A traffic signal monitoring method, device, electronic device and storage medium

Abstract

The present application provides a traffic signal monitoring method, device, electronic device and storage medium, which relates to the technical field of traffic signal monitoring. The key points of its technical solution are: obtaining the environmental light intensity information and traffic flow information of the intersection where the traffic signal is located; calculating the sampling frequency for monitoring the state of the traffic signal according to the environmental light intensity information and traffic flow information; and monitoring the traffic signal according to the calculated sampling frequency. The traffic signal monitoring method, device, electronic device and storage medium provided by the present application have the advantages of being able to dynamically adjust the sampling frequency according to environmental changes, thereby reducing energy consumption while ensuring the monitoring effect.

Description

Technical Field

[0001] This application relates to the technical field of traffic signal monitoring. Specifically, it relates to a traffic signal monitoring method, device, electronic device, and storage medium. Background Art

[0002] In modern urban traffic management, the real-time monitoring of traffic signals is crucial for maintaining traffic order and improving road traffic efficiency. However, in some urban environments, such as busy intersections with unstable power supply and limited sunlight, traditional traffic signal monitoring systems face many challenges.

[0003] Taking a city intersection as an example, this intersection is not only an important node of urban traffic but also a key location for emergency channels. Due to being located at the edge of the power supply network, this intersection often experiences short-term power outages. To solve the power supply problem, the management department has adopted a solar power supply solution. However, the limited sunlight exposure time caused by the surrounding high-rise buildings seriously affects the power generation efficiency of the solar panels. In this case, how to achieve all-weather and high-efficiency traffic signal monitoring under limited power supply conditions has become an urgent problem to be solved.

[0004] Existing traffic signal monitoring systems usually adopt a continuous sampling method with a fixed frequency. Although this method performs well in ensuring monitoring accuracy and real-time performance, it has the problem of excessive energy consumption. In the case of limited power supply, it is difficult to meet the demand for all-weather monitoring. In addition, traditional systems lack the ability to adapt to environmental changes and cannot dynamically adjust the working mode according to the actual situation, resulting in high power consumption during low-demand periods and causing energy waste.

[0005] In view of the above problems, this application proposes a new technical solution. Summary of the Invention

[0006] The purpose of this application is to provide a traffic signal monitoring method, device, electronic device, and storage medium, which have the advantage of being able to dynamically adjust the sampling frequency according to environmental changes, thereby reducing energy consumption while ensuring the monitoring effect.

[0007] This application provides a traffic signal monitoring method, and the technical solution is as follows:

[0008] It includes: obtaining the environmental light intensity information and traffic flow information of the intersection where the traffic signal is located; calculating the sampling frequency for monitoring the traffic signal state according to the environmental light intensity information and traffic flow information; and monitoring the traffic signal according to the calculated sampling frequency.

[0009] Further, the present application also proposes that the step of calculating the sampling frequency for monitoring the traffic signal light status according to the environmental light intensity information and the traffic flow information includes: making the calculation weight of the traffic flow information greater than the calculation weight of the environmental light intensity information; calculating the sampling frequency for monitoring the traffic signal light status according to the traffic flow information, the greater the traffic flow information, the higher the calculated sampling frequency; calculating the sampling frequency for monitoring the traffic signal light status according to the environmental light intensity information, the darker the environmental light intensity information, the higher the calculated sampling frequency.

[0010] Further, the present application also proposes that the step of calculating the sampling frequency for monitoring the traffic signal light status according to the environmental light intensity information and the traffic flow information includes: obtaining the environmental light intensity information and the traffic flow information within a preset time period, and predicting the environmental light intensity and traffic flow in the next time period; calculating the sampling frequency of the traffic signal light status in the next time period according to the predicted environmental light intensity and traffic flow in the next time period, so as to achieve an early response to environmental changes.

[0011] Further, the present application also proposes that the step of calculating the sampling frequency of the traffic signal light status in the next time period according to the predicted environmental light intensity and traffic flow in the next time period includes: obtaining the difference value between the predicted environmental light intensity and traffic flow in the next time period and the environmental light intensity and traffic flow in the current time period; determining whether the difference value exceeds a preset threshold; when it exceeds the preset threshold, increasing the buffer sampling frequency on the basis of the calculated sampling frequency in the next time period to avoid the monitoring blind area caused by environmental mutations.

[0012] Further, the present application also proposes that the step of calculating the traffic signal state sampling frequency for the next time period according to the predicted environmental light intensity and traffic flow in the next time period includes: obtaining the environmental light intensities L(t), L(t - 1) and traffic flows F(t), F(t - 1) in the current time period t and the previous time period t - 1, as well as the predicted environmental light intensity L(t + 1) and traffic flow F(t + 1) in the next time period t + 1; calculating the environmental change difference value D, where D = |L(t + 1) - L(t)| + |F(t + 1) - F(t)|; calculating the environmental change trend T, where T = sign((L(t + 1) - L(t)) * (L(t) - L(t - 1)) + (F(t + 1) - F(t)) * (F(t) - F(t - 1))); calculating the environmental change rate R, where R = (|L(t + 1) - L(t)| / |L(t) - L(t - 1)| + |F(t + 1) - F(t)| / |F(t) - F(t - 1)|) / 2; determining the gain coefficient f(R) according to the environmental change rate R, when R > R1, f(R) = α; when R2 < R ≤ R1, f(R) = β; when R ≤ R2, f(R) = γ; where R1 and R2 are preset change rate thresholds, and R1 > R2, α, β, γ are preset gain coefficients, and α > β > γ; calculating the traffic signal state sampling frequency S for the next time period, where S = S_base + k1 * max(0, D - D_threshold) * (1 + k2 * T) * f(R); where, S_base is the basic sampling frequency, k1 is the first adjustment coefficient, k2 is the second adjustment coefficient, D_threshold is the difference value threshold; ensuring that the calculated sampling frequency S satisfies the constraint condition S_min ≤ S ≤ S_max, where S_min is the minimum sampling frequency and S_max is the maximum sampling frequency; taking the calculated sampling frequency S as the traffic signal state sampling frequency for the next time period.

