Traffic flow real-time signal control system based on multi-sensor fusion

Abstract

The invention relates to the technical field of intelligent traffic, and particularly discloses a traffic flow real-time signal control system based on multi-sensor fusion. According to the method, timestamp detection, missing value filling and data normalization are carried out on traffic flow data collected by different sensors, the consistency of the traffic flow data is effectively ensured, traffic flow data fusion is further carried out through a Kalman filtering algorithm, the measurement noise of the multi-source sensor is continuously corrected, and the accuracy of traffic flow data fusion is improved. The smoothed traffic flow data is output, and powerful data support is provided for analysis of subsequent signal lamp control strategies; the optimal signal lamp period duration is dynamically solved through a Webster formula, historical rules and real-time requirements are considered, the hysteresis of fixed timing is avoided, the actual signal lamp period duration and the optimal signal lamp period duration are compared, the signal lamp period duration is dynamically adjusted, and then a signal lamp timing scheme is matched according to a traffic jam index. Intelligentization and adaptability of real-time control of the signal lamp and overall traffic capacity of a road are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation, and specifically to a real-time traffic flow signal control system based on multi-sensor fusion. Background Art

[0002] With the acceleration of the urbanization process and the continuous growth of the motor vehicle ownership, the problem of urban traffic congestion has become increasingly severe, and traffic flow management and signal control have become one of the core tasks of urban traffic management. Traditional traffic signal control systems mainly rely on a single type of sensor (such as a geomagnetic coil) to collect traffic flow data, which can only obtain traffic volume data, cannot cover multi-dimensional information such as pedestrian flow, vehicle type ratio, and vehicle speed, and is easily interfered by factors such as road construction and weather, resulting in data loss or errors, thus affecting the accuracy of signal control.

[0003] In addition, most traditional traffic signal control systems adopt fixed timing schemes, whose core basis is historical traffic flow statistical data and cannot real-time sense the dynamic changes of traffic states. For example, during the morning and evening rush hours, the traffic volume and pedestrian flow at intersections increase significantly, and fixed timing is likely to lead to the phenomenon that the green light is released in vain in one direction and vehicles queue up and accumulate in the other direction; while during the off-peak hours, if the peak-hour timing scheme is still used, it will cause waste of road resources. This "one-size-fits-all" control mode is difficult to adapt to the spatio-temporal volatility of traffic flow, resulting in low intersection passing efficiency, frequent congestion problems, and insufficient control rationality.

[0004] To solve the above defects, a technical solution is provided now. Summary of the Invention

[0005] The purpose of the present invention is to provide a real-time traffic flow signal control system based on multi-sensor fusion to solve the problems raised in the above background.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A real-time traffic flow signal control system based on multi-sensor fusion, including: A data acquisition module, which is used to integrate various types of sensors such as geomagnetic sensors, cameras, millimeter-wave radar sensors, and meteorological sensors, collect the original traffic flow data of the target intersection during the current monitoring period and transmit it to the data processing module, and collect the meteorological state data of the target intersection during the current monitoring period and transmit it to the server for storage; A data processing module, which is used to receive the original traffic flow data of the target intersection during the current monitoring period and perform preprocessing operations to obtain the preprocessed traffic flow data of the target intersection during the current monitoring period and transmit it to the data fusion module; A data fusion module, which is used to receive the pre - processed traffic flow data of the target intersection during the current monitoring period, perform data recursive estimation and fusion through the Kalman filtering algorithm, obtain the optimal state estimation of the target intersection during the current monitoring period, extract the optimal estimated traffic flow data of the target intersection during the current monitoring period from it, and transmit it to the traffic congestion analysis module; A traffic congestion analysis module, which is used to receive the optimal estimated traffic flow data of the target intersection during the current monitoring period, and at the same time analyze the traffic congestion index correction factor of the target intersection during the current monitoring period, and thus analyze and obtain the traffic congestion index of the target intersection during the current monitoring period; A signal cycle analysis module, which is used to analyze the optimal signal light cycle duration of the target intersection during the current monitoring period based on the traffic data of the target intersection during the preset historical period and the optimal estimated traffic volume of the target intersection during the current monitoring period; A control strategy generation module, which is used to analyze the signal light control instruction and signal light timing plan of the target intersection during the current monitoring period based on the traffic congestion index and the optimal signal light cycle duration of the target intersection during the current monitoring period; A control terminal, which is used to receive the signal light regulation instruction and signal light timing plan and execute corresponding control operations through the signal light controller.

