Building area route planning system applied to building planning design

Abstract

The invention belongs to the technical field of building planning management, and particularly relates to a building area route planning system applied to building planning design, which comprises a geographic information integration module, a function demand analysis module, a people flow prediction module, a route intelligent generation module, a multi-scheme evaluation optimization module and a result visualization display module. According to the invention, various building area route planning schemes are intelligently generated through the route intelligent generation module according to geographic information, function requirements and people flow prediction information, the various schemes are evaluated to select an optimal scheme, and the optimal route planning scheme is visually displayed. According to the method, key factors in multiple aspects of building area planning can be comprehensively considered, and based on tight cooperation and data interaction of all modules, the whole process of route planning from basic data collection to final scheme display is scientific, intelligent and visual, and the quality and level of building area route planning are remarkably improved; and the working difficulty of planning and designing personnel can be reduced.

Description

Technical Field

[0001] This invention relates to the field of architectural planning and management technology, specifically to a building area route planning system applied to architectural planning and design. Background Technology

[0002] Architectural planning and design is a comprehensive process that encompasses multiple stages from the initial planning of a project to the formation of a specific design scheme. It aims to create buildings and their surrounding environment that meet people's material and spiritual needs, are in harmony with the environment, and have a reasonable functional layout and good spatial form. In traditional architectural planning and design, the planning of building areas relies on the designer's experience and manual calculations, which lacks systematicity and scientificity.

[0003] Existing route planning tools are limited in function and can only consider some factors. They cannot comprehensively consider factors such as the geographical environment, functional requirements, and pedestrian flow of the building area. This can lead to unreasonable routes, affecting the efficiency of the building area and the user experience. Furthermore, they cannot securely store and monitor all data generated during the route planning process. Their level of intelligence and automation is low, making planning, design, and management difficult.

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a building area route planning system for architectural planning and design. This system solves the problems of existing technologies, which cannot comprehensively consider factors such as the geographical environment, functional requirements, and pedestrian traffic of building areas for planning, thus hindering the improvement of planning quality and level. Furthermore, it cannot securely store and monitor all data generated during the building area route planning process, making planning and design management difficult.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A building area route planning system applied to architectural planning and design includes a geographic information integration module, a functional requirements analysis module, a pedestrian flow prediction module, a route intelligent generation module, a multi-scheme evaluation and optimization module, and a results visualization module. The geographic information integration module collects and integrates geographic information of the building area to establish a geographic information database for the building area. The functional requirements analysis module analyzes the functional requirements of different areas within the building area and determines the traffic connections and passage requirements between the areas based on the functional requirements. The pedestrian flow prediction module predicts the pedestrian flow situation in different areas and at different times within the building area and sends the pedestrian flow prediction information to the route intelligent generation module.

[0008] The route intelligent generation module generates multiple possible route planning schemes for building areas based on geographic information, functional requirements, and pedestrian flow prediction information; the multi-scheme evaluation and optimization module evaluates and optimizes the generated route planning schemes for building areas, comprehensively considers multiple evaluation indicators, selects the optimal scheme, or further improves the scheme; the result visualization module uses 3D modeling technology and virtual reality and augmented reality technology to visualize the optimal route planning scheme.

[0009] Furthermore, the geographic information integration module acquires raw geographic information data of the building area through various data collection methods, including satellite remote sensing, geographic surveying instruments, and on-site surveys. It then uses geographic information system technology to process and analyze this data, unifying and standardizing data from different sources and formats to establish a geographic information database for the building area.

[0010] Furthermore, the geographic information database classifies and labels the terrain and landforms, including mountains, plains and water areas, accurately models and locates buildings, records their height, area and usage information, and performs topological analysis on surrounding roads to determine road connectivity and traffic capacity.

[0011] Furthermore, the functional requirements analysis module combines the planning and design schemes and relevant standards of the building area to perform functional positioning and analysis of each area within the building area, clarify the specific needs and special requirements of each functional area, and transform these needs into quantifiable indicators.

[0012] Furthermore, the pedestrian flow prediction module collects historical pedestrian flow data for the building area, comprehensively considers the impact of the building area's functional positioning, population size, and surrounding supporting facilities on pedestrian flow, and uses data mining and machine learning algorithms to establish a pedestrian flow prediction model. The geographic information provided by the geographic information integration module and the functional requirement data provided by the functional requirement analysis module are used as input parameters to predict pedestrian flow.

