An AI-based method and system for optimizing municipal engineering construction schemes

Abstract

This invention relates to the field of big data processing technology, specifically disclosing an artificial intelligence-based method and system for optimizing municipal engineering construction plans. The method includes the following steps: S1: Obtain the total length of the construction section, periodically obtain the traffic flow of the construction section, calculate the traffic flow threshold based on the total length of the construction section and the number of remaining lanes, and mark the congestion cycle; S2: When a congestion cycle is detected, obtain monitoring images of the two intersections with the shortest distances from the start and end points of the construction section, obtain the changing patterns of the intersection traffic lights, and determine the number of vehicles waiting to enter based on the direction of the directional arrows; S3: Calculate the theoretical traffic flow and the comprehensive traffic flow, calculate the overflow traffic flow, and adjust the changing patterns of the intersection traffic lights. By judging the road congestion situation based on the traffic flow of the construction section, the corresponding two intersections are linked, and the changing patterns of the intersection signals are reasonably adjusted to reduce the probability of congestion in congested sections and avoid regional traffic paralysis.

Description

Technical Field

[0001] This invention relates to the field of big data processing technology, specifically to a method and system for optimizing municipal engineering construction schemes based on artificial intelligence. Background Technology

[0002] Traffic diversion is a crucial aspect of municipal engineering, considered a lifeline in road, pipeline, and bridge construction. Inadequate planning can not only cause severe traffic congestion but also directly lead to safety accidents, resulting in significant negative public opinion.

[0003] Some construction companies have arbitrarily reduced four-lane roads to two-lane roads, thinking that as long as traffic can pass, it's fine, without conducting on-site surveys and data analysis of existing traffic flow during peak hours and off-peak hours on the construction section and surrounding parallel roads. This approach is highly likely to cause regional traffic paralysis. It's important to understand that when the traffic flow originally handled by four lanes is squeezed into only two lanes, the road's efficiency drops drastically. Furthermore, without prior guidance and diversion of traffic from the surrounding road network, vehicles cannot be dispersed in time, and congestion will quickly spread from the construction site to the entire area, ultimately paralyzing traffic throughout the region, causing great inconvenience to citizens and severely impacting the normal operation of the city. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for optimizing municipal engineering construction schemes based on artificial intelligence, thereby solving the above-mentioned technical problems.

[0005] The objective of this invention can be achieved through the following technical solutions: An artificial intelligence-based method and system for optimizing municipal engineering construction schemes includes the following steps: S1: Obtain the total length L of the construction section, periodically obtain the traffic flow C of the construction section at a preset monitoring period T, and calculate the traffic flow threshold C based on the total length L of the construction section and the number of remaining lanes N. max When traffic volume C > C max The corresponding monitoring period is recorded as the congestion period. S2: When a congestion period is detected, sequentially acquire monitoring images of intersections A and B, which have the shortest distances to the start and end points of the construction section, and perform the following operations on intersections A and B in sequence: Obtain the changing pattern of the traffic lights at the intersection, where the changing pattern refers to the duration T of the left-turn green light at the intersection. L The duration of the green light for straight-ahead traffic is T. S And the duration of the right turn green light (T) R The number of vehicles to enter (CY) is determined based on the direction of the guide arrows; S3: Calculate the theoretical traffic flow C enter =CY / (T) L +T S +T R ), and calculate the total traffic flow C sta =C_A enter +C_B enter , where C_A enter C_B represents the theoretical traffic flow at intersection A. enter This represents the theoretical traffic flow at intersection B; When the total traffic flow C sta >C max At that time, calculate the overflow traffic flow. Based on overflow traffic flow Adjust the changing patterns of traffic lights at intersections.

[0006] As a further aspect of the present invention: in step S3, based on the overflow traffic flow... Methods for adjusting the changing patterns of traffic lights at intersections include: Record the lane where the left-turn green light is on as the target lane, and obtain the number of vehicles (CM) in the target lane. If CM > T... L ×C S At that time, update the left-turn green light duration, where C S This represents the traffic flow when vehicles pass through the intersection. The updated left-turn green light duration will be recorded as TX. L And satisfy the following constraints , Representative to Round down; Based on the above method, the remaining green light duration T for straight-ahead traffic will be calculated. S And the duration of the right turn green light (T) R TX is obtained by updating sequentially. S and TX R .

