Indoor space intelligent planning method and system based on BIM technology and multi-source data fusion
By integrating BIM technology and multi-source data, a teaching objective-driven spatial configuration strategy was generated, which solved the problem of mismatch between spatial planning and teaching intentions in existing technologies and realized visualized and implementable spatial configuration.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGZHOU MODERN INFORMATION ENGINEERING VOCATIONAL & TECHNICAL COLLEGE
- Filing Date
- 2026-01-19
- Publication Date
- 2026-04-28
Smart Images
Figure CN121936029A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of computer science, and in particular relates to an intelligent planning method and system for indoor space based on BIM technology and multi-source data fusion. Background Technology
[0002] School buildings typically feature curriculum-driven interior spaces, frequent class schedule changes, and multi-purpose reuse of the same space. Classrooms often switch between various teaching methods such as lecturing, seminars, presentations, and hands-on activities. If space configuration remains at the level of static design or manual adjustment based on experience, mismatches between spatial form and teaching activities can easily occur. Existing space planning methods struggle to reliably extract structured parameters directly related to spatial organization from teaching objective texts, preventing space planning from truly starting with teaching intentions. Even when incorporating teaching objective texts, neglecting the differences in the variable components of classrooms may still result in configuration suggestions that are impossible to implement in ordinary fixed classrooms, leading to unfeasible planning results or excessively high maintenance costs. Therefore, how to accurately generate space configuration strategies has become an urgent technical problem to be solved. Summary of the Invention
[0003] The purpose of this invention is to design an intelligent indoor space planning method and system based on BIM technology and multi-source data fusion, which can accurately generate space configuration strategies.
[0004] To achieve the above objectives, a method for intelligent indoor space planning based on BIM technology and multi-source data fusion is provided in a first aspect of the present invention, the method comprising: Obtain the teaching objective text, and based on the teaching objective text, obtain the interaction intensity coefficient and spatial partitioning requirement identifier; The interaction intensity coefficient is corrected based on the preset classroom state scalar, the preset spatial variable capability scalar, and the spatial partition requirement identifier to obtain the corrected interaction intensity coefficient. The spatial partition requirement identifier is also corrected based on the classroom state scalar and the spatial variable capability scalar to obtain the corrected spatial partition requirement identifier. Based on the modified interaction intensity coefficient, the modified spatial partition requirement identifier, and the spatial variable capability scalar, the configuration intensity is calculated to obtain the spatial configuration intensity parameter; The activation intensity of each action is calculated based on the spatial configuration intensity parameter, the corrected spatial partition requirement identifier, the preset partition consistency suppression constant, and the preset implementation threshold. Actions with activation intensity greater than the preset intensity threshold are added to the strategy set. The actions in the strategy set are defined as target actions, each target action corresponds to an operation type of a component, and each component corresponds to component parameters. The execution ratio of each type of component is calculated based on the spatial configuration intensity parameters and the corresponding action activation intensity, and a spatial configuration strategy is generated based on the component, the component parameters, and the execution ratio.
[0005] Furthermore, obtaining the interaction intensity coefficient and spatial partitioning requirement identifier based on the teaching objective text includes: The character sequence is obtained by encoding the teaching objective text. The character sequence is input into a preset semantic modeling model to obtain the interaction intensity coefficient and the spatial partitioning requirement identifier.
[0006] Furthermore, the component parameters include variable identifiers. Before correcting the interaction intensity coefficient based on a preset classroom state scalar, a preset spatial variable capability scalar, and the spatial partitioning requirement identifier to obtain the corrected interaction intensity coefficient, the method further includes: Obtaining the classroom state scalar and the spatially variable capability scalar specifically includes: The number of interactive events on the electronic whiteboard, the number of page turning and annotation events on the teacher's courseware, the number of instant responses or submissions on the student's end, and the number of switching events on the classroom projection device are obtained. The number of all events is normalized and summarized using a preset time window as the statistical interval to obtain the classroom state scalar.
[0007] The spatial variable capability scalar is obtained by summarizing and calculating based on all the aforementioned variable identifiers.
[0008] Further, the step of correcting the interaction intensity coefficient based on a preset classroom state scalar, a preset spatial variable ability scalar, and the spatial partitioning requirement identifier to obtain a corrected interaction intensity coefficient includes: The first data is obtained by controlling the fusion ratio of the interaction intensity coefficient and the classroom state scalar according to the preset prior weights; The second data is obtained by multiplying the preset consistency penalty coefficient, the difference between the first preset value and the spatial variable capacity scalar, the difference between the first preset value and the classroom state scalar, and the spatial partitioning requirement identifier. Subtracting the first data from the second data yields the corrected interaction intensity coefficient.
[0009] Further, the step of correcting the spatial partitioning requirement identifier based on the classroom state scalar and the spatial variable capability scalar to obtain the corrected spatial partitioning requirement identifier includes: Subtract the first preset value from the spatial variable capability scalar, multiply by the preset variable capability bias, and add the preset base threshold to obtain the third data. If the classroom status scalar is greater than or equal to the third data, then the corrected spatial partitioning requirement identifier is the first preset value; otherwise, the corrected spatial partitioning requirement identifier is the spatial partitioning requirement identifier.