[0013] Furthermore, the route intelligent generation module uses intelligent algorithms to combine topographic and building distribution data provided by the geographic information integration module, functional connections and traffic requirements determined by the functional requirements analysis module, and pedestrian flow distribution predicted by the pedestrian flow prediction module to generate the initial route.

[0014] During the generation process, several factors are considered, including the shortest route distance, the fewest turns, and obstacle avoidance, while also meeting the traffic needs and pedestrian flow requirements of each functional area. Through continuous iteration and optimization of algorithm parameters, a variety of different route planning schemes are generated.

[0015] Furthermore, the specific operation process of the multi-scheme evaluation and optimization module includes:

[0016] Establish an evaluation index system, including route rationality, traffic efficiency, safety, environmental impact, and construction cost; use the analytic hierarchy process (AHP) or fuzzy comprehensive evaluation method to quantitatively evaluate each scheme; based on the evaluation results, rank and compare the schemes to determine their advantages and disadvantages; for schemes with obvious defects, implement targeted optimization and improvement, including adjusting route layout and increasing or decreasing road width; through multiple evaluations and optimizations, determine the optimal route planning scheme for the building area.

[0017] Furthermore, the geographic information integration module, functional requirements analysis module, pedestrian flow prediction module, route intelligent generation module, multi-scheme evaluation and optimization module, and result visualization module are all communicatively connected to the distributed secure storage module. The distributed secure storage module consists of several storage devices and is used to store all data information in the route planning of the building area.

[0018] The distributed secure storage module communicates with the data storage risk control module. The data storage risk control module analyzes the data storage risk of the distributed secure storage module, generates a storage security signal or a storage risk signal through analysis, and sends the storage security signal or storage risk signal to the backend terminal. When the backend terminal receives the storage risk signal, it issues a corresponding warning.

[0019] Furthermore, the specific analysis process for the data storage risk control module is as follows:

[0020] All storage devices involved in the distributed secure storage module are obtained, and the corresponding storage devices are marked as monitoring objects w, where w is a natural number greater than 1; the time interval between the current date and the production date of monitoring object w is obtained and marked as the existence duration value; all environmental parameters affecting the lifespan of monitoring object w are obtained, and the detection data of the corresponding environmental parameters are collected in real time. If there are environmental parameters whose detection data do not meet the corresponding preset suitable data requirements, it is determined that monitoring object w is in an unsafe state.

[0021] The total duration for which the monitored object w was in an unsafe state during the historical period is obtained and marked as the risk duration value. The existence duration value and the risk duration value are compared with the preset existence duration threshold and the preset risk duration threshold respectively. If the existence duration value or the risk duration value exceeds the corresponding preset threshold, the monitored object w is marked as a target that needs to be controlled.

[0022] If both the existence duration value and the risk duration value do not exceed the corresponding preset thresholds, the number of times the monitored object w experiences anomalies during data storage or data retrieval within the detection period is collected and marked as anomaly detection values. The target risk characteristic value is calculated by weighted summation of the existence duration value, risk duration value, and anomaly detection values. The target risk characteristic value is compared with the preset target risk characteristic threshold. If the target risk characteristic value exceeds the preset target risk characteristic threshold, the monitored object w is marked as a target requiring control. If a target requiring control exists in the distributed secure storage module, a storage risk signal is generated; if no target requiring control exists in the distributed secure storage module, a storage safety signal is generated.

[0023] Furthermore, the data storage risk control module communicates with the storage area management decision evaluation module. The data storage risk control module sends storage security signals to the storage area management decision evaluation module. When the storage area management decision evaluation module receives the storage security signal, it obtains the area where the distributed secure storage module is located and marks it as the target storage area. It analyzes the environmental management performance of the target storage area within a unit of time, generates a storage area risk signal or a storage area optimization signal through analysis, and sends the storage area risk signal or storage area optimization signal to the back-end terminal. When the back-end terminal receives the storage area risk signal, it issues a corresponding warning.