[0007] As a further aspect of the present invention: in step S1, a traffic flow threshold C is calculated based on the total length L of the construction section and the remaining number of lanes N. max The methods include: Calculate the traffic flow threshold C max =N×C lane ×(L sta / L), where C lane L represents the single-lane capacity. sta This represents the preset ideal construction distance.

[0008] As a further aspect of the present invention: in step S3, when the comprehensive traffic flow C sta ≤Cmax When a congestion occurs, stop adjusting the traffic light patterns at the intersection and start the next monitoring cycle until a congestion cycle occurs.

[0009] As a further aspect of the present invention: in step S2, when the actual number of waiting vehicles at intersection A and intersection B exceeds the number recorded in the monitoring screen, the number of waiting vehicles in the monitoring screen shall be taken as the standard.

[0010] As a further aspect of the present invention: when CM≤T L ×C S At that time, the update of the traffic light change pattern at the intersection will cease.

[0011] As a further aspect of the present invention: in step S2, if only one-way passages are retained in the construction section, the data of closed passages will be removed from the subsequent calculations and will not be included in the calculations.

[0012] An artificial intelligence-based municipal engineering construction scheme optimization system includes: Data acquisition module: Obtains the total length L of the construction section, periodically acquires the traffic flow C of the construction section at a preset monitoring period T, and calculates the traffic flow threshold C based on the total length L of the construction section and the number of remaining lanes N. max When traffic volume C > C max The corresponding monitoring period is recorded as the congestion period. Monitoring module: When a congestion period is detected, the module sequentially acquires monitoring images of intersections A and B, which have the shortest distances to the start and end points of the construction section, and performs the following operations on intersections A and B in sequence: Obtain the changing pattern of the traffic lights at the intersection, where the changing pattern refers to the duration T of the left-turn green light at the intersection. L The duration of the green light for straight-ahead traffic is T. S And the duration of the right turn green light (T) R The number of vehicles to enter (CY) is determined based on the direction of the guide arrows; Adjustment module: Calculates theoretical traffic flow C enter =CY / (T) L +T S +T R ), and calculate the total traffic flow C sta =C_A enter +C_B enter , where C_A enter C_B represents the theoretical traffic flow at intersection A. enter This represents the theoretical traffic flow at intersection B; When the total traffic flow C sta >C max At that time, calculate the overflow traffic flow. Based on overflow traffic flow Adjust the changing patterns of traffic lights at intersections.

[0013] The beneficial effects of this invention are as follows: First, this invention achieves dynamic tracking of road conditions by periodically acquiring data on the total length of the construction section, the number of remaining lanes, and traffic flow. Combining these parameters to calculate traffic flow thresholds allows for more accurate identification of congestion cycles. Replacing subjective judgment with objective data provides a reliable basis for subsequent traffic flow control and reduces human error.

[0014] Furthermore, when congestion is detected, the system automatically retrieves monitoring footage from the two key intersections closest to the start and end points of the construction section and analyzes their traffic light patterns. This upstream-downstream collaborative strategy optimizes traffic flow distribution at both entrances and exits, avoiding bottleneck shifts caused by single-node control. Subsequently, directional arrows are used to assist in prediction, and intersection directional signs are used to determine the number of vehicles waiting to enter, anticipating potential conflict points and making signal timing adjustments more targeted.

[0015] It distinguishes between theoretical traffic flow, comprehensive traffic flow, and overflow traffic flow to accurately pinpoint the specific value of excess traffic demand. Theoretical traffic flow represents ideal capacity, while overflow traffic flow represents the portion exceeding the limit. Furthermore, adaptive signal optimization is added, dynamically adjusting traffic light durations based on the above calculations to orderly disperse vehicles exceeding road capacity across time dimensions. This can be achieved by extending green wave intervals and using various methods to limit the total inflow.