[0010] Further, the step of calculating the spatial configuration intensity parameter based on the modified interaction intensity coefficient, the modified spatial partitioning requirement identifier, and the spatial variable capability scalar includes: Multiply the corrected spatial partitioning requirement identifier by the preset partitioning enhancement coefficient, add the first preset value, and then multiply it by the corrected interaction intensity coefficient and the spatial variable capability scalar respectively to obtain the fourth data; Subtract the first preset value from the spatial variable capacity scalar, and then multiply it by the corrected spatial partition requirement identifier and the preset complexity suppression coefficient to obtain the fifth data. The maximum value among the second preset value, the fourth data, and the fifth data is taken as the candidate data, and the maximum value among the candidate data and the first preset value is taken as the spatial configuration intensity parameter.
[0011] Further, the step of calculating the action activation intensity of each action based on the spatial configuration intensity parameter, the corrected spatial partition requirement identifier, the preset partition consistency suppression constant, and the preset implementation threshold includes: Subtract the first preset value from the spatial variable capability scalar, and then multiply by the preset partition consistency suppression constant to obtain the sixth data. Subtract the spatial configuration strength parameter from the implementation threshold and the sixth data to obtain the seventh data. The maximum value between the first preset value and the seventh data is taken as the activation intensity of the action.
[0012] In a second aspect, the present invention provides an intelligent indoor space planning system based on BIM technology and multi-source data fusion, the system comprising: The acquisition unit is used to acquire the teaching objective text and obtain the interaction intensity coefficient and spatial partitioning requirement identifier based on the teaching objective text. The correction unit is used to correct the interaction intensity coefficient according to the preset classroom state scalar, the preset spatial variable capability scalar and the spatial partition requirement identifier to obtain the corrected interaction intensity coefficient, and to correct the spatial partition requirement identifier according to the classroom state scalar and the spatial variable capability scalar to obtain the corrected spatial partition requirement identifier. The first calculation unit is used to perform configuration intensity calculation based on the modified interaction intensity coefficient, the modified spatial partition requirement identifier, and the spatial variable capability scalar to obtain spatial configuration intensity parameters. The second calculation unit is used to calculate the action activation intensity of each action based on the spatial configuration intensity parameter, the corrected spatial partition requirement identifier, the preset partition consistency suppression constant, and the preset implementation threshold, and to add actions whose action activation intensity is greater than the preset intensity threshold to the strategy set; wherein, the actions in the strategy set are defined as target actions, each target action corresponds to an operation type of a component, and each component corresponds to component parameters; The generation unit is used to calculate the execution ratio of each type of component based on the spatial configuration intensity parameters and the corresponding action activation intensity, and to generate a spatial configuration strategy based on the component, the component parameters, and the execution ratio.
[0013] In a third aspect of the invention, an electronic device is provided, the electronic device including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the method described in the first aspect above.
[0014] In a fourth aspect of the invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect above.
[0015] The beneficial technical effects of the present invention are at least as follows: To address the aforementioned issues, this invention provides an intelligent indoor space planning method and system based on BIM technology and multi-source data fusion. Its core lies in transforming teachers' natural language teaching objectives into space usage parameters, enabling teaching intentions to be incorporated into space planning calculations in a unified parameter format. Furthermore, it utilizes existing classroom teaching terminal interaction event traces to construct a classroom state scalar, and combines this with the variable component attributes in the BIM model to form spatial variable capability constraints, thus constraining the space usage parameters and avoiding strategy deviations caused by subjective objectives or occasional states. In the strategy generation stage, using the corrected demand parameters as input, it integrates spatial configuration intensity with action initiation... Using intensity as an intermediate layer, the intensity of teaching interaction, zoning intention, and classroom variable capabilities are uniformly mapped into a set of implementable strategies oriented towards component actions. This reduces the reliance on discrete rule enumeration and adapts to the operational realities of school class switching. In the execution phase, spatial units and configurable component instances are located based on the BIM model. The intensity of action activation is further mapped into the component execution ratio and written into the component parameters. This enables the visualization and implementable output of configuration results such as partition opening and closing, table and chair rearrangement, and lighting zoning in BIM. The planning results can be directly used for pre-class arrangements or interfaced with the intelligent classroom control system, fundamentally implementing "teaching goal-driven" into "component-level spatial configuration". Attached Figure Description
[0016] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.
[0017] Figure 1 This is a flowchart of an intelligent indoor space planning method based on BIM technology and multi-source data fusion provided in an embodiment of this application.
[0018] Figure 2 This is a structural schematic diagram of an intelligent indoor space planning system based on BIM technology and multi-source data fusion provided in an embodiment of this application. Detailed Implementation
[0019] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.
[0020] Please refer to Figure 1 , Figure 1 This is a flowchart of an intelligent indoor space planning method based on BIM technology and multi-source data fusion provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.
