ALC partition panel installation auxiliary analysis method and system based on mirror image model

Abstract

The application belongs to the technical field of optimization analysis, and discloses an ALC partition plate installation auxiliary analysis method and system based on a mirror model, which comprises the following steps: establishing installation dependency relationship describing installation sequence requirements of each installation position based on building construction design files; establishing a mirror information model of a floor space as a dynamic space model; establishing a difficulty evaluation mechanism for calculating a comprehensive difficulty coefficient of any installation position under a given installation sequence based on the dynamic space model; establishing an installation sequence combination optimization problem according to the installation dependency relationship and the difficulty coefficient of all installation positions under the difficulty evaluation mechanism, and solving the installation sequence combination optimization problem to obtain an optimal installation sequence. The application systematically considers the dynamic change of the floor space condition in the installation process before construction, overcomes the limitation that the artificial experience is difficult to comprehensively predict the change of the complex floor space condition, and obtains an installation sequence with low overall construction difficulty, which is helpful to improve the construction efficiency.

Description

Technical Field

[0001] This invention relates to the field of optimization analysis technology, and more specifically, to an ALC partition wall installation auxiliary analysis method and system based on a mirror model. Background Technology

[0002] ALC (autoclaved aerated concrete) partition walls are prefabricated components widely used in interior building construction, particularly in large buildings such as hospitals, office buildings, and commercial complexes. A single ALC panel typically weighs over 100 kilograms, requiring forklifts or other transport equipment to move the panels from the material storage area to the installation location, where workers then position, align, and secure them.

[0003] In actual construction, the installation sequence of ALC partition panels is usually determined manually based on experience. A common practice is to prioritize installing panels farther from the material inlet, while planning the construction sequence for each wall individually. This approach generally meets construction needs when the floor plan is relatively simple. However, in large buildings with complex floor plans, as panels are installed, the passable width of passageways and the operating space around each installation location continuously change. Due to the large number of installation locations and the complex spatial relationships, it is difficult for manual workers to fully predict the impact of spatial changes on subsequent installation steps before construction begins. Panels installed earlier may significantly compress the transport channels or operating space for later panels, significantly worsening the construction conditions for some installation steps, increasing construction difficulty, and affecting overall construction efficiency.

[0004] In summary, how to systematically analyze the installation sequence of ALC partition panels before construction, reasonably consider the dynamic changes in spatial conditions during installation, and form an installation sequence scheme with lower overall construction difficulty, thereby improving construction efficiency, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] To overcome the aforementioned problems in the prior art, this invention proposes an ALC partition wall panel installation auxiliary analysis method and system based on a mirror model to solve the above problems.

[0006] This invention provides the following technical solution: The ALC partition wall installation auxiliary analysis method based on the mirror model includes: The installation dependencies are established based on the building construction design documents. The installation dependencies describe the sequential installation requirements between each installation location due to physical structural constraints. Each installation location corresponds to the design installation space of an ALC board. A dynamic spatial model is established, which is a mirror information model of the floor space, and records the spatial geometric information of the floor space; Based on the dynamic spatial model, a difficulty assessment mechanism is established to calculate the comprehensive difficulty coefficient of any installation position under a given installation sequence. Based on installation dependencies and the difficulty coefficients of all installation locations under the difficulty assessment mechanism, an installation sequence combination optimization problem is established. The optimal installation sequence is obtained by solving the installation sequence combination optimization problem.

[0007] Preferably, establishing the dynamic space model includes: Based on the floor space geometry information in the building construction design documents, an initial mirror information model of the floor space is constructed using spatial information modeling technology. The location of each preset material inlet and the center location of each installation location are included as key spatial points to obtain a dynamic spatial model.

[0008] Preferably, the difficulty assessment mechanism includes: For each installation location, other installation locations that precede it in the installation order are converted into entities in the dynamic space model to obtain the updated dynamic space model; The transportation difficulty coefficient and installation difficulty coefficient of the installation location are calculated based on the updated dynamic spatial model. The transportation difficulty coefficient and installation difficulty coefficient are weighted to obtain the comprehensive difficulty coefficient.