[0007] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention effectively covers all elements of the traffic flow state by integrating various types of sensors, avoiding the limitations of a single sensor. At the same time, it performs timestamp detection, missing value filling, and data normalization on the traffic flow data collected by different sensors, effectively ensuring the consistency of traffic flow data. Further, it performs traffic flow data fusion through the Kalman filtering algorithm, continuously corrects the measurement noise of multi - source sensors, and then outputs smoothed traffic flow data, effectively eliminating the measurement errors of multi - source sensors, improving data reliability, and providing strong data support for the subsequent analysis of signal control strategies.

[0008] The present invention combines road type, flatness, number of lanes, and vehicle type ratio, calculates the first traffic congestion correction factor through the arctangent function, calculates and matches the second traffic congestion correction factor in combination with meteorological state indicators, quantifies the impact of road inherent attributes and meteorological states on congestion, makes the congestion assessment more in line with the actual scenario, converts traffic flow, speed, and spatial occupancy into the length, width, and height of a cuboid, and uses the volume value as the congestion benchmark index, intuitively reflecting the "scale" of intersection congestion, avoiding the one - sidedness of a single index (such as only using traffic volume), and providing a more comprehensive basis for signal control.

[0009] The present invention calculates the flow ratio by statistically analyzing the mean of the phase switching loss duration and the mode of the saturated flow based on historical data, and combines the current optimal estimated traffic volume. The optimal signal light cycle duration is dynamically solved through Webster's formula, taking into account historical rules and real-time demands, avoiding the lag of fixed timing. By comparing the actual signal light cycle duration with the optimal signal light cycle duration, the signal light cycle duration is dynamically adjusted. Then, according to the traffic congestion index, the signal light timing plan is matched, which improves the intelligence and adaptability of real-time signal control to a certain extent, effectively reduces traffic congestion, and improves the overall traffic capacity of the road. Brief Description of the Drawings

[0010] The present invention will be further described below with reference to the accompanying drawings.

[0011] Figure 1 It is a system block diagram of the present invention. Detailed Embodiment

[0012] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0013] Please refer to Figure 1 As shown, the present invention is a real-time traffic flow signal control system based on multi-sensor fusion. The system includes: a data acquisition module 101, a data processing module 102, a data fusion module 103, a traffic congestion analysis module 104, a signal cycle analysis module 105, a control strategy generation module 106, a control terminal 107, and a server 108. Among them, the data acquisition module 101 is connected to the data processing module 102, the data processing module 102 is connected to the data fusion module 103, the data fusion module 103 is connected to the traffic congestion analysis module 104, the signal cycle analysis module 105 is connected to the control strategy generation module 106, the traffic congestion analysis module 104 is connected to the control strategy generation module 106, the control strategy generation module 105 is connected to the control terminal 107, and the data acquisition module 101, the traffic congestion analysis module 104, and the signal cycle analysis module 105 are respectively connected to the server 108.

[0014] The data acquisition module 101 integrates various types of sensors such as geomagnetic sensors, cameras, millimeter-wave radar sensors, and meteorological sensors, collects the original traffic flow data of the target intersection in the current monitoring period and transmits it to the data processing module, and collects the meteorological state data of the target intersection in the current monitoring period and transmits it to the server for storage.

[0015] Among them, the original traffic flow data includes: vehicle flow, passenger flow, vehicle type ratio, vehicle speed, and vehicle queue length.

[0016] The data processing module 102 is used to receive the original traffic flow data of the target intersection in the current monitoring period and perform preprocessing operations to obtain the preprocessed traffic flow data of the target intersection in the current monitoring period and transmit it to the data fusion module.