[0021] Step S101: Obtain the teaching objective text, and obtain the interaction intensity coefficient and spatial partitioning requirement identifier based on the teaching objective text; Step S102: Correct the interaction intensity coefficient according to the preset classroom state scalar, the preset spatial variable ability scalar, and the spatial partition requirement identifier to obtain the corrected interaction intensity coefficient; and correct the spatial partition requirement identifier according to the classroom state scalar and the spatial variable ability scalar to obtain the corrected spatial partition requirement identifier. Step S103: Calculate the configuration intensity based on the corrected interaction intensity coefficient, the corrected spatial partition requirement identifier, and the spatial variable capability scalar to obtain the spatial configuration intensity parameters. Step S104: Calculate the action activation intensity of each action based on the spatial configuration intensity parameter, the corrected spatial partition requirement identifier, the preset partition consistency suppression constant, and the preset implementation threshold. Add actions with an action activation intensity greater than the preset intensity threshold to the strategy set. The actions in the strategy set are defined as target actions. Each target action corresponds to an operation type of a component, and each component has corresponding component parameters. Step S105: Calculate the execution ratio of each type of component based on the spatial configuration strength parameters and the corresponding action activation strength, and generate a spatial configuration strategy based on the component, component parameters and execution ratio.
[0022] In step S101 of some embodiments, the goal is to transform the natural language teaching objective text provided by the teacher into structured parameters required for spatial configuration. To ensure that the parameters have clear spatial semantic relevance and are suitable for the automatic generation of subsequent spatial strategies, this step employs a semantic modeling model based on an attention mechanism. This model is trained using labeled historical corpora to extract content such as the degree of interaction and spatial organization from the teaching objective text, and quantifies this into digital parameters that can be processed in subsequent steps. Since the teaching objectives described by teachers often lack a standard format, the model structure needs to be able to handle complex word order and nested contextual expressions, and the output should be parameters that can be directly used in spatial configuration calculations.
[0023] Specifically, teachers typically fill in teaching objective text in the course scheduling system. This text is in free text input format and generally does not exceed 300 characters. For example, a teacher might enter: "This lesson will focus on cell structure. The first half will be taught by the teacher, and the second half will involve group work assembling cell models and presenting the results." This type of text contains multiple behavioral levels, temporal structures, and spatial meanings. The system receives the teaching objective text at the data access layer through a standard text input interface and encodes it as a character sequence. Each of them For a single character encoded in UTF-8, the maximum length is limited to [number]. This input goes directly into the semantic modeling model for processing.
[0024] The semantic modeling model employs a four-layer attention mechanism encoder, with each layer containing eight attention heads, and an output dimension of 256. (Character sequence) The data is converted into vectors through an embedding layer, then positionally encoded before being fed into the model. The model encodes the entire sentence and uses special position vectors (CLS positions) as semantic aggregation vectors. During the training phase, supervised training is performed using a manually labeled teaching objective dataset. The data comes from real university course systems, and the teaching support platform automatically analyzes behavioral trajectories and labels them with spatial semantic tags, such as "whether there are group activities" and "whether interaction is emphasized."
[0025] The model outputs two structured parameters: interaction strength coefficient. Spatial partitioning requirement identifier .parameter Used to indicate the teacher's desired level of classroom interaction. This indicates whether there is a clear requirement for spatial functional zoning. The parameter calculation formula is as follows: ; ; in, This represents the aggregated vector output of the model for the semantics of the input text; These are the linear weight vectors of the output layer; This corresponds to the bias term. The non-linear mapping in the function structure is implemented using the Sigmoid function, which is used to... The output is normalized; Boolean classification results are then generated through linear projection and threshold discrimination. This indicates that partitioning is required. 0 indicates no partitioning is needed. The model is not updated after deployment and is only used for forward prediction, supporting batch processing. For example, in a real-world course, if the teacher inputs: "Students will work in pairs to design a small project based on mechanical principles and present it to the class," the system's predicted output will be: , This indicates that the teaching objective has a high demand for interactivity and requires spatial support for group partitioning.
[0026] In step S102 of some embodiments, a classroom state scalar is constructed using the "interaction behavior traces" generated by existing teaching terminals in the classroom without introducing additional complex data categories. And based on this, and Perform a constrained correction. Before correction. and The pre-existing teaching objectives text from the teacher represents the "desired classroom organization method"; while the classroom state scalar obtained in this step represents the "intensity of actual interaction in the classroom." The difference between the two is common in school settings: the same course may present completely different levels of interaction and grouping patterns in different classes and different periods; if spatial planning is based solely on textual settings, mismatches can easily occur, such as "expecting grouping but actually lecturing" or "expecting lecturing but actually having intense interaction." The core approach of this step is to explicitly incorporate this mismatch into the correction formula and use "spatial variability" to impose a threshold constraint on the correction range, ensuring that the subsequently generated spatial configuration strategy closely resembles the actual classroom while avoiding unreasonable zoning or arrangement suggestions in classrooms lacking adjustability.
