridge module, used to uniquely identify the corresponding steel temporary bridge module in the material management system. The module type identifies the functional and specification categories of the corresponding steel temporary bridge module. The module specifications include the span and width dimensions of the corresponding steel temporary bridge module. The allocation quantity is the number of modules of the corresponding module type sent from the warehouse to the construction site in this allocation. The on-site assembly record documents the actual module arrangement, splicing sequence, assembled form, and detailed list of modules used in the corresponding historical traffic maintenance projects. The module arrangement refers to the spatial placement of each steel temporary bridge module within the bridge's plan; the splicing sequence refers to the order in which the on-site construction personnel installed each steel temporary bridge module during hoisting and assembly; the assembled form refers to the overall planar outline and opening distribution of the steel temporary bridge formed by assembling multiple modules; and the detailed list of modules used on-site refers to the list of steel temporary bridge modules actually involved in the on-site assembly, including but not limited to the actual quantity used for each module type.

[0021] Methods for collecting construction turnover information for historical traffic maintenance projects include: From the material management system, retrieve the module outbound time, module return time, and return status records for each steel temporary bridge module in each historical road maintenance project. The module outbound time refers to the actual date the corresponding steel temporary bridge module is dispatched from the warehouse to the construction site; the module return time refers to the actual date the corresponding steel temporary bridge module returns from the construction site to the warehouse; and the return status record refers to the module's integrity assessment result upon return, including three states: intact and usable, requiring repair and remodeling, and scrapped. For each historical road maintenance project, the occupancy period of each steel temporary bridge module is calculated. Specifically, for each steel temporary bridge module, the time difference between the module's return to warehouse and its departure from warehouse is calculated to obtain the number of days the corresponding steel temporary bridge module is occupied in the corresponding historical road maintenance project, which is used as the occupancy period of the corresponding steel temporary bridge module in the corresponding historical road maintenance project. The occupancy period is used to characterize the continuous occupancy time of the steel temporary bridge module in the corresponding historical road maintenance project from departure to return to warehouse. The module type, module code, occupancy period, and return status records of all steel temporary bridge modules in each historical road maintenance project are integrated to form the construction turnover information of each historical road maintenance project.

[0022] Methods for collecting module cost information from historical communication maintenance projects include: Obtain the module purchase unit price, on-site modification cost record and turnover loss cost record corresponding to each historical maintenance project from the financial settlement system (i.e., the financial management platform used to record and manage the contract settlement, module procurement cost, on-site modification cost and turnover loss cost of engineering projects); integrate the module purchase unit price, on-site modification cost record and turnover loss cost record corresponding to each historical maintenance project to form the module cost information of each historical maintenance project; Among them, the module purchase unit price refers to the purchase price of a single steel temporary bridge module corresponding to each module type, which is entered into the financial settlement system based on the purchase contract, settlement document, or archive; the on-site modification cost record refers to the additional costs incurred due to on-site modification (on-site cutting, welding, or supplementation, etc.) caused by module size mismatch in the corresponding historical maintenance project, which is filled in by the on-site construction supervisor and then entered into the financial settlement system; the turnover loss cost record refers to the costs incurred due to module loss, repair, or scrapping after the corresponding historical maintenance project is completed, which is calculated by the warehouse management personnel based on the return status record and then entered into the financial settlement system.

[0023] Methods for generating basic datasets for construction management and control include: Using the project's unique code as the primary key, the project requirement information of each historical maintenance project is associated with the corresponding module combination record data, establishing a project-level association between the project requirement information and the module combination record data. The project-level association is used to characterize the correspondence between the specific on-site requirements and the actual module combination scheme adopted in each historical maintenance project, providing a verifiable requirement-combination comparison basis for subsequent requirement clustering and adaptation evaluation. All historical road maintenance project requirements, module combination records, construction turnover information, module cost information, and project-level relationships are collected and integrated to form a basic construction management dataset. The basic construction management dataset uses historical road maintenance projects as the basic record unit. Each record fully covers the entire chain of data, from on-site requirements parameters, actual module combination schemes, module turnover and occupancy, to module cost composition, providing a unified data foundation for subsequent functional modules.

[0024] The demand clustering module is used to perform clustering analysis based on project demand information in the construction management and control basic dataset, according to the requirements of span length, passage width, work opening retention, traffic maintenance duration, and road traffic guarantee level, and generate demand feature profiles corresponding to each demand category. Methods for generating requirement feature profiles corresponding to each requirement category include: In the construction management and control basic dataset, the project demand information corresponding to each historical traffic maintenance project is structured and encoded to form numerical and categorical feature vectors for each historical traffic maintenance project. The numerical and categorical feature vectors corresponding to the same historical traffic maintenance project are then concatenated to form the demand feature vector for each historical traffic maintenance project. Specifically: For the opening dimensions in the requirements of span length, passage width, traffic maintenance duration, and work access retention, the corresponding values ​​are extracted from the project requirement information of each historical traffic maintenance project and marked as numerical requirement feature values. A set of graded mapping tables is pre-established, including span length graded mapping tables, passage width graded mapping tables, duration graded mapping tables, and opening size graded mapping tables. Each graded mapping table contains multiple graded segments and corresponding level codes. Each graded mapping table is pre-set by those skilled in the art based on the typical distribution range of the corresponding requirement parameters in historical traffic maintenance projects, the adaptation boundary of the standard module system, and the scenario division requirements in actual engineering applications. The numerical requirement feature values ​​corresponding to each historical traffic maintenance project are compared with the corresponding graded mapping tables in the graded mapping table set. Based on the graded segment in which each numerical requirement feature value falls, the level code corresponding to each numerical requirement feature value is determined. The level codes of the numerical requirement feature values ​​corresponding to the same historical traffic maintenance project are concatenated to form a numerical feature vector corresponding to each historical traffic maintenance project. For the location of construction operation openings in the requirements for road traffic protection level and the requirements for retaining operation openings, the corresponding category descriptions are extracted from the project requirement information of each historical traffic maintenance project and marked as category-type requirement feature values. A set of category coding tables is pre-established, including a protection level category coding table and an opening location category coding table. Each category coding table contains multiple category descriptions and corresponding category codes. Each category coding table is pre-set by those skilled in the art based on the traffic capacity level classification standards in road traffic maintenance scenarios, common layout methods of steel temporary bridge deck openings, and category differentiation requirements in actual engineering applications. The category-type requirement feature values ​​corresponding to each historical traffic maintenance project are compared with the corresponding category coding tables in the set of category coding tables. Based on the category descriptions matched by each category-type requirement feature value, the category code corresponding to each category-type requirement feature value is determined. The category codes of each category-type requirement feature value corresponding to the same historical traffic maintenance project are concatenated to form a category-type feature vector corresponding to each historical traffic maintenance project.

[0025] Based on the demand feature vectors corresponding to each historical traffic maintenance project, demand clustering analysis was performed to obtain multiple demand categories. Specifically, for each dimension in the demand feature vector, it was divided into demand skeleton dimensions and flexible subdivision dimensions. The demand skeleton dimension includes length level codes (level codes corresponding to span length requirements), width level codes (level codes corresponding to passage width requirements), size level codes (level codes corresponding to opening sizes), and location category codes (category codes corresponding to construction operation opening locations), used to characterize whether there are essential differences in the structural dimensions of the steel temporary bridge module combinations. The flexible subdivision dimension includes period level codes (level codes corresponding to traffic maintenance duration requirements). The code (code) and the guarantee category code (category code corresponding to the road traffic guarantee level requirement) are used to characterize the differences in continuous use pressure and traffic guarantee pressure of different historical traffic maintenance projects under the premise of roughly the same structural size; the values ​​of each requirement feature vector corresponding to each requirement skeleton dimension are combined to form the skeleton code corresponding to each requirement feature vector; historical traffic maintenance projects with the same skeleton code are grouped into the same initial skeleton group; the initial skeleton group is used to ensure that historical traffic maintenance projects entering the same group do not have essential structural conflicts in terms of span length, traffic width and operation port retention method, thereby avoiding the conventional clustering algorithm from erroneously aggregating projects with structurally incompatible similar module schemes; Within each initial skeleton group, secondary clustering is further performed based on the flexible segmentation dimension. Specifically, for each historical traffic maintenance project within the initial skeleton group, the corresponding cycle level code and guarantee category code are extracted and combined into a joint code combination. The joint code combination refers to the ordered value pairs of the same historical traffic maintenance project across the two flexible segmentation dimensions of cycle level code and guarantee category code. The joint code combinations of all historical traffic maintenance projects within the initial skeleton group are statistically analyzed to obtain the frequency of occurrence of each different joint code combination, forming a demand pressure distribution table. This demand pressure distribution table reflects the distribution concentration of usage time pressure and traffic guarantee pressure of historical traffic maintenance projects under the same structural skeleton conditions. From the demand pressure distribution table, joint code combinations with a frequency greater than or equal to a preset segmentation trigger threshold are identified and used as sub-classification center labels. The segmentation trigger threshold is preset by those skilled in the art based on the number of projects within the group, the dispersion of the flexible segmentation dimension, and the minimum group size requirement for the demand category. For each historical maintenance project within the initial skeleton group, calculate the segmentation matching deviation between the corresponding historical maintenance project and each sub-category center label. Specifically, for each historical maintenance project, compare the category code or level code of each elastic segmentation dimension with each sub-category center label. If the category code or level code of the elastic segmentation dimension matches the sub-category center label, set the dimension deviation of the corresponding elastic segmentation dimension relative to the corresponding sub-category center label to zero. If the category code or level code of the elastic segmentation dimension does not match the sub-category center label, set the dimension deviation of the corresponding elastic segmentation dimension relative to the corresponding sub-category center label to one. Sum the dimension deviations of each elastic segmentation dimension relative to the same sub-category center label for the same historical maintenance project to obtain the segmentation matching deviation of each historical maintenance project relative to each sub-category center label. Each sub-category center label is used as a category identifier for a sub-category, and corresponding sub-categories are constructed based on each sub-category center label. According to the sub-matching deviation of each historical maintenance project relative to each sub-category center label, each historical maintenance project is assigned to the sub-category corresponding to the sub-category with the smallest sub-matching deviation. If multiple sub-category center labels have the smallest sub-matching deviation, the corresponding historical maintenance project is preferentially assigned to the sub-category containing the most historical maintenance projects. Each sub-category within each initial skeleton group is used as a candidate requirement category, and the results are aggregated to form the candidate requirement category division. Based on the candidate demand category classification results, further sparse category trimming is performed. Specifically, for each candidate demand category, the number of historical maintenance projects is counted to obtain the corresponding number of historical projects. A minimum category size threshold is preset, which is pre-set by those skilled in the art based on the stability requirements of the demand category profile. If the number of historical projects corresponding to a candidate demand category is less than the minimum category size threshold, the corresponding candidate demand category is marked as a sparse category. If the number of historical projects corresponding to a candidate demand category is greater than or equal to the minimum category size threshold, the corresponding candidate demand category is marked as a non-sparse category. For each sparse category, the category compatibility between the corresponding sparse category and each non-sparse category is calculated. The calculation method for category compatibility is as follows: if the sparse category and the non-sparse category do not belong to the same initial skeleton group, the category compatibility between the sparse category and the non-sparse category is set to zero; if the sparse category and the non-sparse category belong to the same initial skeleton group, the category compatibility between the sparse category and the non-sparse category is set to zero. For the same initial skeleton group, the most frequently occurring cycle level code and guarantee category code for sparse and non-sparse categories are counted and marked as cycle-dominant and guarantee-dominant codes, respectively. The number of differences between sparse and non-sparse categories in the cycle-dominant and guarantee-dominant codes is counted, and the difference number is added by one and the reciprocal is taken to obtain the category compatibility between the sparse category and each non-sparse category. Each sparse category is merged into the non-sparse category with the highest category compatibility, completing the sparse category trimming. If there are multiple non-sparse categories with the same category compatibility, the corresponding sparse category is merged into the non-sparse category with the most historical projects. After the sparse category trimming is completed, all non-sparse categories are treated as requirement categories. It should be understood that the clustering path of grouping first with the skeleton, then subdividing flexibly, and trimming after sparseness can avoid the problem of confusion between structural requirements and operational requirements in the scenario of road maintenance steel temporary bridges, which occurs with conventional clustering algorithms.

