Automatic configuration method and query system of intelligent transportation equipment

Abstract

The invention discloses an automatic configuration method and a query system of intelligent transportation equipment, and relates to the technical field of intelligent transportation. An equipment quantity calculation algorithm library associated with scene codes and equipment types is established; receiving core parameters of a target traffic scene input by a user, and performing validity verification on the core parameters; determining a target scene code through a scene mapping function based on the core parameters passing the verification; and calling an algorithm in an equipment quantity calculation algorithm library corresponding to the target scene code, and calculating the installation quantity of various types of intelligent transportation equipment in combination with the dynamic variables in the core parameters. According to the invention, standardization, automation and precision of intelligent transportation equipment configuration are realized, and the problems of low manual configuration efficiency, large deviation and poor normalization are effectively solved. Through scene coding and construction of an algorithm library, dynamic quantitative calculation of the number of devices is realized, device redundancy or insufficient configuration is avoided, and the engineering cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, and in particular to an automatic configuration method and query system for intelligent transportation equipment. Background Technology

[0002] With the rapid development of the intelligent transportation industry, the demand for intelligent transportation equipment in urban roads and highways is increasing. Currently, the configuration of intelligent transportation equipment mainly relies on manual labor. The configuration personnel need to be proficient in various traffic design specifications, combine their own engineering experience, and determine the type, quantity, and installation requirements of equipment after judging different traffic scenarios.

[0003] The existing configuration method has many shortcomings. There are many types of traffic scenarios, and the requirements for equipment configuration vary greatly depending on the road level and the type of intersection. Manual judgment is prone to errors, resulting in redundant or insufficient equipment configuration, which increases engineering costs and may affect the effectiveness of traffic management. Manual configuration is inefficient, requiring a lot of time to sort out the standard provisions and calculate the number of equipment, and the consistency and standardization of the configuration results are difficult to guarantee.

[0004] Furthermore, the existing configuration process lacks standardized parameter input and verification mechanisms, which makes it impossible to guarantee the accuracy of core parameters and further affects the reliability of the configuration scheme.

[0005] At the same time, once configured, it is difficult to quickly adjust and optimize the solution, and it cannot flexibly adapt to changes in the needs of actual projects, which brings inconvenience to subsequent construction and operation and maintenance. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides an automatic configuration method and query system for intelligent transportation equipment. The technical solution adopted is as follows: The automatic configuration method for intelligent transportation equipment includes the following steps: Step 1: Based on traffic design specifications, define different traffic scenario codes and create an associated equipment configuration table for each scenario code. The equipment configuration table includes equipment type, configuration necessity, and calculation benchmark rules. Step 2: Establish a library of device quantity calculation algorithms associated with the scene encoding and device type; Step 3: Receive the core parameters of the target traffic scenario input by the user, and verify the validity of the core parameters; Step 4: Based on the verified core parameters, determine the target scene code through the scene mapping function; call the algorithm in the device quantity calculation algorithm library corresponding to the target scene code, and combine it with the dynamic variables in the core parameters to calculate the installation quantity of various intelligent transportation devices; Step 5: Based on the equipment configuration table corresponding to the target scenario code and the calculated number of equipment to be installed, automatically generate and output the equipment configuration list and the engineering quantity statistics table.

[0007] Optionally, in step 1, the traffic scenario includes intersection scenarios and road segment scenarios; The method for generating scene codes is as follows: For intersection scenes, the code adopts a three-dimensional coding rule of first road level, second road level, and intersection type; for road segment scenes, the code adopts a two-dimensional coding rule of road segment level and scene type, and the code also includes road segment type information.

[0008] Optionally, in step 3, the core parameters are categorized by scenario type using the following method: For intersection scenarios, the core parameters include the type of the first intersecting road. Second type of intersecting road The intersection layout and the number of lanes in the four directions of east, south, west, and north. , , , ; For road segment scenarios, the core parameters include: road segment level R, road segment length. And the list of key nodes N.

[0009] Optionally, in step 3, the validity check includes range check, logical check, and integrity check; Range verification is used to check whether the values ​​of each core parameter are within the preset enumeration range or numerical range; logic verification is used to check the matching between the intersection type and the number of lanes at the approach; integrity verification is used to check whether the core parameters of the corresponding scenario type are missing. If the verification fails, prompt the user to re-enter the information and explain the error type and correction direction; if the verification passes, proceed to step 4.

[0010] Optionally, in step 4, the scene mapping function expression is: ;in Encode the target scene. In an intersection scenario, the road segment scenario represents the hierarchy of two intersecting roads. Omitted, only input the road segment level R; T is the scene type identifier, T=1 represents the intersection scene, T=2 represents the road segment scene; F(·) is the preset mapping function, used to map the input parameters to a unique scene code.

[0011] Optionally, in step 4, the device quantity calculation algorithm library includes quantitative calculation formulas for video surveillance devices, and the number of video surveillance devices is calculated according to scene type: Number of video surveillance devices at intersections Based on the road level in the scene code, distinguish between high-demand scenes and low-demand scenes; High-demand scenarios are intersections where at least one intersecting road is an expressway or arterial road, and the calculation formula is: ; The low-demand scenario is an intersection where both intersecting roads are secondary arterial roads or local roads. The calculation formula is: ; Road segment scenario, number of video surveillance devices The calculation formula is: ; in It is a cross-form correction coefficient. Let i be the number of lanes at the i-th entrance. , For the preset lane number threshold and D is the preset equipment spacing benchmark value based on the road section level. It is a rounding function. This is the floor function. For conditional functions, The number of critical nodes. To sum the parameters of the four inlet channels.