[0016] In summary, this solution integrates real-time data from construction sections with signal control at related intersections to construct a dynamic traffic optimization system. Its core objective is to drive an intelligent response mechanism using precise, quantified congestion assessment criteria. When anomalies are detected, it automatically captures monitoring images of key nodes and analyzes traffic light cycle patterns, combining this with vehicle queuing characteristics in guide lanes to predict merging demand. It calculates overflow load using the difference between theoretical and actual capacity, dynamically adjusting intersection timing schemes accordingly to achieve coordinated traffic flow between upstream and downstream areas. This closed-loop data management model not only prevents localized congestion from spreading to other areas in advance but also significantly improves the overall traffic efficiency of the road network through flexible allocation of spatiotemporal resources, effectively preventing cascading traffic paralysis caused by construction and maintaining relative stability during project implementation. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart illustrating an artificial intelligence-based municipal engineering construction scheme optimization system according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 As shown, this invention is a method and system for optimizing municipal engineering construction schemes based on artificial intelligence, comprising the following steps: S1: Obtain the total length L of the construction section, periodically obtain the traffic flow C of the construction section at a preset monitoring period T, and calculate the traffic flow threshold C based on the total length L of the construction section and the number of remaining lanes N. max When traffic volume C > C max The corresponding monitoring period is recorded as the congestion period. S2: When a congestion period is detected, sequentially acquire monitoring images of intersections A and B, which have the shortest distances to the start and end points of the construction section, and perform the following operations on intersections A and B in sequence: Obtain the changing pattern of the traffic lights at the intersection, where the changing pattern refers to the duration T of the left-turn green light at the intersection. L The duration of the green light for straight-ahead traffic is T. S And the duration of the right turn green light (T) R The number of vehicles to enter (CY) is determined based on the direction of the guide arrows; S3: Calculate the theoretical traffic flow C enter =CY / (T) L +T S +T R ), and calculate the total traffic flow C sta =C_A enter +C_B enter , where C_A enter C_B represents the theoretical traffic flow at intersection A. enter This represents the theoretical traffic flow at intersection B; When the total traffic flow C sta >C max At that time, calculate the overflow traffic flow. Based on overflow traffic flow Adjust the changing patterns of traffic lights at intersections.

[0021] It should be noted that, firstly, this invention achieves dynamic monitoring and precise tracking of road traffic conditions by periodically acquiring data on the total length of the construction section, the number of remaining lanes, and real-time traffic flow. Based on this road data, the system can dynamically calculate reasonable traffic flow thresholds by combining actual road capacity with changes in traffic demand, thereby identifying the occurrence cycle and severity of traffic congestion. This method, based on real-time monitoring data, replaces traditional subjective judgments relying on human experience, significantly improving the reliability and timeliness of congestion identification. Furthermore, the system provides reliable decision support for traffic management departments, enabling them to implement timely traffic control measures, such as dynamic signal control, lane allocation optimization, and route guidance, effectively alleviating traffic pressure and comprehensively improving the intelligence level and operational efficiency of traffic organization in construction sections.

[0022] Furthermore, when the system detects congestion in a construction zone, it automatically retrieves monitoring footage from the nearest upstream and downstream key intersections to the start and end points of the construction area, and analyzes the phase and cycle changes of their traffic lights in real time. Through this upstream and downstream coordinated control mechanism, the system can simultaneously optimize vehicle traffic sequences in the entrance and exit directions, effectively avoiding traffic bottleneck shifts or secondary congestion caused by single-point signal adjustments. Further, the system introduces intelligent recognition of directional arrow signs, accurately predicting the conflict trends and accumulation patterns of traffic flows in different directions by analyzing lane guidance signs at intersections and the number of corresponding waiting vehicles. Based on this prediction, the system can proactively and differentiately adjust signal timing schemes, dynamically extending or shortening specific phases to more effectively alleviate congestion points and improve overall road network traffic efficiency.