[0026] Based on the demand feature vectors corresponding to each historical maintenance project, demand feature profiles are generated for each demand category. Specifically, each dimension in the demand feature vector is labeled as a feature dimension. For each demand category, the values ​​corresponding to each feature dimension are extracted from the demand feature vectors of all historical maintenance projects and labeled as dimension feature values. For each feature dimension, the frequency of occurrence of each feature value in the same demand category is counted, and the feature value with the highest frequency is taken as the dominant demand value of the corresponding feature dimension in the corresponding demand category. For each feature dimension, the number of historical maintenance projects in each demand category whose dimension feature value is the same as the corresponding dominant demand value is counted, and then divided by the total number of historical maintenance projects in the corresponding demand category to obtain the demand concentration of each feature dimension under each demand category. The demand concentration reflects the degree of demand consistency of the demand category under the corresponding feature dimension. The higher the demand concentration, the more stable the demand of the corresponding demand category under the corresponding feature dimension, and the easier it is for the subsequent standard module system to cover the corresponding demand category with a smaller number of highly adaptable module types. The number of historical traffic maintenance projects included in each demand category is counted to obtain the corresponding number of historical demands. The dominant demand value, demand concentration and historical demand quantity of each demand category are integrated to form a demand feature profile of the corresponding demand category. The demand feature profile is used to characterize the typical demand profile of each demand category in five dimensions: span length, traffic width, maintenance access retention, traffic maintenance duration and road traffic guarantee level.

[0027] The adaptation assessment module is used to extract the historical module combination usage patterns corresponding to each demand category based on the demand feature profile and project-level correlation, and combined with construction turnover information, calculate the matching gap value, modification additional cost, turnover retention value and combination complexity value of the predefined standard module system under each demand category, forming a module system adaptation assessment result set. Methods for extracting historical module combination usage patterns corresponding to each requirement category include: Extract the historical module combination usage patterns corresponding to each demand category to reveal the module selection rules and combination characteristics actually adopted in historical maintenance projects under the actual demand conditions of the corresponding demand category, and provide a historical practical comparison benchmark for subsequent evaluation of the actual adaptability of the predefined standard module system. Specifically, from the construction management and control basic dataset, based on project-level relationships, module combination record data corresponding to each historical traffic maintenance project for each demand category is extracted; for each demand category, the usage frequency of different module types in all corresponding module combination record data is counted, and all module types are sorted from high to low according to usage frequency to generate a type sequence; the sum of usage frequencies of all module types corresponding to each demand category is calculated to obtain the corresponding total usage frequency; for each sequence position in the type sequence, the usage frequency of the corresponding module type is calculated and the sum of the usage frequencies of all module types ranked before the corresponding module type to obtain the corresponding cumulative usage frequency; The cumulative coverage rate is obtained by calculating the ratio between the cumulative usage frequency of each sequence position and the total usage frequency of the corresponding demand category. For each demand category, the sequence position in the corresponding type sequence where the cumulative coverage rate first exceeds or equals the preset backbone module coverage rate threshold is identified and marked as the backbone position. For each type sequence, the module type corresponding to the backbone position and all module types preceding the backbone position are marked as backbone types. All backbone types corresponding to the same demand category are summarized to form a backbone module set. The backbone module set is used to characterize the module types that frequently appear in historical maintenance projects under the demand category and undertake core crossing and carrying functions. The backbone module coverage rate threshold is preset by those skilled in the art based on the representativeness requirements of the backbone modules for historical usage.

[0028] Based on the module combination record data of each historical maintenance project under each demand category, the module combination patterns corresponding to each historical maintenance project are extracted. The module combination pattern is a historical expression of module combination determined by the module type composition, the actual usage quantity of each module type, the module arrangement, the splicing order, and the combination form. The module combination patterns of any two historical maintenance projects corresponding to the same demand category are compared. If the two module combination patterns have the same module type composition, the same actual usage quantity of each module type, and the same module arrangement, splicing order, and combination form, then the two corresponding module combination patterns are determined to be the same module combination pattern. The same module combination patterns are merged, and each merged result forms a module combination pattern class. A module combination pattern class refers to a pattern category formed by merging multiple identical module combination patterns. The number of historical maintenance projects corresponding to each module combination pattern class is counted and used as the frequency of occurrence of the corresponding module combination pattern class. The module combination pattern classes corresponding to the same requirement category are sorted from high to low frequency of occurrence, and the module combination pattern classes with the highest frequency of occurrence are extracted. The module combination patterns corresponding to each extracted module combination pattern class are used as typical combination configurations for the corresponding requirement category. Among them, the typical combination configurations are used to record the most widely used module type composition, module quantity composition, module arrangement, splicing order and combination form under the corresponding requirement category. The number of typical combination configurations is preset by those skilled in the art according to the representativeness requirements of typical configurations.

[0029] The core module sets and typical combination configurations of each demand category are integrated to form the historical module combination usage patterns of the corresponding demand category; the historical module combination usage patterns of all demand categories are summarized to form a set of historical module combination usage patterns.

[0030] Methods for calculating the matching gap values ​​of the standard module system under each demand category include: A predefined standard module system is obtained. For each requirement category, the predefined standard module system is compared dimension by dimension with the dominant requirement value in the corresponding requirement feature profile to determine whether there is a matching gap for the corresponding requirement category. For requirement categories with matching gaps, the corresponding matching gap value is calculated. Specifically, the predefined standard module system is obtained from the standard module management system (i.e., the module standardization management platform used to record and manage the current standard module types, module specifications, standard splicing rules, and standard application scope of the enterprise's steel temporary bridges). The predefined standard module system refers to the current standardized module configuration system of steel temporary bridges that the enterprise has long adopted before this system optimization. It includes a set of module types, the specifications corresponding to each module type, the working opening adaptation parameters, and the standard splicing rules. The specifications include the span and width dimensions corresponding to each module type. The working opening adaptation parameters include the construction opening position and opening size adaptation level that each module type can achieve without on-site modification. The standard splicing rules include the splicing combination relationship between each module type, and the span, width, construction opening position, and opening size adaptation level of the module combination formed by combining according to the splicing combination relationship. For each demand category, the dominant demand values ​​corresponding to each feature dimension in the corresponding demand feature profile are extracted, resulting in dominant demand values ​​for span length, passage width, opening location, and opening size. Each module type in the standard module system is marked as a standard type, and the span size, width size, and construction opening location corresponding to each standard type are structurally encoded to obtain the corresponding length level code, width level code, and location category code. It is determined whether there exists a standard type in the standard module system that has the same length level code as the span length dominant demand value, the same width level code as the passage width dominant demand value, the same location category code as the opening location dominant demand value, and the same opening size adaptation level as the opening size dominant demand value. If it exists, it is determined that the standard module system can directly satisfy the corresponding demand category through a single module type, that is, there is no matching gap in the corresponding demand category. If it does not exist, all module combinations are further enumerated according to the standard splicing rules, and the span size, width size, construction opening location, and opening size adaptation level corresponding to each module combination are obtained. Similarly, the span size, width size, construction operation opening location, and opening size adaptation level corresponding to each module combination are structurally coded to obtain the corresponding length level code, width level code, and location category code. It is determined whether there are module combinations whose length level code is consistent with the dominant demand value of the span length, whose width level code is consistent with the dominant demand value of the passage width, whose location category code is consistent with the dominant demand value of the opening location, and whose opening size adaptation level is consistent with the dominant demand value of the opening size. If they exist, it is determined that the standard module system can meet the corresponding demand category through module combination, that is, there is no matching gap in the corresponding demand category. If they do not exist, it is determined that there is a matching gap in the corresponding demand category, and the matching gap value of the corresponding demand category is calculated. The method for calculating the matching gap value is as follows: For the dominant demand values ​​of span length, passage width, opening location, and opening size corresponding to the demand category, if there is a dominant demand value that is inconsistent with all standard types and module combinations, then the feature dimension corresponding to the dominant demand value is marked as the gap dimension; calculate the ratio of the number of gap dimensions to four to obtain the dimension gap ratio; obtain the on-site modification cost records of each historical traffic maintenance project in the corresponding demand category from the construction management basic dataset; count the historical traffic maintenance projects with non-zero on-site modification cost records to obtain the number of historical modification projects; calculate the historical modification costs. The historical reconfiguration ratio is obtained by comparing the quantity with the total number of historical maintenance projects in the corresponding demand category. The dimensional gap ratio is compared with the historical reconfiguration ratio, and the larger value is taken as the matching gap value for the corresponding demand category. The theoretical demand gap analysis and the historical on-site reconfiguration occurrences corroborate each other. When the gap degree reflected by either source is higher, the higher value should be used to avoid underestimating the actual degree of inadequacy of the predefined standard module system under the corresponding demand category. The larger the matching gap value, the more difficult it is for the predefined standard module system to directly meet the on-site needs under the corresponding demand category, and the higher the urgency for system optimization.

