BIM-based traffic engineering electronic sand table simulation and analysis system

Abstract

The present application relates to the BIM-based traffic engineering electronic sand table simulation and analysis system, relates to the traffic engineering data processing technical field, including: obtaining the calibration influence range, collecting the building layout topology according to the calibration influence range, obtaining the primary sample traffic flow record data set, the secondary sample traffic flow record data set, carrying out recursive fusion, obtaining the traffic flow calibration result;Obtain the first traffic engineering BIM model of sand table simulation area;According to the traffic flow calibration result, traffic test is carried out, and the first sand table simulation test result is obtained;When the first sand table simulation test result is test qualified mark, the first traffic engineering BIM model is sent to the traffic engineering layout design client. The technical problem that the subsequent traffic engineering model application process iteration optimization times are more due to the traffic engineering electronic sand table simulation relying on subjective setting use case in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of traffic engineering data processing, and particularly relates to a traffic engineering electronic sand table simulation and analysis system based on BIM. BACKGROUND

[0002] With the development of information technology, traffic engineering electronic sand table simulation technology based on building information modeling (BIM) has emerged, which simulates traffic flow, traffic management and planning by creating digital models to improve the accuracy and efficiency of design.

[0003] Although the application of BIM technology in traffic engineering provides more intuitive simulation and analysis means, existing electronic sand table simulation systems still rely on subjective experience when configuring use cases, and lack sufficient consideration of the complexity of actual traffic scenarios. This method may result in the need for multiple iterations of adjusting the designed traffic road layout in actual application, thereby reducing work efficiency and project progress. SUMMARY

[0004] The present application provides a traffic engineering electronic sand table simulation and analysis system based on BIM to solve the technical problem that the number of iterations of subsequent traffic engineering model application processes is large due to the dependence of traffic engineering electronic sand table simulation on subjective setting of use cases in the prior art.

[0005] The technical solution of the present application to solve the above technical problems is as follows:

[0006] The present application provides a traffic engineering electronic sand table simulation and analysis system based on BIM, the system execution steps comprising: performing influence range analysis based on the sand table simulation area to obtain a calibrated influence range, collecting building layout topology according to the calibrated influence range; performing first-level matching of associated samples based on the building layout topology to obtain a first-level sample traffic flow record data set, wherein the first-level sample traffic flow record data set has a building layout topology tag set; performing second-level matching of associated samples based on the building layout topology tag set by traversing the first-level sample traffic flow record data set to obtain a second-level sample traffic flow record data set; performing recursive fusion on the first-level sample traffic flow record data set and the second-level sample traffic flow record data set to obtain a traffic flow calibration result; obtaining a first traffic engineering BIM model of the sand table simulation area; performing traffic testing on the first traffic engineering BIM model according to the traffic flow calibration result to obtain a first sand table simulation test result; when the first sand table simulation test result is a test qualified identification, sending the first traffic engineering BIM model to a traffic engineering layout design client.

[0007] The beneficial effects of the present application are: by using two-stage scene sample matching, the traffic flow calibration result of the associated sample is collected; the traffic flow calibration result is used as a use case of the traffic engineering sand table simulation test, and the first traffic engineering BIM model is further optimized based on the test result, and the use case of the traffic engineering sand table simulation test determined by big data has strong objectivity, which can improve the stability of the test result, and can achieve the technical effect of reducing the optimization frequency of the subsequent first traffic engineering BIM model application. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 A flowchart of a BIM-based traffic engineering electronic sand table simulation and analysis system is provided for the present application.

[0009] Figure 2 A structural schematic diagram of an electronic device is provided for the present application.

[0010] Figure 3 A structural schematic diagram of a computer-readable storage medium is provided for the present application.