[0009] Preferably, the calculation of the transportation difficulty coefficient includes: In the dynamic space model, a path search is performed with each preset material inlet as the starting point and the installation location as the ending point to obtain the feasible transportation path corresponding to each inlet. The sum of the plate width at the installation location and the fixed allowance on both sides of the transport equipment is used as the passage width of the transport equipment. The passable width at each point on each path is obtained, and paths with a minimum passable width that is less than the passage width of the transport equipment are filtered out. For each remaining path, calculate the segment transportation difficulty of each segment on the path, and use the weighted sum of the segment transportation difficulties as the path transportation difficulty value. Select the path with the lowest transportation difficulty value from each remaining path, and use that path's transportation difficulty value as the transportation difficulty coefficient.

[0010] Preferably, the method for calculating the transportation difficulty of the road segment includes: The difference between the passable width of the road segment and the passable width of the transport equipment is taken as the width margin of the road segment; The transportation difficulty of a road segment is determined by the ratio of the width margin to the passage width of transportation equipment. The smaller the width margin, the higher the transportation difficulty of the segment. Furthermore, the rate of reduction in transportation difficulty gradually decreases as the width margin increases.

[0011] Preferably, the calculation of the installation difficulty coefficient includes: In the dynamic spatial model, a spatial distance query is performed along the normal direction of the wall to which the installation location belongs to determine the positive net distance from the front of the installation location to the nearest obstacle. The difference between the positive clearance and the preset minimum depth for worker operation is used as the positive margin. The difficulty of front installation is determined according to the ratio of the positive margin to the minimum depth for worker operation. The smaller the positive margin, the higher the difficulty of front installation. As the positive margin increases, the reduction in the difficulty of front installation gradually decreases. Perform bidirectional spatial distance queries along the wall extension direction, and take the smaller value between the net distances from both sides to the nearest obstacle as the lateral net distance; The difference between the lateral clearance and half the width of the plate at the installation position is taken as the lateral margin. The lateral installation difficulty is determined according to the ratio of the lateral margin to half the width of the plate at the installation position. The smaller the lateral margin, the higher the lateral installation difficulty. And the reduction in lateral installation difficulty gradually decreases as the lateral margin increases. The installation difficulty coefficient is obtained by weighting and combining the difficulty of frontal installation and side installation.

[0012] Preferably, the optimization problem of establishing installation sequence combinations includes: For each pair of installation locations in the installation dependency relationship, determine whether there is a directed path between them consisting of the dependency pair, and record the determination result as a dependency reachability matrix; The feasible solution space is the topological order that satisfies the installation dependencies. The objective function is to calculate the comprehensive difficulty coefficient bit by bit and sum the results of calling the difficulty evaluation mechanism for a given installation sequence. The optimization objective is to minimize the objective function, thus obtaining the installation sequence combinatorial optimization problem.

[0013] Preferably, solving the installation sequence combination optimization problem includes: Based on installation dependencies, topological sorting and sampling are performed to generate an initial installation sequence. The difficulty assessment mechanism is then invoked to calculate the objective function value as the current solution. Randomly select two installation locations from the current installation sequence, query the dependency reachability matrix to determine if there is a directed path between them. If there is, discard and reselect; otherwise, perform an exchange to generate a candidate sequence. The difficulty assessment mechanism is invoked to calculate the objective function value of the candidate sequence. If the value is lower than the current solution, it is accepted directly; otherwise, it is accepted with a probability that decreases monotonically as the temperature parameter decreases. The temperature parameters are gradually reduced according to the preset cooling strategy. Random selection and exchange are continuously performed until the termination condition is met. The installation sequence with the minimum objective function value during the search process is output as the optimal installation sequence.

[0014] Preferably, the termination conditions include: the temperature parameter dropping to a preset minimum temperature threshold; the cumulative number of iterations reaching a preset upper limit; All installation location pairs in the current installation sequence, as confirmed by the dependency reachability matrix, do not have directed paths. All have been randomly selected and swapped under the current temperature parameters, and no candidate sequences with objective function values ​​lower than the current optimal solution have been generated.

[0015] This invention also provides an ALC partition wall panel installation auxiliary analysis system based on a mirror model, used to implement an ALC partition wall panel installation auxiliary analysis method based on a mirror model, including: The dependency construction module is used to establish installation dependencies based on building construction design documents. The installation dependencies describe the sequential installation requirements between each installation location due to physical structural constraints. Each installation location corresponds to the design installation space of an ALC board. The dynamic space modeling module is used to establish a dynamic space model, which is a mirror information model of the floor space and records the spatial geometric information of the floor space. The difficulty assessment module is used to establish a difficulty assessment mechanism based on a dynamic spatial model to calculate the comprehensive difficulty coefficient of any installation position under a given installation sequence. The optimization problem building module is used to create an installation sequence combination optimization problem based on installation dependencies and the difficulty coefficients of all installation locations under the difficulty assessment mechanism. An optimization solution module is used to solve the installation sequence combination optimization problem to obtain the optimal installation sequence.