[0031] Methods for calculating the added value of reconfiguration of standard module systems under various demand categories include: The module cost information for each historical maintenance project corresponding to each demand category is obtained from the construction management and control basic dataset, and the on-site modification cost records for each historical maintenance project are extracted from it. Based on the on-site modification cost records for each historical maintenance project, the average modification cost per instance for each demand category is calculated. Based on the typical combination configurations of each demand category, the corresponding typical scheme procurement cost benchmark is calculated. Based on the average modification cost per instance and the typical scheme procurement cost benchmark, the modification-added value for each demand category is calculated. Specifically, the on-site modification cost records of all historical maintenance projects within the same demand category are summed and then divided by the total number of historical maintenance projects in the corresponding demand category to obtain the average modification cost per instance for each demand category. The average modification cost per instance is used to reflect the economic burden of modification caused by module incompatibility for each historical maintenance project under the corresponding demand category. From the module cost information corresponding to each historical maintenance project, obtain the module purchase unit price of each module type in the module allocation list of each historical maintenance project; for each typical combination configuration, calculate the product of the actual usage quantity of each module type and the corresponding module purchase unit price, and sum the products to obtain the corresponding single configuration purchase cost; for each typical combination configuration, obtain the frequency of occurrence in the corresponding demand category; for each demand category, calculate the product of the single configuration purchase cost of each typical combination configuration and the frequency of occurrence, and sum the products. Summing the results and then dividing the sum by the total frequency of occurrence of all typical combination configurations, we obtain the typical solution procurement cost benchmark for the corresponding demand category. We calculate the ratio of the average reconfiguration cost per instance to the typical solution procurement cost benchmark for each demand category, thus obtaining the reconfiguration additional cost for the corresponding demand category. Here, the reconfiguration additional cost is a dimensionless ratio used to characterize the degree of additional burden of reconfiguration costs relative to the normal module procurement cost. The larger the reconfiguration additional cost, the higher the implicit cost caused by system mismatch under the corresponding demand category, and the greater the potential for economic benefits from system optimization.

[0032] Methods for calculating the turnover retention value of the standard module system under each demand category include: From the construction management and control basic dataset, obtain the on-site modification and equipment cost records corresponding to each historical traffic maintenance project; based on the on-site modification and equipment cost records, divide all historical traffic maintenance projects into historical modification and equipment projects and historical normal projects; based on the construction turnover information of each historical modification and equipment project, calculate the average modification and equipment occupancy period corresponding to each demand category; based on the construction turnover information of each historical normal project, calculate the normal average occupancy period corresponding to each demand category; based on the average modification and equipment occupancy period and the normal average occupancy period, calculate the turnover retention value corresponding to each demand category. Specifically, historical maintenance projects with non-zero on-site modification costs are all marked as historical modification projects; historical maintenance projects with zero on-site modification costs are all marked as historical normal projects. For each demand category, the average occupancy period of each steel temporary bridge module in all corresponding historical modification projects is calculated to obtain the corresponding average modification occupancy period. For each demand category, the average occupancy period of each steel temporary bridge module in all corresponding historical normal projects is calculated to obtain the corresponding normal average occupancy period. For each demand category, the difference between the corresponding average modification occupancy period and the normal average occupancy period is calculated to obtain the corresponding modification occupancy period. Excess value; among which, the excess value of the reconfiguration occupancy period is used to reflect the number of extra days that the module is occupied due to the module reconfiguration operation, which exceeds the time required for normal operation. The larger the excess value, the more serious the damage to the circulation efficiency of the steel temporary bridge module caused by the reconfiguration. The ratio of the excess value of the reconfiguration occupancy period corresponding to each demand category to the corresponding normal average occupancy period is calculated to obtain the turnover retention value of the corresponding demand category. Among which, the turnover retention value is a dimensionless ratio used to characterize the multiple of the turnover occupancy excess caused by the reconfiguration relative to the normal turnover baseline. The larger the turnover retention value, the more serious the erosion of the circulation capacity of the steel temporary bridge module due to the module's unsuitability for the corresponding demand category.

[0033] Methods for calculating the combinatorial complexity of the standard module system under various requirement categories include: For each requirement category, based on the corresponding typical combination configurations, calculate the category-level module type ratio; obtain the assembly error records from the historical maintenance projects corresponding to each requirement category, and calculate the assembly error rate for each requirement category; based on the category-level module type ratio and the assembly error rate, calculate the corresponding combination complexity value for each requirement category; specifically, for each requirement category, extract all corresponding typical combination configurations from the corresponding historical module combination usage patterns; for each typical combination configuration, count the number of different module types involved to obtain the number of module types for the corresponding typical combination configuration; calculate the sum of the actual usage quantities of all module types in each typical combination configuration to obtain the total number of modules for the corresponding typical combination configuration; for each typical combination configuration, calculate the ratio of the corresponding number of module types to the total number of modules to obtain the configuration module type ratio for the corresponding typical combination configuration; where the configuration module type ratio is used to reflect the degree of dispersion of module types relative to the total number of modules in a single typical combination configuration; the higher the configuration module type ratio, the more module types there are in the corresponding typical combination configuration, the less repeated use of a single module type, and the more dispersed the combination and matching; For each demand category, the frequency of occurrence of each typical combination configuration is counted. The product of the proportion of configuration module types of each typical combination configuration under the same demand category and the corresponding frequency of occurrence is calculated, and the product results are summed. Then, the summation result is divided by the total frequency of occurrence of all typical combination configurations in the corresponding demand category to obtain the category-level module type proportion of each demand category. The category-level module type proportion is used to reflect the degree of module type dispersion of the mainstream module combination schemes under the corresponding demand category, which can avoid the bias caused by the randomness of a single typical combination configuration in the evaluation of combination complexity. From the project management system, retrieve assembly error records from each historical maintenance project corresponding to each requirement category. These records document rework, misassembly, or temporary adjustments caused by complex module combination schemes. Count the number of historical maintenance projects with assembly error records for each requirement category to obtain the historical error count. Calculate the ratio of the historical error count to the total number of historical maintenance projects for each requirement category to obtain the assembly error rate for each requirement category. The assembly error rate reflects the frequency of operational problems arising during on-site execution in historical maintenance projects under the corresponding requirement category due to complex combination schemes. The sum of the category-level module type ratio and the assembly error rate for the same requirement category is used to calculate the combinatorial complexity value for each requirement category. The assembly error rate is introduced because simply relying on the category-level module type ratio can only reflect the complexity of the combinatorial scheme in terms of theoretical structure, but cannot fully reflect the actual operational difficulty in the field execution process. The assembly error rate directly records the practical execution consequences of complex combinatorial schemes in historical projects. By combining the category-level module type ratio and the assembly error rate, the combinatorial complexity of the standard module system under the corresponding requirement category can be characterized from two dimensions: theoretical configuration complexity and field execution complexity.

[0034] Methods for forming a modular system adaptation evaluation result set include: The matching gap value, reconfiguration added value, turnover retention value and combination complexity value corresponding to each demand category are integrated and recorded by demand category to form a module system adaptation evaluation result set, thereby fully recording the multi-dimensional adaptation performance of the predefined standard module system under each demand category.