[0011] In the drawings, the components represented by the numbers are described as follows:

[0012] The electronic device 500, the memory 510, the processor 520, the first computer program 511, the computer-readable storage medium 600, and the second computer program 611. DETAILED DESCRIPTION

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0014] In the description of the present application, the terms "first" and "second" are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first" and "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0015] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0016] Example 1:

[0017] like Figure 1 As shown, an embodiment of the present invention provides a BIM-based electronic sandbox simulation and analysis system for traffic engineering, and the system execution steps include:

[0018] S10: performing an influence range analysis based on the sandbox simulation area to obtain a calibrated influence range, and collecting a building layout topology based on the calibrated influence range;

[0019] Furthermore, based on the sandbox simulation area, an impact range analysis is performed to obtain a calibrated impact range. Step S10 includes the following steps:

[0020] Step 1: Setting a first radius constraint interval through a user terminal, wherein the first radius constraint interval includes a first radius lower limit value and a first radius upper limit value;

[0021] Step 2: Taking the median of the first radius constraint interval to obtain a first median radius;

[0022] Step 3: Using the first median radius, construct a first influence range to be analyzed, and overlap the center of the sandbox simulation area with the center of the first influence range to be analyzed to obtain a pseudo-circular area;

[0023] Step 4: Performing flow correlation analysis on the pseudo-circular area and the sandbox simulation area to obtain a Pearson correlation coefficient;

[0024] Step 5A: When the Pearson correlation coefficient is less than or equal to a Pearson correlation coefficient threshold, constructing a second radius constraint interval based on the first radius lower limit value and the first median radius;

[0025] Step 6A: When the interval step size of the second radius constraint interval is greater than or equal to the convergence step size threshold, the second radius constraint interval is returned to step 2 to execute a loop;

[0026] Step seven A: when the interval step of the second radius constraint interval is less than the convergence step threshold, take the median of the second radius constraint interval as the calibration impact range for the sand table simulation area.

[0027] Further, the step S10 executing step further comprises:

[0028] Step five B: when the Pearson correlation coefficient is greater than the Pearson correlation coefficient threshold, constructing a third radius constraint interval based on the first median radius and the first radius upper limit value;

[0029] Step six B: when the interval step of the third radius constraint interval is greater than or equal to the convergence step threshold, returning the third radius constraint interval to step two for execution loop;

[0030] Step seven B: when the interval step of the third radius constraint interval is less than the convergence step threshold, take the median of the third radius constraint interval as the calibration impact range for the sand table simulation area.

[0031] In particular, the sand table simulation area refers to a specific geographical area used to simulate traffic flow and layout in a traffic engineering electronic sand table simulation system; the calibration impact range refers to the geographical range that will have an impact on the traffic flow of the sand table simulation area; the building layout topology refers to the collection of layout information of buildings in the calibration impact range, including the location, shape and connection relationship between buildings.

[0032] The impact range analysis process is as follows:

[0033] The user end refers to the operation interface, which allows the user to input and adjust parameters; the first radius constraint interval refers to a range defined by the length of the radius, which is used to preliminarily determine the impact range of the sand table simulation area; the first radius lower limit value and the first radius upper limit value are the two boundary values of this constraint interval, representing the possible minimum and maximum radius of the impact range, respectively. Generally speaking, the minimum radius is 0m by default, and the maximum radius is 300000m by default.

[0034] In detail, the user inputs two key parameters, the lower limit value and the upper limit value of the first radius, through the operation of the user end interface, based on the lower limit value and the upper limit value of the first radius, a first radius constraint interval is constructed, which defines a preliminary circular area. This area is considered as the geographical range that the sand table simulation area may affect, which is beneficial to filter out the areas that have actual impact on traffic flow in a wide geographical range, thereby providing a reasonable starting point for subsequent detailed analysis and simulation.

[0035] The median value refers to the calculation of the geometric midpoint of the first radius constraint interval, that is, the average of the first radius lower limit value and the first radius upper limit value. The first median radius is this median value, which represents the central radius value of the sand table simulation area impact range in the preliminary analysis, and this value will be used to build the impact range to be analyzed.

[0036] The first impact range to be analyzed refers to the circular area determined by taking the sand table simulation area as the center using the median radius, and this area will be used for further traffic flow analysis; the pseudo-annular area refers to the annular area between the center of the sand table simulation area and the center of the first impact range to be analyzed after they coincide, and this annular area represents a certain range of areas outside the sand table simulation area, which together with the sand table simulation area constitutes a complete analysis area.

[0037] The first traffic flow record data set of the pseudo-annular area and the second traffic flow record data set of the sand table simulation area are collected, and the first traffic flow record data and the second traffic flow record data set correspond one-to-one at the collection time; the first traffic flow record data set is arranged from small to large to construct a first traffic flow record data sequence; based on the one-to-one correspondence at the collection time, the second traffic flow record data set is sorted based on the first traffic flow record data sequence to obtain a second traffic flow record data sequence.