[0016] This invention provides an ALC partition wall panel installation auxiliary analysis method and system based on a mirror model, which has the following beneficial effects: This invention establishes a dynamic spatial model of the floor space. Given an installation sequence, it sequentially converts preceding installation positions into entities to update the dynamic spatial model. Based on the updated model, it calculates the transportation difficulty coefficient and installation difficulty coefficient for each installation position and synthesizes them into a comprehensive difficulty coefficient. The optimal installation sequence is found by minimizing the sum of the comprehensive difficulty coefficients of all installation positions. Therefore, this invention systematically considers the dynamic changes in floor space conditions as the panels gradually occupy their positions during installation before construction begins. This overcomes the limitations of human experience in fully predicting the dynamic changes in spatial conditions in complex floors. The resulting installation sequence has superior overall transportation channel conditions and installation operation space conditions, helping to reduce construction difficulty and improve construction efficiency.

[0017] This invention queries the forward and lateral clearances along the normal and extension directions of the wall to which the installation location belongs in the updated dynamic space model. It quantifies the abundance of forward operating space and lateral positioning space by the margin between each clearance and the corresponding benchmark value. The installation difficulty coefficient is calculated by monotonically decreasing the difficulty as the margin increases and the decrease rate gradually narrows. This transforms the installation operating space conditions, which originally relied on subjective human judgment, into quantifiable and comparable numerical indicators, enabling the optimization of installation sequences to incorporate operating space factors into a systematic consideration. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the ALC partition wall panel installation auxiliary analysis method based on a mirror model according to the present invention. Figure 2 This is a schematic diagram of the ALC partition wall panel installation auxiliary analysis system based on the mirror model of the present invention. Detailed Implementation

[0019] 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.

[0020] Example 1

[0021] Please see Figure 1 In this embodiment, the ALC partition wall installation auxiliary analysis method based on the mirror model includes: S1. Establish installation dependencies based on building construction design documents. The installation dependencies describe the sequential installation requirements between each installation location due to physical structural constraints. Each installation location corresponds to the design installation space of an ALC board. In this embodiment, it should be noted that ALC partition panels often have structural features such as lintel supports and L-shaped interlocking joints at corners. These structural features create a sequential installation requirement between the relevant installation locations. For example, the lintel panel above the opening rests on top of the side panels and is usually installed only after the side panels are in place; the closing panel in the L-shaped interlocking joint at the corner is usually installed first. These sequential requirements can be identified from the BIM model and related design documents. Dependency pairs are generated for each pair of installation locations with sequential requirements, and the installation dependency relationship is obtained by summing all dependency pairs. The installation dependency relationship is represented as a directed graph in the data structure, where each installation location is a node and each dependency pair is a directed edge.

[0022] The pre-designated material inlet is the location on the floor where materials are transported to the construction site in advance. It is usually set near the stairwell, elevator hall or external window opening, which is convenient for vertical transportation. The specific location is determined according to the actual construction conditions, and the number is no less than one.

[0023] S2. Establish a dynamic spatial model, which is a mirror information model of the floor space, and records the spatial geometric information of the floor space; The establishment of the dynamic space model includes: Based on the floor space geometry information in the building construction design documents, an initial mirror information model of the floor space is constructed using spatial information modeling technology. The location of each preset material inlet and the center location of each installation location are included as key spatial points to obtain a dynamic spatial model.

[0024] In this embodiment, it should be noted that the dynamic spatial model is a digital mirror of the floor space in a computer. Its foundation is the geometric information of the floor space contained in the architectural construction design documents, including wall locations and thicknesses, door and window opening locations and dimensions, and spatial boundaries of corridors and rooms. The three-dimensional spatial geometric data of the floor can be exported using BIM software (such as Autodesk Revit), or the floor space can be discretized at a certain resolution using a three-dimensional voxelization method to construct an initial model that reflects the floor space layout and the dimensions of various passageways. Sampling nodes are generated at certain intervals within the floor space, typically between 200mm and 500mm. Smaller intervals result in higher model accuracy but also higher computational load; the appropriate interval can be selected based on the floor size and computing resources. The location of each preset material inlet and the center location of each installation location are included as forced nodes to ensure that subsequent path searches and spatial distance queries accurately use these locations as the starting or ending point. The initial dynamic spatial model reflects the spatial state of the floor before any ALC panels are installed, prior to the start of construction.