[0023] Then, by distinguishing between theoretical traffic volume, total traffic volume, and overflow traffic volume, the system achieves precise quantification and positioning of excess traffic demand. Theoretical traffic volume refers to the maximum designed capacity of the construction section under ideal road conditions, representing the theoretical upper limit of traffic flow. Total traffic volume is the actual traffic load sensed in real time by sensors and monitoring systems. Overflow traffic volume is the difference between total traffic volume and theoretical traffic volume; it precisely characterizes the specific value of excess traffic demand exceeding the road's inherent carrying capacity and is the direct cause of congestion.

[0024] Based on the precisely calculated overflow flow figures, the system integrates an adaptive signal optimization module. The core strategy of this module is no longer simply to meet all traffic demands, but rather to intelligently and systematically distribute vehicles exceeding the physical space's carrying capacity across time dimensions by dynamically adjusting the signal timings at key upstream intersections. Specifically, the system can achieve refined control of the total inflow through various collaborative methods: for example, dynamically extending the green wave interval of upstream intersection signals to moderately increase vehicle travel time within the road network without causing secondary congestion, thereby widening the spatiotemporal distribution of vehicles arriving at construction sites; or by shortening the green light duration towards construction sites and increasing red light waiting times to suppress inflows at the source. This series of operations collectively constitutes a negative feedback control system, the fundamental purpose of which is to match the actual inflow rate with the dynamic capacity of the road, thereby systematically alleviating congestion from both spatiotemporal dimensions and improving the overall operational efficiency of the regional road network.

[0025] In another preferred embodiment of the invention, based on overflow traffic flow Methods for adjusting the changing patterns of traffic lights at intersections include: Record the lane where the left-turn green light is on as the target lane, and obtain the number of vehicles (CM) in the target lane. If CM > T... L ×C S At that time, update the left-turn green light duration, where C S This represents the traffic flow when vehicles pass through the intersection. The updated left-turn green light duration will be recorded as TX. L And satisfy the following constraints , Representative to Round down; Based on the above method, the remaining green light duration T for straight-ahead traffic will be calculated. S And the duration of the right turn green light (T) R TX is obtained by updating sequentially. S and TX R .

[0026] It is worth noting that the first step is to determine whether there are enough vehicles to enter to make an adjustment. When there are few vehicles or no vehicles waiting in the target lane, it will not cause any impact even if vehicles enter the construction section. On the contrary, if an adjustment is made, although the impact on the subsequent construction section is small, it will cause other impacts because the traffic lights at the intersection are connected to other intersections. Therefore, the principle of this invention is to avoid making adjustments as much as possible while ensuring normal traffic flow in the construction section.

[0027] First, obtain the weight percentage of the traffic lights when different turns are lit. Then, use the obtained weight percentage to calculate the matched overflow traffic flow. Next, use the ratio of the matched overflow traffic flow to the traffic flow when vehicles pass through the intersection to calculate the ratio. At this point, the unit of the ratio has been eliminated. Multiply the ratio by the original green light duration to obtain the excess green light duration. Finally, subtract the two obtained times to get an updated green light duration.

[0028] The scheme was designed with full consideration of the complexity and chain reaction characteristics of the traffic system, establishing an intelligent decision-making logic centered on cautious intervention and necessary adjustments. Its core concept is that if the number of vehicles merging into the construction section is small—for example, the traffic request per unit time does not reach the system's set safety threshold, or the monitoring screen shows no obvious queuing or congestion in that direction—then it is determined that even if a few vehicles merge into the construction area during this period, it will not cause a significant decrease in traffic efficiency. In this case, the system will proactively maintain the existing signal timing scheme to avoid disturbing the already stable regional road network operation rhythm due to unnecessary adjustments. This restrained approach particularly emphasizes controlling the indirect impact on related intersections, because a signal change at any node in the urban road network can trigger a cascading response at multiple surrounding intersections through traffic flow transmission effects; excessively frequent intervention may lead to larger-scale traffic fluctuations. Only when the system comprehensively assesses and confirms that the current lane's traffic density is approaching or exceeding the road's carrying capacity threshold, and predicts that continued neglect will lead to a precipitous drop in the construction area's traffic efficiency, will precise local optimization measures be initiated. This hierarchical response mechanism based on dynamic threshold judgment not only ensures that the basic traffic function of the construction section is not slightly disturbed, but also minimizes the butterfly effect on the overall urban road network. It realizes the transformation of traffic management from passive firefighting to proactive prevention, and effectively improves the overall coordination capability under complex road conditions.