[0038] Further, the Pearson correlation analysis function is obtained: Wherein, r represents the Pearson correlation coefficient, x i represents the i-th serial number traffic flow record data of the first traffic flow record data sequence, y i represents the i-th serial number traffic flow record data of the second traffic flow record data sequence, represents the mean value of the traffic flow record data of the first traffic flow record data sequence, represents the mean value of the traffic flow record data of the second traffic flow record data sequence. The Pearson correlation coefficient is obtained by processing the first traffic flow record data sequence and the second traffic flow record data sequence according to the Pearson correlation analysis function.

[0039] Generally speaking, the stronger the correlation, the greater the traffic flow of the pseudo-annular area, and the greater the traffic flow of the sand table simulation area, so there is only a positive correlation between the two, so the analysis result for the negative correlation is treated as a weak correlation. Preferably, the user sets the Pearson correlation coefficient threshold, which is 0.2 by default, and the user sets the convergence step threshold, which is 2000m by default;

[0040] In an implementation, when the Pearson correlation coefficient is less than or equal to the Pearson correlation coefficient threshold value, it indicates that the radius is large, so there is no correlation, and the radius needs to be reduced. At this time, a second radius constraint interval is constructed based on the first lower radius limit value and the first median radius. If the interval step length of the second radius constraint interval is greater than or equal to the convergence step length threshold value, it indicates that the constraint interval is still too large and cannot converge. Then the second radius constraint interval is returned to step two for execution of the loop. When the interval step length of the second radius constraint interval is less than the convergence step length threshold value, it indicates that the constraint interval can converge. The median of the second radius constraint interval is taken as the calibrated influence range of the sand table simulation area. At this time, the calibrated influence range has a greater impact on the traffic flow of the sand table simulation area, which can ensure the accuracy of subsequent sand table simulation.

[0041] In another implementation, when the Pearson correlation coefficient is greater than the Pearson correlation coefficient threshold value, it indicates that the correlation is strong, and the radius needs to be increased for further analysis. At this time, a third radius constraint interval is constructed based on the first median radius and the first upper radius limit value. Similarly, when the interval step length of the third radius constraint interval is greater than or equal to the convergence step length threshold value, the third radius constraint interval is returned to step two for execution of the loop. When the interval step length of the third radius constraint interval is less than the convergence step length threshold value, the median of the third radius constraint interval is taken as the calibrated influence range of the sand table simulation area.

[0042] By identifying an accurate calibrated influence range, it can be ensured that the subsequent statistical traffic flow data is within the range of the influence, avoiding the interference of redundant data, thereby improving the accuracy of the analysis results.

[0043] S20: Perform first-level matching of associated samples according to the building layout topology to obtain a first-level sample traffic flow record data set, wherein the first-level sample traffic flow record data set has a building layout topology label set;

[0044] Specifically, the building layout topology refers to the spatial layout and mutual connection structure of buildings in the sand table simulation area. The first-level matching of associated samples refers to comparing the building layout topology of the sand table simulation area with historical or existing traffic flow data to find a sample data set with similar building layout characteristics. The first-level sample traffic flow record data set refers to the traffic flow data set obtained after first-level matching, which is similar to the building layout topology of the sand table simulation area. The building layout topology label set refers to a set of building layout characteristics corresponding to the first-level sample traffic flow record data set. These labels are used to identify and distinguish different building layout topology characteristics. The first-level sample traffic flow record data set obtained through first-level matching can be used as the basis for traffic flow prediction and analysis of the sand table simulation area.

[0045] S30: traversing the primary sample traffic flow record dataset based on the building layout topology label set to perform associated sample secondary matching, to obtain a secondary sample traffic flow record dataset;

[0046] Specifically, the associated sample secondary matching refers to further refining the matching process on the basis of the primary matching to find samples similar to the building layout topology label set; the secondary sample traffic flow record dataset refers to the traffic flow dataset matched with the building layout topology label set after secondary matching.