[0027] Classroom status scalar The data is obtained by summarizing the event streams from existing teaching terminals in the classroom. Typical sources include: interactive events on the electronic whiteboard, events related to page turning and annotation on the teacher's courseware, events related to students' real-time responses / submissions, and events related to switching between classroom projection devices. The preset time window can be set to a single class period. Using this "single class period" as the statistical interval, the above event counts are normalized and summed. and will Limited to the range [0,1], so that it can be used with Fusion within the same numerical domain. To avoid relying solely on... right Simple weighting results in "jittering corrections," so two constraints strongly correlated with school teaching spaces are introduced: one is the zoning consistency constraint, that is, if It points to "needs partitioning", and If the intensity of the actual interaction reflected is very low, then the interaction should be suppressed. The adjustment is influenced by two factors: first, the upward adjustment of space capacity; and second, the constraint of spatial variability, meaning that whether the classroom itself has the conditions for zoning and flexible arrangement will affect the magnitude of the adjustment. (Spatial variability scalar) The system calculates the information based on the component attributes of the classroom in the BIM model, such as the proportion of movable tables and chairs, the presence of openable and closable partitions, and the presence of reconfigurable power / network points. The system then assigns "variable identifiers" to these components in the BIM model, and the results are aggregated from these identifiers. Specifically, This is achieved by reading the component attributes directly related to spatial adjustment in the BIM model and summarizing them according to preset rules. For example, the system sequentially reads the "movable" attribute of tables and chairs, the "openable / closeable" attribute of partitions, and the "reconfigurable" attribute of power or network points in the BIM model, generating movable table and chair ratio, partition availability indicator, and infrastructure variability indicator, respectively. Then, according to a pre-set aggregation logic, the above results are combined into a scalar representing the overall variability of the classroom space. The range is defined as [0,1]. The proportion of movable tables and chairs is obtained by comparing the number of table and chair components marked "movable" in the BIM model with the total number of table and chair components. This ratio serves as an input factor for calculating the space's variable capacity. Furthermore, it incorporates component-level Boolean identifiers such as the presence of openable / closed partitions and reconfigurable power or network points, and aggregates these according to preset combination rules. This ensures that adjustments are only made when multiple key components simultaneously possess adjustment capabilities. This ensures a higher value, thus avoiding the misjudgment of a classroom's high spatial variability based solely on a single component condition. Based on the above design, a structure of "weighted fusion + consistency penalty + variable capability threshold" is adopted when correcting the interaction intensity coefficient. The fusion ratio of the interaction intensity coefficient and the classroom state scalar is controlled according to preset prior weights to obtain the first data. The preset consistency penalty coefficient, the difference between the first preset value and the spatial variability scalar, the difference between the first preset value and the classroom state scalar, and the spatial zoning requirement identifier are multiplied to obtain the second data. The first and second data are subtracted to obtain the corrected interaction intensity coefficient, as shown in the following formula: ; in, The corrected interaction strength parameters; This is the interaction intensity coefficient. Identify spatial zoning requirements; As a scalar for classroom status; This refers to the spatial variable capacity scalar obtained from the aggregation of variable attributes of BIM components. As a priori weight, it is used to control the integration ratio of "teacher settings" and "classroom status"; This is a consistency penalty coefficient used to suppress the occurrence of partitions where "marking requires partitioning" ( However, classroom interaction was low. The overestimation of interaction intensity in the case of "smaller"; the first preset value is set to 1. (in the formula...) This makes punishment more effective when variable abilities are insufficient, thus avoiding the pitfalls of using fixed desks and chairs in ordinary classrooms without partitions. The occasional fluctuations may mislead teachers into making overly drastic adjustments to the intensity of interaction. For example, if a teacher's lesson objective text emphasizes group presentations, then... High and =1, but in reality, classroom teaching was changed to lectures and reviews due to exam arrangements, resulting in a significant reduction in terminal interaction events. If it is too low, then in the above formula... It will make Actively pulled back; if the classroom is marked as having variable height in the BIM ( The penalty has been reduced (larger), allowing the system to more nimbly follow changes in classroom conditions in variable classrooms.
[0028] In obtaining Subsequently, when correcting the spatial partitioning requirement identifier, continuous mapping is not used; instead, a threshold judgment with a "variable capability bias" is employed to avoid frequent flipping of the partitioning identifier under similar conditions. Specifically, the spatial partitioning requirement identifier is only confirmed as 1 when the classroom state sufficiently supports group interaction and the classroom possesses a certain degree of variable capability; otherwise, the original spatial partitioning requirement identifier remains unchanged, thus taking "teacher intention" as the default and triggering correction only when sufficient evidence is available. The first preset value is subtracted from the spatial variable capability scalar, multiplied by the preset variable capability bias, and then added to the preset base threshold. This yields the third data; if the classroom state scalar is greater than or equal to the third data, the corrected spatial partitioning requirement identifier is the first preset value; otherwise, the corrected spatial partitioning requirement identifier is the original spatial partitioning requirement identifier. As shown in the following formula: ; in, This is the corrected spatial partitioning requirement identifier; As a scalar for classroom status; For spatially variable capability scalar; It serves as a base threshold to distinguish between "low-interaction lecture mode" and "high-interaction group mode"; For variable capability bias, when At lower Larger classrooms require stronger evidence of interaction before setting the space zoning requirement flag to 1. This design addresses the real-world constraints of school settings: in a typical fixed classroom, even some interaction may not be sufficient to trigger a space zoning configuration suggestion; however, in an innovative classroom with movable furniture and openable partitions, weaker evidence of interaction may be enough to support zoning configuration.