[0025] S3. Based on the dynamic space model, establish a difficulty assessment mechanism to calculate the comprehensive difficulty coefficient of any installation position under a given installation sequence; The difficulty assessment mechanism includes: For each installation location, other installation locations that precede it in the installation order are converted into entities in the dynamic space model to obtain the updated dynamic space model; The transportation difficulty coefficient and installation difficulty coefficient of the installation location are calculated based on the updated dynamic spatial model. The transportation difficulty coefficient and installation difficulty coefficient are weighted to obtain the comprehensive difficulty coefficient.

[0026] The calculation of the transportation difficulty coefficient includes: In the dynamic space model, a path search is performed with each preset material inlet as the starting point and the installation location as the ending point to obtain the feasible transportation path corresponding to each inlet. The sum of the plate width at the installation location and the fixed allowance on both sides of the transport equipment is used as the passage width of the transport equipment. The passable width at each point on each path is obtained, and paths with a minimum passable width that is less than the passage width of the transport equipment are filtered out. For each remaining path, calculate the segment transportation difficulty of each segment on the path, and use the weighted sum of the segment transportation difficulties as the path transportation difficulty value. Select the path with the lowest transportation difficulty value from each remaining path, and use that path's transportation difficulty value as the transportation difficulty coefficient.

[0027] The methods for calculating the transportation difficulty of the aforementioned road section include: The difference between the passable width of the road segment and the passable width of the transport equipment is taken as the width margin of the road segment; The transportation difficulty of a road segment is determined by the ratio of the width margin to the passage width of transportation equipment. The smaller the width margin, the higher the transportation difficulty of the segment. Furthermore, the rate of reduction in transportation difficulty gradually decreases as the width margin increases.

[0028] The calculation of the installation difficulty coefficient includes: In the dynamic spatial model, a spatial distance query is performed along the normal direction of the wall to which the installation location belongs to determine the positive net distance from the front of the installation location to the nearest obstacle. The difference between the positive clearance and the preset minimum depth for worker operation is used as the positive margin. The difficulty of front installation is determined according to the ratio of the positive margin to the minimum depth for worker operation. The smaller the positive margin, the higher the difficulty of front installation. As the positive margin increases, the reduction in the difficulty of front installation gradually decreases. Perform bidirectional spatial distance queries along the wall extension direction, and take the smaller value between the net distances from both sides to the nearest obstacle as the lateral net distance; The difference between the lateral clearance and half the width of the plate at the installation position is taken as the lateral margin. The lateral installation difficulty is determined according to the ratio of the lateral margin to half the width of the plate at the installation position. The smaller the lateral margin, the higher the lateral installation difficulty. And the reduction in lateral installation difficulty gradually decreases as the lateral margin increases. The installation difficulty coefficient is obtained by weighting and combining the difficulty of frontal installation and side installation.

[0029] In this embodiment, the core of the difficulty assessment mechanism is that, for any installation position in a given installation sequence, the spatial state upon which the difficulty assessment is based is the spatial state after all installation positions preceding it in that installation sequence have been simulated and occupied. The simulated occupation operation involves determining the physical area occupied by the ALC board in the floor space based on the width and thickness of the ALC board corresponding to that installation position, using the center coordinates of the installation position as a reference. This physical area is marked as impassable in the dynamic space model, and the available width of the passageway adjacent to this area is recalculated. The later the installation position in the sequence, the more positions in the corresponding dynamic space model are marked as physical entities, and the more constrained the spatial conditions are generally.

[0030] When performing path search on the updated dynamic space model, existing graph-based search algorithms (such as Dijkstra's algorithm or A* algorithm) can be used to search for paths from each preset material inlet to the center of the installation location among passable nodes. The passable width of each segment on the path is directly read from the dynamic space model. The passable width of the transport equipment is determined by the sum of the width of the ALC plate corresponding to the installation location and the fixed allowance on both sides of the transport equipment (such as an electric forklift) bracket. The fixed allowance is pre-entered according to the model of the transport equipment used, reflecting the required operating distance on both sides after the equipment clamps the plate, and is usually 50mm to 150mm. The passable width of each segment is compared with the passable width of the transport equipment, and paths with a minimum passable width less than the passable width of the transport equipment are eliminated. The remaining paths are the actually feasible transport paths.