[0035] The system optimization module is used to perform reverse optimization on a predefined standard module system based on the module system adaptation evaluation result set and module cost information, and to determine the target standard module type set, target reserve quantity range, target combination priority rules, and module retention and replacement rules. Methods for determining the target standard module type set include: Based on the module system adaptation evaluation result set and the historical module combination usage pattern set, the retention value of each standard type in the predefined standard module system is evaluated, low-value modules are eliminated and missing modules are added to form a target standard module type set. Specifically, in the construction management basic dataset, the number of cross-category occurrences of each standard type in all historical maintenance projects corresponding to all demand categories is counted. The number of cross-category occurrences refers to the number of demand categories in which the corresponding standard type is used in historical maintenance projects of different demand categories, which is used to reflect the general coverage capability of the corresponding standard type in demand diversity scenarios. For each standard type, it is determined whether to mark it as a low-value candidate elimination type. Specifically, a cross-category threshold is preset, and the number of cross-category occurrences of each standard type is compared with the cross-category threshold. The cross-category threshold is preset by those skilled in the art based on the minimum cross-demand scenario coverage capability that the module type should have. If the historical module combination usage pattern set does not contain the corresponding standard type, or the number of cross-category occurrences is less than the preset cross-category threshold, the corresponding standard type is marked as a low-value candidate elimination type. For each low-value candidate elimination type, an alternative feasibility verification is performed, and the verification results determine whether to remove the corresponding low-value candidate elimination type from the standard module system. Specifically, from the construction management basic dataset, all historical maintenance projects containing the corresponding low-value candidate elimination type in the module combination record data are extracted and marked as candidate maintenance projects. The demand categories corresponding to each candidate maintenance project are summarized to form a set of demand categories to be verified. For each low-value candidate elimination type, the corresponding low-value candidate elimination type is removed from the standard module system to obtain a temporary alternative verification system. For each demand category in the set of demand categories to be verified, it is sequentially determined whether there is a matching gap under the temporary alternative verification system. If there is no matching gap in all demand categories in the set of demand categories to be verified, the corresponding low-value candidate elimination type is removed from the standard module system. If at least one demand category in the set of demand categories to be verified has a matching gap, the corresponding low-value candidate elimination type is retained and marked as a scarce scenario support type. For demand categories whose matching gap values ​​exceed a preset gap trigger threshold based on the module system adaptation assessment results, the gap dimensions with matching gaps within the corresponding demand categories are identified, and a suggested list of module types for gap filling is generated. Specifically, from the demand categories whose matching gap values ​​exceed the gap trigger threshold, historical maintenance projects with non-zero on-site modification costs are extracted and marked as gap modification projects. From the project demand information of each gap modification project, the demand feature values ​​of the corresponding gap dimension are extracted. If the demand feature value is a numerical demand feature value, all demand feature values ​​of the corresponding gap dimension are summarized, and the minimum and maximum values ​​are taken to form the gap size segment of the corresponding demand category on the corresponding gap dimension. The demand feature values ​​of each gap size segment are statistically analyzed. The frequency of occurrence of characteristic values ​​is calculated, and the characteristic value with the highest frequency is taken as the dominant gap size value of the corresponding gap dimension. If the demand characteristic value is a categorical demand characteristic value, the frequency of occurrence of each categorical demand characteristic value under the corresponding gap dimension is counted, and the categorical demand characteristic value with the highest frequency is taken as the dominant gap position category of the corresponding demand category in the corresponding gap dimension. Based on the dominant gap size value and dominant gap position category of each gap dimension corresponding to the demand category, it is recommended to add a new module type with corresponding specification parameters and working port adaptation parameters, and marked as the recommended addition type to form a list of recommended gap addition module types. Among them, the gap trigger threshold is preset by those skilled in the art based on the maximum allowable system coverage gap degree. All standard types not marked as low-value candidate elimination types, all scarce scenario support types, and all suggested filling types in the gap-filling module type suggestion list in the standard module system are merged to form the target standard module type set; the target standard module type set is the result of the evolution and optimization of the original standard module system driven by both historical needs and system gaps.

[0036] Methods for determining the target reserve quantity range include: Each module type in the target standard module type set is taken as a target type; for each target type, the corresponding demand weighted quantity benchmark is calculated; based on construction turnover information, the turnover occupancy rate corresponding to each target type is calculated, and combined with the turnover retention value corresponding to each demand category, turnover compensation is performed on the demand weighted quantity benchmark corresponding to each target type to obtain the corresponding turnover compensation reserve benchmark; based on the turnover compensation reserve benchmark, the target reserve quantity range corresponding to each target type is determined. Specifically, for each target type in the target standard module type set that is not a suggested addition type, the actual usage quantity of the corresponding target type in each typical combination configuration corresponding to each demand category is extracted from the historical module combination usage pattern set. The actual usage quantity of the corresponding target type in each typical combination configuration is weighted and averaged according to the frequency of occurrence of the corresponding typical combination configuration to obtain the typical demand quantity of the corresponding target type under the corresponding demand category. The ratio of the historical demand quantity of each demand category to the total number of all historical maintenance projects is calculated to obtain the quantity proportion of each demand category. The product of the typical demand quantity and the corresponding quantity proportion for each demand category is calculated to obtain the demand weighted contribution of each demand category to each target type. For each target type, the demand weighted contributions of all corresponding demand categories are summed to obtain the corresponding demand weighted quantity benchmark. For each target type, the occupancy period of all historical traffic maintenance projects is extracted from the construction management and control basic dataset, and the average occupancy period is calculated by averaging. The project start date and completion date of each historical traffic maintenance project are obtained from the construction management and control basic dataset. The time difference between the project completion date and the project start date of each historical traffic maintenance project is calculated to obtain the construction duration days of each historical traffic maintenance project. The average construction duration days of all historical traffic maintenance projects are averaged to obtain the average construction period of historical projects. The ratio of the average occupancy period to the average construction period of historical projects is calculated to obtain the turnover occupancy rate for each target type. The turnover retention value corresponding to each demand category is extracted from the module system adaptation evaluation result set. For each target type, the demand categories corresponding to the target type are identified in the historical module combination usage pattern set, and the turnover retention value of each demand category is calculated by weighting the number of historical traffic maintenance projects corresponding to each demand category to obtain the turnover retention correction value for the corresponding target type.

[0037] For each target type belonging to the suggested supplement type in the target standard module type set, the number of corresponding gap reconfiguration projects is counted and used as the benchmark for the expected usage frequency of the corresponding target type. At the same time, the typical estimated usage of a single project for each target type is preset. The typical estimated usage of a single project is preset by those skilled in the art based on the specification parameters, gap size range, or dominant gap location category of each suggested supplement type. For each target type, the product of the corresponding typical estimated usage of a single project and the benchmark for expected usage frequency is calculated, and then the product result is divided by the total number of all historical maintenance projects to obtain the demand weighted quantity benchmark for the corresponding target type. A preset turnover occupancy rate estimate is used as the turnover occupancy rate for each target type. The turnover occupancy rate estimate is preset by those skilled in the art based on historical turnover experience of similar steel temporary bridge modules with similar specifications to the suggested replacement type and the cycle characteristics of typical traffic maintenance projects. For each target type, the demand category corresponding to the corresponding gap relocation project is extracted, and the turnover retention value of each demand category is calculated by weighting the number of historical traffic maintenance projects corresponding to each demand category, so as to obtain the turnover retention correction value for the corresponding target type.

[0038] Based on the demand-weighted quantity benchmark, turnover occupancy rate, and turnover retention correction value for each target type, the turnover compensation reserve benchmark for each target type is calculated. Specifically, the difference between the demand-weighted quantity benchmark and the turnover occupancy rate is calculated to obtain the turnover release rate. A minimum reserve threshold and an extreme turnover compensation coefficient are preset, both of which are pre-set by those skilled in the art based on inventory safety requirements and extreme turnover risk tolerance requirements. If the turnover release rate is less than or equal to the preset minimum reserve threshold, the demand-weighted quantity benchmark is multiplied by the sum of the demand-weighted quantity benchmark and the preset extreme turnover compensation coefficient to obtain the turnover compensation reserve benchmark. If the turnover release rate is greater than the minimum reserve threshold, the ratio of the demand-weighted quantity benchmark to the turnover release rate is calculated to obtain the basic reserve quantity. The basic reserve quantity is multiplied by the sum of the demand-weighted quantity benchmark and the turnover retention correction value to obtain the turnover compensation reserve benchmark. Taking the turnover compensation reserve benchmark of each target type as the center, multiply it downward by a preset reserve lower limit coefficient to obtain the reserve quantity lower limit; multiply it upward by a preset reserve upper limit coefficient to obtain the reserve quantity upper limit; wherein, the reserve lower limit coefficient is preset by those skilled in the art based on the minimum supply guarantee tolerance level, and the reserve upper limit coefficient is preset by those skilled in the art based on the upper limit of inventory capital occupation; combine the reserve quantity lower limit and the reserve quantity upper limit to form the target reserve quantity range for each target type; summarize the target reserve quantity ranges corresponding to all target types in the target standard module type set to form the target reserve quantity range system.