[0029] In step S103 of some embodiments, the "requirement parameters that have taken into account both the teacher's vision and the actual classroom situation" are further transformed into "spatial configuration strategies that can be actually organized and implemented in school classrooms." The results of the previous stage addressed the credibility and feasibility boundaries on the demand side, while this step addresses the engineering implementation problem on the strategy side: In a school setting, the same classroom may continuously host different courses, and spatial adjustments must minimize interference with the next class and must be limited by the variable component conditions of the classroom itself as represented in BIM. Therefore, this step does not employ a large number of discrete rule enumerations, but instead compresses the modified requirement parameters into an adjustable configuration strength, and limits the number and magnitude of strategy actions through an "action complexity suppression term," making the output strategy more in line with the actual operational habits of daily school maintenance and break preparation.
[0030] First, generate a spatial configuration strength parameter. This is used to uniformly describe "how much space is worth investing in adjusting movements for this course". The design must reflect two real-world constraints of the school setting: First, higher interaction intensity necessitates greater spatial organization support, but this support is only meaningful when the classroom has flexible components; second, zoning requirements in schools often imply higher organizational costs (e.g., the need to set up group areas or create non-interfering discussion zones). If the classroom lacks partitions or movable furniture, zoning strategies should be proactively suppressed to avoid providing impractical configuration suggestions. Therefore, a "motion complexity suppression term" is introduced into the configuration intensity, which only applies when... and When the value is low, it is significantly effective, used to limit the unreasonable result of the strategy jumping directly from "want partitioning" to "must partitioning". The calculation of the spatial configuration strength parameter is as follows: multiply the corrected spatial partitioning demand identifier by the preset partitioning enhancement coefficient, add the first preset value, and then multiply by the corrected interaction strength coefficient and the spatial variable capability scalar respectively to obtain the fourth data; subtract the first preset value from the spatial variable capability scalar, and then multiply by the corrected spatial partitioning demand identifier and the preset complexity suppression coefficient respectively to obtain the fifth data; take the maximum value among the second preset value, the fourth data, and the fifth data as the candidate data, and take the maximum value among the candidate data and the first preset value as the spatial configuration strength parameter. As shown in the following formula: ; in, Configure strength parameters for the space; This is the corrected interaction strength coefficient. This is the corrected spatial partitioning requirement identifier; Spatial variable capacity scalar provided for BIM; This is the partition enhancement factor, used to increase configuration strength when there is a genuine need for partitioning; This is a complexity suppression coefficient, used to reduce the policy strength caused by partitioning when variable capabilities are insufficient. The outer truncation operator in the formula will... Limiting it to [0,1] avoids excessive intensity in extreme course descriptions or under extreme device conditions. The interpretation of this expression in a school setting can be illustrated with a common example: when course objectives heavily emphasize interaction (…). (Larger) but the classrooms have fixed seating arrangements. When it is smaller, Will be The item is naturally lowered; when grouping is required at the same time ( When ), the inhibition term Will further This pulls the strategy back, thus avoiding the implementation of "strong partitioning" strategies in regular classrooms.
[0031] In step S104 of some embodiments, the following is obtained: Next, the "intensity" needs to be translated into a "strategy set". This step adopts a BIM component-based action library-driven approach: the "implementation threshold" of each type of adjustable action is pre-marked in the family / type attributes of the BIM model, and the threshold value is solidified into action parameters. . The source of these thresholds does not rely on external data, but is defined by the existence and adjustment range of variable components in the BIM. For example, adjusting only the lighting zones usually has a low threshold; rearranging tables and chairs requires a higher threshold; and opening and closing partitions requires the existence of corresponding components and has an even higher threshold. When reading the classroom BIM model, the system generates a set of implementation thresholds according to a pre-configured mapping table. Based on this, the activation status of each action is calculated. Action activation does not employ a hard rule of "activation if all conditions are met," but rather a filtering method based on "intensity exceeding a threshold and semantic consistency across zones," allowing the strategy to automatically adapt across different classrooms. The first preset value is subtracted from the spatial variable capacity scalar, and then multiplied by a preset zone consistency suppression constant to obtain the sixth data point. The spatial configuration intensity parameter is subtracted from both the implementation threshold and the sixth data point to obtain the seventh data point. The maximum value between the first preset value and the seventh data point is taken as the action activation intensity. As shown in the following formula: ; in, Indicates the first The activation intensity of each action; Configure strength parameters for the space; For the first The implementation threshold for each action is generated by the properties of the BIM components; This is a partition consistency suppression constant, used to suppress the consistency of partitions that do not require ( However, additional suppression is applied to situations where "partition-based actions are forcibly activated." Specifically, the preset intensity threshold can be set to 0. At that time, the first Each action is selected into the policy set and then... The size determines the range of motion (e.g., the extent of table and chair rearrangement, the opening ratio of partitions, the number of lighting zones, etc.). (Scale its maximum magnitude). This expression can link the three factors of "demand parameters - classroom capacity - action feasibility" without introducing additional data categories, and naturally form the result of "prioritizing fewer actions": when When the threshold is low, only actions with low barriers to entry are selected; when... Very high and As the learning curve widens, more advanced actions are gradually implemented, aligning with the tiered renovation of school classrooms from ordinary to innovative models. It's important to note that each action corresponds to a specific type of component operation, such as "rearranging desks and chairs," "opening and closing partitions," or "adjusting lighting zones." Each type of action corresponds to a set of instances of the same type of component in the BIM model.