[0047] S40: recursively fusing the primary sample traffic flow record dataset and the secondary sample traffic flow record dataset to obtain a traffic flow calibration result;

[0048] Further, the primary sample traffic flow record dataset and the secondary sample traffic flow record dataset are recursively fused to obtain a traffic flow calibration result, and the execution step includes:

[0049] According to the primary sample traffic flow record dataset, the secondary sample traffic flow record dataset is grouped to obtain multiple groups of secondary sample traffic flow record data;

[0050] The multiple groups of secondary sample traffic flow record data are respectively subjected to mode analysis to obtain multiple secondary sample traffic flow mode values;

[0051] The multiple secondary sample traffic flow mode values and the primary sample traffic flow record dataset are subjected to mode analysis to obtain the traffic flow calibration result.

[0052] Specifically, the traffic flow calibration result represents the result of recursively fusing the primary sample traffic flow record dataset and the secondary sample traffic flow record dataset, and the detailed process is as follows: since each primary sample traffic flow record data corresponds to multiple secondary sample traffic flow record data, first, the secondary sample traffic flow record dataset is grouped according to the primary sample traffic flow record dataset to obtain multiple groups of secondary sample traffic flow record data; further, each group of secondary sample traffic flow record data of the multiple groups of secondary sample traffic flow record data is subjected to mode analysis to obtain multiple secondary sample traffic flow mode values; and further, the multiple secondary sample traffic flow mode values and the primary sample traffic flow record dataset are subjected to mode analysis to obtain the traffic flow calibration result.

[0053] The mode analysis process is preferably as follows:

[0054] With the mode analysis of any one set of secondary sample traffic flow record data as an example, a user presets a traffic flow deviation threshold, and then performs clustering analysis on the set of secondary sample traffic flow record data according to the traffic flow deviation threshold to obtain multiple clusters of secondary sample traffic flow record data; then, clusters in which the number of samples is less than a user-preset fitting number threshold are deleted to obtain updated multiple clusters of secondary sample traffic flow record data, and mean value calculation is performed on the remaining multiple clusters of secondary sample traffic flow record data to obtain secondary sample traffic flow mode values corresponding to the set.

[0055] Through two-stage sampling, the data quantity is improved to ensure objectivity, and the traffic flow calibration result obtained through recursive fusion can provide more reliable test cases for subsequent sand table simulation.

[0056] S50: Obtain a first traffic engineering BIM model of a sand table simulation area;

[0057] S60: Perform traffic testing on the first traffic engineering BIM model according to the traffic flow calibration result to obtain a first sand table simulation test result;

[0058] S70: When the first sand table simulation test result is a test qualified identifier, send the first traffic engineering BIM model to a traffic engineering layout design client.

[0059] Specifically, the first traffic engineering BIM model refers to a traffic engineering design model to be constructed, the traffic testing refers to traffic simulation on the first traffic engineering BIM model according to the traffic flow calibration result, and test indicators in the simulation process are collected, preferably including traffic volume, average vehicle speed, number of vehicles per unit length of road, ratio of real-time density to road congestion density, etc. Through the user end, threshold intervals of each test indicator are set; when any one of the test indicators does not belong to the corresponding test indicator threshold interval, the first sand table simulation test result is a test unqualified identifier, otherwise the first sand table simulation test result is a test qualified identifier. Since sand table simulation traffic simulation is relatively mature, no more details are given here.

[0060] The traffic engineering layout design client refers to a traffic engineering layout management end. When the first sand table simulation test result is a test qualified identifier, it indicates that the current design can meet the needs of most traffic flows, and at this time the first traffic engineering BIM model can be sent to the traffic engineering layout design client.

[0061] Further, the execution step further includes step S80, and step S80 further includes steps of:

[0062] S81: When the first sand table simulation test result is a test unqualified identifier, performing traffic topology optimization on the first traffic engineering BIM model to obtain a second traffic engineering BIM model, wherein a second sand table simulation test result of the second traffic engineering BIM model is a test qualified identifier;

[0063] S82: Sending the second traffic engineering BIM model to the traffic engineering layout design client.

[0064] Further, when the first sand table simulation test result is a test unqualified identifier, performing traffic topology optimization on the first traffic engineering BIM model to obtain a second traffic engineering BIM model, the execution step includes:

[0065] Performing random adjustment on the first traffic engineering BIM model for several times to obtain several initial traffic engineering BIM models;

[0066] According to the traffic flow calibration result, traversing the several initial traffic engineering BIM models to perform traffic test to obtain several sand table simulation test results;

[0067] When any one of the several sand table simulation test results is a test qualified identifier, obtaining the second traffic engineering BIM model;

[0068] When the several sand table simulation test results are all test unqualified identifiers, configuring a topology taboo neighborhood for the several initial traffic engineering BIM models based on a taboo structure similarity threshold, wherein the topology taboo neighborhood represents that a traffic engineering BIM model with a topology structure similarity less than or equal to the taboo structure similarity threshold does not participate in subsequent traffic topology optimization;

[0069] According to the topology taboo neighborhood, performing traffic topology optimization on the first traffic engineering BIM model in a cycle.