[0012] Optionally, the device quantity calculation algorithm library contains quantitative calculation formulas for the electronic police system, which includes an electronic police capture unit and supporting supplementary lighting. Number of electronic police camera units The calculation formula is: ; in To match the number of lanes on the import lane Related piecewise functions; Number of supporting supplementary lights The calculation formula is: ; in This is the average number of lanes for each approach lane. , The calculation result is rounded to the nearest integer.

[0013] Optionally, in step 4, the equipment quantity calculation algorithm library also includes quantitative calculation formulas for traffic light systems, traffic flow detection equipment, and road segment radar checkpoint equipment: Number of traffic light groups for motor vehicles : Where B represents the number of standard configuration sets for a single entrance lane. This is a correction factor based on the number of lanes in the approach lane; Number of pedestrian traffic lights : ; Number of traffic flow detection devices : ; Road section radar equipment : ; This refers to the number of lanes in a road segment. Number of checkpoint capture units : This means that every 3 radar devices integrate 1 checkpoint capture unit.

[0014] Optionally, it also includes solution interaction optimization steps, specifically: It provides a visual user interface that divides the devices in the device configuration table into mandatory devices and optional devices according to their configuration necessity. Mandatory devices cannot be adjusted, and users can enable or disable optional devices. After the user makes adjustments, the system recalculates the number of related dependent devices in real time, updates the device configuration list and engineering quantity statistics table, and can call the graphics engine to generate a visual device layout diagram and output a complete configuration plan report.

[0015] An automatic configuration query system for intelligent transportation equipment includes a memory, a processor, and a display. The memory stores a computer program designed using an automatic configuration method for intelligent transportation equipment. The processor executes the computer program to output query results, and the display shows the query results.

[0016] In summary, the present invention has at least one of the following beneficial technical effects: This invention provides an automatic configuration method and query system for intelligent transportation equipment, achieving standardization, automation, and precision in equipment configuration, effectively solving the problems of low efficiency, large deviations, and poor standardization in manual configuration. Through scene coding and algorithm library construction, dynamic quantitative calculation of equipment quantity is achieved, avoiding equipment redundancy or under-configuration and reducing engineering costs. A standardized parameter verification mechanism ensures the accuracy of core parameters and improves the reliability of configuration schemes. The scheme interaction optimization function can flexibly adapt to changes in requirements, and the visual output facilitates construction and operation and maintenance, comprehensively improving the efficiency and quality of intelligent transportation equipment configuration. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the automatic configuration method and query system for intelligent transportation equipment of the present invention. Figure 2This is a schematic diagram on the first page of the report on the equipment configuration scheme for the intersection of main roads and secondary roads in City A, according to a specific embodiment of the present invention. Figure 3 This is a schematic diagram on the second page of the report on the equipment configuration scheme for the intersection of main roads and secondary roads in City A, according to a specific embodiment of the present invention. Figure 4 This is a schematic diagram on page 3 of the report on the equipment configuration scheme for the intersection of main roads and secondary roads in City A, a specific embodiment of the present invention. Figure 5 This is a schematic diagram on page four of the report on the equipment configuration scheme for the intersection of main roads and secondary roads in City A, a specific embodiment of the present invention. Figure 6 This is a schematic diagram on page five of the report on the equipment configuration scheme for the intersection of main roads and secondary roads in City A, which is a specific embodiment of the present invention. Detailed Implementation

[0018] The present invention will be further described in detail below with reference to the accompanying drawings.

[0019] This invention discloses an automatic configuration method and query system for intelligent transportation equipment.

[0020] Reference Figures 1-6 Example 1: An automatic configuration method for intelligent transportation equipment, comprising the following steps: Step 1: Based on traffic design specifications, define different traffic scenario codes and create an associated equipment configuration table for each scenario code. The equipment configuration table includes equipment type, configuration necessity, and calculation benchmark rules. Step 2: Establish a library of device quantity calculation algorithms associated with the scene encoding and device type; Step 3: Receive the core parameters of the target traffic scenario input by the user, and verify the validity of the core parameters; Step 4: Based on the verified core parameters, determine the target scene code through the scene mapping function; call the algorithm in the device quantity calculation algorithm library corresponding to the target scene code, and combine it with the dynamic variables in the core parameters to calculate the installation quantity of various intelligent transportation devices; Step 5: Based on the equipment configuration table corresponding to the target scenario code and the calculated number of equipment to be installed, automatically generate and output the equipment configuration list and the engineering quantity statistics table.

[0021] Example 2, in step 1, the traffic scenario includes intersection scenario and road segment scenario; The method for generating scene codes is as follows: For intersection scenes, the code adopts a three-dimensional coding rule of first road level, second road level, and intersection type; for road segment scenes, the code adopts a two-dimensional coding rule of road segment level and scene type, and the code also includes road segment type information.

[0022] By adopting the above technical solution, step 1 establishes the basic configuration benchmark. Traffic design specifications are the core basis for equipment configuration. Different traffic scenarios have fundamental differences in the type, specifications, and quantity requirements of equipment. By defining scenario codes, different scenarios can be accurately distinguished. By associating each scenario code with an equipment configuration table, the configuration boundaries, necessity, and calculation prerequisites of equipment under various scenarios can be clearly defined, avoiding arbitrariness in configuration and ensuring that configuration behavior complies with industry standard requirements.

[0023] Step 2 provides quantitative calculation support. The core challenge of equipment configuration lies in the accurate calculation of the number of devices in different scenarios. By establishing an algorithm library associated with scenario coding and device type, it can provide exclusive quantitative calculation logic for different scenarios and devices, solving the problems of low efficiency and large errors in manual calculation, and providing algorithmic support for subsequent automatic calculation.

