A method and system for recommending beam storage platforms based on multi-factor constraints

Abstract

This application discloses a method and system for recommending beam storage platforms based on multi-factor constraints, belonging to the field of construction management. The method includes: acquiring status information data of beam storage platforms and demand information data of beam components, and preprocessing the information data; based on the preprocessed data, filtering a list of candidate beam storage platforms through constraints; the constraints include availability constraints and beam type adaptability constraints; sorting the candidate beam storage platform list in ascending order using a proximity coefficient to obtain a location-priority candidate list; selecting candidate beam storage platforms to be verified from the location-priority candidate list for storage order conflict verification to obtain the target beam storage platform, and outputting recommendation information. This application constructs a multi-dimensional constraint system of "availability status + beam type adaptability + location optimization + multi-level date conflict," solving the problem of unreasonable decision-making caused by a single constraint dimension.

Description

Technical Field

[0001] This application belongs to the field of construction management of building engineering, and specifically relates to a method and system for recommending beam storage platforms based on multi-factor constraints. Background Technology

[0002] In bridge engineering (especially large-scale cross-river and cross-river bridges), the storage and management of precast beam components (such as box girders, small box girders, T-beams, etc.) is one of the core aspects of construction organization. The rational allocation of storage platforms, as temporary storage carriers for precast beams, directly affects three key indicators:

[0003] 1) Efficiency of beam erection: The distance between the platform and the beam erection point determines the length of the component transfer path. An unreasonable allocation will lead to increased transfer time and increased mechanical energy consumption.

[0004] 2) Site utilization: The beam storage platform is mostly designed in a multi-story (1-2 stories) manner. If the storage sequence rules of the multi-story platform are not considered (e.g., the 2-story components need to be erected before the 1-story components), the beam erection sequence may conflict, resulting in multiple beam relocations or the platform being idle.

[0005] 3) Management Costs: Traditional pier allocation relies on manual processing by management personnel. This requires manually querying information such as pier occupancy status, component beam type requirements, and estimated beam erection dates, and then allocating piers based on experience. This method has the following significant drawbacks:

[0006] Inefficient: Large bridge projects may have dozens to hundreds of beam abutments and various component types (such as 32m and 24m). Manually querying and comparing information can take tens of minutes, making it difficult to respond quickly to the needs of rapid operations.

[0007] The decision-making process is highly subjective: the occupancy status of the piers and the updating of component information (such as the expected beam erection date) rely on manual recording, which can easily lead to a disconnect between the data and the actual situation. Manual decision-making can easily overlook implicit constraints such as "regional differences" (e.g., cross-regional use of piers from different construction areas will increase transportation costs) and "multi-level conflicts" (e.g., the beam erection date of components stored on the second-level piers is later than that on the first-level piers, resulting in multiple beam relocations).

[0008] Lack of visualization: Under the manual management model, the distribution of storage bases and storage status are only recorded in tables or drawings. Managers cannot intuitively grasp the overall storage layout and find it difficult to quickly identify resource waste or conflict risks.

[0009] In existing technologies, some construction projects attempt to manage pedestal information using Excel spreadsheets, but these only achieve data recording functions and cannot complete automatic filtering, optimization calculations, and conflict verification. Although a few intelligent systems introduce "location priority" recommendation logic, they do not consider key constraints such as beam type adaptation and date conflicts of multi-level pedestals, resulting in insufficient practicality of the recommendation results.

[0010] Therefore, there is an urgent need for an intelligent recommendation algorithm for storage beams and pedestals that can construct a multi-factor constraint system and take into account both efficiency and rationality, in order to solve the pain points of traditional management. Summary of the Invention

[0011] To address the aforementioned problems, this application provides a method for recommending beam storage platforms based on multi-factor constraints, the method comprising:

[0012] Acquire the status information data of the beam storage platform and the demand information data of the beam components, and preprocess the information data;

[0013] Based on the preprocessed data, a list of candidate beam storage platforms is selected through constraints; the constraints include the availability constraints of the beam storage platforms and the beam type adaptability constraints.