[0024] Furthermore, the specific analysis process of the storage area management decision evaluation module is as follows:

[0025] All environmental parameters affecting the lifespan of the monitored object w are acquired, and the detection data of the corresponding environmental parameters in the target storage area are collected in real time. The duration for which the detection data of the corresponding environmental parameters does not meet the corresponding preset suitable data requirements within a unit time is marked as an untimely measurement value, and the number of times the detection data of the corresponding environmental parameters does not meet the corresponding preset suitable data requirements within a unit time is marked as an untimely measurement value. The maximum duration of a single instance for which the detection data of the corresponding environmental parameters does not meet the corresponding preset suitable data requirements within a unit time is marked as an untimely amplitude.

[0026] The parameter fitness value is obtained by weighted summation of non-timely measurement values, non-frequency measurement values, and non-timely amplitude values. The parameter fitness value is compared with the corresponding preset parameter fitness threshold. If the parameter fitness value exceeds the preset parameter fitness threshold, the corresponding environmental parameter is marked as a risk control parameter. If a risk control parameter exists in the target storage area within a unit of time, a storage area risk control signal is generated.

[0027] If no risk control parameters exist in the target storage area within a unit of time, a set of preset weight values ​​are assigned to each environmental parameter in advance. The parameter suitability value is calculated by comparing the parameter suitability value of the corresponding environmental parameter with the corresponding preset parameter suitability threshold. The parameter suitability value is multiplied by the corresponding preset weight value to obtain the parameter suitability table value. The parameter suitability table values ​​of all environmental parameters are summed to obtain the storage area management decision value. The storage area management decision value is compared with the preset storage area management decision threshold. If the storage area management decision value exceeds the preset storage area management decision threshold, a storage area risk signal is generated; if the storage area management decision value does not exceed the preset storage area management decision threshold, a storage area optimization signal is generated.

[0028] Compared with the prior art, the beneficial effects of the present invention are:

[0029] 1. In this invention, multiple route planning schemes for building areas are intelligently generated by using geographic information, functional requirements and traffic flow prediction information. Multiple schemes are evaluated to select the optimal scheme and visualize it. It can comprehensively consider multiple key factors of building area planning and improve the quality and level of building area route planning based on close collaboration and data interaction among modules. It is conducive to reducing the work difficulty of planning and design personnel and has a high level of intelligence and automation.

[0030] 2. In this invention, by analyzing the data storage risk of the distributed secure storage module and promptly replacing or strengthening the management of the corresponding storage devices, it is beneficial to avoid the loss of relevant planning data. Furthermore, when generating a storage security signal, the environmental management performance of the target storage area is analyzed within a unit of time. When generating a storage area risk signal, the environmental management of the area where the distributed secure storage module is located is strengthened, further improving the storage security of relevant planning data. Attached Figure Description

[0031] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0032] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0033] Figure 2 This is a system block diagram of Embodiments 2 and 3 of the present invention. Detailed Implementation

[0034] 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.

[0035] Example 1: As Figure 1 As shown, the building area route planning system proposed in this invention for architectural planning and design includes a geographic information integration module, a functional requirement analysis module, a pedestrian flow prediction module, a route intelligent generation module, a multi-scheme evaluation and optimization module, and a result visualization module.

[0036] The geographic information integration module collects and integrates geographic information of the building area, including topography, building distribution, surrounding roads, etc., and establishes a geographic information database for the building area. By comprehensively and accurately integrating geographic information, it provides real and reliable basic data for route planning, avoids route planning errors caused by inaccurate or incomplete geographic information, and improves the scientificity and rationality of planning.

[0037] Specifically, the geographic information integration module first acquires raw geographic information data of the building area through various data collection methods, such as satellite remote sensing, geographic surveying instruments, and on-site surveys; then, it uses geographic information system (GIS) technology to process and analyze these data, unifying and standardizing data from different sources and in different formats to establish a geographic information database for the building area.

[0038] Furthermore, the geographic information database provides detailed classification and labeling of topography, such as mountains, plains, and water bodies; precise modeling and positioning of buildings, recording information such as their height, area, and purpose; and topological analysis of surrounding roads to determine their connectivity and traffic capacity.

[0039] The functional requirements analysis module analyzes the functional requirements of different areas within the building area, such as residential, commercial, office, and leisure. Based on the functional requirements, it determines the traffic connections and passage requirements between each area, ensuring that the route planning meets the actual use requirements of the building area, improving the functionality and convenience of the building area, and providing users with a better user experience.