[0024] Step 3 ensures accurate configuration through proactive prevention. Core parameters are the core inputs for scene recognition and device calculation. The accuracy of the parameters directly determines the reliability of the configuration results. Through validity verification, parameters that do not conform to the actual scenario, have logical contradictions, or are missing can be filtered out, preventing erroneous parameters from entering the subsequent calculation process and reducing the risk of configuration deviation from the source.

[0025] Step 4 achieves precise matching and dynamic quantization between the scene and the calculation logic. Based on the verified core parameters, the target scene code can be quickly locked through the scene mapping function, and then the corresponding calculation algorithm can be called. Combined with the dynamic variables in the parameters, it can adapt to the detailed differences of different scenes, realize the dynamic calculation of the number of devices, and ensure that the calculation results fit the actual needs of the target scene, without redundancy or omission.

[0026] Step 5 integrates the calculation results to form a feasible configuration outcome. The equipment configuration table clarifies the configuration requirements of the equipment, and the calculated number of equipment clarifies the configuration scale. The two can be automatically integrated to form an equipment configuration list and a project quantity statistics table, directly outputting standardized results that can be used for construction, quotation and operation and maintenance, realizing a closed loop in the configuration process and improving configuration efficiency and feasibility.

[0027] Traffic scenarios are divided into intersection scenarios and road segment scenarios. The principle behind this is that the traffic characteristics and equipment configuration requirements of the two are significantly different. The core of the intersection scenario is the connection between different roads, involving multiple lanes in different directions, traffic conflict points, and other elements. The focus of equipment configuration is on monitoring, signal control, and violation capture in the intersection area. The core of the road segment scenario is the linear extension of the road, involving road segment length, key nodes, and other elements. The focus of equipment configuration is on full coverage monitoring and speed recognition of the road segment. The classification allows for targeted design of coding rules and configuration logic.

[0028] The intersection scenario adopts a three-dimensional coding rule. The core features of the intersection are jointly determined by the first road level, the second road level, and the intersection type. These three factors together constitute the core differences of the intersection scenario. The three-dimensional coding can comprehensively cover the key features of the intersection, ensuring that each intersection scenario has a unique coding identifier, avoiding coding duplication of intersection scenarios with different features. At the same time, it facilitates subsequent association with the corresponding device configuration table and calculation algorithm to achieve accurate matching.

[0029] The road segment scenario adopts a two-dimensional coding rule and includes road segment type information. The core characteristics of the road segment scenario are determined by the road segment level and scenario type. The road segment level determines the configuration standards and density requirements of the equipment, while the scenario type determines the configuration focus of the equipment. Two-dimensional coding can accurately identify the core differences of road segment scenarios. At the same time, adding road segment type information can further refine the characteristics of road segment scenarios, adapt to the configuration requirements of different types of road segments, and ensure that the coding can fully reflect the actual characteristics of the road segment scenario, providing support for subsequent accurate configuration.

[0030] The overall coding rule design takes into account both the accuracy and scalability of scene recognition. It can cover the current mainstream traffic scenarios and also facilitate the expansion of coding based on the existing coding rules when adding new scenario types in the future, without the need to reconstruct the entire coding system. This ensures the long-term availability of the coding system and provides a stable and accurate scene recognition foundation for the automatic configuration of intelligent transportation equipment.

[0031] In Example 3, step 3, the core parameters are categorized by scenario type as follows: For intersection scenarios, the core parameters include the type of the first intersecting road. Second type of intersecting road The intersection layout and the number of lanes in the four directions of east, south, west, and north. , , , ; For road segment scenarios, the core parameters include: road segment level R, road segment length. And the list of key nodes N.

[0032] Example 4, in step 3, the validity check includes range check, logical check and integrity check; Range verification is used to check whether the values ​​of each core parameter are within the preset enumeration range or numerical range; logic verification is used to check the matching between the intersection type and the number of lanes at the approach; integrity verification is used to check whether the core parameters of the corresponding scenario type are missing. If the verification fails, prompt the user to re-enter the information and explain the error type and correction direction; if the verification passes, proceed to step 4.

[0033] By adopting the above technical solution, the core parameters of the intersection scenario are clearly defined because the core characteristics of the intersection scenario are concentrated in the road connection relationship and the configuration of the approach lanes. The types of the first and second intersecting roads determine the level and traffic flow scale of the intersection, which directly affects the standard and density of equipment configuration. The intersection form determines the distribution of traffic conflict points and traffic logic of the intersection, which is an important basis for equipment layout and quantity calculation. The number of lanes in the four approach lane directions is directly related to the coverage and configuration quantity of the equipment. Different numbers of lanes correspond to different monitoring and signal control requirements. Clarifying these parameters can ensure that the subsequent calculation logic can accurately adapt to the actual situation of the intersection.

[0034] The core parameters for defining road segment scenarios are crucial because their key characteristics are concentrated in the road's inherent attributes and key control areas. The road segment level determines the baseline standards and density requirements for equipment configuration, with significant differences in monitoring and speed measurement needs across different road segment levels. The road segment length directly determines the number of coverage devices required, with varying lengths resulting in differences in device spacing and total number. The list of key nodes reflects the areas on the road segment that require focused monitoring. These areas have specific equipment configuration needs, and clearly defining key nodes ensures effective coverage of key areas, avoids monitoring blind spots, and ensures that the configuration scheme aligns with the actual control needs of the road segment.