[0014] The candidate beam storage platform list is sorted in ascending order using the proximity coefficient to obtain the position-priority candidate list;

[0015] Select the candidate beam storage platform to be verified from the position priority candidate list, perform storage order conflict verification, obtain the target beam storage platform, and output recommendation information.

[0016] According to some embodiments of this disclosure, the status information data of the beam storage platform includes: serial number, beam storage platform number, region, row number, column number, layer number, proximity coefficient, weight, availability status, available beam type, number of guide beams, current beam component number, current beam frame number, expected beam erection date of the current beam, and current beam storage date;

[0017] The beam component requirements data includes: component number, component type, component category, process status, and expected beam erection date.

[0018] According to some embodiments of this disclosure, the preprocessing of information data includes:

[0019] Convert empty values ​​in the information data to None;

[0020] Convert numeric fields in the information data to integer types; and,

[0021] Format the date field in the information data to a standard format.

[0022] According to some embodiments of this disclosure, the availability constraints of the beam storage platform include:

[0023] In the preprocessed status information data, the available status is "available" and the current beam component number is "None".

[0024] According to some embodiments of this disclosure, the beam adaptability constraint includes:

[0025] Extract the component category of the component to be assigned;

[0026] Read the "Storable Beam Type" field of the storage platform obtained after filtering by the availability constraint of the storage platform, and split it into a list of beam types by commas;

[0027] The list of retained beam types includes the beam storage platforms of the current component category, forming a candidate beam storage platform list.

[0028] According to some embodiments of this disclosure, the acquisition of the proximity coefficient includes:

[0029] Determine the source platform for the component to be assigned;

[0030] The planar distance between the storage platform and the source platform is determined by the Manhattan distance method, and a penalty value is added in combination with regional differences to obtain the proximity coefficient.

[0031] According to some embodiments of this disclosure, the expression for the proximity coefficient is:

[0032] ;

[0033] Where PC represents the proximity coefficient; d represents the planar distance between the storage beam platform and the source platform; T area Indicates the region; S area Indicates a specified area.

[0034] According to some embodiments of this disclosure, the candidate beam storage platform to be verified is subjected to storage order conflict verification to obtain the target beam storage platform, including:

[0035] Determine the target number of layers for the beam storage platform;

[0036] Find the bottom beam storage platform at the same location. If the bottom beam storage platform is idle, set the bottom beam storage platform as the target beam storage platform.

[0037] If the bottom-level beam storage platform has already stored components, check whether the beam erection date of the bottom-level component is later than the beam erection date of the current component. Only if the condition is met will the current upper-level beam storage platform be set as the target beam storage platform.

[0038] According to some embodiments of this disclosure, after obtaining the target beam support and outputting recommendation information, the method further includes:

[0039] Update the beam storage platform data, mark the target beam storage platform as occupied, and clear the source platform status.

[0040] According to some embodiments of this disclosure, the output includes a graphical display of the distribution and storage status of the beam storage platforms, which is visualized through coordinate mapping and beam storage platform annotation.

[0041] This application also provides a beam storage platform recommendation system based on multi-factor constraints, the system comprising:

[0042] The acquisition module is used to acquire the status information data of the beam storage platform and the demand information data of the beam components, and to preprocess the information data;

[0043] The filtering module is used to filter out a list of candidate beam storage platforms based on preprocessed data and by means of constraints; the constraints include the availability constraints of the beam storage platforms and the beam type adaptability constraints.

[0044] The sorting module is used to sort the candidate beam storage platform list in ascending order using the proximity coefficient to obtain the position-priority candidate list.

[0045] The verification module is used to select candidate beam storage platforms to be verified from the position priority candidate list, perform storage order conflict verification, obtain the target beam storage platform, and output recommendation information.

[0046] According to some embodiments of this disclosure, the system further includes:

[0047] The marking module is used to update the beam storage platform data, mark the target beam storage platform as occupied, and clear the source platform status.