[0040] Specifically, the functional requirements analysis module, combining the planning and design schemes and relevant standards and regulations of the building area, performs functional positioning and analysis of each area within the building area. For example, for commercial areas, considering their high pedestrian traffic and frequent freight transport, it is necessary to determine convenient connections with surrounding roads and other functional areas, as well as the required passage space for loading and unloading goods. For residential areas, the focus is on residents' travel convenience and safety, analyzing their transportation connections with public transportation stations, schools, hospitals, and other supporting facilities. Through communication and exchange with planning designers, owners, and other relevant parties, the specific needs and special requirements of each functional area are further clarified, and these needs are transformed into quantifiable indicators, such as passage width and traffic flow.

[0041] The pedestrian flow prediction module predicts pedestrian flow in different time periods and areas within the building area and sends the pedestrian flow prediction information to the route intelligent generation module. Accurate pedestrian flow prediction can avoid overly conservative or wasteful route planning, improve route utilization efficiency, and reduce traffic congestion.

[0042] Specifically, the pedestrian flow prediction module collects historical pedestrian flow data for the building area, such as pedestrian flow statistics for similar building areas in the surrounding area, and pedestrian flow patterns during holidays and weekdays. Simultaneously, it comprehensively considers the impact of factors such as the functional positioning of the building area, population size, and surrounding supporting facilities on pedestrian flow. Using data mining and machine learning algorithms, it establishes a pedestrian flow prediction model, using geographic information provided by the geographic information integration module and functional requirement data provided by the functional requirement analysis module as input parameters to predict pedestrian flow. For example, based on the area and business type distribution of a commercial area, it predicts pedestrian flow at different times after its opening; based on the number of households and population structure of a residential area, it predicts pedestrian flow for residents' daily travel.

[0043] The intelligent route generation module generates multiple possible route planning schemes for the building area based on geographic information, functional requirements, and pedestrian flow prediction information. This provides more possibilities for subsequent evaluation and selection, improving the quality and flexibility of route planning.

[0044] Specifically, the route intelligent generation module uses intelligent algorithms, such as genetic algorithms and ant colony algorithms, to generate the initial route by combining the topographic data and building distribution provided by the geographic information integration module, the functional connections and passage requirements determined by the functional requirements analysis module, and the pedestrian flow distribution predicted by the pedestrian flow prediction module.

[0045] During the generation process, several factors are considered, including the shortest route distance, the fewest turns, and obstacle avoidance. At the same time, the traffic needs and pedestrian flow requirements of each functional area are met. Through continuous iteration and optimization of algorithm parameters, a variety of different route planning schemes are generated, each with different route layouts and characteristics.

[0046] The multi-scheme evaluation and optimization module evaluates and optimizes various generated route planning schemes for building areas. It comprehensively considers multiple evaluation indicators, selects the optimal scheme, or further improves existing schemes. This ensures that the final selected route planning scheme achieves a good level in all aspects, improving the overall performance and economic benefits of the building area. The specific operation process of the multi-scheme evaluation and optimization module is as follows:

[0047] An evaluation index system was established, including route rationality, traffic efficiency, safety, environmental impact, and construction cost. Methods such as the Analytic Hierarchy Process (AHP) or fuzzy comprehensive evaluation were used to quantitatively evaluate each scheme. Based on the evaluation results, the schemes were ranked and compared to determine their advantages and disadvantages. Schemes with obvious defects were optimized and improved in a targeted manner, such as adjusting the route layout or increasing or decreasing road width. Through multiple evaluations and optimizations, the optimal route planning scheme for the built-up area was finally determined.

[0048] The results visualization module utilizes 3D modeling and virtual reality / augmented reality technologies to visualize the optimal route planning scheme. It clearly displays information such as the topography, building distribution, and route layout of the building area. Users can also view the route planning scheme from different angles through interactive operations such as zooming, rotating, and panning. It can also generate 2D plan views and cross-sectional views to meet the needs of different users.

[0049] Furthermore, the geographic information integration module, functional requirements analysis module, pedestrian flow prediction module, route intelligent generation module, multi-scheme evaluation and optimization module, and results visualization module are all communicatively connected to the distributed secure storage module. The distributed secure storage module consists of several storage devices and is used to store all data information in the route planning of the building area.

[0050] Example 2: Figure 2 As shown, the difference between this embodiment and Embodiment 1 is that the distributed secure storage module is connected to the data storage risk control module. The data storage risk control module analyzes the data storage risk of the distributed secure storage module and generates a storage security signal or a storage risk signal through the analysis.