[0031] The transportation difficulty of a road segment is characterized by the ratio of width margin to the width for transport equipment passage. Width margin is the difference between the passable width of the road segment and the width for transport equipment passage, reflecting the space available on both sides when transport equipment passes through the segment. The ratio of width margin to transport equipment passage width serves as a normalized indicator of sufficiency. The difficulty decreases as this ratio increases, but the rate of decrease gradually narrows, reflecting the diminishing marginal utility: the difficulty significantly decreases when the passageway changes from extremely cramped to slightly more spacious, while further increasing the width when the passageway is already relatively wide has limited improvement in difficulty. The transportation difficulty of each road segment is weighted and summed using the segment's own difficulty value as a weight. That is, segments with higher difficulty values ​​contribute a larger weight to the weighted summation, ensuring that the overall route transportation difficulty reflects the passage conditions of the most difficult segments in the route. The route with the lowest transportation difficulty value is selected from all feasible transportation routes; the corresponding material inlet is the optimal material inlet for this installation step, and the transportation difficulty value of this route is used as the transportation difficulty coefficient.

[0032] The installation difficulty coefficient is quantified from two dimensions: frontal operating space and lateral positioning space. The frontal clearance is the distance from the front of the installation location along the normal direction of the wall to the nearest obstacle (simulated occupied panel entity or fixed wall) in the dynamic space model, reflecting the available depth for the worker to operate from the front at that location. The preset minimum worker operating depth is the minimum frontal space required for the worker to complete the panel positioning operation. It is usually determined with reference to construction operation specifications and on-site worker operating habits, generally taken as 800mm, but can be adjusted according to actual construction conditions. The frontal margin is the difference between the frontal clearance and the minimum worker operating depth. The smaller the frontal margin, the more cramped the frontal operating space, and the higher the frontal installation difficulty. The calculation method for difficulty is the same as that for road transport difficulty, determined by the ratio of the frontal margin to the minimum worker operating depth. The difficulty decreases as this ratio increases, but the rate of decrease gradually narrows. Lateral clearance is the smaller of the clearances on both sides of the wall extension direction to the nearest obstacle, reflecting the space allowance for lateral sliding of the panel during installation. Lateral margin is the difference between the lateral clearance and half the panel width at the installation location. Half the panel width is the minimum single-sided reference space required for lateral panel placement. A smaller lateral margin makes placement more difficult. The difficulty of lateral installation is determined by the ratio of lateral margin to half the panel width, calculated in the same way as frontal installation difficulty. The installation difficulty coefficient is obtained by weighting and combining the frontal and lateral installation difficulties. The weights of both can be set according to construction conditions; by default, they are combined with equal weight.

[0033] When the transportation difficulty coefficient and installation difficulty coefficient are weighted and combined into a comprehensive difficulty coefficient, since both are calculated based on the ratio of margin to benchmark value and have the same monotonically decreasing characteristic, their values ​​are of the same order of magnitude and can be directly summed according to the preset weights. The weights reflect the relative importance of transportation conditions and installation operation conditions at the construction site, with a default value of 0.5 for each. This can be adjusted if site conditions are special, and the sum of the two is 1.

[0034] S4. Based on the installation dependencies and the difficulty coefficients of all installation locations under the difficulty assessment mechanism, establish an installation sequence combination optimization problem; The optimization problem for establishing installation sequence combinations includes: For each pair of installation locations in the installation dependency relationship, determine whether there is a directed path between them consisting of the dependency pair, and record the determination result as a dependency reachability matrix; The feasible solution space is the topological order that satisfies the installation dependencies. The objective function is to calculate the comprehensive difficulty coefficient bit by bit and sum the results of calling the difficulty evaluation mechanism for a given installation sequence. The optimization objective is to minimize the objective function, thus obtaining the installation sequence combinatorial optimization problem.

[0035] In this example, it's important to note that performing transitive closure computation on the directed graph representing installation dependencies yields an N×N dependency reachability matrix (where N is the total number of installation locations). A true value in the i-th row and j-th column of the matrix indicates a direct or indirect dependency between installation location i and installation location j. This matrix is ​​calculated and stored once; subsequent queries to determine if a dependency exists between two installation locations only require reading the value at the corresponding position in the matrix.