[0048] Compared with the prior art, this application has the following advantages:

[0049] (1) This application breaks through the traditional manual query and experience-based decision-making model, realizes the automatic screening and calculation of seat allocation, and shortens the decision-making time;

[0050] (2) This application makes up for the shortcomings of the prior art that only considers the single constraint of "location" or "availability status", and constructs a multi-dimensional constraint system of "availability status + beam type adaptation + location optimization + multi-level date conflict", which solves the problem of unreasonable decision-making caused by the single constraint dimension;

[0051] (3) This application solves the problem of relying solely on subjective human judgment for "nearby allocation" by using mathematical models to quantify the degree of optimization of the pedestal location, thereby ensuring the objectivity of the decision-making process;

[0052] (4) This application designs a beam erection date verification rule for setting the number of pedestal layers to avoid beam erection conflicts caused by improper storage order;

[0053] (5) This application is equipped with a graphical display of the distribution and storage status of the base, which solves the problem of "information is not intuitive" under manual management and helps managers to quickly grasp the overall situation.

[0054] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A diagram illustrating a method for recommending beam storage platforms based on multi-factor constraints according to an embodiment of this application is shown.

[0057] Figure 2 A diagram of a beam storage platform recommendation system based on multi-factor constraints according to an embodiment of this application is shown. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0059] To address the aforementioned technical problems, this application provides a method for recommending beam storage platforms based on multi-factor constraints. The overall process is divided into "data input and preprocessing". Multi-dimensional constraint screening Optimize calculation Conflict checking The five modules, "Results Output and Visualization," work together to achieve intelligent recommendations.

[0060] like Figure 1 As shown, this application provides a method for recommending beam storage platforms based on multi-factor constraints, the method comprising:

[0061] Acquire the status information data of the beam storage platform and the demand information data of the beam components, and preprocess the information data;

[0062] Based on the preprocessed data, a list of candidate beam storage platforms is selected through constraints; the constraints include the availability constraints of the beam storage platforms and the beam type adaptability constraints.

[0063] The candidate beam storage platform list is sorted in ascending order using the proximity coefficient to obtain the position-priority candidate list;

[0064] Select the candidate beam storage platform to be verified from the position priority candidate list, perform storage order conflict verification, obtain the target beam storage platform, and output recommendation information.

[0065] Specifically, the status information data of the beam storage platform includes: serial number, beam storage platform number, region, row number, column number, layer number, proximity coefficient, weight, availability status, available beam type, number of guide beams, current beam component number, current beam frame number, current beam estimated erection date, and current beam storage date;

[0066] The beam component requirements data includes: component number, component type, component category, process status, and expected beam erection date.

[0067] Specifically, the preprocessing of the information data includes:

[0068] Convert empty values ​​in the information data to None;

[0069] Convert numeric fields in the information data to integer types; and,

[0070] Format the date field in the information data to a standard format.

[0071] Specifically, the availability constraints of the beam storage platform include:

[0072] In the preprocessed status information data, the available status is "available" and the current beam component number is "None".

[0073] Specifically, the beam type adaptability constraint includes:

[0074] Extract the component category of the component to be assigned;

[0075] Read the "Storable Beam Type" field of the storage platform obtained after filtering by the availability constraint of the storage platform, and split it into a list of beam types by commas;

[0076] The list of retained beam types includes the beam storage platforms of the current component category, forming a candidate beam storage platform list.

[0077] Specifically, obtaining the proximity coefficient includes:

[0078] Determine the source platform for the component to be assigned;

[0079] The planar distance between the storage platform and the source platform is determined by the Manhattan distance method, and a penalty value is added in combination with regional differences to obtain the proximity coefficient.

[0080] Specifically, the expression for the proximity coefficient is:

[0081] ;

[0082] Where PC represents the proximity coefficient; d represents the planar distance between the storage beam platform and the source platform; T area Indicates the region; S area Indicates a specified area.

[0083] Specifically, the candidate beam storage platforms to be verified undergo storage order conflict verification to obtain the target beam storage platforms, including:

[0084] Determine the target number of layers for the beam storage platform;

[0085] Find the bottom beam storage platform at the same location. If the bottom beam storage platform is idle, set the bottom beam storage platform as the target beam storage platform.