[0017] Specifically, the execution process of the preprocessing operation is as follows: 201: Timestamp detection: The original traffic flow data of the target intersection during the current monitoring period , extract all raw traffic flow data recorded during the current monitoring period The timestamps are arranged according to the size of the time to generate a timestamp sequence ; The original traffic flow data Including traffic flow , Traffic 、Model ratio , vehicle speed , Vehicle queue length ; In a timestamp sequence, calculate the time interval between all two adjacent timestamps , when the time interval between two adjacent timestamps is greater than the preset maximum time interval threshold , it is determined that the time interval between the original traffic flow data records corresponding to the two adjacent timestamps is too large, that is, there is data missing between the two adjacent timestamps, and step 202 is executed.

[0018] 202: Missing value calculation and filling: Let the missing timestamp be , and the known traffic flow data before and after it is and , calculated by linear interpolation formula Get missing timestamp and missing timestamps corresponding to missing values ​​of raw traffic flow , and fill in the missing values ​​accordingly.

[0019] It should be noted that timestamp detection, missing value calculation and filling are used to coordinate multi-source data (data collected by different sensors) to maintain consistency in the time base. Its core goal is to solve data asynchrony problems caused by factors such as sensor sampling frequency, clock deviation, and data transmission delay, and to ensure the accuracy of subsequent data processing and fusion.

[0020] 203: Traversal filling: Perform traversal operations on all adjacent two timestamps in all original traffic flow data timestamp sequences in the same operation mode as in steps 201 - 202 to complete the filling of all missing values, and obtain the traffic flow data after filling the missing values for the current monitoring period of the target intersection.

[0021] 204: Data normalization: Normalize the traffic flow data after filling the missing values for the current monitoring period of the target intersection to obtain the preprocessed traffic flow data for the current monitoring period of the target intersection.

[0022] It should be noted that the normalization method is min - max normalization. Normalization is to make the original traffic flow data have the same scale and range, which is convenient for subsequent fusion calculations.

[0023] The data fusion module 103 is used to receive the preprocessed traffic flow data of the target intersection in the current monitoring period and perform data recursive estimation fusion through the Kalman filter algorithm to obtain the optimal traffic flow data estimation values at each moment in the current monitoring period of the target intersection, and extract the optimal traffic flow data estimation values in the current monitoring period of the target intersection and transmit them to the traffic congestion analysis module.

[0024] It should be noted that the optimal traffic flow data estimation values include the optimal vehicle flow estimation value, the optimal pedestrian flow estimation value, the optimal driving speed estimation value, the optimal vehicle type proportion estimation value, and the optimal vehicle queue length estimation value; The method for obtaining the optimal traffic flow data estimation values of the target intersection in the current monitoring period is as follows: Calculate the mean value of the optimal traffic flow data estimation values at each moment in the current monitoring period of the target intersection to obtain the mean value of the optimal traffic flow data estimation of the target intersection in the current monitoring period, and use it as the optimal traffic flow data estimation value of the target intersection in the current monitoring period.

[0025] Specifically, the data recursive estimation fusion through the Kalman filter algorithm includes: 301: Initialization: Preset the initial traffic flow data estimation value and the initial traffic flow data estimation error ; It should be noted that the initial traffic flow data estimation value can be set according to historical data or experience; the initial traffic flow data estimation error represents the degree of uncertainty of the initial traffic flow data estimation value. This value reflects the confidence level in the initial traffic flow data estimation value. The larger the value, the greater the possible deviation between the initial traffic flow data estimation value and the true value, and the higher the uncertainty; the smaller the value, the more confident in the initial traffic flow data estimation value.

[0026] 302: Kalman Gain Calculation: According to the formula calculate the Kalman gain corresponding to the traffic flow data , where represents the sensor measurement error corresponding to the traffic flow data at the current moment. That is, the current moment is the k-th moment, represents the estimated error of the traffic flow data at the previous moment. That is, the previous moment is the (k - 1)-th moment; It should be noted that the Kalman gain is used to balance the estimated error of the traffic flow data at the previous moment and the measurement error of the current traffic flow data to determine the weight of the measurement value when updating the estimated value; the measurement error is obtained from the factory usage instructions of the above-mentioned various types of sensors.