[0035] By employing multi-dimensional and targeted validation logic, the core parameters input by users are comprehensively reviewed, and erroneous, invalid, or incomplete parameters are eliminated. This ensures the accuracy, logic, and completeness of the input parameters, providing reliable data support for subsequent scenario matching and device calculations. It avoids configuration deviations from the source and guarantees the reliability and compliance of the automatic configuration solution.

[0036] Example 5, in step 4, the scene mapping function expression is: ;in Encode the target scene. In an intersection scenario, the road segment scenario represents the hierarchy of two intersecting roads. Omitted, only input the road segment level R; T is the scene type identifier, T=1 represents the intersection scene, T=2 represents the road segment scene; F(·) is the preset mapping function, used to map the input parameters to a unique scene code.

[0037] Example 6, in step 4, the device quantity calculation algorithm library includes a quantitative calculation formula for video surveillance devices, and the number of video surveillance devices is calculated according to scene type: Number of video surveillance devices at intersections Based on the road level in the scene code, distinguish between high-demand scenes and low-demand scenes; High-demand scenarios are intersections where at least one intersecting road is an expressway or arterial road, and the calculation formula is: ; The low-demand scenario is an intersection where both intersecting roads are secondary arterial roads or local roads. The calculation formula is: ; Road segment scenario, number of video surveillance devices The calculation formula is: ; in It is a cross-form correction coefficient. Let i be the number of lanes at the i-th entrance. , For the preset lane number threshold and D is the preset equipment spacing benchmark value based on the road section level. It is a rounding function. This is the floor function. For conditional functions, The number of critical nodes. To sum the parameters of the four inlet channels.

[0038] Example 7: The device quantity calculation algorithm library contains quantitative calculation formulas for the electronic police system, which includes an electronic police capture unit and supporting supplementary lighting. Number of electronic police camera units The calculation formula is: ; in To match the number of lanes on the import lane Related piecewise functions; Number of supporting supplementary lights The calculation formula is: ; in This is the average number of lanes for each approach lane. , The calculation result is rounded to the nearest integer.

[0039] In Example 8, step 4, the equipment quantity calculation algorithm library also includes quantitative calculation formulas for traffic light systems, traffic flow detection equipment, and road section radar checkpoint equipment: Number of traffic light groups for motor vehicles : Where B represents the number of standard configuration sets for a single entrance lane. This is a correction factor based on the number of lanes in the approach lane; Number of pedestrian traffic lights : ; Number of traffic flow detection devices : ; Road section radar equipment : ; This refers to the number of lanes in a road segment. Number of checkpoint capture units : This means that every 3 radar devices integrate 1 checkpoint capture unit.

[0040] By adopting the above technical solution, the design of the scene mapping function expression essentially involves linking and integrating scene encoding rules and core parameters, clarifying the correspondence between function input and output, and enabling scattered core parameters to be transformed into unique scene codes. All parameters included in the function correspond to the core features of the target scene, ensuring that the mapping logic aligns with the essential needs of scene classification.

[0041] Different input parameters are designed to distinguish between intersection scenarios and road segment scenarios. The core parameters of the two types of scenarios are fundamentally different. Intersection scenarios need to reflect the connection characteristics of two intersecting roads, so the first road level and the second road level are retained as inputs. Road segment scenarios only need to reflect their own level characteristics and do not require additional road level parameters, so the second road level is omitted and only the road segment level is retained as input to avoid redundant parameters affecting mapping efficiency and accuracy.

[0042] By assigning simple identifiers, the function can quickly identify whether the target scene belongs to an intersection or a road segment, and then call the corresponding encoding mapping logic. This avoids confusion between the mapping rules of the two types of scenes and improves mapping efficiency. The design principle of the preset mapping function is to solidify the scene encoding rules, ensuring that the same parameter input can produce a unique scene code, guaranteeing the consistency and stability of scene matching, avoiding errors from manual mapping, and realizing the automation of scene recognition.

[0043] The monitoring needs of intersection scenarios and road segment scenarios are significantly different. Intersection scenarios focus on monitoring the traffic status of multi-directional approach lanes, while road segment scenarios focus on achieving full coverage of linear areas and monitoring of key nodes. Differentiated calculation can ensure that the formula is adapted to the monitoring focus of various scenarios and improve the accuracy of calculation results.

[0044] High-demand scenarios involve large traffic volumes and stringent control requirements, necessitating precise equipment configuration based on the number of lanes at entrances to ensure effective coverage of each high-lane entrance. Low-demand scenarios, on the other hand, involve smaller traffic volumes and lower control requirements, allowing for simplified calculations based on the total number of lanes, balancing monitoring effectiveness with cost control.

[0045] The settings of various parameters and functions are all in line with the actual working characteristics of video surveillance equipment and traffic engineering specifications. The intersection form correction coefficient adapts to the differences in monitoring range of different intersection forms. The lane number threshold distinguishes different coverage requirements. Rounding up, rounding down and conditional functions ensure that the calculation results are in line with the actual equipment installation requirements. The number of key nodes supplements the monitoring needs of key areas in road segment scenarios. The equipment spacing benchmark value adapts to the monitoring density requirements of different levels of road segments, jointly ensuring the rationality and feasibility of the calculation results.

[0046] Based on the violation capture function requirements of the electronic police system, a dedicated quantitative formula was designed for the electronic police capture unit and its supporting supplementary lighting. This ensures that the capture unit can accurately cover violations at each entrance lane, and that the supplementary lighting can provide sufficient illumination to meet the capture requirements, guaranteeing the clarity of nighttime captures. At the same time, it achieves a balance between the economy and functionality of the equipment configuration.