[0086] If the bottom-level beam storage platform has already stored components, check whether the beam erection date of the bottom-level component is later than the beam erection date of the current component. Only if the condition is met will the current upper-level beam storage platform be set as the target beam storage platform.

[0087] Specifically, after obtaining the target beam support and outputting the recommendation information, the process also includes:

[0088] Update the beam storage platform data, mark the target beam storage platform as occupied, and clear the source platform status.

[0089] Specifically, the output includes a graphical display of the distribution and storage status of the beam storage platforms, which is visualized through coordinate mapping and beam storage platform annotation.

[0090] The detailed technical solution of this method is as follows:

[0091] 1. Data Input and Preprocessing Module

[0092] The algorithm requires two types of basic data, which are imported into an Excel file and then standardized in format:

[0093] 1) Foundation information data of beam storage platform

[0094] It mainly includes the following fields, which are used to describe the attributes and current status of the pedestal. The field definitions are shown in the table below:

[0095]

[0096] 2) Foundation information data of beam components

[0097] It contains 5 core fields used to describe component requirements and construction plans. The field definitions are shown in the table below:

[0098]

[0099] 3) Data preprocessing logic

[0100] The algorithm standardizes data using the following rules:

[0101] i. Handling empty values: Convert all empty cells in Excel to "None" to avoid calculation errors;

[0102] ii. Numerical conversion: Convert fields such as "row number", "column number", "layer number", and "availability status" from strings to integers to ensure the accuracy of subsequent calculations;

[0103] iii. Date formatting: Convert "Expected beam erection date" and "storage date" to the standard "yyyy-MM-dd" format for easy date comparison.

[0104] 2. Multi-dimensional constraint screening

[0105] First, candidate pedestals are screened based on two types of core constraints, eliminating pedestals that clearly do not meet the requirements to reduce subsequent computational workload. The specific screening rules are as follows:

[0106] 1) Basic availability constraints

[0107] Select beam storage platform information that meets the following two conditions:

[0108] i. Available status = 1 (The pedestal structure is intact and components can be stored normally).

[0109] ii. Current beam component number = None (the platform is idle and there are no stored components).

[0110] 2) Beam type adaptability constraints

[0111] For the pedestals selected based on basic availability constraints, further verification of "beam type adaptability" is performed:

[0112] i. Extract the "component category" (e.g., "box girder") of the currently assigned component;

[0113] ii. Read the "Saveable Beam Types" field of the platform and split it into a list of beam types by commas (e.g., "24, 32");

[0114] iii. Only retain the pedestals in the beam type list that contain the current component's "component category" to form a "candidate pedestal list".

[0115] If the candidate pedestal list is empty (i.e., there is no pedestal that meets the criteria of "available + compatible"), the algorithm will directly output the message "No suitable pedestal found" and terminate the subsequent process.

[0116] 3. Optimize calculation (calculate the nearest coefficient)

[0117] To quantify the degree of optimization of candidate pedestals, a "proximity coefficient" index is introduced, which is calculated using a "Manhattan distance + regional penalty" model. The specific logic is as follows:

[0118] 1) Source pedestal information extraction

[0119] First, locate the "source platform" (i.e., the platform where the component is currently stored) of the component to be assigned:

[0120] i. Based on the "component number" of the component to be assigned, find the source platform in the stored beam platform information where "current beam component number = this component number";

[0121] ii. Extract the "region", "row number", and "column number" information from the source platform.

[0122] 2) Manhattan distance calculation

[0123] For each stilt in the candidate stilt list, calculate its Manhattan distance to the source stilt (a measure of straight-line distance in planar coordinates, suitable for stilts with a grid-like distribution):

[0124] Manhattan distance formula:

[0125]

[0126] Where: Trow: row number of the candidate platform; Tcol: column number of the candidate platform; Srow: row number of the source platform; Scol: column number of the source platform.