[0029] This scheme constructs an intelligent signal optimization mechanism based on dynamic weight allocation and flow matching. Its core lies in achieving adaptive adjustment of green light duration through refined calculation. The system first uses sensing devices to continuously monitor traffic flow distribution characteristics when different turn signals are illuminated, calculating the weight ratio of each direction within a unit cycle. This data intuitively reflects the urgency of different driving demands and the rationality of resource utilization. Based on this weight system, the algorithm further derives the corresponding overflow traffic flow, i.e., the additional traffic load exceeding the current road infrastructure capacity. Subsequently, this overflow amount is compared with the total traffic flow when vehicles actually pass through the intersection. Since both use standardized measurement methods, this step naturally eliminates the constraints of traditional units, forming a dimensionless congestion coefficient indicator. This key parameter is cleverly applied to the recalibration process of green light duration. Multiplying the calculated ratio by the original green light duration yields the theoretically required additional green light duration. Finally, by subtracting or adding this difference from the original set duration, a dynamically optimized new green light timing scheme is generated. This real-time data-driven signal control strategy not only enables precise, drip-irrigation-style allocation of traffic resources, but also transforms complex road conditions into quantifiable decision-making criteria through mathematical modeling, allowing traffic lights to automatically adjust traffic flow like intelligent valves, effectively improving intersection efficiency and stability.

[0030] In another preferred embodiment of the present invention, the traffic flow threshold C is calculated based on the total length L of the construction section and the remaining number of lanes N. max The methods include: Calculate the traffic flow threshold C max =N×C lane ×(L sta / L), where C lane L represents the single-lane capacity. sta This represents the preset ideal construction distance.

[0031] Understandably, the number of currently available lanes on the road section is multiplied by the theoretical capacity of each lane, and then adjusted based on road geometry. The adjustment factor here is the construction section length L obtained from on-site measurements and the industry-recognized ideal construction distance standard value L. sta The proportional relationship between them determines the efficiency of traffic flow. This calculation method fully considers the impact of changes in road conditions on traffic flow. When the construction area is too long, restricting driver behavior and causing frequent lane changes, the effective traffic efficiency will decrease accordingly; conversely, if the construction section is close to the ideal length, it can better maintain a stable driving rhythm and speed. By introducing this dynamic adjustment factor, L... sta / L, so that the calculated traffic flow threshold C maxIt can reflect both the actual supply level of lane resources and the comprehensive impact of road space layout on driving safety and efficiency, thus providing a scientific basis for traffic organization plans.

[0032] In another preferred embodiment of the present invention, when the comprehensive traffic flow C sta ≤C max When a congestion occurs, stop adjusting the traffic light patterns at the intersection and start the next monitoring cycle until a congestion cycle occurs.

[0033] It should be noted that when the total traffic volume C sta ≤C max When the existing road resources are sufficient to handle traffic demand, the system will proactively maintain the existing traffic light timing scheme and change patterns at intersections. This stability control strategy aims to avoid unnecessary frequent interventions that could cause traffic fluctuations. Instead, it enters a continuous monitoring mode, collecting dynamic parameters such as vehicle trajectories, speed distribution, and queue lengths in real time, forming a closed-loop feedback mechanism. Only when traffic flow is detected to be continuously increasing and approaching the warning line within multiple consecutive monitoring cycles, or when a sudden event causes a surge in instantaneous traffic flow triggering a congestion warning, will the system activate an emergency response mechanism to reassess the signal optimization strategy. This working mode ensures smooth and orderly daily traffic flow while allowing for rapid intervention and adjustment when abnormal situations first appear, achieving an organic balance between refined management and efficient handling.

[0034] In another preferred embodiment of the present invention, when the actual number of waiting vehicles at intersection A and intersection B exceeds the number recorded in the monitoring screen, the number of waiting vehicles in the monitoring screen shall prevail.