[0047] The calculation of the electronic traffic enforcement camera unit and the supporting supplementary lighting is based on the fact that the two have different functions and configuration logic. The core of the camera unit is to capture violations, and its number is directly related to the number of lanes in the approach lane. The core of the supplementary lighting is to improve the nighttime capture effect, and its number is related to the number of camera units and the width of the approach lane. Calculating them separately can ensure that the number of each type of equipment is accurately matched to its own functional requirements.

[0048] The electronic traffic enforcement camera unit uses a piecewise function to calculate the entry lanes with different numbers of lanes. The resolution and coverage requirements of the camera unit vary. The piecewise function can adapt different camera unit configuration logic according to the number of lanes, avoiding unclear captures or equipment waste caused by a single configuration for a single number of lanes, and ensuring that violations at each entry lane can be accurately captured.

[0049] The intersection pattern affects the layout of the approach lanes and the capture range, which is directly related to the configuration requirements of electronic police systems at intersections. Using this coefficient ensures that the calculation results closely match the actual intersection layout, improving configuration accuracy. The supplementary lighting is calculated based on the number of capture units and the average number of lanes. The principle is that the average number of lanes reflects the average width of the approach lanes; the wider the lane, the higher the required supplementary lighting intensity, and the more supplementary lights need to be added accordingly. This ensures uniform lighting and avoids insufficient or excessive lighting, meeting the actual needs of nighttime capture.

[0050] Example 8 further improves the equipment quantity calculation algorithm library, supplements the quantitative formulas for traffic light systems, traffic flow detection equipment and road section radar checkpoint equipment, and designs targeted quantitative logic based on the functional positioning of various equipment and the configuration requirements of different scenarios to ensure that the quantity of various equipment is accurately adapted to the scenario management and control requirements, realizes the coordinated implementation of functions such as traffic signal control, traffic flow collection, and speed measurement and recognition, and improves the configuration system of intelligent transportation equipment.

[0051] The design of the motor vehicle traffic light group formula conforms to the traffic signal control principle. The standard configuration of a single approach lane ensures that each effective approach lane has complete signal control functions. The lane number correction coefficient adapts to the signal visibility requirements of multi-lane approach lanes. The intersection form correction coefficient adapts to the differences in the layout of approach lanes with different intersection forms, ensuring that the traffic lights can be clearly identified by drivers and ensuring orderly traffic flow.

[0052] The design of the pedestrian traffic light formula focuses on pedestrian safety, configuring pedestrian traffic lights only on valid approach lanes, and assigning a reasonable number of traffic lights to each valid approach lane to ensure that pedestrians can clearly see the signal instructions, avoid pedestrian risks, and meet the actual needs of pedestrian management at intersections.

[0053] The design of the traffic flow detection equipment formula is precisely matched to the application scenarios of traffic flow detection. The detection equipment is only configured on the entrance lanes with a certain scale and signal control requirements to avoid redundant configuration, ensure that the detection equipment can collect effective traffic flow data, provide reliable support for adaptive signal control, and meet the intelligent needs of traffic management.

[0054] The design of the road section radar checkpoint equipment formula is in line with the functional requirements of road section speed measurement and vehicle identification. The radar equipment is configured according to the number of lanes to ensure that each lane can achieve accurate speed measurement. The checkpoint capture unit is integrated according to the number of radar equipment, taking into account the vehicle identification function and the equipment integration requirements, reducing installation complexity and equipment cost, and achieving a balance between functionality and economy.

[0055] Example 9 also includes a solution interaction optimization step, specifically: It provides a visual user interface that divides the devices in the device configuration table into mandatory devices and optional devices according to their configuration necessity. Mandatory devices cannot be adjusted, and users can enable or disable optional devices. After the user makes adjustments, the system recalculates the number of related dependent devices in real time, updates the device configuration list and engineering quantity statistics table, and can call the graphics engine to generate a visual device layout diagram and output a complete configuration plan report.

[0056] By adopting the above technical solutions, the design of a visual user interface transforms abstract device configuration information into an intuitive visual presentation, avoiding the difficulty for users to understand complex parameters and data. This allows users to clearly grasp the configuration status of various devices, facilitates quick judgment on whether the configuration scheme meets actual needs, and improves the efficiency of scheme adjustment.

[0057] Different devices play different roles in traffic management. Mandatory devices are set based on traffic regulations and core requirements of scenario management. Their absence will cause management functions to fail or fail to meet industry standards, so they cannot be adjusted. Optional devices are set based on additional management requirements. They are not necessary for all scenarios and allow users to adjust the cost budget and management focus of different projects to achieve personalized adaptation of the solution.

[0058] The configurations of various devices are inherently related. Enabling or disabling optional devices will affect the quantity requirements of related dependent devices. Real-time linkage calculation can avoid device quantity contradictions caused by manual adjustments, ensure the accuracy of configuration lists and engineering quantity statistics tables, eliminate the need for users to manually recalculate, and improve operational convenience.

[0059] The system uses a graphics engine to generate visual equipment layout diagrams, intuitively presenting the installation locations and coverage areas of the equipment. This allows users to anticipate the rationality of the equipment layout, promptly identify blind spots or equipment conflicts, and reduce adjustment costs during the construction phase. It also outputs a complete configuration plan report, which integrates all configuration information to form standardized, archiveable, and deliverable results. This provides a unified basis for subsequent stages such as construction, pricing, and operation and maintenance, achieving closed-loop optimization of the configuration process and further improving the overall efficiency and reliability of intelligent transportation equipment configuration.