[0036] It should be noted that the objective function of the optimization problem exhibits path dependence: when calculating the objective function value for a given installation sequence, the difficulty assessment mechanism must be called sequentially according to the sequence. Before calculating the comprehensive difficulty coefficient of each installation position, all installation positions preceding that position are simulated for occupation in the dynamic space model. Then, based on the updated model state, the comprehensive difficulty coefficient of that position is calculated. The sum of the comprehensive difficulty coefficients of all installation positions is the objective function value of the installation sequence. The smaller the objective function value, the better the overall transportation and operational space conditions of the installation sequence.

[0037] S5. Solve the installation sequence combination optimization problem to obtain the optimal installation sequence.

[0038] Solving the installation sequence combination optimization problem includes: Based on installation dependencies, topological sorting and sampling are performed to generate an initial installation sequence. The difficulty assessment mechanism is then invoked to calculate the objective function value as the current solution. Randomly select two installation locations from the current installation sequence, query the dependency reachability matrix to determine if there is a directed path between them. If there is, discard and reselect; otherwise, perform an exchange to generate a candidate sequence. The difficulty assessment mechanism is invoked to calculate the objective function value of the candidate sequence. If the value is lower than the current solution, it is accepted directly; otherwise, it is accepted with a probability that decreases monotonically as the temperature parameter decreases. The temperature parameters are gradually reduced according to the preset cooling strategy. Random selection and exchange are continuously performed until the termination condition is met. The installation sequence with the minimum objective function value during the search process is output as the optimal installation sequence.

[0039] The termination conditions include: the temperature parameter dropping to a preset minimum temperature threshold; the cumulative number of iterations reaching a preset upper limit; All installation location pairs in the current installation sequence, as confirmed by the dependency reachability matrix, do not have directed paths. All have been randomly selected and swapped under the current temperature parameters, and no candidate sequences with objective function values ​​lower than the current optimal solution have been generated.

[0040] In this embodiment, it should be noted that the generation of the initial installation sequence is based on topological sorting sampling of the directed graph of installation dependencies: initially, all installation positions (i.e., nodes with an in-degree of zero in the directed graph) without any prerequisites in the installation dependencies are identified from the dependency reachability matrix to form an initial installable set. One of these positions is randomly selected to be added to the sequence. After each selection, the selected position is removed from the installable set, and the installation positions with it as a prerequisite in the installation dependencies are checked. If all prerequisites of a position have been added to the sequence, it is added to the installable set. This process is repeated until all installation positions have been included in the sequence. This process ensures that the generated initial sequence satisfies the installation dependencies, while introducing diversity in the initial solutions through random sampling.

[0041] During the solution process, after randomly selecting two installation positions from the current installation sequence each time, the dependency reachability matrix is ​​queried to determine whether there is a directed path between them. If a directed path exists, swapping the order of these two positions will cause one of them to be placed before its predecessor, violating the installation dependency relationship. In this case, the position is discarded and a new random selection is made. If no directed path exists, there is no mandatory order constraint between the two positions in terms of installation order. The sequence after the swap still satisfies the installation dependency relationship, and the swap is performed to generate a candidate sequence.

[0042] The settings of temperature parameters and cooling strategies affect the balance between solution quality and computation time. The initial temperature is typically determined by randomly generating several valid sequences (usually 20 to 50) that satisfy installation dependencies, calculating their objective function values, and using the average change in the objective function value as a reference. The initial temperature is then set to 1 to 2 times this average change, ensuring that the probability of accepting a suboptimal solution in the initial stage is between 70% and 90%. The cooling strategy typically employs exponential cooling, where the temperature is multiplied by a cooling coefficient after each iteration. The cooling coefficient is generally between 0.95 and 0.99; a larger coefficient results in a more thorough search but longer computation time, and can be selected based on the total number of installation locations and available computation time. The specific implementation of accepting suboptimal solutions with a monotonically decreasing probability as the temperature parameter decreases is as follows: calculate the difference between the candidate sequence and the objective function value of the current solution, divide this difference by the current temperature parameter, take the negative value as the exponent, and calculate the natural constant e raised to this power as the acceptance probability. When a randomly generated uniformly random number between 0 and 1 is less than this acceptance probability, the candidate sequence is accepted as the new current solution.