[0027] 303: Prediction: Based on the estimated value of the traffic flow data at the previous moment , the Kalman gain of the traffic flow data at the current moment and the measured value of the traffic flow data at the current moment, use the formula to predict the optimal estimated value of the traffic flow data at the current moment , where represents the measured value of the traffic flow data at the current moment; It should be noted that all the traffic flow data after preprocessing at the target intersection during the current monitoring period are used as the measured values of the traffic flow data at each moment of the current monitoring period.

[0028] 304: Update: According to the formula calculate the estimated error of the traffic flow data at the current moment ; 305: Iterative Operation: Use the optimal estimated value of the traffic flow data at the current moment and the estimated error of the traffic flow data as the input for step 302 at the next moment, and continuously repeat the execution operations of steps 302 - 304, thereby obtaining the optimal estimated values of the traffic flow data at each moment of the target intersection during the current monitoring period.

[0029] It should be noted that the Kalman filter algorithm is a recursive optimal estimation algorithm. Through the prediction-update mechanism, it continuously corrects the sensor measurement noise (such as random interference and inherent errors of the sensor), and outputs the smoothed traffic flow data. When collecting data with multiple sensors, it will be affected by various random interferences and inherent errors. For example, the geomagnetic sensor may be interfered by the surrounding magnetic field changes, and the camera will produce image blurring and data errors in poor light conditions, resulting in insufficient accuracy of the collected data. The Kalman filter algorithm can effectively reduce the influence of noise on the data and make the output traffic flow data smoother and more accurate.

[0030] In a specific embodiment, the present invention effectively covers all elements of the traffic flow state by integrating various types of sensors, avoiding the limitations of a single sensor. At the same time, timestamp detection, missing value filling, and data normalization are performed on the traffic flow data collected by different sensors to effectively ensure the consistency of traffic flow data. Further, the Kalman filtering algorithm is used for traffic flow data fusion to continuously correct the measurement noise of multi-source sensors, and then smooth traffic flow data is output, effectively eliminating the measurement error of multi-source sensors and improving data reliability, providing strong data support for the analysis of subsequent signal light control strategies.

[0031] The traffic congestion analysis module 104 is used to receive the optimal traffic flow data estimation value of the target intersection in the current monitoring period, and at the same time analyze the traffic congestion index correction factor of the target intersection in the current monitoring period, thereby obtaining the traffic congestion index of the target intersection in the current monitoring period.

[0032] Specifically, the analysis process of the traffic congestion index correction factor of the target intersection in the current monitoring period is as follows: Obtain the road type, road flatness, and number of lanes of the target intersection from the server, and at the same time extract the optimal estimated vehicle type proportion of the target intersection in the current monitoring period, and match them with the traffic congestion index correction factor influence values corresponding to each road type, each road flatness, each number of lanes, and each vehicle type proportion stored in the server to obtain the traffic congestion index correction factor influence values corresponding to the road type, road flatness, number of lanes, and vehicle type proportion of the target intersection in the current monitoring period, which are respectively denoted as ; It should be noted that the road flatness is collected by a laser profilometer.

[0033] Substitute into the preset arctangent function Calculate to obtain the first traffic congestion index correction factor of the target intersection in the current monitoring period ; Extract the meteorological state data of the target intersection in the current monitoring period from the server, and screen out each meteorological state index of the target intersection in the current monitoring period. Compare each meteorological state index with its corresponding standard index. If a certain climate environment index is greater than its corresponding standard index, then calculate the difference between the two to obtain the high index difference. If a certain climate environment index is less than its corresponding standard index, then calculate the difference between the two to obtain the low index difference; It should be noted that the meteorological state indicators include visibility, wind speed, rainfall, snowfall, visibility.