[0070] Specifically, when the first sand table simulation test result is a test unqualified identifier, it indicates that the first traffic engineering BIM model needs to be optimized, at this time, performing traffic topology optimization on the first traffic engineering BIM model to obtain a second traffic engineering BIM model, wherein the second traffic engineering BIM model is an optimized traffic engineering BIM model, a second sand table simulation test result of the second traffic engineering BIM model is a test qualified identifier, and then sending the second traffic engineering BIM model to the traffic engineering layout design client.

[0071] The traffic topology optimization process is as follows:

[0072] Random adjustment: multiple random adjustments are made to the first traffic engineering BIM model, generating multiple initial traffic engineering BIM models. The purpose of this step is to explore different traffic layout possibilities.

[0073] Traffic test: according to the traffic flow calibration results, traffic tests are conducted on each initial traffic engineering BIM model, obtaining several sand table simulation test results. This step aims to evaluate the traffic performance of each initial model.

[0074] Result evaluation: check the sand table simulation test results. If any result is the test qualified identifier, the corresponding BIM model is determined as the second traffic engineering BIM model.

[0075] Configure topology taboo neighborhood: if all sand table simulation test results are test unqualified identifiers, determine the topology taboo neighborhood based on the taboo structure similarity threshold, and exclude those models that are too similar to the initial model topology structure.

[0076] Optimization cycle: according to the topology taboo neighborhood, the first traffic engineering BIM model is subjected to continuous traffic topology optimization cycle until a model that meets the test requirements is found.

[0077] Preferably, a structure similarity analysis function is constructed: Where SIM represents the structure similarity, A and B represent two topology structures respectively, |A| represents the volume of A topology structure, |B| represents the volume of B topology structure, and |A∩B| represents the intersection volume of A and B.

[0078] By random adjustment and iterative optimization, a better traffic layout scheme is found. This method helps to systematically explore the possible solution space and gradually approach the optimal solution. By excluding models with high structural similarity, it can avoid falling into local optimum, thereby improving the probability of finding the global optimal solution. This step reflects the flexibility and adaptability of the scheme, which can continuously adjust and optimize the BIM model according to the test results until it meets the actual traffic engineering requirements.

[0079] Further, according to the building layout topology, a first-level sample traffic flow record data set is obtained by performing a first-level matching of the building layout topology. The execution steps include:

[0080] Obtain the building layout topology label and traffic flow record data of the sample to be analyzed;

[0081] Perform a similar dichotomy evaluation on the building layout topology label and the building layout topology to obtain a similar dichotomy parameter;

[0082] When the similar dichotomy parameter is 1, add the traffic flow record data to the first-level sample traffic flow record data set;

[0083] updating the sample to be analyzed when the similarity dichotomy parameter is 0.

[0084] Specifically, traffic flow record data refers to data recording traffic flow in a specific time period, which can be used to analyze traffic patterns and trends; similarity dichotomy evaluation is an evaluation method for quantifying the similarity between two building layout topologies, which usually produces a parameter between 0 and 1, called similarity dichotomy parameter; when the similarity dichotomy parameter is 1, it means that the two building layout topologies are exactly the same; when it is 0, it means that they are completely different; the first-level sample traffic flow record data set refers to the traffic flow data set similar to the building layout topology of the sand table simulation area after first-level matching.

[0085] The specific steps of matching the associated sample are as follows:

[0086] Data acquisition: First, collect the building layout topology label of the sample to be analyzed and the corresponding traffic flow record data.

[0087] Similarity evaluation: Then, perform similarity dichotomy evaluation on the collected building layout topology label and the building layout topology of the sand table simulation area, and calculate a similarity dichotomy parameter.

[0088] Data set updating:

[0089] If the similarity dichotomy parameter is 1, i.e. the building layout topology of the sample to be analyzed is completely matched with that of the sand table simulation area, then the traffic flow record data of the sample is added to the first-level sample traffic flow record data set.