[0039] Methods for determining the priority rules for target combinations include: Based on the demand feature profiles of each demand category, historical module combination usage patterns, and module system adaptation evaluation results, the executable combination configurations corresponding to each demand category are extracted. Then, based on the combination complexity value, turnover occupancy rate, turnover delay correction value, and module purchase unit price, the executable combination configurations under each demand category are prioritized to form target combination priority rules. Specifically, for each requirement category, all typical combination configurations are extracted from the historical module combination usage pattern set, and the module types contained in each typical combination configuration are identified; if all module types in the typical combination configuration are included in the target standard module type set, the corresponding typical combination configuration is marked as an executable combination configuration; if there are module types in the typical combination configuration that are not included in the target standard module type set, the corresponding typical combination configuration is not marked. From the module system adaptation evaluation results set, obtain the combinatorial complexity value corresponding to each requirement category; for each executable combinatorial configuration corresponding to each requirement category, count the number of module types and the total number of modules respectively, and use the ratio of the number of module types to the total number of modules as the configuration complexity ratio of the corresponding executable combinatorial configuration; where, a lower configuration complexity ratio means that fewer module types are required to implement the requirements under the same requirement category, the reuse ratio is higher, and the difficulty of on-site organization is lower; The frequency of each executable configuration in all historical maintenance projects corresponding to the corresponding demand category is counted and marked as historical frequency. The total purchase price of each module type in each executable configuration is calculated to obtain the configuration purchase cost of each executable configuration. At the same time, the turnover occupancy rate and turnover retention correction value corresponding to each module type are obtained, and the average turnover occupancy rate and turnover retention correction value of each module type in the same executable configuration are calculated to obtain the configuration average turnover occupancy rate and configuration average turnover retention correction value of each executable configuration. The feasible combinatorial configurations within the same requirement category are sorted in the following order: First priority: select executable combinatorial configurations with lower configuration complexity. The second priority is to select executable combinatorial configurations that have appeared more frequently in the past, provided that the configuration complexity ratio is the same. The third priority is to select executable combinatorial configurations with lower average turnover rate when the configuration complexity ratio and the frequency of occurrence in history are the same. The fourth priority is to select the executable combination configuration with the lower average turnover retention correction value when the configuration complexity ratio, historical occurrence frequency and configuration average turnover retention rate are the same. The fifth priority is to select the executable combination configuration with the lower procurement cost, provided that the configuration complexity ratio, historical frequency, average configuration turnover rate and average configuration turnover retention correction value are all the same. The ranking results of the executable combination configurations corresponding to each requirement category are used as the combination priority sequence of the corresponding requirement category. The dominant requirement values ​​of each feature dimension in the requirement feature profile of each requirement category are associated with the combination priority sequence of the corresponding requirement category to form the target combination priority rule. The target combination priority rule is recorded in a structured form of condition-ranking result, which is used to express that when the project requirement features match the requirement feature profile of the requirement category, the executable combination configuration ranked first in the corresponding requirement category should be adopted first.

[0040] Methods for determining module retention and replacement rules include: From the material management system, retrieve all steel temporary bridge modules currently in stock and mark them as existing modules; based on the module system adaptation evaluation result set, target standard module type set, module cost information, turnover retention value and combination complexity value, establish decision rules for retention, gradual withdrawal and replacement of all existing modules, forming module retention and replacement rules; Specifically, the system retrieves the module type, remaining useful life, and current book value of all existing modules from the material management system; it retrieves historical secondary disposal price records for each module type from the financial settlement system; it marks the corresponding module type of each existing module as an existing type, and for each existing type, it determines whether it is included in the target standard module type set; if the existing type is not included in the target standard module type set, it determines that the corresponding existing type should be gradually phased out of the standard module system; further, it combines the current book value with historical secondary disposal price records to determine the corresponding disposal method recommendation; if the historical secondary disposal price is higher than the preset realization trigger value, the disposal method recommendation is early realization; if the historical secondary disposal price is lower than the preset realization trigger value, but the remaining useful life is still higher than the preset minimum remaining useful life threshold, the disposal method recommendation is to retain it until natural scrapping without replenishment; the realization trigger value and minimum remaining useful life threshold are both preset by those skilled in the art based on asset residual value recovery requirements and subsequent engineering safety requirements; if the existing type is included in the target standard module type set, the corresponding remaining useful life is further compared with the minimum remaining useful life threshold; If the remaining useful life is higher than the minimum remaining useful life threshold, the corresponding stock type should be retained; if the remaining useful life is lower than or equal to the minimum remaining useful life threshold, the corresponding stock type should be replaced, and the priority replacement procurement object for the corresponding stock type is identified from the list of recommended gap-filling module types. Specifically, the span and width dimensions of the stock type are obtained, and the absolute values ​​of the differences in span and width dimensions between each recommended gap-filling type and the corresponding stock type are calculated. The absolute values ​​of the differences in span and width dimensions corresponding to the same recommended gap-filling type are summed to obtain the size proximity deviation value for each recommended gap-filling type. The recommended gap-filling type with the smallest size proximity deviation value is selected as the priority replacement procurement object for the corresponding stock type. From the module system adaptation evaluation results set, obtain the turnover retention value and combination complexity value corresponding to the dominant demand category for each stock type; where the dominant demand category refers to the demand category that appears most frequently for the corresponding stock type in the historical module combination usage pattern set; if the stock type is included in the target standard module type set and its remaining service life is higher than the minimum remaining service life threshold, but the turnover retention value corresponding to the dominant demand category is greater than the preset turnover optimization threshold, and the corresponding combination complexity value is greater than the preset complexity optimization threshold, and there are suggested replacement types generated for the corresponding dominant demand category in the gap filling module type suggestion list, then the corresponding stock type is marked as an optimization replacement candidate type; where the turnover optimization threshold and complexity optimization threshold are preset by those skilled in the art based on the system operation efficiency requirements and the on-site implementation complexity upper limit requirements; For stock types determined to be retained, they are recorded as retention types; for stock types determined to be phased out, they are recorded as phased out types; for stock types determined to be replaced or marked as optimized replacement candidates, they are recorded as replacement types, and the corresponding priority replacement procurement objects are recorded. The decision rules corresponding to retention types, phased out types, and replacement types are summarized to form module retention and replacement rules. Among them, the module retention and replacement rules take the stock type and current status as inputs and the retention, phased out, replacement, or optimized replacement as outputs, providing a standardized judgment basis for the enterprise's module procurement and disposal decisions during the asset renewal cycle.

[0041] The simulation and deployment module is used to obtain project demand forecast information during the planning period. Based on the target standard module type set, target reserve quantity range, target combination priority rules, module retention and replacement rules and construction turnover information, it constructs multiple candidate deployment strategies, performs scenario simulations on each candidate deployment strategy based on the project demand forecast information during the planning period, and selects the implementation deployment strategy based on the scenario simulation results.

[0042] Methods for obtaining project demand forecasting information during the planning period include: From the engineering planning coordination system (i.e., a government engineering information integrated management platform used to collect and manage municipal engineering project approval information, planned construction time windows, and engineering type classification records within the region), obtain a list of road maintenance projects that have been approved or are in the planning application stage within the planning period, and mark each road maintenance project in the list as a predicted project; where the planning period refers to the target management time interval extending from the current time point to the future, which is pre-set by those skilled in the art based on the enterprise asset planning cycle and the local municipal engineering plan release cycle; For approved and initiated forecast projects, basic engineering information is obtained from the engineering planning and coordination system. This basic engineering information refers to fundamental information characterizing the construction sequence, engineering attributes, regional location, and initial traffic maintenance needs of the forecast project. Based on a pre-built set of demand estimation rules, the predicted demand values ​​for the span length, traffic width, work access retention, traffic maintenance duration, and road traffic assurance level are estimated from the basic engineering information. The demand estimation rule set is developed by those skilled in the art based on the correspondence between basic engineering information and actual project demand information in historical traffic maintenance projects, adaptation experience of the steel temporary bridge standard module system, and the typical distribution of road traffic maintenance needs under different project types, scale levels, and regional conditions. The process involves: pre-setting rules; structuring and encoding each predicted demand value to form a predicted demand description group for the corresponding predicted project; comparing the predicted demand description group with the demand feature profiles corresponding to each demand category dimension by dimension, identifying the feature dimensions that are the same as the corresponding dominant demand value after structuring and encoding, and marking them as matching dimensions; counting the number of matching dimensions for each demand category to obtain the corresponding number of matches, and labeling the demand category with the largest number of matches as the predicted demand category for the corresponding predicted project; if multiple demand categories have the same and largest number of matches, extracting the demand concentration of each demand category on each matching dimension, and summing them to obtain the cumulative concentration of each demand category; and labeling the demand category with the largest cumulative concentration as the predicted demand category for the corresponding predicted project. For predicted projects still in the planning and application stage, the region, type, and scale of the corresponding predicted project are obtained from the engineering planning and coordination system. Historical projects that match the predicted project in terms of region, type, and scale are extracted from the construction management and control dataset to form a set of matching historical projects. The frequency of occurrence of each demand category for all historical projects in the matching historical project set is counted, and the ratio of the frequency of occurrence of each demand category to the total number of historical projects in the matching historical project set is calculated to obtain the probability of occurrence of each demand category. The probabilistic demand category labels for the same predicted project are summarized to obtain the probabilistic demand category labels for the corresponding predicted project. The probabilistic demand category labels refer to the demand category labels composed of multiple demand categories and their corresponding probabilities, used to characterize the probability distribution of predicted projects still in the planning and application stage across different demand categories. The basic engineering information, predicted demand category labels, and probabilistic demand category labels of all predicted projects within the planning period are integrated to form the project demand prediction information for the planning period. Among them, the project demand prediction information for the planning period uses the predicted project as the basic recording unit and records the composition and time distribution of the predicted demand categories that will occur within the planning period.

[0043] Methods for constructing multiple candidate deployment strategies include: Based on the target standard module type set, the target reserve quantity range system, and the project demand forecast information during the planning period, multiple reserve plans under the reserve focus dimension and multiple turnover operation modes under the turnover efficiency dimension are generated respectively. The reserve plans and turnover operation modes are combined to form multiple candidate deployment strategies. At the same time, the module retention and replacement rules are incorporated into each candidate deployment strategy to form the initial module inventory structure of each candidate deployment strategy at the beginning of the planning period.

[0044] Specifically, a target reserve quantity range system is used as the reserve quantity constraint boundary for each candidate deployment strategy; a candidate strategy generation dimension is preset, including a reserve focus dimension and a turnover efficiency dimension; the reserve focus dimension is used to characterize the different orientations of the candidate deployment strategy in terms of module inventory configuration, such as low inventory, balanced inventory, or high inventory; the turnover efficiency dimension is used to characterize the different orientations of the candidate deployment strategy in terms of module return and reallocation rhythm, such as regular turnover or accelerated turnover; the candidate strategy generation dimension is preset by those skilled in the art based on the balance requirements of guarantee capacity and capital efficiency in the asset planning of steel temporary bridge maintenance.