[0034] Calculate the mean values of the high-index differences and low-index differences respectively to obtain the high-index difference mean value and the low-index difference mean value. Input the high-index difference mean value and the low-index difference mean value into the image processor. The graphics processor converts them into numerical values according to a certain ratio and inputs them into the line chart respectively to obtain two corresponding points. Connect the two points in sequence with line segments to obtain a broken line. Draw perpendicular lines to the X-axis at the two endpoints of the broken line, so that the broken line and the two perpendicular lines form a closed figure with the X-axis. Identify the area of the closed figure as the meteorological state index of the target intersection during the current monitoring period; Match the meteorological state index of the target intersection during the current monitoring period with the meteorological state indices corresponding to each preset second traffic index correction factor to obtain the second traffic congestion index correction factor of the target intersection during the current monitoring period; Perform a summation calculation on the first traffic congestion index correction factor and the second traffic congestion index correction factor to obtain the comprehensive traffic congestion index correction factor.

[0035] Specifically, the analysis process of the traffic congestion index of the target intersection during the current monitoring period is as follows: Obtain the traffic flow value, traffic speed value, and space occupancy rate of the target intersection during the current monitoring period, and perform ratio calculations on them with the preset maximum traffic flow value, minimum traffic speed value, and maximum space occupancy rate respectively to obtain the traffic flow ratio, traffic speed ratio, and space occupancy rate ratio of the target intersection during the current monitoring period. Convert them into lengths according to a preset ratio. Construct a cuboid with the lengths of the traffic flow ratio, traffic speed ratio, and queue length ratio as the length, width, and height respectively. Extract the numerical value of the volume of the cuboid as the congestion benchmark index of the target intersection during the current monitoring period. Perform a multiplication calculation on the congestion benchmark index and the comprehensive traffic congestion index correction factor to obtain the traffic congestion index of the target intersection during the current monitoring period.

[0036] It should be noted that the acquisition process of the traffic flow value, traffic speed value, and space occupancy rate is as follows: Extract the estimated optimal vehicle flow value and the estimated optimal pedestrian flow value of the target intersection during the current monitoring period from the estimated optimal traffic flow data value of the target intersection during the current monitoring period, and perform a summation calculation to obtain the traffic flow. Extract the estimated optimal vehicle driving speed value and the estimated optimal pedestrian driving speed value of the target intersection during the current monitoring period, and perform a summation calculation to obtain the traffic speed value. Extract the estimated optimal vehicle queue length value of the target intersection during the current monitoring period, and perform a ratio calculation with the total length of the target intersection to obtain the space occupancy rate of the target intersection during the current monitoring period.

[0037] In a specific embodiment, the present invention combines road type, flatness, number of lanes, and vehicle type ratio, calculates the first traffic congestion correction factor through the arctangent function, and calculates and matches the second traffic congestion correction factor in combination with meteorological state indicators, quantifying the impact of road inherent attributes and meteorological states on congestion, making the congestion assessment more in line with the actual scenario, converting traffic flow, speed, and spatial occupancy into the length, width, and height of a cuboid, and using the volume value as the congestion benchmark index to intuitively reflect the "scale" of intersection congestion, avoiding the one-sidedness of a single indicator (such as only using traffic flow), and providing a more comprehensive basis for signal control.

[0038] The signal cycle analysis module 105 is used to analyze the optimal signal cycle duration of the target intersection in the current monitoring period based on the traffic data of the target intersection in the preset historical period and the optimal estimated traffic flow of the target intersection in the current monitoring period.

[0039] Specifically, the analysis process of the optimal signal cycle duration of the target intersection in the current monitoring period is as follows: Extract the total loss duration of phase switching within each signal cycle duration of the target intersection in the preset historical period from the server, calculate its mean value, obtain the mean value of the total loss duration of phase switching of the target intersection in the preset historical period, and use it as the total loss duration of phase switching of the target intersection in the current monitoring period, denoted as ; Extract the number of vehicles passing through the stop line during the green light period within each signal cycle duration of the target intersection in the preset historical period from the server, obtain the saturated flow rate within each signal cycle duration of the target intersection through the saturated flow rate calculation formula, and extract the mode of the saturated flow rate within each signal cycle duration as the saturated flow rate of the target intersection in the current monitoring period; Calculate the ratio of the optimal traffic flow estimate value of the target intersection in the current monitoring period to the saturated flow rate, obtain the optimal estimated flow ratio of the target intersection in the current monitoring period, denoted as ; Through the Webster algorithm formula Calculate the optimal signal cycle duration of the target intersection in the current monitoring period 。

[0040] The control strategy generation module 106 is used to analyze the signal control instruction and signal timing plan of the target intersection in the current monitoring period based on the traffic congestion index and the optimal signal cycle duration of the target intersection in the current monitoring period.