[0090] If the similarity dichotomy parameter is 0, i.e. the building layout topology of the sample to be analyzed is completely unmatched with that of the sand table simulation area, then the sample to be analyzed needs to be updated, which may be done by reselecting the sample or adjusting the building layout topology label of the sample to improve the accuracy of matching.

[0091] Further, the similarity dichotomy evaluation is performed on the building layout topology label and the building layout topology to obtain a similarity dichotomy parameter, and the execution step includes:

[0092] Obtaining a building layout topology comparison network, wherein the building layout topology comparison network includes a first topology feature extraction channel, a second topology feature extraction channel and a similarity dichotomy channel, and the first topology feature extraction channel and the second topology feature extraction channel are a twin neural network;

[0093] Inputting the building layout topology label into the first topology feature extraction channel to obtain first topology features;

[0094] Inputting the building layout topology into the second topology feature extraction channel to obtain second topology features;

[0095] inputting the first topological feature and the second topological feature into the similarity binary channel to obtain the similarity binary parameter, wherein the similarity binary parameter is equal to 1 when the structural similarity is greater than or equal to a structural similarity threshold, and the similarity binary parameter is equal to 0 when the structural similarity is less than the structural similarity threshold.

[0096] In particular, the building layout topology comparison network refers to a specially designed neural network architecture for comparing and evaluating the similarity between two building layout topologies; the first topological feature extraction channel and the second topological feature extraction channel are two parts of the comparison network, which process building layout topology labels and actual building layouts respectively to extract key features; the twin neural network refers to two neural networks with the same structure but processing different inputs, which are used here to extract and compare the features of building layouts; the similarity binary channel is another part of the comparison network, which receives the outputs of the two topological feature extraction channels and calculates a similarity binary parameter; the structural similarity refers to the degree of structural similarity between two building layout topologies, while the structural similarity threshold is a preset standard for determining whether two layouts are similar enough; the structural similarity is preferably calculated by the aforementioned structural similarity analysis function.

[0097] The similarity binary channel of the building layout topology comparison network is a linear analysis channel based on the structural similarity analysis function, and the first topological feature extraction channel and the second topological feature extraction channel are machine learning networks trained based on building layout topology historical data and topological feature identification data. Preferably, a convolutional neural network is trained with topological feature identification data as supervision and building layout topology historical data as input to obtain the first topological feature extraction channel and the second topological feature extraction channel.

[0098] The specific steps are as follows:

[0099] Feature extraction:

[0100] Input the building layout topology label into the first topological feature extraction channel to extract the topological features related to the label.

[0101] Input the actual building layout into the second topological feature extraction channel to extract the topological features of the actual layout.

[0102] Similarity evaluation: input the topological features obtained by the two topological feature extraction channels into the similarity binary channel, which will calculate a similarity binary parameter based on the comparison of the structural similarity with the preset structural similarity threshold, as follows:

[0103] If the structural similarity is greater than or equal to the structural similarity threshold, the similarity binary parameter is set to 1, indicating that the two building layout topologies are similar enough.

[0104] If the structural similarity is less than the structural similarity threshold, the similarity binary parameter is set to 0, indicating that the two building layout topologies are not similar.

[0105] The BIM-based traffic engineering electronic sand table simulation and analysis system provided by the embodiment has at least the following technical effects:

[0106] 1. By using two-stage scene sample matching, the traffic flow calibration results of the associated samples are collected; the traffic flow calibration results are used as use cases for traffic engineering sand table simulation testing, and the first traffic engineering BIM model is further optimized based on the test results. The use cases for traffic engineering sand table simulation testing determined by big data are objective, which can improve the stability of the test results and reduce the number of subsequent optimization of the first traffic engineering BIM model application.

[0107] 2. A better traffic layout scheme is found by random adjustment and iterative optimization. This method helps to systematically explore the possible solution space and gradually approach the optimal solution. By excluding models with high structural similarity, it can avoid being trapped in local optimum, thereby improving the probability of finding the global optimal solution. This step reflects the flexibility and adaptability of the scheme, which can continuously adjust and optimize the BIM model according to the test results until the actual traffic engineering requirements are met.