[0060] Example 10: An automatic configuration query system for intelligent transportation equipment includes a memory, a processor, and a display. The memory stores a computer program designed using an automatic configuration method for intelligent transportation equipment. The processor executes the computer program to output query results, and the display shows the query results.

[0061] The following specific embodiments illustrate the implementation principle of the present invention: Taking the scenarios of crossroads formed by urban arterial roads and secondary arterial roads, and linear sections of urban secondary arterial roads as practical application scenarios, this paper fully implements the automatic configuration method of intelligent transportation equipment, and at the same time, it is equipped with a corresponding automatic configuration query system to realize the automation, accuracy and visualization of equipment configuration. The following is the specific implementation process and results.

[0062] Specific Implementation Example A: Scenario Configuration of an Intersection of a Main Road and a Secondary Road in an Urban Area: Step 1: Associate scene coding with device configuration table: Based on urban road traffic design specifications, the three-dimensional coding rule for intersection scenarios is defined as: First Road Level - Second Road Level - Intersection Type, where the main road is coded as Z, the secondary road as C, and the cross intersection type as S. A dedicated equipment configuration table is associated with the scenario code ZCS. The table specifies that mandatory equipment includes video surveillance equipment, electronic police systems, vehicle traffic lights, pedestrian traffic lights, and traffic flow detection equipment. The necessity of configuring these devices is mandatory, and the calculation benchmark rules use the number of lanes at the approach and the intersection type as the core calculation factors. Optional equipment includes environmental monitoring equipment, with its necessity being configured as needed, and the calculation benchmark rules using the intersection area as the core calculation factor.

[0063] Step 2: Matching device quantity calculation algorithm library: The system is a scene-coding ZCS matching algorithm library for calculating the number of intersection-type devices. This library includes dedicated quantitative calculation logic for video surveillance, electronic police, traffic light systems, and traffic flow detection equipment. All algorithms are associated with core parameters such as intersection type correction coefficients and approach lane number thresholds. Among them, the cross intersection type correction coefficient... A value of 1.2 represents the threshold for the number of lanes in high-demand scenarios. The value is 3, the standard configuration number of single-entry lane traffic lights (B) is 1, and the lane number correction coefficient is 1. For lanes with 3 or more lanes, take 1.1; for lanes with less than 3 lanes, take 1.0.

[0064] Step 3: Input and validate core parameters. The user submits the core parameters of the intersection, including the type of the first intersecting road, through the input interface of the automatic configuration query system. Main road, second intersecting road type It is a secondary arterial road with a cross intersection. The number of lanes at the east entrance is [number missing]. =4. Number of lanes at the south entrance =3. Number of lanes at the west entrance =4. Number of lanes on the north entrance =3.

[0065] The system performs validity checks on the above parameters. The range check shows that all road types, intersection types, and number of lanes are within the preset enumeration and value range. The logic check shows that the cross intersection type matches the configuration of the number of lanes of the four-way approach lanes, and there is no logical contradiction. The integrity check shows that the core parameters required for the intersection scenario are not missing. The check passes and proceeds to the subsequent calculation stage.

[0066] Step 4: Scene Mapping and Device Quantity Calculation The system uses scene mapping functions Perform scene matching, where =Z、 =C, intersection scene identifier T=1, the target scene code is calculated as ZCS through the mapping function; the system calls the algorithm library corresponding to this code, and calculates the number of various required devices by combining the core parameters and preset coefficients. Since the intersection has at least one intersecting road as a main road, it is determined to be a high demand scene for video surveillance equipment, and the calculation is completed according to the high demand scene formula; the electronic police system, traffic light system, and traffic flow detection equipment are all quantified according to their respective exclusive formulas.

[0067] Step 5: Generate basic configuration results The system automatically generates an equipment configuration list and a quantity statistics table based on the equipment configuration table corresponding to ZCS and the calculated equipment installation quantity. The configuration list specifies the specifications, quantity, and installation location of various mandatory equipment, while the quantity statistics table counts the unit quantity, total quantity, and basic installation hours of various equipment. Both types of results are stored synchronously in the memory of the automatic configuration query system, and the processor completes the standardization processing of the result data.

[0068] Step 6: Solution Interaction Optimization Users can view the basic configuration results on the system's visual interface and select to enable optional environmental monitoring equipment. After recognizing this operation, the system automatically calculates the number of environmental monitoring devices based on the intersection area, while simultaneously verifying in real time that the installation positions of this device do not conflict with other devices, and updates the equipment configuration list and engineering quantity statistics table. The system calls the graphics engine to generate a visual equipment layout diagram of the intersection, which accurately marks the installation points and coverage areas of various devices at the four-way approach lanes. Finally, the configuration list, engineering quantity statistics table, and layout diagram are integrated to generate a complete intersection equipment configuration plan report, which is displayed visually on the system monitor and also supports exporting and printing the report.

[0069] Specific Implementation Example B: Urban Secondary Trunk Road Linear Section Scenario Configuration: Step 1: Associate scene coding with device configuration table: Based on urban road traffic design specifications, a two-dimensional coding rule for road segment scenarios is defined as road segment level - scenario type. Secondary arterial roads are coded as C, and ordinary linear road segments are coded as X. The code includes information indicating that the road segment type is an urban road. A dedicated equipment configuration table is associated with the scenario code CX. The table specifies that mandatory equipment includes video surveillance equipment, road segment radar equipment, and checkpoint capture units, with configuration necessity set as mandatory. The calculation benchmark rule uses road segment length, number of lanes, and number of key nodes as core calculation factors. Optional equipment includes emergency broadcast equipment, with configuration necessity set as on demand. The calculation benchmark rule uses the number of key nodes as the core calculation factor.