[0043] As the search progresses, if the dependency reachability matrix confirms that there are no directed path pairs of installation locations in the current installation sequence, and all legally swappable pairs have already been swapped at the current temperature without improving the objective function value, continuing the search is meaningless and can be terminated early to save computational resources.

[0044] The final output of the optimal installation sequence provides the suggested installation sequence number and corresponding optimal material inlet for each installation location. Construction organizers can use this sequence to develop a construction plan, pre-plan the transportation routes and material inlets for each step, reduce the risk of obstructed passageways or insufficient operating space due to improper sequence during construction, and improve construction efficiency.

[0045] Example 2

[0046] Please see Figure 2 This invention provides an ALC partition wall panel installation auxiliary analysis system based on a mirror model, used to implement an ALC partition wall panel installation auxiliary analysis method based on a mirror model, including: The dependency construction module is used to establish installation dependencies based on building construction design documents. The installation dependencies describe the sequential installation requirements between each installation location due to physical structural constraints. Each installation location corresponds to the design installation space of an ALC board. The dynamic space modeling module is used to establish a dynamic space model, which is a mirror information model of the floor space and records the spatial geometric information of the floor space. The difficulty assessment module is used to establish a difficulty assessment mechanism based on a dynamic spatial model to calculate the comprehensive difficulty coefficient of any installation position under a given installation sequence. The optimization problem building module is used to create an installation sequence combination optimization problem based on installation dependencies and the difficulty coefficients of all installation locations under the difficulty assessment mechanism. An optimization solution module is used to solve the installation sequence combination optimization problem to obtain the optimal installation sequence.

[0047] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0048] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

[0049] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. 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.

Claims

1. An ALC partition wall panel installation auxiliary analysis method based on a mirror model, characterized in that, include: The installation dependencies are established based on the building construction design documents. The installation dependencies describe the sequential installation requirements between each installation location due to physical structural constraints. Each installation location corresponds to the design installation space of an ALC board. A dynamic spatial model is established, which is a mirror information model of the floor space, and records the spatial geometric information of the floor space; Based on the dynamic spatial model, a difficulty assessment mechanism is established to calculate the comprehensive difficulty coefficient of any installation position under a given installation sequence. Based on installation dependencies and the difficulty coefficients of all installation locations under the difficulty assessment mechanism, an installation sequence combination optimization problem is established. The optimal installation sequence is obtained by solving the installation sequence combination optimization problem.

2. The ALC partition wall panel installation auxiliary analysis method based on a mirror model according to claim 1, characterized in that, The establishment of the dynamic space model includes: Based on the floor space geometry information in the building construction design documents, an initial mirror information model of the floor space is constructed using spatial information modeling technology. The location of each preset material inlet and the center location of each installation location are included as key spatial points to obtain a dynamic spatial model.

3. The ALC partition wall panel installation auxiliary analysis method based on a mirror model according to claim 2, characterized in that, The difficulty assessment mechanism includes: For each installation location, other installation locations that precede it in the installation order are converted into entities in the dynamic space model to obtain the updated dynamic space model; The transportation difficulty coefficient and installation difficulty coefficient of the installation location are calculated based on the updated dynamic spatial model. The transportation difficulty coefficient and installation difficulty coefficient are weighted to obtain the comprehensive difficulty coefficient.

4. The ALC partition wall panel installation auxiliary analysis method based on a mirror model according to claim 3, characterized in that, The calculation of the transportation difficulty coefficient includes: In the dynamic space model, a path search is performed with each preset material inlet as the starting point and the installation location as the ending point to obtain the feasible transportation path corresponding to each inlet. The sum of the plate width at the installation location and the fixed allowance on both sides of the transport equipment is used as the passage width of the transport equipment. The passable width at each point on each path is obtained, and paths with a minimum passable width that is less than the passage width of the transport equipment are filtered out. For each remaining path, calculate the segment transportation difficulty of each segment on the path, and use the weighted sum of the segment transportation difficulties as the path transportation difficulty value. Select the path with the lowest transportation difficulty value from each remaining path, and use that path's transportation difficulty value as the transportation difficulty coefficient.

5. The ALC partition wall panel installation auxiliary analysis method based on a mirror model according to claim 4, characterized in that, The methods for calculating the transportation difficulty of the aforementioned road section include: The difference between the passable width of the road segment and the passable width of the transport equipment is taken as the width margin of the road segment; The transportation difficulty of a road segment is determined by the ratio of the width margin to the passage width of transportation equipment. The smaller the width margin, the higher the transportation difficulty of the segment. Furthermore, the rate of reduction in transportation difficulty gradually decreases as the width margin increases.