[0032] In one example, a course emphasizes group collaboration in its objectives and higher =1. If the classroom has openable / closable partitions and movable tables and chairs in the BIM model ( (larger), then This will be magnified, thus affecting actions such as opening and closing partitions and rearranging table and chair arrays. If positive, the strategy set will include a combination of "zoning + group arrangement"; while in another classroom with fixed seating ( In smaller quantities, even with the same course, the inhibitory term will decrease. Ultimately, only low-threshold actions (such as lighting zoning or seating orientation cues) are retained to avoid outputting unfeasible spatial configuration strategies.
[0033] In step S105 of some embodiments, parametric execution of specific components in the BIM model is completed, ensuring that spatial planning truly translates into engineering results that are "viewable, implementable, and comparable." Spatial configuration strength parameters. With action activation intensity set The previous step already answered the question of "which types of spatial actions should be adjusted and to what extent in this teaching context." This step shifts the focus from judgment and decision-making to execution within the BIM platform: clarifying which components and parameters in the BIM model correspond to each type of action, and forming a clear spatial configuration strategy according to a predetermined execution ratio. This process emphasizes adaptability to the school setting, not assuming fully automated execution capabilities in the classroom. Instead, it generates configuration results that can be invoked by the system or implemented manually based on drawings through BIM parameter updates. At the start of execution, the system first determines the classroom space unit corresponding to the current course through the BIM platform's spatial positioning interface and reads all component instances within that unit. Whether a component is configurable is not determined ad hoc in this step but is already labeled through component family parameters during the BIM modeling stage. For example, during modeling, movable table and chair families will include "rotatable" and "rearrangeable" labels, openable partition families will include "opening ratio" parameters, and lighting component families will include parameters such as "zone number" and "brightness ratio." This step only operates on the components that have been marked as configurable; the remaining components remain in their original state.
[0034] For each type of target action, iterate through its corresponding set of component instances and enable intensity based on the action. Calculate the uniform execution ratio of this type of component. This execution ratio controls the adjustment range of this type of component, such as the movement ratio of rearranging tables and chairs, the opening ratio of partitions, or the activation ratio of lighting zones. Instead of calculating different execution intensities for each component individually, consistency in component adjustments under the same action type is ensured, and execution complexity is reduced. Execution Ratio Used to describe the The component should be adjusted to a percentage of its maximum capacity under the current configuration. The class component is the first in the strategy set. The components of a target action. This proportion is constrained by both the overall configuration strength and the activation strength of a single action, and its calculation method is shown in the following formula: ; in, For the first The execution ratio of class components; For the first The activation intensity of each action; The space is configured with intensity parameters. This design ensures two things: first, when the space configuration intensity parameter is low, even if an action is selected in the policy set, its execution ratio will be compressed overall; second, when an action is not selected in the policy set... It will not be falsely triggered due to high spatial configuration strength parameters.
[0035] In obtaining Then, a spatial configuration strategy is generated based on the components, component parameters, and execution ratio. That is, the system performs corresponding parameter writing operations for different types of components. Specifically, the target adjustment values for each component parameter are first calculated based on the execution ratio, and then these target adjustment values are written to the corresponding component parameters through the parameter interface of the BIM model. This results in an updated component state and spatial layout in the BIM model, and this updated model state constitutes the specific implementation of the spatial configuration strategy. For example, for movable tables and chairs, the system reads the default orientation and spacing parameters of all table and chair instances in the classroom and, according to... The target orientation offset and row / column spacing adjustment are linearly scaled and then written into the BIM parameters; for operable partitions, the system will... The mapping is to the partition opening ratio parameter, which changes continuously from fully closed to fully open; for lighting components, it is based on... The system determines the number of lighting zones to be activated and the brightness ratio of each zone. All these operations are completed through the parameter interface of the BIM platform. The parameter values before and after writing are recorded in the model properties for later viewing or rollback. Among them, Building Information Modeling (BIM model) is a new tool applied to the fields of architecture, engineering, and civil engineering. It is mainly based on three-dimensional graphics and can be applied to the entire life cycle of construction projects.
[0036] In school teaching settings, a significant practical problem is the limited preparation time during breaks, making it unsuitable to adjust too many components at once. Therefore, this step introduces a "priority execution mechanism" at the execution layer: when multiple... When classifying components, the system follows... Sort by size from largest to smallest, and only write parameters to a few types of components with a high execution rate, leaving the remaining components unchanged. For example, in a typical classroom, even if the strategy simultaneously suggests rearranging desks and chairs and adjusting lighting zones, if the desk and chair rearrangement corresponds to... If the parameters are significantly higher, the system will prioritize generating a table and chair arrangement plan, while the lighting configuration will remain at its default state. This mechanism ensures that the final output better aligns with the actual operating habits of teachers and administrators. After all selected component parameters have been written, the system generates an updated BIM model and simultaneously outputs the corresponding spatial configuration strategy. The spatial configuration strategy includes a floor plan view and a component status list. This view can be viewed directly in the BIM software or exported as an image or layout description for teachers' pre-class preparation or for unified adjustments by teaching administrators.