[0041] Specifically, the process of analyzing the signal control instruction and signal timing plan of the target intersection in the current monitoring period is as follows: Extract the actual signal light cycle duration of the target intersection during the current monitoring period, and compare and analyze it with the optimal signal light cycle duration of the target intersection during the current monitoring period. If the optimal signal light cycle duration is inconsistent with the actual signal light cycle duration, generate a signal light control instruction and send the signal light control instruction to the control terminal; Extract the traffic congestion index of the target intersection during the current monitoring period, and match the traffic congestion index of the target intersection during the current monitoring period with the signal light timing plan to which each optimal signal light cycle duration corresponding to each traffic congestion index in the preset belongs, to obtain the signal light timing plan of the target intersection during the current monitoring period. Among them, the signal light timing plan includes the green light duration, the red light duration, and the yellow light duration, and send the signal light timing plan to the control terminal.

[0042] The control terminal 107 is used to receive the signal light control instruction and the signal light timing plan and execute corresponding control operations through the signal light controller.

[0043] In a specific embodiment, the present invention statistically calculates the mean value of the phase switching loss duration and the mode of the saturated flow based on historical data, combines the current optimal estimated traffic flow to calculate the flow ratio, dynamically solves the optimal signal light cycle duration through the Webster formula, takes into account historical laws and real-time requirements, avoids the lag of fixed timing, compares the actual signal light cycle duration and the optimal signal light cycle duration, dynamically adjusts the signal light cycle duration, and then matches the signal light timing plan according to the traffic congestion index, to improve the intelligence and adaptability of the signal light real-time control to a certain extent, effectively reduce traffic congestion phenomena, and improve the overall traffic capacity of the road.

[0044] The server 108 is used to store the meteorological state data of the target intersection during the current monitoring period, store the road type, road flatness, and number of lanes of the target intersection, store the influence values of the traffic congestion index correction factors corresponding to each road type, each road flatness, each number of lanes, and each vehicle type ratio, and store the total phase switching loss duration and the number of vehicles passing through the stop line during the green light period within each signal light cycle duration in the preset historical period.

[0045] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of the present technology make various modifications or supplements or use similar methods to replace the specific embodiments described, as long as they do not deviate from the structure of the invention or exceed the scope defined by this claims, they should all belong to the protection scope of the present invention.

Claims

1. A real-time traffic flow signal control system based on multi-sensor fusion, characterized in that, Including: A data acquisition module, configured to acquire the original traffic flow data and meteorological state data of a target intersection during a current monitoring period and transmit them to a data processing module and a server respectively; A data processing module, configured to receive the original traffic flow data of a target intersection during a current monitoring period and perform preprocessing operations to obtain traffic flow data and transmit it to a data fusion module; A data fusion module, which preprocesses the traffic flow data of a target intersection during a current monitoring period and performs data recursive estimation fusion through a Kalman filter algorithm to obtain the optimal traffic flow data estimation values at each moment of the target intersection during the current monitoring period, and extracts the optimal traffic flow data estimation values of the target intersection during the current monitoring period and transmits them to a traffic congestion analysis module; A traffic congestion analysis module, configured to receive the optimal traffic flow data estimation values of a target intersection during a current monitoring period and analyze the traffic congestion index correction factor at the same time to obtain a traffic congestion index; A signal cycle analysis module, configured to analyze the optimal signal light cycle duration of a target intersection during a current monitoring period based on the traffic data of the target intersection during a preset historical period and the optimal estimated traffic volume of the target intersection during the current monitoring period; A control strategy generation module, configured to analyze the signal light control instruction and signal light timing plan of a target intersection during a current monitoring period based on the traffic congestion index and the optimal signal light cycle duration; A control terminal, configured to receive the signal light regulation instruction and signal light timing plan and execute corresponding control operations through a signal light controller.