[0045] In terms of reserve focus, the candidate reserve quantities are set as the lower limit, upper limit and average of the lower limit and upper limit of the reserve quantity for each target type, and a conservative reserve plan, a balanced reserve plan and an ample reserve plan are generated. Among them, the conservative reserve plan prioritizes controlling the capital occupation of inventory, the balanced reserve plan takes into account both guarantee capacity and capital efficiency, and the ample reserve plan prioritizes guarantee capacity during peak periods.

[0046] In terms of turnover efficiency, the average occupancy period for each target type, the product of the average occupancy period and the preset aggressive turnover compression coefficient are used as candidate occupancy times to generate standard turnover mode and aggressive turnover mode. The aggressive turnover compression coefficient is preset by those skilled in the art based on the ratio of the shortest occupancy period to the average occupancy period of the corresponding module type in the construction turnover information. The standard turnover mode schedules modules according to the normal circulation rhythm, while the aggressive turnover mode compresses the occupancy time of modules in the predicted project by strengthening logistics scheduling.

[0047] By combining various reserve plans under the reserve focus dimension with various turnover modes under the turnover efficiency dimension, multiple candidate deployment strategies are formed. Each candidate deployment strategy records the corresponding reserve plan and turnover operation mode, which serves as a global configuration rule for unified resource scheduling across all target types. The actual inventory quantity of each stock type at the start of the planning period is obtained from the material management system. The stock types marked as replacement types and their corresponding priority replacement procurement objects are extracted from the module retention and replacement rules, and the actual inventory quantity of each stock type at the start of the planning period is added to the actual inventory quantity of the corresponding priority replacement procurement object. Meanwhile, the actual inventory quantity corresponding to the retention type remains unchanged, and the actual inventory quantity corresponding to the exit type only participates in allocation and use and does not participate in quantity replenishment calculation. The candidate reserve quantity and actual inventory quantity of each target type in the reserve plan corresponding to the candidate deployment strategy are compared, and the larger value is taken as the number of available modules in inventory for the corresponding target type at the start of the planning period, forming the initial module inventory structure of each candidate deployment strategy at the start of the planning period.

[0048] Methods for scenario simulation of each candidate deployment strategy include: Based on the project demand forecast information during the planning period and the initial module inventory structure corresponding to each candidate deployment strategy, the module allocation and module storage process of each candidate deployment strategy at different time steps is simulated step by step along the planning period time axis. According to the forecast project demand and the number of available modules in the inventory at each time step, allocation gaps and support failure events are identified. After completing the simulation of the entire planning period, the expected value of the cumulative support failure number and the average inventory holding cost corresponding to each candidate deployment strategy are calculated. The expected value of the cumulative support failure number and the average inventory holding cost are integrated to form the scenario extrapolation results of each candidate deployment strategy. Specifically, a preset scenario simulation time step is used to divide continuous simulation intervals on the planning period time axis. This time step is pre-set by those skilled in the art based on the typical time granularity of project initiation, continuation, and termination within the planning period. The planning period time axis is divided according to the scenario simulation time step, resulting in multiple consecutive time steps. Each time window within a time step corresponds to a simulation interval. For each candidate deployment strategy, the number of available modules in inventory for each target type in the corresponding initial module inventory structure is used as the number of available modules in inventory for each target type before the start of the first time step. The process proceeds sequentially along the planning period time axis according to the scenario simulation time step, and the currently advanced time step is taken as the current time step. The number of available modules in inventory for each target type within the current time step is extracted. If the current time step is the first time step on the planning period time axis, the number of available modules in inventory in the initial module inventory structure is used as the number of available modules in inventory for the current time step. If the current time step is not the first time step, the number of available modules updated in the previous time step is used as the number of available modules in inventory for the current time step. Within the current time step, extract the predicted projects expected to be launched within the corresponding projection interval from the project demand forecast information during the planning period. For predicted projects with predicted demand category labels, extract the highest-ranked executable combination configuration under the corresponding predicted demand category label from the target combination priority rules, and extract the corresponding target type and actual usage quantity from them as the module requirements to be allocated for the corresponding predicted project within the current time step. Compare the module requirements to be allocated with the number of available modules in the database for each target type item by item. If the actual usage quantity of any target type in the module requirements to be allocated is greater than the number of available modules in the database, it is determined that there is an allocation shortage for the corresponding predicted project within the current time step. The corresponding predicted project is recorded as a guarantee failure event at the current time step. If there is no allocation gap for the corresponding predicted project at the current time step, the actual usage quantity of each target type in the allocation module requirements is taken as the actual allocation quantity of the corresponding target type at the current time step. If there is an allocation gap for the corresponding predicted project at the current time step, for target types whose actual usage quantity is greater than the number of available modules in the database, the number of available modules in the database for the corresponding target type at the current time step is taken as the actual allocation quantity. For target types whose actual usage quantity is less than or equal to the number of available modules in the database, the actual usage quantity of the corresponding target type at the current time step is still taken as the actual allocation quantity. For forecast projects with probabilistic demand category labels, the same guarantee failure event determination method as for forecast projects with predictive demand category labels is used for each demand category in the corresponding probabilistic demand category label to determine whether a guarantee failure event has occurred in the current time step. The guarantee failure status corresponding to the demand category that has a guarantee failure event is recorded as one, and the guarantee failure status corresponding to the demand category that has not a guarantee failure event is recorded as zero. The product of the guarantee failure status corresponding to each demand category and the occurrence probability of the corresponding demand category is calculated, and the product results are summed to obtain the expected number of guarantee failures for the corresponding forecast project in the current time step. Among them, the forecast projects with probabilistic demand category labels are only used to calculate the expected number of guarantee failures in the corresponding time step, and the module requirements under each demand category scenario are not included in the physical inventory deduction process. At the end of each time step, the number of available modules in the database for each target type is updated based on the construction turnover information and the turnover operation mode corresponding to the candidate deployment strategy. Specifically, for each target type, the occupancy period of the corresponding target type is divided by the scenario simulation time step and rounded up to obtain the occupancy period of the corresponding target type. The occupancy period in the standard turnover mode is the average occupancy period of the corresponding target type, while the occupancy period in the aggressive turnover mode is the product of the average occupancy period of the corresponding target type and the aggressive turnover compression coefficient. For each target type, the corresponding number of occupancy periods is traced back within the current time step to determine the allocation source time step for each target type. If an allocation source time step exists, the actual allocation of the corresponding target type recorded in the corresponding allocation source time step is read. The allocation quantity is recorded as the corresponding target type's inventory quantity within the current time step. If the allocation source time step does not exist or exists but the actual allocation quantity for the corresponding target type is not recorded, the inventory quantity for the corresponding target type within the current time step is recorded as zero. The actual allocation quantity for each target type corresponding to all prediction projects with prediction demand category labels within the current time step is obtained and summed according to target type to obtain the total allocation quantity for each target type within the current time step. The inventory quantity for each target type within the current time step is added to the number of available modules in the corresponding target type, and then the total allocation quantity for each target type within the current time step is subtracted to obtain the updated number of available modules in the inventory, which is used as the number of available modules in the inventory for each target type before the start of the next time step. After completing the step-by-step extrapolation of the planning period timeline, the number of all assurance failure events or the expected number of assurance failures within each time step are summed to obtain the expected cumulative number of assurance failures for the corresponding candidate deployment strategy within the planning period. Simultaneously, the number of available modules in inventory for each target type within each time step is extracted and multiplied by the corresponding module purchase price to obtain the inventory value for each target type. The inventory values ​​for all target types within the same time step are summed to obtain the corresponding total inventory value. The average inventory value across all time steps within the planning period is calculated to obtain the average inventory cost for the corresponding candidate deployment strategy. The expected cumulative number of assurance failures and the average inventory cost are integrated to form the scenario extrapolation results for the corresponding candidate deployment strategy.

[0049] Methods for selecting implementation and deployment strategies based on scenario simulation results include: From the scenario simulation results of each candidate deployment strategy, the expected value of the cumulative number of assurance failures and the average inventory cost are extracted; based on the preset assurance failure tolerance threshold, candidate deployment strategies with insufficient assurance capabilities are screened out to obtain a set of feasible assurance candidate strategies; the candidate deployment strategy with the lowest average inventory cost in the set of feasible assurance candidate strategies is selected as the preliminary preferred strategy; assurance safety margin verification is performed on the preliminary preferred strategy, and it is determined as the implementation deployment strategy after passing the verification. Specifically, a default protection failure tolerance threshold is preset. This threshold represents the upper limit of the cumulative number of protection failures that an enterprise can accept within the planning period. It is preset by those skilled in the art based on the enterprise's maximum tolerance for default risk in the protection task. The expected value of the cumulative number of protection failures for each candidate deployment strategy is compared with the default protection failure tolerance threshold. Candidate deployment strategies with an expected value of the cumulative number of protection failures greater than the threshold are eliminated to obtain a set of feasible candidate strategies. If the set of feasible candidate strategies is empty, the candidate deployment strategy with the smallest expected value of the cumulative number of protection failures is selected from all candidate deployment strategies and directly used as the implementation deployment strategy. If the set of feasible candidate strategies is not empty, the candidate deployment strategy with the lowest average inventory holding cost is identified in the set and determined as the preliminary preferred strategy. Further verification of the preliminary preferred strategy is performed to ensure a safety margin. Specifically, from the project demand forecast information during the planning period, all forecast projects with probabilistic demand category labels are extracted and marked as verification projects. For each verification project, the actual usage quantity of each target type under the executable combination configuration corresponding to each demand category is extracted, and the actual usage quantities of each target type under the same demand category are summed to obtain the total module demand corresponding to each demand category. The demand category with the largest total module demand is identified as the stress demand category, and the demand category with the highest probability of occurrence is identified as the baseline demand category. For each target type, the following steps are performed: Calculate the difference between the actual usage quantity of the corresponding target type under the pressure demand category and the actual usage quantity under the baseline demand category; if the difference is greater than zero, the difference is taken as the maximum potential incremental allocation gap of the corresponding predicted project in the corresponding target type; if the difference is less than or equal to zero, the maximum potential incremental allocation gap of the corresponding target type is recorded as zero; for each verification project, the corresponding maximum potential incremental allocation gap is added to the allocation module demand of the preliminary selection strategy within the expected start time step of the corresponding verification project, and the allocation gap judgment process in the scenario simulation is re-executed to obtain the expected value of the cumulative number of assurance failures under the assurance safety margin verification; if the expected value of the cumulative number of assurance failures is still less than or equal to the assurance failure tolerance threshold, the preliminary selection strategy is confirmed to have passed the assurance safety margin verification and is determined as the implementation deployment strategy; If the initial selection strategy fails the safety margin verification, the remaining candidate deployment strategies in the feasible candidate strategy set will be verified in order of average inventory holding cost from low to high until the first candidate deployment strategy that passes the verification is found and determined as the implementation deployment strategy.