[0127] 3) Superposition of regional penalty coefficients

[0128] If the candidate pedestal and the source pedestal belong to different "regions", a fixed penalty value (set to 1000 in this algorithm, which can be adjusted according to the actual regional span of the project) is added to the Manhattan distance to form the final "proximity coefficient".

[0129] Proximity coefficient formula:

[0130]

[0131] Among them: PC proximity coefficient (the smaller the value, the better the pedestal location, and the more preferred).

[0132] 4) Ranking of candidate pedestals

[0133] Sort the candidate pedestals in ascending order of "proximity coefficient" to obtain the "position-priority candidate list". Pedestals at the top of the list have better positions and are given priority to enter the subsequent conflict verification stage.

[0134] 4. Conflict checking (for multi-layer bases only)

[0135] For platforms with two layers, a "beam erection date conflict check" rule is added to avoid beam erection conflicts caused by improper storage order (i.e., components from layer 1 can only be retrieved after components from layer 2 have been erected). The specific check logic is as follows:

[0136] 1) Determining the number of pedestal layers

[0137] The pedestals in the "preferred location candidate list" are verified sequentially:

[0138] i. If the number of pedestal layers = 1: No date verification is required. Directly determine this pedestal as the "optimal target pedestal" and terminate the verification process;

[0139] ii. If the number of pedestal layers is 2: proceed to the date conflict check stage.

[0140] 2) Search for the first-floor pedestal in the same location

[0141] Find the first-level platform (i.e., the bottom-level platform whose physical location completely overlaps with the current second-level candidate platform) that is in the same region, row number, and column number, and record it as "associated first-level platform".

[0142] 3) Judgment of conflicting beam erection dates

[0143] i. If the "Current Beam Component Number = None" of the associated 1st floor platform (the 1st floor platform is idle): then it is recommended to associate the 1st floor platform (set the associated 1st floor platform as the "optimal target platform");

[0144] ii. If the "Current Beam Component Number" of the associated 1st floor platform is not equal to None (the 1st floor platform has stored components): Extract the "Estimated Beam Erection Date" (denoted as D1) of the 1st floor component and the "Estimated Beam Erection Date" (denoted as D2) of the currently assigned component. Only when D1>D2 (the beam erection date of the 1st floor component is later than that of the current component) does the current 2nd floor platform meet the requirements and is set as the "Optimal Target Platform". If D1≤D2, it is determined as a "Date Conflict", and the platform is skipped and the next candidate platform is checked.

[0145] If all candidate pedestals fail the conflict check, the algorithm outputs the message "No suitable pedestal found".

[0146] 5. Results Output and Visualization Module

[0147] After determining the "optimal target platform", perform the following operations:

[0148] 1) Data update

[0149] i. Target platform data update: Write the "component number" and "estimated beam erection date" of the component to be assigned into the "current beam component number" and "current beam estimated beam erection date" fields of the target platform, and record the current date as the "current beam storage date";

[0150] ii. Clear source platform data: Set the "Current Beam Component Number", "Current Beam Expected Erection Date", and "Current Beam Storage Date" fields of the source platform to None, and mark it as idle.

[0151] 2) Visualization

[0152] The demo program uses Matplotlib to build a visualization of the pedestal distribution. The core display logic is as follows:

[0153] i. Coordinate mapping: Construct a planar coordinate system with the "column number" of the platform as the X-axis and the "row number" as the Y-axis;

[0154] ii. Draw the pedestal: Draw a rectangle with a width of 0.8 and a height of 0.4 below the Y-axis (e.g., Y-0.2) for the first-layer pedestal; draw a rectangle of the same size above the Y-axis (e.g., Y+0.2) for the second-layer pedestal.

[0155] iii. Information labeling: Label the platform number, layer number, and proximity coefficient (if any) within the rectangle. If the platform has already stored components, additionally label the component number.

[0156] iv. Style settings: Set the coordinate axis scale to integers, add grid lines to assist in positioning, and set the title to "Beam Storage Platform Distribution Map" to ensure that managers can intuitively distinguish the platform location and storage status.