[0070] Step 2 Matching device quantity calculation algorithm library The system is a scene-coded CX matching road segment equipment quantity calculation algorithm library. This algorithm library contains dedicated quantitative calculation logic for video surveillance, road segment radar, and checkpoint capture units. All algorithms are associated with core parameters such as the equipment spacing benchmark value and rounding function rules corresponding to the road segment level. Among them, the secondary arterial road video surveillance equipment spacing benchmark value D is 200 meters. Road segment radar equipment is configured one-to-one according to the number of lanes, and checkpoint capture units are configured according to the rule of integrating 3 radar equipment into 1 unit.

[0071] Step 3: Input and validate core parameters The user submits the core parameters of the road segment through the input interface of the automatic configuration query system. The road segment level R is a secondary arterial road, and the segment length is... =1800 meters, the road section has 4 lanes in both directions, and the list of key nodes N includes 3 key nodes: school gate, community entrance and exit, and bus stop.

[0072] The system performs validity checks on the above parameters. The range check shows that the road segment level, length, number of lanes, and key node type are all within the preset enumeration and value range. The logic check shows that the configuration of the road segment length and number of lanes matches, and there is no logical contradiction between the distribution of key nodes and the linear characteristics of the road segment. The integrity check shows that there are no missing core parameters required for the road segment scenario. The check passes and the system proceeds to the subsequent calculation stage.

[0073] Step 4: Scene Mapping and Device Quantity Calculation The system uses scene mapping functions Scene matching is performed, where the road segment level R=C and the road segment scene identifier T=2. The target scene code is CX, calculated by the mapping function. The system calls the algorithm library corresponding to this code and calculates the number of various required devices by combining the core parameters and preset benchmark values. The video surveillance equipment is calculated according to the road segment length, the benchmark value of the device spacing and the number of key nodes. The road segment radar equipment is calculated according to the number of lanes in the road segment. The checkpoint capture unit is calculated according to the number of radar devices and the integration rules.

[0074] Step 5: Generate basic configuration results Based on the equipment configuration table corresponding to CX and the calculated equipment installation quantity, the system automatically generates an equipment configuration list and a project quantity statistics table. The configuration list specifies the specifications, quantity, installation spacing, and key node installation points of various mandatory equipment. The project quantity statistics table counts the unit project quantity, total project quantity, and pipeline laying length of various equipment. Both types of results are stored synchronously in the memory of the automatic configuration query system, and the processor completes the standardization processing of the result data.

[0075] Step 6: Solution Interaction Optimization Users can view the basic configuration results in the system's visual interface and choose to disable the optional emergency broadcast equipment. After the system recognizes this operation, it directly removes the relevant information of the emergency broadcast equipment from the configuration list without adjusting the quantity of other required equipment, and quickly updates the equipment configuration list and engineering quantity statistics table. The system calls the graphics engine to generate a visual equipment layout diagram of the road section. The diagram marks the linear installation points of video surveillance and road radar equipment according to the installation spacing, and marks the installation points of dedicated equipment at key nodes, clearly showing the equipment coverage and pipeline routing. Finally, the configuration list, engineering quantity statistics table, and layout diagram are integrated to generate a complete road section equipment configuration plan report, which is displayed visually on the system monitor. The report can also be exported and printed.

[0076] Example C: Application of the Automatic Configuration Query System for Intelligent Transportation Equipment The automatic configuration query system for intelligent transportation equipment in this embodiment includes a memory, a processor, a display, and supporting input peripherals. The memory stores a computer program designed based on the above-mentioned automatic configuration method for intelligent transportation equipment, and also stores basic data resources such as a traffic design specification database, a scenario coding database, an equipment configuration table database, an equipment quantity calculation algorithm library, and a core parameter preset threshold database. All databases support real-time updates and expansion.

[0077] The processor is an industrial-grade embedded processor that can efficiently execute various instructions in a computer program, sequentially completing operations such as receiving core parameters, validating validity, mapping scenarios, calculating the number of devices, generating configuration results, and optimizing and updating solutions. It has the ability to perform parallel computing in multiple scenarios and real-time data updates, and can quickly handle the configuration requirements of different types of traffic scenarios such as intersections and road sections.

[0078] The display is a high-definition touch screen, serving as the system's visual user interface. It supports touch input of core parameters, visual display of configuration results, and touch adjustment of optional devices. It can also display high-definition diagrams of device layout and configuration reports, and supports gesture zooming, point marking, content retrieval, and other operations.

[0079] Input peripherals include a keyboard, mouse, and barcode scanner, which can be adapted to the operating habits of different users. The barcode scanner can directly scan the basic information QR code in the traffic scene, quickly import core parameters, and reduce the workload of manual input.

[0080] The system's workflow is as follows: Users submit core parameters of the target traffic scenario through input peripherals or a touchscreen interface. After receiving the parameters, the processor calls a validity verification program to complete the review. Once the verification is successful, the scenario mapping program is called to match the target scenario code, and then the corresponding device quantity calculation algorithm library is called to complete the quantitative calculation. Subsequently, the configuration result generation program is called to output a device configuration list and a project quantity statistics table. Finally, the basic configuration results are displayed on the screen. Users can enable or disable optional devices on the screen interface. The processor performs linked calculations and updates the configuration results in real time. At the same time, the graphics engine is called to generate a visual device layout diagram. Finally, all results are integrated to generate a configuration scheme report, which is displayed on the screen and supports export in multiple formats. The entire process realizes the automation, visualization, and personalized optimization of intelligent transportation equipment configuration, greatly improving configuration efficiency and feasibility.