[0035] It should be noted that the intelligent traffic management system employs a rigorous data calibration mechanism for vehicle queue management at intersections A and B. When the actual number of waiting vehicles captured by the real-time sensing equipment exceeds the baseline value statistically recorded in the historical monitoring footage, the system automatically annotates vehicle outlines and generates a structured dataset through a background image recognition algorithm. Simultaneously, on-duty personnel conduct regular spot checks and comparisons to ensure that the recorded results are unaffected by transient interference. This strategy effectively avoids short-term data distortion caused by sudden traffic surges and prevents the control system from frequently switching signal timing schemes due to overreaction, thereby establishing a stable and reliable decision-making anchor point in a dynamic traffic environment.

[0036] In a preferred embodiment, when CM≤T L ×C S At that time, the update of the traffic light change pattern at the intersection will cease.

[0037] Understandably, on the one hand, T L ×C SThe dynamic safety boundary has been validated through historical big data modeling and can fully accommodate traffic demand within the normal fluctuation range. On the other hand, avoiding frequent changes to the signal plan can prevent chain reactions caused by minor disturbances, especially during sensitive periods such as morning and evening rush hours. A stable signal rhythm helps to form drivers' expected driving patterns. In another preferred embodiment of the present invention, if the construction section only retains a one-way passage, the data of the closed passage will be excluded from the subsequent calculation and will not be included in the calculation.

[0038] It is worth noting that when a road requires only one-way traffic due to engineering needs, the system will automatically activate a data filtering mechanism. This filtering mechanism is based on two considerations: first, in a physically isolated state, there is no legal driving path in the reverse lane, as stationary obstacles or temporary facilities will completely block vehicles from entering; second, avoiding the inclusion of irrelevant data in the algorithm model prevents misleading conclusions and ensures that core calculations such as the capacity assessment of the remaining one-way lane, traffic flow simulation, and signal timing optimization are all based on valid datasets. This mechanism not only improves computational efficiency under complex conditions but also significantly enhances the reliability and feasibility of traffic control plans by eliminating interference factors.

[0039] An artificial intelligence-based municipal engineering construction scheme optimization system includes: Data acquisition module: Obtains the total length L of the construction section, periodically acquires the traffic flow C of the construction section at a preset monitoring period T, and calculates the traffic flow threshold C based on the total length L of the construction section and the number of remaining lanes N. max When traffic volume C > C max The corresponding monitoring period is recorded as the congestion period. Monitoring module: When a congestion period is detected, the module sequentially acquires monitoring images of intersections A and B, which have the shortest distances to the start and end points of the construction section, and performs the following operations on intersections A and B in sequence: Obtain the changing pattern of the traffic lights at the intersection, where the changing pattern refers to the duration T of the left-turn green light at the intersection. L The duration of the green light for straight-ahead traffic is T. S And the duration of the right turn green light (T) R The number of vehicles to enter (CY) is determined based on the direction of the guide arrows; Adjustment module: Calculates theoretical traffic flow C enter =CY / (T) L +T S +T R ), and calculate the total traffic flow C sta =C_A enter +C_B enter , where C_A enter C_B represents the theoretical traffic flow at intersection A.enter This represents the theoretical traffic flow at intersection B; When the total traffic flow C sta >C max At that time, calculate the overflow traffic flow. Based on overflow traffic flow Adjust the changing patterns of traffic lights at intersections.

[0040] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A method for optimizing municipal engineering construction schemes based on artificial intelligence, characterized in that, Includes the following steps: S1: acquire the total length L of the construction section, periodically acquire the traffic volume C of the construction section with a preset monitoring period T, calculate the traffic volume threshold C based on the total length L of the construction section and the remaining lane number N max , when the traffic volume C>C max , the corresponding monitoring period is recorded as the congestion period; S2: When a congestion period is detected, sequentially acquire monitoring images of intersections A and B, which have the shortest distances to the start and end points of the construction section, and perform the following operations on intersections A and B in sequence: Obtain the change rule of the intersection signal light, the change rule indicates the left turn green light time T of the intersection signal light L , the straight green light time T S And the right turn green light time T R , determine the number CY of the vehicle to be driven into based on the direction of the guide arrow; S3: Calculate the theoretical traffic volume C enter = CY / (T L + T S + T R ), and calculate the comprehensive traffic volume C sta = C_A enter + C_B enter , wherein C_A enter represents the theoretical traffic volume of intersection A, and C_B enter represents the theoretical traffic volume of intersection B; When the total traffic flow C sta >C max At that time, calculate the overflow traffic flow. Based on overflow traffic flow Adjust the pattern of traffic light changes at intersections.