[0157] Example

[0158] 1. System Architecture

[0159] The algorithm of this invention is implemented through a software system. The demonstration system is developed based on the Python language (the final production system is built using the Java language) and adopts a three-layer architecture of "data layer - algorithm layer - interaction layer", as follows:

[0160]

[0161] System operating environment requirements:

[0162] 1) Hardware: CPU Intel Core i5 or higher, memory 8GB or higher, hard disk free space 10GB or higher;

[0163] 2) Software: Operating system Windows 10 / 11, Python 3.8 and above, dependent libraries include pandas 1.5.0+, numpy 1.21.0+, matplotlib 3.5.0+, PySide 66.4.0+, openpyxl (Excel parser).

[0164] 2. Data Structure Design

[0165] The system uses PandasDataFrame as the core data structure to store two types of key data. The specific structure and examples are as follows:

[0166] i. Information on beam storage platform

[0167]

[0168] ii. Beam component information DataFrame (component_df)

[0169]

[0170] In the above data structure, None indicates that the field is empty (such as the "current beam component number" of an idle platform), and available status = 0 indicates that the platform is unavailable (such as A2-01 which cannot be used due to maintenance). Process status = prefabrication completion is a prerequisite for the component to be stored in the warehouse (such as L003 which is not assigned a platform because tensioning is not completed).

[0171] This method addresses the problems of manual allocation of precast beam storage platforms in bridge construction, which is inefficient and subject to strong subjective decision-making. It achieves intelligent matching and recommendation of storage platforms by constructing a multi-dimensional constraint system and an optimized calculation model. First, candidate platforms are screened based on platform availability and beam type compatibility. Then, a "proximity coefficient" is calculated using Manhattan distance combined with regional penalties to quantify the optimization degree of platform location. Finally, a beam erection date conflict verification rule is added for multi-layered platforms to ensure that the recommendation results are optimal under multi-factor constraints. Simultaneously, a data visualization function is implemented to intuitively present the distribution and storage status of platforms. Applying this method can reduce platform allocation decision-making time from tens of minutes to seconds, significantly improving the efficiency and collaboration of bridge construction beam storage management. This method is applicable to various scenarios involving the storage of precast beams for large bridges.

[0172] like Figure 2 As shown, this application also provides a beam storage platform recommendation system based on multi-factor constraints, the system comprising:

[0173] The acquisition module is used to acquire the status information data of the beam storage platform and the demand information data of the beam components, and to preprocess the information data;

[0174] The filtering module is used to filter out a list of candidate beam storage platforms based on preprocessed data and by means of constraints; the constraints include the availability constraints of the beam storage platforms and the beam type adaptability constraints.

[0175] The sorting module is used to sort the candidate beam storage platform list in ascending order using the proximity coefficient to obtain the position-priority candidate list.

[0176] The verification module is used to select candidate beam storage platforms to be verified from the position priority candidate list, perform storage order conflict verification, obtain the target beam storage platform, and output recommendation information.

[0177] Specifically, the system also includes:

[0178] The marking module is used to update the beam storage platform data, mark the target beam storage platform as occupied, and clear the source platform status.

[0179] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for recommending beam storage platforms based on multi-factor constraints, characterized in that, The method comprises: acquiring state information data of the beam storage pedestal and demand information data of the beam member, and preprocessing the information data; based on the preprocessed data, screening a candidate beam storage pedestal list through constraint conditions; the constraint conditions include beam storage pedestal availability constraint and beam type adaptability constraint; The candidate beam storage pedestal list is sorted in ascending order by using a near coefficient to obtain a position priority candidate list; the near coefficient is obtained by: determining a source pedestal of a current component to be allocated; determining a planar distance between the beam storage pedestal and the source pedestal by using a Manhattan distance method, and adding a penalty value in combination with a regional difference to obtain the near coefficient; and an expression of the near coefficient is: ; wherein PC represents the near coefficient; d represents the planar distance between the beam storage pedestal and the source pedestal; Tarea represents a region; and Sarea represents a specified region. selecting a candidate beam storage pedestal to be checked from the position priority candidate list for storage sequence conflict checking to obtain a target beam storage pedestal, and outputting recommendation information.