[0081] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. An automatic configuration method for intelligent transportation equipment, characterized in that: Includes the following steps: Step 1: Based on traffic design specifications, define different traffic scenario codes and create an associated equipment configuration table for each scenario code. The equipment configuration table includes equipment type, configuration necessity, and calculation benchmark rules. Step 2: Establish a library of device quantity calculation algorithms associated with the scene encoding and device type; Step 3: Receive the core parameters of the target traffic scenario input by the user, and verify the validity of the core parameters; Step 4: Based on the core parameters that have passed verification, determine the target scene code using the scene mapping function; The algorithm in the device quantity calculation algorithm library corresponding to the target scene code is called, and the dynamic variables in the core parameters are combined to calculate the installation quantity of various intelligent transportation devices; Step 5: Based on the equipment configuration table corresponding to the target scenario code and the calculated number of equipment to be installed, automatically generate and output the equipment configuration list and the engineering quantity statistics table.

2. The automatic configuration method for intelligent transportation equipment according to claim 1, characterized in that: In step 1, the traffic scenario includes intersection scenarios and road segment scenarios; The method for generating scene codes is as follows: For intersection scenes, the code adopts a three-dimensional coding rule of first road level, second road level, and intersection type; for road segment scenes, the code adopts a two-dimensional coding rule of road segment level and scene type, and the code also includes road segment type information.

3. The automatic configuration method for intelligent transportation equipment according to claim 2, characterized in that: In step 3, the core parameters are categorized by scene type as follows: For intersection scenarios, the core parameters include the type of the first intersecting road. Second type of intersecting road The intersection layout and the number of lanes in the four directions of east, south, west, and north. , , , ; For road segment scenarios, the core parameters include: road segment level R, road segment length. And the list of key nodes N.

4. The automatic configuration method for intelligent transportation equipment according to claim 3, characterized in that: In step 3, validity verification includes range verification, logical verification, and integrity verification; Range checking is used to check whether the values ​​of each core parameter are within the preset enumeration range or numerical range; Logical checks are used to verify the match between the intersection configuration and the number of lanes at the approach. Integrity verification is used to check whether the core parameters of the corresponding scenario type are missing; If the verification fails, the user will be prompted to re-enter the information, and the error type and correction instructions will be explained. If the verification passes, proceed to step 4.

5. The automatic configuration method for intelligent transportation equipment according to claim 4, characterized in that: In step 4, the scene mapping function expression is: ;in Encode the target scene. In an intersection scenario, the road segment scenario represents the hierarchy of two intersecting roads. Omitted, only input the road segment level R; T is the scene type identifier, T=1 represents an intersection scene, and T=2 represents a road segment scene; F(·) is a preset mapping function used to map the input parameters to a unique scene code.

6. The automatic configuration method for intelligent transportation equipment according to claim 5, characterized in that: In step 4, the device quantity calculation algorithm library includes quantitative calculation formulas for video surveillance devices. The number of video surveillance devices is calculated according to scene type: Number of video surveillance devices at intersections Based on the road level in the scene code, distinguish between high-demand scenes and low-demand scenes; High-demand scenarios are intersections where at least one intersecting road is an expressway or arterial road, and the calculation formula is: ； The low-demand scenario is an intersection where both intersecting roads are secondary arterial roads or local roads. The calculation formula is: ； Road segment scenario, number of video surveillance devices The calculation formula is: ； in It is a cross-form correction coefficient. Let i be the number of lanes at the i-th entrance. , For the preset lane number threshold and D is the preset equipment spacing benchmark value based on the road section level. It is a rounding function. This is the floor function. For conditional functions, The number of critical nodes. To sum the parameters of the four inlet channels.

7. The automatic configuration method for intelligent transportation equipment according to claim 6, characterized in that: The device quantity calculation algorithm library contains quantitative calculation formulas for electronic police systems, which include electronic police capture units and supporting supplementary lighting. Number of electronic police camera units The calculation formula is: ； in To match the number of lanes on the import lane Related piecewise functions; Number of supporting supplementary lights The calculation formula is: ； in This is the average number of lanes for each approach lane. , The calculation result is rounded to the nearest integer.

8. The automatic configuration method for intelligent transportation equipment according to claim 7, characterized in that: In step 4, the equipment quantity calculation algorithm library also includes quantitative calculation formulas for traffic light systems, traffic flow detection equipment, and road section radar checkpoint equipment: Number of traffic light groups for motor vehicles : Where B represents the number of standard configuration sets for a single entrance lane. This is a correction factor based on the number of lanes in the approach lane; Number of pedestrian traffic lights : ； Number of traffic flow detection devices : ； Road section radar equipment : ; This refers to the number of lanes in a road segment. Number of checkpoint capture units : This means that every 3 radar devices integrate 1 checkpoint capture unit.

9. The automatic configuration method for intelligent transportation equipment according to claim 8, characterized in that: It also includes steps for optimizing the solution interaction, specifically: It provides a visual user interface that divides the devices in the device configuration table into mandatory devices and optional devices according to their configuration necessity. Mandatory devices cannot be adjusted, and users can enable or disable optional devices. After the user makes adjustments, the system recalculates the number of related dependent devices in real time, updates the device configuration list and engineering quantity statistics table, and can call the graphics engine to generate a visual device layout diagram and output a complete configuration plan report.

10. An automatic configuration query system for intelligent transportation equipment, characterized in that, The device includes a memory, a processor, and a display. The memory stores a computer program designed using the automatic configuration method for the intelligent transportation equipment according to claim 9. The processor executes the computer program to output query results, and the display shows the query results.

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