[0051] Furthermore, a safe or risky storage signal is sent to the backend terminal. Upon receiving a risky storage signal, the backend terminal issues a corresponding warning to remind backend personnel to promptly replace or strengthen the control of the corresponding storage devices, ensuring the security of all storage devices, reducing the storage risk of the distributed secure storage module, and helping to avoid the loss of relevant planned data information. The specific analysis process of the data storage risk control module is as follows:

[0052] All storage devices involved in the distributed secure storage module are obtained, and the corresponding storage devices are marked as monitoring objects w, where w is a natural number greater than 1; the time interval between the current date and the production date of monitoring object w is obtained and marked as the existence duration value; all environmental parameters (including temperature, humidity, vibration, etc.) that affect the service life of monitoring object w are obtained, and the detection data of the corresponding environmental parameters are collected in real time. If there are environmental parameters whose detection data do not meet the corresponding preset suitable data requirements, it is determined that monitoring object w is in an unsafe state.

[0053] The total duration for which the monitored object w was in an unsafe state in the historical period is obtained and marked as the risk duration value. The existence duration value and the risk duration value are compared with the preset existence duration threshold and the preset risk duration threshold respectively. If the existence duration value or the risk duration value exceeds the corresponding preset threshold, it indicates that the storage security risk of the monitored object w is high, and the monitored object w is marked as a target that needs to be controlled.

[0054] If neither the existence duration value nor the risk duration value exceeds the corresponding preset threshold, the number of times the monitored object w experiences anomalies during data storage or data retrieval within the detection period is collected and marked as anomaly detection value. The target risk characteristic value is obtained by weighted summation of the existence duration value, risk duration value, and anomaly detection value. Specifically, the existence duration value, risk duration value, and anomaly detection value are each assigned a corresponding preset weight coefficient, and the existence duration value, risk duration value, and anomaly detection value are each multiplied by the corresponding preset weight coefficient. The sum of the three product results is then marked as the target risk characteristic value.

[0055] It should be noted that the larger the value of the target risk characteristic, the higher the overall storage security risk of the monitored object w. The target risk characteristic value is compared with the preset target risk characteristic threshold. If the target risk characteristic value exceeds the preset target risk characteristic threshold, it indicates that the overall storage security risk of the monitored object w is relatively high. In this case, the monitored object w is marked as a target that needs to be controlled.

[0056] If there are targets requiring control in the distributed secure storage module, it indicates that the current situation is not conducive to ensuring the storage security of relevant planning data information, and storage control needs to be strengthened and corresponding improvement measures need to be taken, then a storage risk signal is generated; if there are no targets requiring control in the distributed secure storage module, it indicates that the current storage risk of relevant planning data information is relatively small, then a storage security signal is generated.

[0057] Example 3: Figure 2As shown, the difference between this embodiment and Embodiments 1 and 2 is that the data storage risk control module is connected to the storage area management decision evaluation module. The data storage risk control module sends the storage security signal to the storage area management decision evaluation module. When the storage area management decision evaluation module receives the storage security signal, it obtains the area where the distributed secure storage module is located and marks it as the target storage area. It analyzes the environmental management performance of the target storage area within a unit of time and generates a storage area risk signal or a storage area optimization signal through analysis.

[0058] Furthermore, the system sends risk or optimization signals for the storage area to the backend terminal. Upon receiving the risk signal, the backend terminal issues a corresponding warning to remind backend personnel to strengthen environmental control over the area where the distributed secure storage module is located, ensuring the storage environment remains in a suitable state. This helps guarantee the safe and stable operation of all storage devices, prevents damage to storage devices, and further enhances the storage security of relevant planned data. The specific analysis process of the storage area management decision-making evaluation module is as follows:

[0059] All environmental parameters affecting the lifespan of the monitored object w are acquired, and the detection data of the corresponding environmental parameters in the target storage area are collected in real time. The duration for which the detection data of the corresponding environmental parameters does not meet the corresponding preset suitable data requirements within a unit time is marked as an untimely measurement value, and the number of times the detection data of the corresponding environmental parameters does not meet the corresponding preset suitable data requirements within a unit time is marked as an untimely measurement value. The maximum duration of a single instance for which the detection data of the corresponding environmental parameters does not meet the corresponding preset suitable data requirements within a unit time is marked as an untimely amplitude.