2. The multi-factor constraint-based beam storage pedestal recommendation method according to claim 1, wherein the state information data of the beam storage pedestal comprises: serial number, beam storage pedestal number, area, row number, column number, layer number, proximity coefficient, weight, available state, storable beam type, guide beam number, current beam member number, current beam frame number, current beam scheduled erection date and current beam storage date; and the demand information data of the beam member comprises: member number, member type, member category, process state and scheduled erection date.

3. The multi-factor constraint-based beam storage pedestal recommendation method according to claim 1, wherein the preprocessing of the information data comprises: converting null values in the information data into None; converting numerical fields in the information data into integer type; and formatting date fields in the information data into standard format.

4. The multi-factor constraint-based beam storage pedestal recommendation method according to claim 2, wherein the beam storage pedestal availability constraint comprises: in the preprocessed state information data, the available state is "available" and the current beam member number is "None".

5. The multi-factor constraint-based beam storage pedestal recommendation method according to claim 4, wherein the beam type adaptability constraint comprises: extracting the member category of the current member to be allocated; reading the "storable beam type" field of the beam storage pedestal obtained after the beam storage pedestal availability constraint screening, and separating the beam type list according to commas; retaining the beam storage pedestals containing the member category of the current member in the beam type list to form the candidate beam storage pedestal list.

6. The multi-factor constraint-based beam storage pedestal recommendation method according to claim 1, wherein the storage sequence conflict checking of the candidate beam storage pedestal to be checked to obtain the target beam storage pedestal comprises: determining the target layer number of the beam storage pedestal; finding the same-position bottom layer beam storage pedestal, and setting the bottom layer beam storage pedestal as the target beam storage pedestal if the bottom layer beam storage pedestal is idle; if the bottom layer beam storage pedestal has stored members, checking whether the erection date of the bottom layer members is later than the erection date of the current members, and setting the current upper layer beam storage pedestal as the target beam storage pedestal only when the condition is met. After obtaining the target beam storage pedestal and outputting the recommendation information, the method further comprises: updating the beam storage pedestal data, marking the target beam storage pedestal as occupied, and clearing the source pedestal state.

8. The multi-factor constraint-based beam storage pedestal recommendation method according to claim 1, wherein the output of the recommendation information comprises graphical display of the beam storage pedestal distribution and storage state, and visualization is realized through coordinate mapping and beam storage pedestal annotation. The system comprises: ​ ​ 7. The multi-factor constraint based storage beam pedestal recommendation method of claim 1, wherein, ​ ​ ​ ​ 9. A multi-factor constraint based storage beam pedestal recommendation system, comprising: ​ An acquisition module is configured to acquire state information data of the beam storage pedestal and demand information data of the beam member, and to pre-process the information data; A screening module is configured to screen a candidate beam storage pedestal list based on the pre-processed data through constraint conditions, wherein the constraint conditions include a beam storage pedestal availability constraint and a beam type adaptability constraint; The sorting module is configured to sort the candidate beam storage pedestal list in ascending order by using a near coefficient to obtain a position priority candidate list. The near coefficient is obtained by determining a source pedestal of a current component to be allocated, determining a planar distance between the beam storage pedestal and the source pedestal by using a Manhattan distance method, and adding a penalty value in combination with a regional difference to obtain the near coefficient. An expression of the near coefficient is as follows: ; wherein PC represents the near coefficient; d represents the planar distance between the beam storage pedestal and the source pedestal; T area represents a region; S area represents a specified region. A verification module is configured to select a candidate beam storage pedestal to be verified from the position priority candidate list for storage sequence conflict verification, to obtain a target beam storage pedestal, and to output recommendation information.

10. The multi-factor constraint based storage beam pedestal recommendation system of claim 9, wherein, The system further comprises: A marking module is configured to update the beam storage pedestal data, mark the target beam storage pedestal as occupied, and clear the source pedestal state.

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