[0037] For example, in a course that primarily involves group discussions, the previous stage might have yielded higher scores. It also has significant advantages in rearranging tables and chairs and opening and closing partitions. In classrooms equipped with movable tables and chairs and retractable partitions, this step will be based on... The system automatically generates a table and chair layout for a four-person group and marks the open / closed status of the partitions in the BIM model. However, in a regular classroom without partitions, even if the course requirements are the same, the system will only generate table and chair adjustment plans and will not output unfeasible partition configurations due to the lack of relevant components.
[0038] Steps S101 to S105 of this embodiment involve obtaining the teaching objective text and then deriving the interaction intensity coefficient and spatial partitioning requirement identifier based on it. The interaction intensity coefficient is corrected based on a preset classroom state scalar, a preset spatial variable capability scalar, and the spatial partitioning requirement identifier to obtain a corrected interaction intensity coefficient. Similarly, the spatial partitioning requirement identifier is corrected based on the classroom state scalar and the spatial variable capability scalar to obtain a corrected spatial partitioning requirement identifier. Configuration intensity is calculated based on the corrected interaction intensity coefficient, the corrected spatial partitioning requirement identifier, and the spatial variable capability scalar to obtain spatial configuration intensity parameters. The action activation intensity of each action is calculated based on the spatial configuration intensity parameters, the corrected spatial partitioning requirement identifier, a preset partition consistency suppression constant, and a preset implementation threshold. Actions with activation intensities greater than the preset intensity threshold are added to the strategy set. Actions in the strategy set are defined as target actions, each target action corresponds to an operation type of a component, and each component has corresponding component parameters. The execution ratio of each type of component is calculated based on the spatial configuration intensity parameters and the corresponding action activation intensity. A spatial configuration strategy is generated based on the component, component parameters, and execution ratio, achieving accurate generation of spatial configuration strategies.
[0039] Please see Figure 2 This application also provides an intelligent indoor space planning system based on BIM technology and multi-source data fusion, which can realize the above-mentioned intelligent indoor space planning method based on BIM technology and multi-source data fusion. The system includes: Unit 201 is used to acquire the teaching objective text and obtain the interaction intensity coefficient and spatial partitioning requirement identifier based on the teaching objective text. The correction unit 202 is used to correct the interaction intensity coefficient according to the preset classroom state scalar, the preset spatial variable ability scalar and the spatial partition requirement identifier to obtain the corrected interaction intensity coefficient, and to correct the spatial partition requirement identifier according to the classroom state scalar and the spatial variable ability scalar to obtain the corrected spatial partition requirement identifier. The first calculation unit 203 is used to perform configuration intensity calculation based on the modified interaction intensity coefficient, the modified spatial partition requirement identifier and the spatial variable capability scalar, and obtain the spatial configuration intensity parameters. The second calculation unit 204 is used to calculate the action activation intensity of each action based on the spatial configuration intensity parameter, the corrected spatial partition requirement identifier, the preset partition consistency suppression constant and the preset implementation threshold, and to add actions with an action activation intensity greater than the preset intensity threshold to the strategy set; wherein, the actions in the strategy set are defined as target actions, each target action corresponds to an operation type of a component, and each component corresponds to component parameters; The generation unit 205 is used to calculate the execution ratio of each type of component based on the spatial configuration strength parameters and the corresponding action activation strength, and to generate a spatial configuration strategy based on the component, component parameters and execution ratio.
[0040] The specific implementation of the intelligent indoor space planning system based on BIM technology and multi-source data fusion is basically the same as the specific implementation of the intelligent indoor space planning method based on BIM technology and multi-source data fusion described above, and will not be repeated here.
[0041] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.
Claims
1. An intelligent indoor space planning method based on BIM technology and multi-source data fusion, characterized in that, The method includes: Obtain the teaching objective text, and based on the teaching objective text, obtain the interaction intensity coefficient and spatial partitioning requirement identifier; The interaction intensity coefficient is corrected based on the preset classroom state scalar, the preset spatial variable capability scalar, and the spatial partition requirement identifier to obtain the corrected interaction intensity coefficient. The spatial partition requirement identifier is also corrected based on the classroom state scalar and the spatial variable capability scalar to obtain the corrected spatial partition requirement identifier. Based on the modified interaction intensity coefficient, the modified spatial partition requirement identifier, and the spatial variable capability scalar, the configuration intensity is calculated to obtain the spatial configuration intensity parameter; The activation intensity of each action is calculated based on the spatial configuration intensity parameter, the corrected spatial partition requirement identifier, the preset partition consistency suppression constant, and the preset implementation threshold. Actions with activation intensity greater than the preset intensity threshold are added to the strategy set. The actions in the strategy set are defined as target actions, each target action corresponds to an operation type of a component, and each component corresponds to component parameters. The execution ratio of each type of component is calculated based on the spatial configuration intensity parameters and the corresponding action activation intensity, and a spatial configuration strategy is generated based on the component, the component parameters, and the execution ratio.
2. The intelligent indoor space planning method based on BIM technology and multi-source data fusion according to claim 1, characterized in that, The process of obtaining the interaction intensity coefficient and spatial partitioning requirement identifier based on the teaching objective text includes: The character sequence is obtained by encoding the teaching objective text. The character sequence is input into a preset semantic modeling model to obtain the interaction intensity coefficient and the spatial partitioning requirement identifier.