6. The ALC partition wall panel installation auxiliary analysis method based on a mirror model according to claim 5, characterized in that, The calculation of the installation difficulty coefficient includes: In the dynamic spatial model, a spatial distance query is performed along the normal direction of the wall to which the installation location belongs to determine the positive net distance from the front of the installation location to the nearest obstacle. The difference between the positive clearance and the preset minimum depth for worker operation is used as the positive margin. The difficulty of front installation is determined according to the ratio of the positive margin to the minimum depth for worker operation. The smaller the positive margin, the higher the difficulty of front installation. As the positive margin increases, the reduction in the difficulty of front installation gradually decreases. Perform bidirectional spatial distance queries along the wall extension direction, and take the smaller value between the net distances from both sides to the nearest obstacle as the lateral net distance; The difference between the lateral clearance and half the width of the plate at the installation position is taken as the lateral margin. The lateral installation difficulty is determined according to the ratio of the lateral margin to half the width of the plate at the installation position. The smaller the lateral margin, the higher the lateral installation difficulty. And the reduction in lateral installation difficulty gradually decreases as the lateral margin increases. The installation difficulty coefficient is obtained by weighting and combining the difficulty of frontal installation and side installation.

7. The ALC partition wall panel installation auxiliary analysis method based on a mirror model according to claim 6, characterized in that, The optimization problem for establishing installation sequence combinations includes: For each pair of installation locations in the installation dependency relationship, determine whether there is a directed path between them consisting of the dependency pair, and record the determination result as a dependency reachability matrix; The feasible solution space is the topological order that satisfies the installation dependencies. The objective function is to calculate the comprehensive difficulty coefficient bit by bit and sum the results of calling the difficulty evaluation mechanism for a given installation sequence. The optimization objective is to minimize the objective function, thus obtaining the installation sequence combinatorial optimization problem.

8. The ALC partition wall panel installation auxiliary analysis method based on a mirror model according to claim 7, characterized in that, Solving the installation sequence combination optimization problem includes: Based on installation dependencies, topological sorting and sampling are performed to generate an initial installation sequence. The difficulty assessment mechanism is then invoked to calculate the objective function value as the current solution. Randomly select two installation locations from the current installation sequence, query the dependency reachability matrix to determine if there is a directed path between them. If there is, discard and reselect; otherwise, perform an exchange to generate a candidate sequence. The difficulty assessment mechanism is invoked to calculate the objective function value of the candidate sequence. If the value is lower than the current solution, it is accepted directly; otherwise, it is accepted with a probability that decreases monotonically as the temperature parameter decreases. The temperature parameters are gradually reduced according to the preset cooling strategy. Random selection and exchange are continuously performed until the termination condition is met. The installation sequence with the minimum objective function value during the search process is output as the optimal installation sequence.

9. The ALC partition wall panel installation auxiliary analysis method based on a mirror model according to claim 8, characterized in that, The termination conditions include: the temperature parameter dropping to a preset minimum temperature threshold; the cumulative number of iterations reaching a preset upper limit; All installation location pairs in the current installation sequence, as confirmed by the dependency reachability matrix, do not have directed paths. All have been randomly selected and swapped under the current temperature parameters, and no candidate sequences with objective function values ​​lower than the current optimal solution have been generated.

10. An ALC partition wall panel installation auxiliary analysis system based on a mirror model, used to implement the ALC partition wall panel installation auxiliary analysis method based on a mirror model as described in any one of claims 1-9, characterized in that, include: The dependency construction module is used to establish installation dependencies based on building construction design documents. The installation dependencies describe the sequential installation requirements between each installation location due to physical structural constraints. Each installation location corresponds to the design installation space of an ALC board. The dynamic space modeling module is used to establish a dynamic space model, which is a mirror information model of the floor space and records the spatial geometric information of the floor space. The difficulty assessment module is used to establish a difficulty assessment mechanism based on a dynamic spatial model to calculate the comprehensive difficulty coefficient of any installation position under a given installation sequence. The optimization problem building module is used to create an installation sequence combination optimization problem based on installation dependencies and the difficulty coefficients of all installation locations under the difficulty assessment mechanism. An optimization solution module is used to solve the installation sequence combination optimization problem to obtain the optimal installation sequence.

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