2. The method for optimizing municipal engineering construction schemes based on artificial intelligence according to claim 1, characterized in that, In step S3, based on the overflow traffic flow Methods for adjusting the changing patterns of traffic lights at intersections include: The left-turn green light is turned on, and the corresponding lane is recorded as a target lane, and the number of vehicles CM on the target lane is obtained; when CM > T L × C S , the duration of the left-turn green light is updated, wherein C S represents the traffic volume when the vehicle passes through the intersection. The updated left-turn green light duration will be recorded as TX. L And satisfy the following constraints , Representative to Round down; Based on the above method, the remaining green light duration T for straight-ahead traffic will be calculated. S And the duration of the right turn green light (T) R TX is obtained by updating sequentially. S and TX R .

3. The method for optimizing municipal engineering construction schemes based on artificial intelligence according to claim 1, characterized in that, In step S1, the traffic flow threshold C is calculated based on the total length L of the construction section and the number of remaining lanes N. max The methods include: Calculate the traffic flow threshold C max =N×C lane ×(L sta / L), where C lane L represents the single-lane capacity. sta This represents the preset ideal construction distance.

4. The method for optimizing municipal engineering construction schemes based on artificial intelligence according to claim 1, characterized in that, In step S3, when the total traffic flow C sta ≤C max When a congestion occurs, stop adjusting the traffic light patterns at the intersection and start the next monitoring cycle until a congestion cycle occurs.

5. The method for optimizing municipal engineering construction schemes based on artificial intelligence according to claim 1, characterized in that, In step S2, when the actual number of vehicles waiting at intersection A and intersection B exceeds the number recorded in the monitoring screen, the number of vehicles waiting in the monitoring screen shall prevail.

6. The method for optimizing municipal engineering construction schemes based on artificial intelligence according to claim 2, characterized in that, When CM≤T L ×C S At that time, the update of the traffic light change pattern at the intersection will cease.

7. The method for optimizing municipal engineering construction schemes based on artificial intelligence according to claim 1, characterized in that, In step S2, if the construction section only retains a one-way passage, the data of the closed passage will be removed from the subsequent calculations and will not be included in the calculations.

8. A municipal engineering construction scheme optimization system based on artificial intelligence, characterized in that, include: Data acquisition module: Obtains the total length L of the construction section, periodically acquires the traffic flow C of the construction section at a preset monitoring period T, and calculates the traffic flow threshold C based on the total length L of the construction section and the number of remaining lanes N. max When traffic volume C > C max The corresponding monitoring period is recorded as the congestion period. Monitoring module: When a congestion period is detected, the module sequentially acquires monitoring images of intersections A and B, which have the shortest distances to the start and end points of the construction section, and performs the following operations on intersections A and B in sequence: Obtain the changing pattern of the traffic lights at the intersection, where the changing pattern refers to the duration T of the left-turn green light at the intersection. L The duration of the green light for straight-ahead traffic is T. S And the duration of the right turn green light (T) R The number of vehicles to enter (CY) is determined based on the direction of the guide arrows; Adjustment module: Calculates theoretical traffic flow C enter =CY / (T) L +T S +T R ), and calculate the total traffic flow C sta =C_A enter +C_B enter , where C_A enter C_B represents the theoretical traffic flow at intersection A. enter This represents the theoretical traffic flow at intersection B; When the total traffic flow C sta >C max At that time, calculate the overflow traffic flow. Based on overflow traffic flow Adjust the pattern of traffic light changes at intersections.

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