[0050] This embodiment breaks down the information barrier between on-site demand data and warehouse asset data in the traditional standardized management system for steel temporary bridges by constructing a basic dataset for construction management that includes project demand information, module combination record data, construction turnover information, and module cost information. This achieves a fundamental shift from passive selection for single projects to proactive evolution of a standard system driven by multi-project demand profiles. Through multi-dimensional structured coding and cluster analysis of project demand information from historical road maintenance projects, a demand feature profile system with span length, traffic width, work access retention, maintenance duration, and road traffic guarantee level as core dimensions was established. This reveals the heterogeneous patterns of demand under different urban road maintenance scenarios, effectively overcoming the fundamental defects of relying on manual experience to piece together solutions from atlases and failing to systematically identify demand classification characteristics. By integrating and analyzing the predefined standard module system with historical module combination usage patterns and construction turnover information for each demand category, a four-dimensional adaptation evaluation index was calculated: matching gap value, additional cost of modification, turnover retention value, and combination complexity value. This achieves a comprehensive quantitative diagnosis of the original system in terms of demand coverage, cost burden, turnover efficiency, and operational complexity. It compensates for the lack of a system-level dynamic assessment mechanism for adaptation; by using the adaptation assessment results to drive the retention and elimination of module types, the calculation of reserve quantity ranges, the generation of combination priority rules, and the establishment of rules for retaining and replacing existing modules, it realizes the historical practice-driven evolution and optimization of the standard module system. This makes the system configuration results no longer dependent on static predefined subjective experience, but rather on objective reverse solutions derived from historical real demand and turnover efficiency data. By introducing project demand forecast information during the planning period and constructing three reserve focus strategies (conservative, balanced, and ample) and two combination candidate deployment strategies (standard and aggressive turnover efficiency modes), a time-step-driven planning period scenario simulation mechanism is used to gradually simulate each candidate strategy. Combined with a three-level screening logic of first ensuring feasibility screening, then minimizing costs and verifying the safety margin pressure, the implementation deployment strategy is determined. This enables the use of scientific and quantitative forward-looking simulation to replace blind resource allocation in highly uncertain multi-project concurrent maintenance scenarios. It systematically solves the dilemma of long-term idleness of non-adaptive modules and sudden shortage of core modules in the asset scheduling of steel temporary bridge maintenance, and comprehensively improves the company's overall management level between capital efficiency and maintenance capabilities.

[0051] Example 2: Please refer to Figure 2As shown, for parts not described in detail in this embodiment, please refer to the description in Embodiment 1. A standardized construction management method for temporary steel bridges used for road maintenance based on big data is provided. The method includes: Collect project requirement information, module combination record data, construction turnover information and module cost information from historical traffic maintenance projects, and establish project-level correlation between project requirement information and module combination record data to form a basic dataset for construction management and control; Based on the project demand information in the construction management and control basic dataset, cluster analysis is performed according to the requirements of span length, traffic width, work opening retention, traffic maintenance duration, and road traffic guarantee level to generate demand feature profiles for each demand category. Based on the demand feature profile and project-level correlation, extract the historical module combination usage patterns corresponding to each demand category, and combine them with construction turnover information to calculate the matching gap value, modification additional cost, turnover retention value and combination complexity value of the predefined standard module system under each demand category, forming a module system adaptation evaluation result set. Based on the module system adaptation evaluation result set and module cost information, the predefined standard module system is back-optimized to determine the target standard module type set, target reserve quantity range, target combination priority rules and module retention and replacement rules. Obtain project demand forecast information during the planning period, construct multiple candidate deployment strategies based on the target standard module type set, target reserve quantity range, target combination priority rules, module retention and replacement rules, and construction turnover information, and conduct scenario simulations for each candidate deployment strategy based on the project demand forecast information during the planning period, and select the implementation deployment strategy based on the scenario simulation results.

[0052] Example 3: This application also provides an electronic device. The electronic device may include one or more processors and one or more memories. The memories store computer-readable code, which, when executed by the one or more processors, can perform the standardized construction management method for maintaining traffic flow on steel temporary bridges based on big data, as described above.

[0053] The method or system according to the embodiments of this application can also be implemented using the architecture of the electronic device shown in this application. The electronic device may include a bus, one or more CPUs, ROM, RAM, a communication port connected to a network, input / output, a hard disk, etc. The storage device in the electronic device, such as a ROM or hard disk, may store the standardized construction management method for road maintenance steel temporary bridges based on big data provided in this application. Furthermore, the electronic device may also include a user interface. Of course, the architecture shown in this application is merely exemplary; when implementing different devices, one or more components of the electronic device shown in this application may be omitted according to actual needs.

[0054] Example 4: One embodiment of this application discloses a computer-readable storage medium. The computer-readable storage medium stores computer-readable instructions. When the computer-readable instructions are executed by a processor, the standardized construction management method for maintaining traffic flow on steel temporary bridges based on big data, as described in the above-described embodiments of this application, can be executed. The storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0055] Furthermore, according to embodiments of this application, the processes described in the above-described flowcharts can be implemented as computer software programs. For example, this application provides a non-transitory machine-readable storage medium storing machine-readable instructions that can be executed by a processor to perform instructions corresponding to the method steps provided in this application, such as a standardized construction management method for road maintenance steel temporary bridges based on big data. When this computer program is executed by a central processing unit (CPU), it performs the functions defined in the method of this application.

[0056] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0057] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0058] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A standardized construction management method for temporary steel bridges used for road maintenance based on big data, characterized in that: include: Collect project requirement information, module combination record data, construction turnover information and module cost information from historical traffic maintenance projects, and establish project-level correlation between project requirement information and module combination record data to form a basic dataset for construction management and control; Based on the project demand information in the construction management and control basic dataset, cluster analysis is performed according to the requirements of span length, traffic width, work opening retention, traffic maintenance duration, and road traffic guarantee level to generate demand feature profiles for each demand category. Based on the demand feature profile and project-level correlation, extract the historical module combination usage patterns corresponding to each demand category, and combine them with construction turnover information to calculate the matching gap value, modification additional cost, turnover retention value and combination complexity value of the predefined standard module system under each demand category, forming a module system adaptation evaluation result set. Based on the module system adaptation evaluation result set and module cost information, the predefined standard module system is back-optimized to determine the target standard module type set, target reserve quantity range, target combination priority rules and module retention and replacement rules. Obtain project demand forecast information during the planning period, construct multiple candidate deployment strategies based on the target standard module type set, target reserve quantity range, target combination priority rules, module retention and replacement rules, and construction turnover information, and conduct scenario simulations for each candidate deployment strategy based on the project demand forecast information during the planning period, and select the implementation deployment strategy based on the scenario simulation results.

2. The standardized construction management and control method for road maintenance steel temporary bridges based on big data as described in claim 1, characterized in that, Methods for generating requirement feature profiles corresponding to each requirement category include: In the construction management and control basic dataset, the project demand information corresponding to each historical traffic maintenance project is structured and encoded to form numerical feature vectors and categorical feature vectors for each historical traffic maintenance project. The numerical feature vectors and categorical feature vectors corresponding to the same historical traffic maintenance project are concatenated to form the demand feature vectors corresponding to each historical traffic maintenance project. Based on the demand feature vectors corresponding to each historical traffic maintenance project, demand clustering analysis is performed to obtain multiple demand categories. Each dimension in the demand feature vector is labeled as a feature dimension. For each demand category, the dimension feature value corresponding to each feature dimension is extracted from the demand feature vector of all historical maintenance projects. Based on the dimension feature value, the dominant demand value of each dimension feature corresponding to each demand category is determined. Based on the dimension feature value and the dominant demand value, the demand concentration of each dimension feature corresponding to each demand category is determined. The number of historical maintenance projects included in each demand category is counted to obtain the corresponding number of historical demands. The dominant demand value, demand concentration and number of historical demands of each feature dimension corresponding to each demand category are integrated to form the demand feature profile of the corresponding demand category.