3. The intelligent indoor space planning method based on BIM technology and multi-source data fusion according to claim 1, characterized in that, The component parameters include variable identifiers. Before correcting the interaction intensity coefficient based on a preset classroom state scalar, a preset spatial variable capability scalar, and the spatial partitioning requirement identifier to obtain the corrected interaction intensity coefficient, the method further includes: Obtaining the classroom state scalar and the spatially variable capability scalar specifically includes: The number of interactive events on the electronic whiteboard, the number of page turning and annotation events on the teacher's courseware, the number of instant responses or submissions on the student's end, and the number of switching events on the classroom projection device are obtained. The number of all events is normalized and summarized using a preset time window as the statistical interval to obtain the classroom state scalar. The spatial variable capability scalar is obtained by summarizing and calculating based on all the aforementioned variable identifiers.
4. The intelligent indoor space planning method based on BIM technology and multi-source data fusion according to claim 1, characterized in that, The step of correcting the interaction intensity coefficient based on a preset classroom state scalar, a preset spatial variable capability scalar, and the spatial partitioning requirement identifier to obtain the corrected interaction intensity coefficient includes: The first data is obtained by controlling the fusion ratio of the interaction intensity coefficient and the classroom state scalar according to the preset prior weights; The second data is obtained by multiplying the preset consistency penalty coefficient, the difference between the first preset value and the spatial variable capacity scalar, the difference between the first preset value and the classroom state scalar, and the spatial partitioning requirement identifier. Subtracting the first data from the second data yields the corrected interaction intensity coefficient.
5. The intelligent indoor space planning method based on BIM technology and multi-source data fusion according to claim 1, characterized in that, The step of correcting the spatial partitioning requirement identifier based on the classroom state scalar and the spatial variable capability scalar to obtain the corrected spatial partitioning requirement identifier includes: Subtract the first preset value from the spatial variable capability scalar, multiply by the preset variable capability bias, and add the preset base threshold to obtain the third data. If the classroom status scalar is greater than or equal to the third data, then the corrected spatial partitioning requirement identifier is the first preset value; otherwise, the corrected spatial partitioning requirement identifier is the spatial partitioning requirement identifier.
6. The intelligent indoor space planning method based on BIM technology and multi-source data fusion according to claim 1, characterized in that, The step of calculating the spatial configuration intensity parameters based on the modified interaction intensity coefficient, the modified spatial partitioning requirement identifier, and the spatial variable capability scalar includes: Multiply the corrected spatial partitioning requirement identifier by the preset partitioning enhancement coefficient, add the first preset value, and then multiply it by the corrected interaction intensity coefficient and the spatial variable capability scalar respectively to obtain the fourth data; Subtract the first preset value from the spatial variable capacity scalar, and then multiply it by the corrected spatial partition requirement identifier and the preset complexity suppression coefficient to obtain the fifth data. The maximum value among the second preset value, the fourth data, and the fifth data is taken as candidate data, and the maximum value among the candidate data and the first preset value is taken as the spatial configuration intensity parameter.
7. The intelligent indoor space planning method based on BIM technology and multi-source data fusion according to claim 1, characterized in that, The calculation of the action activation strength for each action based on the spatial configuration strength parameter, the corrected spatial partition requirement identifier, the preset partition consistency suppression constant, and the preset implementation threshold includes: Subtract the first preset value from the spatial variable capability scalar, and then multiply by the preset partition consistency suppression constant to obtain the sixth data. Subtract the spatial configuration strength parameter from the implementation threshold and the sixth data to obtain the seventh data. The maximum value between the first preset value and the seventh data is taken as the activation intensity of the action.
8. An intelligent indoor space planning system based on BIM technology and multi-source data fusion, characterized in that, The system includes: The acquisition unit is used to acquire the teaching objective text and obtain the interaction intensity coefficient and spatial partitioning requirement identifier based on the teaching objective text. The correction unit is used to correct the interaction intensity coefficient according to the preset classroom state scalar, the preset spatial variable capability scalar and the spatial partition requirement identifier to obtain the corrected interaction intensity coefficient, and to correct the spatial partition requirement identifier according to the classroom state scalar and the spatial variable capability scalar to obtain the corrected spatial partition requirement identifier. The first calculation unit is used to perform configuration intensity calculation based on the modified interaction intensity coefficient, the modified spatial partition requirement identifier, and the spatial variable capability scalar to obtain spatial configuration intensity parameters. The second calculation unit is used to calculate the action activation intensity of each action based on the spatial configuration intensity parameter, the corrected spatial partition requirement identifier, the preset partition consistency suppression constant, and the preset implementation threshold, and to add actions whose action activation intensity is greater than the preset intensity threshold to the strategy set; wherein, the actions in the strategy set are defined as target actions, each target action corresponds to an operation type of a component, and each component corresponds to component parameters; The generation unit is used to calculate the execution ratio of each type of component based on the spatial configuration intensity parameters and the corresponding action activation intensity, and to generate a spatial configuration strategy based on the component, the component parameters, and the execution ratio.
9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the intelligent indoor space planning method based on BIM technology and multi-source data fusion as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the indoor space intelligent planning method based on BIM technology and multi-source data fusion as described in any one of claims 1 to 7.