2. The real-time traffic flow signal control system based on multi-sensor fusion according to claim 1, wherein The execution process of the preprocessing operation is as follows: 201: Timestamp Detection: For the original traffic flow data of the target intersection during the current monitoring period , extract all the timestamps of the original traffic flow data recorded during the current monitoring period and arrange them in ascending order of time to generate a timestamp sequence ; The original traffic flow data includes traffic volume , pedestrian volume , vehicle type proportion , vehicle driving speed and vehicle queue length ; In the timestamp sequence, calculate the time interval between all adjacent timestamps , when there is a time interval between two adjacent timestamps greater than the preset maximum threshold of the time interval , it is determined that the time interval of the original traffic flow data corresponding to the two adjacent timestamps is too large, that is, there is data missing between the two adjacent timestamps, and step 202 is executed; 202: Missing value calculation and filling: Let the missing timestamp be , and the known data points adjacent to it before and after are and . Through the linear interpolation calculation formula , the missing timestamp and the missing value of the original traffic flow corresponding to the missing timestamp are obtained, and the corresponding missing value filling is performed; 203: Traversal and filling: Traverse all adjacent two timestamps in all original traffic flow data timestamp sequences in the same operation mode as steps 201 - 202 to complete the filling of all missing values, and obtain the traffic flow data after missing value filling of the target intersection during the current monitoring period; 204: Data normalization: Normalize the traffic flow data after missing value filling of the target intersection during the current monitoring period to obtain the preprocessed traffic flow data of the target intersection during the current monitoring period.

3. The real-time traffic flow signal control system based on multi-sensor fusion according to claim 1, characterized in that The data recursive estimation fusion through the Kalman filter algorithm includes: 301: Initialization: Preset the estimated value of the initial traffic flow data and the estimated error of the initial traffic flow data ; 302: Kalman gain calculation: According to the formula the Kalman gain corresponding to the traffic flow data is calculated , where represents the sensor measurement error corresponding to the traffic flow data at the current moment, that is, the current moment is the k-th moment, represents the estimated error of the traffic flow data at the previous moment, that is, the previous moment is the (k-1)-th moment; 303: Prediction: Estimate based on the traffic flow data estimate at the previous moment , the Kalman gain of the traffic flow data at the current moment and the measured value of the traffic flow data at the current moment, using the formula to predict the optimal traffic flow data estimate at the current moment , where represents the measured value of the traffic flow data at the current moment; 304: Update: Calculate the estimated error of traffic flow data at the current moment according to the formula ; ; 305: Iterative operation: Take the optimal traffic flow data estimation value and traffic flow data estimation error at the current moment as the input of step 302 at the next moment, and continuously repeat the execution operations of steps 302 - 304, thereby obtaining the optimal traffic flow data estimation values at each moment of the target intersection during the current monitoring period.