3. The standardized construction management and control method for road maintenance steel temporary bridges based on big data as described in claim 2, characterized in that, Methods for performing demand clustering analysis include: For each feature dimension in the demand feature vector, it is divided into demand skeleton dimension and elastic segmentation dimension; the dimension feature values ​​of each demand feature vector corresponding to each demand skeleton dimension are combined to form the corresponding skeleton code; historical projects with identical skeleton codes are grouped into the same initial skeleton group. Within each initial skeleton group, a secondary clustering subdivision is further performed based on the elastic subdivision dimension: the dimensional feature values ​​of each historical maintenance project corresponding to each elastic subdivision dimension are combined to form a corresponding joint encoding combination, and the subdivision center label is identified; the subdivision matching deviation between each historical maintenance project and its corresponding subdivision center label is calculated, and each historical maintenance project is assigned to the subdivision category corresponding to the subdivision center label with the smallest subdivision matching deviation; each subdivision category within each initial skeleton group is used as a candidate demand category; For all candidate requirement categories, further sparsity category trimming is performed: all candidate requirement categories are divided into sparse categories and non-sparse categories, and the category compatibility between each sparse category and each non-sparse category is calculated; each sparse category is merged into the non-sparse category with the highest corresponding category compatibility, and all non-sparse categories are used as requirement categories.

4. The standardized construction management and control method for road maintenance steel temporary bridges based on big data as described in claim 3, characterized in that, Methods for extracting historical module combination usage patterns include: For each requirement category, the usage frequency of different module types in all corresponding module combination records is counted, and sorted from high to low usage frequency to generate a type sequence; for each sequence position in the type sequence, the corresponding cumulative coverage rate is calculated; for each requirement category, the sequence position in the corresponding type sequence where the cumulative coverage rate first exceeds or equals the preset backbone module coverage rate threshold is identified and marked as the backbone position; for each type sequence, the module type corresponding to the backbone position is summarized with all module types ranked before the backbone position to form a backbone module set; Based on the module combination record data of each historical maintenance project under each demand category, the module combination patterns corresponding to each historical maintenance project are extracted; the module combination patterns of any two historical maintenance projects corresponding to the same demand category are compared, and it is determined whether they are the same module combination pattern based on the comparison results; the same module combination patterns are merged to form a module combination pattern class; the frequency of occurrence of each module combination pattern class is counted, the multiple module combination pattern classes with the highest frequency of occurrence are extracted, and the corresponding module combination patterns are used as the typical combination configurations of the corresponding demand category. The core modules of each demand category are integrated with typical combination configurations to form historical module combination usage patterns for the corresponding demand category.

5. The standardized construction management and control method for road maintenance steel temporary bridges based on big data as described in claim 4, characterized in that, Methods for forming a modular system adaptation evaluation result set include: Obtain a predefined standard module system. For each requirement category, compare the standard module system with the dominant requirement value in the corresponding requirement feature profile dimension by dimension to determine whether there is a matching gap for the corresponding requirement category. For requirement categories with matching gaps, calculate the corresponding matching gap value respectively. Extract the corresponding on-site modification cost records from the module cost information of each historical maintenance project, and calculate the average modification cost per time for each demand category; calculate the procurement cost benchmark of typical solutions based on typical combination configurations, and calculate the additional value of modification for each demand category in combination with the average modification cost per time. Based on the on-site modification cost records, all historical maintenance projects are divided into historical modification projects and historical normal projects; based on the construction turnover information of each historical modification project and historical normal project, the turnover retention value corresponding to each demand category is calculated. Based on typical combinatorial configurations, calculate the category-level module type ratio for each requirement category; obtain assembly error records for each historical maintenance project and calculate the assembly error rate for each requirement category; based on the category-level module type ratio and the assembly error rate, calculate the combinatorial complexity value for each requirement category. The matching gap value, reconfiguration added value, turnover retention value and combination complexity value of each demand category are integrated to form a module system adaptation evaluation result set.

6. The standardized construction management and control method for road maintenance steel temporary bridges based on big data as described in claim 5, characterized in that, Methods for determining the target standard module type set and the target reserve quantity range include: For each module type in the standard module system, a retention value assessment is performed to identify low-value candidate elimination types. For each low-value candidate elimination type, an alternative feasibility verification is performed, and the corresponding low-value candidate elimination type is removed from the standard module system based on the verification results. For the demand categories whose matching gap values ​​exceed the preset gap trigger threshold in the module system adaptation assessment results, a list of recommended module types for gap filling is generated. All module types not marked as low-value candidate elimination types in the standard module system, all low-value candidate elimination types that have not been deleted, and all recommended filling types in the list of recommended module types for gap filling are merged to form a target standard module type set. Each module type in the target standard module type set is taken as a target type; for each target type, the corresponding demand weighted quantity benchmark is calculated; based on construction turnover information, the turnover occupancy rate corresponding to each target type is calculated, and combined with the turnover retention value corresponding to each demand category, turnover compensation is performed on the demand weighted quantity benchmark corresponding to each target type to obtain the corresponding turnover compensation reserve benchmark; based on the turnover compensation reserve benchmark, the target reserve quantity range corresponding to each target type is determined.

7. The standardized construction management and control method for road maintenance steel temporary bridges based on big data as described in claim 6, characterized in that, The methods for determining the priority rules for target combinations and the rules for retaining and replacing modules include: Based on the demand characteristic profiles of each demand category, historical module combination usage patterns, and module system adaptation evaluation results, the executable combination configurations corresponding to each demand category are extracted; the module purchase unit price is obtained from the module cost information of each historical maintenance project; and the executable combination configurations under each demand category are prioritized according to the combination complexity value, turnover occupancy rate, turnover retention value, and module purchase unit price to form target combination priority rules. Obtain all steel temporary bridge modules currently in stock and mark them as existing modules; based on the module system adaptation evaluation result set, target standard module type set, module cost information, turnover retention value and combination complexity value, establish decision rules for retention, gradual withdrawal and replacement of all existing modules, forming module retention and replacement rules.

8. The standardized construction management and control method for road maintenance steel temporary bridges based on big data as described in claim 7, characterized in that, Methods for obtaining project demand forecasting information during the planning period include: Obtain road maintenance projects that have been approved or are in the planning application stage within the planning period and mark them as predicted projects; for predicted projects that have been approved, obtain the corresponding basic project information and calculate the matching quantity of each demand category, and use the demand category with the largest matching quantity as the corresponding predicted demand category label; for predicted projects that are still in the planning application stage, calculate the occurrence probability of each demand category and form the corresponding probabilistic demand category label by summarizing; integrate the basic project information, predicted demand category labels and probabilistic demand category labels of all predicted projects within the planning period to form the project demand prediction information for the planning period. Methods for constructing multiple candidate deployment strategies include: Based on the target standard module type set, the target reserve quantity range, and the project demand forecast information during the planning period, multiple reserve plans under the reserve focus dimension and multiple turnover operation modes under the turnover efficiency dimension are generated respectively. The reserve plans and turnover operation modes are combined to form multiple candidate deployment strategies. At the same time, the module retention and replacement rules are incorporated into each candidate deployment strategy to form the initial module inventory structure of each candidate deployment strategy at the beginning of the planning period.

9. The standardized construction management and control method for road maintenance steel temporary bridges based on big data as described in claim 8, characterized in that, Methods for scenario simulation of each candidate deployment strategy include: Based on the project demand forecast information during the planning period and the initial module inventory structure corresponding to each candidate deployment strategy, the module allocation and module storage process of each candidate deployment strategy at different time steps is simulated step by step along the planning period time axis, and allocation gaps and guarantee failure events are identified; after completing the simulation of the entire planning period, the expected value of the cumulative guarantee failure number and the average inventory holding cost corresponding to each candidate deployment strategy are calculated; the expected value of the cumulative guarantee failure number and the average inventory holding cost corresponding to the same candidate deployment strategy are integrated to form the scenario extrapolation results of each candidate deployment strategy; Methods for selecting implementation and deployment strategies based on scenario simulation results include: From the scenario simulation results of each candidate deployment strategy, the expected value of the cumulative number of assurance failures and the average inventory cost are extracted. Based on the preset assurance failure tolerance threshold, candidate deployment strategies with the expected value of the cumulative number of assurance failures greater than the assurance failure tolerance threshold are eliminated, resulting in a set of feasible assurance candidate strategies. The candidate deployment strategy with the lowest average inventory cost in the set of feasible assurance candidate strategies is selected as the preliminary preferred strategy. Assurance safety margin verification is performed on the preliminary preferred strategy, and it is determined as the implementation deployment strategy after passing the verification.

10. A standardized construction management and control system for temporary steel bridges for road maintenance based on big data, implementing the standardized construction management and control method for temporary steel bridges for road maintenance based on big data as described in any one of claims 1-9, characterized in that, include: The data collection module is used to collect project requirement information, module combination record data, construction turnover information and module cost information of historical traffic maintenance projects, and establish project-level correlation between project requirement information and module combination record data to form a basic dataset for construction management and control. The demand clustering module is used to perform clustering analysis based on project demand information in the construction management and control basic dataset, according to the requirements of span length, passage width, work opening retention, traffic maintenance duration, and road traffic guarantee level, and generate demand feature profiles corresponding to each demand category. The adaptation assessment module is used to extract the historical module combination usage patterns corresponding to each demand category based on the demand feature profile and project-level correlation, and combined with construction turnover information, calculate the matching gap value, modification additional cost, turnover retention value and combination complexity value of the predefined standard module system under each demand category, forming a module system adaptation assessment result set. The system optimization module is used to perform reverse optimization on a predefined standard module system based on the module system adaptation evaluation result set and module cost information, and to determine the target standard module type set, target reserve quantity range, target combination priority rules, and module retention and replacement rules. The simulation and deployment module is used to obtain project demand forecast information during the planning period. Based on the target standard module type set, target reserve quantity range, target combination priority rules, module retention and replacement rules and construction turnover information, it constructs multiple candidate deployment strategies, performs scenario simulations on each candidate deployment strategy based on the project demand forecast information during the planning period, and selects the implementation deployment strategy based on the scenario simulation results.

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