[0060] The parameter fitness value is obtained by weighted summation of out-of-time measurements, out-of-frequency measurements, and out-of-time amplitudes. Specifically, out-of-time measurements, out-of-frequency measurements, and out-of-time amplitudes are each assigned a corresponding preset weight coefficient, and then multiplied by their respective preset weight coefficients. The sum of these three products is then marked as the parameter fitness value. It should be noted that the larger the parameter fitness value, the worse the overall management performance for the corresponding environmental parameter per unit time.

[0061] The parameter fitness value is compared with the corresponding preset parameter fitness threshold. If the parameter fitness value exceeds the preset parameter fitness threshold, it indicates that the overall management performance of the corresponding environmental parameter is poor within a unit of time. In this case, the corresponding environmental parameter is marked as a risk control parameter. If there is a risk control parameter in the target storage area within a unit of time, it indicates that the environmental management risk of the target storage area is high within a unit of time. It is necessary to strengthen the environmental control of the target storage area in the future. In this case, a storage area risk control signal is generated.

[0062] Furthermore, if there are no risk control parameters in the target storage area within a unit of time, a set of preset weight values ​​with values ​​greater than zero are assigned to each environmental parameter in advance. The higher the security risk caused by the abnormality of the corresponding environmental parameter to the storage device, the larger the value of the preset weight value that matches it. The parameter fitness value is calculated by the ratio of the parameter fitness value of the corresponding environmental parameter to the corresponding preset parameter fitness threshold. The parameter fitness value is then multiplied by the corresponding preset weight value to obtain the parameter fitness table value.

[0063] The system calculates the storage area management decision value by summing the values ​​of all environmental parameters. This decision value is then compared with a preset storage area management decision threshold. If the decision value exceeds the threshold, it indicates a high environmental management risk for the target storage area within a given time period, requiring enhanced environmental control. In this case, a storage area risk signal is generated. Conversely, if the decision value does not exceed the threshold, it indicates a low environmental management risk for the target storage area within a given time period, and a storage area optimization signal is generated.

[0064] The working principle of this invention is as follows: In use, the geographic information integration module collects and integrates geographic information of the building area; the functional requirements analysis module analyzes the functional requirements of different areas within the building area; the pedestrian flow prediction module predicts the pedestrian flow in different time periods and different areas within the building area; the intelligent route generation module intelligently generates multiple route planning schemes for the building area based on geographic information, functional requirements, and pedestrian flow prediction information; the multi-scheme evaluation and optimization module selects the optimal scheme or further improves the scheme; and the result visualization display module visualizes the optimal route planning scheme. This invention comprehensively considers multiple key factors in building area planning. Based on the close collaboration and data interaction of each module, it achieves a scientific, intelligent, and visualized process for route planning from basic data collection to final scheme display, significantly improving the quality and level of building area route planning and reducing the workload of planning and design personnel.

[0065] In this invention, the threshold, preset value, or preset range settings are for result comparison and analysis to determine whether the result is good or bad. The magnitude of these values ​​is determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions. Similarly, the preset weight coefficients and influence factors are assigned specific values ​​based on the magnitude of each parameter's influence on the result, ultimately reflecting the impact on the result. These settings are also determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions.

[0066] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A building area route planning system applied to architectural planning and design, characterized in that, It includes a geographic information integration module, a functional requirements analysis module, a pedestrian flow prediction module, a route intelligent generation module, a multi-scheme evaluation and optimization module, and a results visualization module; the geographic information integration module collects and integrates geographic information of the building area to establish a geographic information database for the building area; The functional requirements analysis module analyzes the functional requirements of different areas within the building area and determines the traffic connections and passage requirements between each area based on the functional requirements; the pedestrian flow prediction module predicts the pedestrian flow situation in different areas and at different times within the building area. The route intelligent generation module generates multiple route planning schemes for building areas based on geographic information, functional requirements, and pedestrian flow prediction information; the multi-scheme evaluation and optimization module evaluates and optimizes the generated multiple route planning schemes for building areas, selects the optimal scheme, or further improves the scheme; the result visualization module uses 3D modeling technology and virtual reality and augmented reality technology to visualize the optimal route planning scheme.

2. The building area route planning system applied to architectural planning and design according to claim 1, characterized in that, The geographic information integration module acquires raw geographic information data of the building area through various data collection methods, processes and analyzes this data using geographic information system technology, and integrates and standardizes data from different sources and in different formats to establish a geographic information database for the building area.