[0108] Embodiment two:

[0109] Please refer to Figure 2 , Figure 2 The embodiment of the electronic device provided by the embodiment of the present application is shown in the embodiment of the electronic device. As shown in Figure 2 The embodiment of the present application provides an electronic device 500, which includes a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, the following steps are implemented:

[0110] Based on the sand table simulation area, the influence range analysis is performed to obtain the calibration influence range, and based on the calibration influence range, the building layout topology is collected;

[0111] According to the building layout topology, the first-stage matching of the associated samples is performed to obtain the first-stage sample traffic flow record data set, wherein the first-stage sample traffic flow record data set has a building layout topology label set;

[0112] Based on the building layout topology label set, the second-stage matching of the associated samples is performed by traversing the first-stage sample traffic flow record data set to obtain the second-stage sample traffic flow record data set;

[0113] recursively fuse the first sample traffic flow record data set and the second sample traffic flow record data set, and obtain a traffic flow calibration result;

[0114] obtain a first traffic engineering BIM model of the sand table simulation area;

[0115] perform traffic testing on the first traffic engineering BIM model according to the traffic flow calibration result, and obtain a first sand table simulation test result;

[0116] when the first sand table simulation test result is a test qualified identifier, send the first traffic engineering BIM model to a traffic engineering layout design client.

[0117] Embodiment three:

[0118] Please refer to Figure 3 , Figure 3 An embodiment of a computer readable storage medium provided for the embodiments of the present application is shown in FIG. 6. As shown in FIG. 6, the embodiment provides a computer readable storage medium 600, which stores a second computer program 611, and the second computer program 611 is executed by a processor to implement the following steps: Figure 3

[0119] perform influence range analysis based on the sand table simulation area, obtain a calibration influence range, collect building layout topology according to the calibration influence range;

[0120] perform associated sample first matching according to the building layout topology, and obtain a first sample traffic flow record data set, wherein the first sample traffic flow record data set has a building layout topology tag set;

[0121] based on the building layout topology tag set, traverse the first sample traffic flow record data set to perform associated sample second matching, and obtain a second sample traffic flow record data set;

[0122] recursively fuse the first sample traffic flow record data set and the second sample traffic flow record data set, and obtain a traffic flow calibration result;

[0123] obtain a first traffic engineering BIM model of the sand table simulation area;

[0124] perform traffic testing on the first traffic engineering BIM model according to the traffic flow calibration result, and obtain a first sand table simulation test result;

[0125] when the first sand table simulation test result is a test qualified identifier, send the first traffic engineering BIM model to a traffic engineering layout design client.

[0126] ​It should be noted that the above-mentioned embodiments have been described by way of example only and that modifications and additions can be made thereto without departing from the scope of the application.

[0127] Those skilled in the art will appreciate that embodiments of the application can be situated as a system, a method, or a computer program product. Accordingly, the application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the application can take the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0128] The application is described with reference to the flowchart and / or block diagrams of the systems, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 means for performing each of the functions specified in the flowchart and / or block diagram block or blocks.

[0129] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 means for performing each of the functions specified in the flowchart and / or block diagram block or blocks.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 means for performing each of the functions specified in the flowchart and / or block diagram block or blocks.

[0131] Although preferred embodiments of the application have been described, those skilled in the art will appreciate that additional modifications and alterations can be made thereto without departing from the scope of the application.

[0132] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover modifications and variations of this application provided they come within the scope of the application and their equivalent technology.

Claims

1. The BIM-based traffic engineering electronic sandbox simulation and analysis system is characterized by: The BIM-based traffic engineering electronic sandbox simulation and analysis system execution steps include: Performing an influence range analysis based on a sandbox simulation area to obtain a calibrated influence range, and collecting building layout topology based on the calibrated influence range; Performing primary matching of associated samples according to the building layout topology to obtain a primary sample traffic flow record dataset, wherein the primary sample traffic flow record dataset has a building layout topology label set; Based on the building layout topology label set, traverse the first-level sample traffic flow record data set to perform associated sample second-level matching to obtain a second-level sample traffic flow record data set; Recursively fusing the first-level sample traffic flow record dataset and the second-level sample traffic flow record dataset to obtain a traffic flow calibration result; Obtained the first traffic engineering BIM model of the sandbox simulation area; Performing a traffic test on the first traffic engineering BIM model according to the traffic flow calibration result to obtain a first sandbox simulation test result; When the first sandbox simulation test result is a test pass mark, the first traffic engineering BIM model is sent to the traffic engineering layout design client.