4. The real-time traffic flow signal control system based on multi-sensor fusion according to claim 1, characterized in that, The analysis process of the traffic congestion index correction factor of the target intersection during the current monitoring period is as follows: Extract the road type, road flatness, number of lanes, and vehicle type ratio of the target intersection during the current monitoring period, and match them with the traffic congestion index correction factor influence values corresponding to each road type, each road flatness, each number of lanes, and each vehicle type ratio stored in the cloud database to obtain the traffic congestion index correction factor influence values corresponding to the road type, road flatness, number of lanes, and vehicle type ratio of the target intersection during the current monitoring period; Substitute into a preset arctangent function to calculate the first traffic congestion index correction factor of the target intersection during the current monitoring period; Extract the meteorological status data of the target intersection during the current monitoring period, and filter to obtain each meteorological status index of the target intersection during the current monitoring period. Compare each meteorological status index with its corresponding standard index. If a certain climate environment index is greater than its corresponding standard index, calculate the difference between the two to obtain the high-index difference. If a certain climate environment index is less than its corresponding standard index, calculate the difference between the two to obtain the low-index difference; Calculate the mean value of each high-index difference and low-index difference respectively to obtain the high-index difference mean and the low-index difference mean. Input the high-index difference mean and the low-index difference mean into the image processor. The graphics processor converts them into numerical values according to a certain ratio and inputs them into the line chart respectively to obtain two corresponding points. Connect the two points in sequence with line segments to obtain a broken line. Draw perpendicular lines to the X-axis at the two endpoints of the broken line, so that the broken line and the two perpendicular lines form a closed figure with the X-axis. Identify the area of the closed figure as the meteorological status index of the target intersection during the current monitoring period; Match the meteorological status index of the target intersection during the current monitoring period with the meteorological status indices corresponding to each preset second traffic index correction factor to obtain the second traffic congestion index correction factor of the target intersection during the current monitoring period; Perform a summation calculation on the first traffic congestion index correction factor and the second traffic congestion index correction factor to obtain the comprehensive traffic congestion index correction factor.

5. The real-time traffic flow signal control system based on multi-sensor fusion according to claim 1, characterized in that, The analysis process of the traffic congestion index of the target intersection during the current monitoring period is as follows: Obtain the traffic flow value, traffic speed value, and space occupancy rate of the target intersection during the current monitoring period, and calculate their ratios with the preset maximum traffic flow value, minimum traffic speed value, and maximum space occupancy rate respectively to obtain the traffic flow ratio, traffic speed ratio, and space occupancy rate ratio of the target intersection during the current monitoring period. Convert them into lengths according to the preset ratio. Construct a cuboid with the lengths of the traffic flow ratio, traffic speed ratio, and queue length ratio as the length, width, and height respectively. Extract the numerical value of the volume of the cuboid as the congestion benchmark index of the target intersection during the current monitoring period. Perform a multiplication calculation on the congestion benchmark index and the comprehensive traffic congestion index correction factor to obtain the traffic congestion index of the target intersection during the current monitoring period.

6. The real-time traffic flow signal control system based on multi-sensor fusion according to claim 1, characterized in that, The analysis process of the optimal signal cycle duration of the target intersection during the current monitoring period is as follows: Extract the total loss duration of phase switching within each signal cycle duration of the target intersection during the preset historical period, and calculate its mean value to obtain the mean value of the total loss duration of phase switching of the target intersection during the preset historical period, and use it as the total loss duration of phase switching of the target intersection during the current monitoring period; Extract the number of vehicles passing through the stop line during the green light period within each signal cycle duration of the target intersection during the preset historical period. Obtain the saturated flow rate within each signal cycle duration of the target intersection during the preset historical period through the saturated flow rate calculation formula, and extract the mode of the saturated flow rate within each signal cycle duration as the saturated flow rate of the target intersection during the current monitoring period; Calculate the ratio of the optimal traffic flow estimate of the target intersection in the current monitoring period to the saturation flow to obtain the optimal estimated flow ratio of the target intersection in the current monitoring period. Calculate the optimal signal cycle length of the target intersection in the current monitoring period through the Webster algorithm formula.

7. The real-time traffic flow signal control system based on multi-sensor fusion according to claim 1, characterized in that The process of analyzing the signal control instructions and signal timing plan of the target intersection in the current monitoring period is as follows: Extract the actual signal cycle length of the target intersection in the current monitoring period, and compare and analyze it with the optimal signal cycle length of the target intersection in the current monitoring period. If the optimal signal cycle length is inconsistent with the actual signal cycle length, generate a signal regulation instruction and send the signal regulation instruction to the control terminal. Extract the traffic congestion index of the target intersection in the current monitoring period, and match the traffic congestion index of the target intersection in the current monitoring period with the signal timing plans corresponding to the optimal signal cycle lengths of each traffic congestion index preset to obtain the signal timing plan of the target intersection in the current monitoring period. Among them, the signal timing plan includes green light duration, red light duration, and yellow light duration, and send the signal timing plan to the control terminal.

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