3. The building area route planning system applied to architectural planning and design according to claim 1, characterized in that, The functional requirements analysis module combines the planning and design schemes and relevant standards of the building area to perform functional positioning and analysis of each area within the building area, clarify the specific needs and special requirements of each functional area, and transform these needs into quantifiable indicators.

4. The building area route planning system applied to architectural planning and design according to claim 1, characterized in that, The pedestrian flow prediction module collects historical pedestrian flow data for the building area, comprehensively considers the impact of the building area's functional positioning, population size, and surrounding supporting facilities on pedestrian flow, and uses data mining and machine learning algorithms to establish a pedestrian flow prediction model. It uses geographic information provided by the geographic information integration module and functional requirement data provided by the functional requirement analysis module as input parameters to predict pedestrian flow.

5. The building area route planning system applied to architectural planning and design according to claim 1, characterized in that, The route intelligent generation module uses intelligent algorithms to combine topographic and building distribution data provided by the geographic information integration module, functional connections and traffic requirements determined by the functional requirements analysis module, and pedestrian flow distribution predicted by the pedestrian flow prediction module to generate the initial route. During the generation process, several factors are considered, including the shortest route distance, the fewest turns, and obstacle avoidance, while also meeting the traffic needs and pedestrian flow requirements of each functional area. Through continuous iteration and optimization of algorithm parameters, a variety of different route planning schemes are generated.

6. The building area route planning system applied to architectural planning and design according to claim 1, characterized in that, The specific operation process of the multi-scheme evaluation and optimization module is as follows: determine the evaluation index system, use the analytic hierarchy process or fuzzy comprehensive evaluation method to quantitatively evaluate each scheme; based on the evaluation results, rank and compare the schemes to determine the optimal route planning scheme for the building area.

7. The building area route planning system applied to architectural planning and design according to claim 1, characterized in that, The geographic information integration module, functional requirements analysis module, pedestrian flow prediction module, route intelligent generation module, multi-scheme evaluation and optimization module, and result visualization module are all communicatively connected to the distributed secure storage module. The distributed secure storage module stores all data information in the route planning of the building area. Furthermore, the distributed secure storage module is communicatively connected to the data preservation risk control module, which analyzes the data preservation risk of the distributed secure storage module and sends preservation safety signals or preservation risk signals to the backend terminal.

8. The building area route planning system applied to architectural planning and design according to claim 7, characterized in that, The specific analysis process for the data storage risk management module is as follows: All storage devices involved in the distributed security storage module are obtained, and the corresponding storage devices are marked as monitoring objects w, where w is a natural number greater than 1. If the duration value or the duration of the risk exceeds the corresponding preset threshold, the monitoring object w is marked as a target that needs to be controlled. If neither the duration value nor the duration of the risk exceeds the corresponding preset threshold, the target risk characteristic value is calculated by weighted summation of the duration value, the duration of the risk, and the storage-read anomaly detection value. If the target risk characteristic value exceeds the preset target risk characteristic threshold, the monitoring object w is marked as a target that needs to be controlled. If there are targets that need to be managed in the distributed secure storage module, a storage risk signal will be generated; Otherwise, a save security signal will be generated.

9. The building area route planning system applied to architectural planning and design according to claim 8, characterized in that, The data storage risk control module communicates with the storage area management decision evaluation module. When the storage area management decision evaluation module receives the storage security signal, it obtains the area where the distributed security storage module is located and marks it as the target storage area. It analyzes the environmental management performance of the target storage area within a unit of time and sends the storage area risk signal or storage area optimization signal to the back-end terminal.

10. The building area route planning system applied to architectural planning and design according to claim 9, characterized in that, The specific analysis process of the storage area management decision-making evaluation module is as follows: All environmental parameters affecting the lifespan of the monitored object w are acquired. If risk control parameters exist in the target storage area within a unit of time, a storage area risk control signal is generated. If no risk control parameters exist in the target storage area within a unit of time, the parameter values ​​of all environmental parameters are summed to obtain the storage area management decision value. If the storage area management decision value exceeds the preset storage area management decision threshold, a storage area risk control signal is generated. If the storage area management decision value does not exceed the preset storage area management decision threshold, a storage area optimization signal is generated.

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