2. The system according to claim 1, wherein The execution steps also include: When the first sandbox simulation test result is a test failure mark, traffic topology optimization is performed on the first traffic engineering BIM model to obtain a second traffic engineering BIM model, wherein the second sandbox simulation test result of the second traffic engineering BIM model is a test pass mark; The second traffic engineering BIM model is sent to the traffic engineering layout design client.

3. The system according to claim 1, wherein: Perform an impact range analysis based on the sandbox simulation area to obtain a calibrated impact range. The execution steps include: Step 1: Set a first radius constraint interval through the user terminal, wherein the first radius constraint interval includes a first radius lower limit value and a first radius upper limit value, which are two boundary values ​​of the constraint interval and represent the minimum and maximum radii of the impact range, respectively; Step 2: Taking the median of the first radius constraint interval to obtain a first median radius; Step 3: Using the first median radius, construct a first influence range to be analyzed, and overlap the center of the sandbox simulation area with the center of the first influence range to be analyzed to obtain a pseudo-circular area; Step 4: Performing flow correlation analysis on the pseudo-circular area and the sandbox simulation area to obtain a Pearson correlation coefficient; Step 5A: When the Pearson correlation coefficient is less than or equal to a Pearson correlation coefficient threshold, constructing a second radius constraint interval based on the first radius lower limit value and the first median radius; Step 6A: When the interval step size of the second radius constraint interval is greater than or equal to the convergence step size threshold, the second radius constraint interval is returned to step 2 to execute a loop; Step 7A: When the interval step size of the second radius constraint interval is less than the convergence step size threshold, the median value of the second radius constraint interval is taken to configure the calibrated influence range for the sandbox simulation area.

4. The system according to claim 3, wherein: The execution steps also include: Step 5B: When the Pearson correlation coefficient is greater than the Pearson correlation coefficient threshold, constructing a third radius constraint interval based on the first median radius and the first radius upper limit value; Step 6B: When the interval step size of the third radius constraint interval is greater than or equal to the convergence step size threshold, the third radius constraint interval is returned to step 2 to execute a loop; Step 7B: When the interval step size of the third radius constraint interval is less than the convergence step size threshold, the median value of the third radius constraint interval is taken to configure the calibration influence range for the sandbox simulation area.

5. The system according to claim 1, wherein: Performing first-level matching of associated samples based on the building layout topology to obtain a first-level sample traffic flow record dataset includes the following steps: Obtain building layout topology labels and traffic flow record data of samples to be analyzed; Performing a similarity binary evaluation on the building layout topology label and the building layout topology to obtain a similarity binary parameter; When the similarity binary parameter is 1, the traffic flow record data is added to the first-level sample traffic flow record data set; When the similarity binary parameter is 0, the sample to be analyzed is updated.

6. The system according to claim 5, wherein: Performing similarity binary evaluation on the building layout topology label and the building layout topology to obtain similarity binary parameters, the execution steps including: Obtaining a building layout topology comparison network, wherein the building layout topology comparison network includes a first topology feature extraction channel, a second topology feature extraction channel, and a similarity bisection channel, and the first topology feature extraction channel and the second topology feature extraction channel are twin neural networks; Inputting the building layout topology label into the first topology feature extraction channel to obtain a first topology feature; Inputting the building layout topology into the second topological feature extraction channel to obtain a second topological feature; The first topological feature and the second topological feature are input into the similarity bipartition channel to obtain the similarity bipartition parameter, wherein when the structural similarity is greater than or equal to the structural similarity threshold, the similarity bipartition parameter is equal to 1, and when the structural similarity is less than the structural similarity threshold, the similarity bipartition parameter is equal to 0.

7. The system according to claim 1, wherein: Recursively fusing the first-level sample traffic flow record dataset and the second-level sample traffic flow record dataset to obtain a traffic flow calibration result, the execution steps including: Grouping the secondary sample traffic flow record data set according to the primary sample traffic flow record data set to obtain multiple groups of secondary sample traffic flow record data; Performing mode analysis on the multiple groups of secondary sample traffic flow record data to obtain multiple secondary sample traffic flow mode values; A mode analysis is performed on the plurality of secondary sample traffic flow mode values ​​and the primary sample traffic flow record data set to obtain the traffic flow calibration result.

Images