[0013] Further, the present application also proposes that the step of obtaining the environmental light intensity information and traffic flow information within a preset time period and predicting the environmental light intensity and traffic flow in the next time period includes: obtaining the historical environmental light intensity information and historical traffic flow information corresponding to the next time period to be predicted in the preset time period according to the preset time period; obtaining the mutation points based on the historical environmental light intensity information and historical traffic flow information and their corresponding time information; predicting the environmental light intensity and traffic flow in the next time period according to the time information of the detected light intensity and traffic flow mutation points.

[0014] Further, the present application also proposes that the step of predicting the ambient light intensity and traffic flow in the next time period according to the detected light intensity and the time information of the traffic flow mutation point includes: counting the occurrence frequencies of the detected light intensity and traffic flow mutation points within a preset time period in multiple historical time periods; calculating the probabilities of light intensity and traffic flow mutations occurring in the next time period based on the occurrence frequencies of the mutation points within the time period; and adjusting the predicted ambient light intensity and traffic flow values in the next time period according to the probabilities of light intensity and traffic flow mutations occurring in the next time period.

[0015] Further, the present application also proposes a traffic signal monitoring device, including: an acquisition module for acquiring the ambient light intensity information and traffic flow information of the intersection where the traffic signal is located; a calculation module for calculating the sampling frequency for monitoring the state of the traffic signal according to the ambient light intensity information and traffic flow information; and a monitoring module for monitoring the traffic signal according to the calculated sampling frequency.

[0016] Further, the present application also proposes an electronic device, including a processor and a memory, where the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the above method are run.

[0017] Further, the present application also proposes a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above method are run.

[0018] As can be seen from the above, a traffic signal monitoring method, device, electronic device and storage medium provided by the present application dynamically adjust the sampling frequency according to the ambient light intensity and traffic flow, reducing energy consumption while ensuring the monitoring effect, and having the advantages of being able to dynamically adjust the sampling frequency according to environmental changes, thereby reducing energy consumption while ensuring the monitoring effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic flow chart of a traffic signal monitoring method provided by the present application.

[0020] Figure 2 It is a schematic structural diagram of a traffic signal monitoring method provided by the present application.

[0021] In the figure: 210, acquisition module; 220, calculation module; 230, monitoring module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the present application usually described and illustrated in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application claimed, but only represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0023] It should be noted that: similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0024] At busy urban intersections, traffic signal monitoring systems face a trade-off between energy consumption and monitoring efficiency. Although the existing fixed-frequency continuous sampling method can ensure monitoring accuracy and real-time performance, it is difficult to meet the all-weather monitoring requirements in the case of limited power supply. This method cannot dynamically adjust the working mode according to the actual environmental changes, resulting in high power consumption during low-demand periods and causing energy waste. At the same time, the system lacks the ability to quickly respond to emergencies and is difficult to quickly switch to the high-frequency sampling mode to meet the emergency monitoring requirements while ensuring low power consumption during daily operation.

[0025] In response to this, referring to Figure 1 , the present application proposes a traffic signal monitoring method, including:

[0026] S110. Obtain the ambient light intensity information and traffic flow information of the intersection where the traffic signal is located;

[0027] S120. Calculate the sampling frequency for monitoring the traffic signal status according to the ambient light intensity information and traffic flow information;

[0028] S130. Monitor the traffic signal according to the calculated sampling frequency.

[0029] Among them, the ambient light intensity information refers to the light intensity data of the intersection where the traffic signal is located, and can be specifically realized by a light sensor for real-time measurement. This information is used to evaluate the influence degree of the current light conditions on the signal lamp monitoring.

[0030] Among them, the traffic flow information refers to the number and density of vehicles passing through the intersection. Specifically, it can be realized by vehicle detectors or cameras for statistics. This information is used to judge the traffic conditions and monitoring requirements at the current intersection.

[0031] Among them, the sampling frequency refers to the time interval at which the monitoring system samples the traffic signal status. Specifically, it can be realized by an adjustable timer for dynamic control. The adjustment of this frequency directly affects the energy consumption and monitoring accuracy of the system.

[0032] In some specific embodiments, the monitoring system can specifically be a camera. By aiming the camera at the traffic signal, the status of the traffic signal is monitored based on image recognition, and the traffic flow information can be collected simultaneously based on this camera.

[0033] The core innovation of this application lies in proposing a traffic signal monitoring method that dynamically adjusts the sampling frequency based on ambient light intensity and traffic flow. This method obtains real-time environmental data and calculates the optimal sampling frequency, thereby significantly reducing the system's energy consumption while ensuring the monitoring effect.

[0034] The working principle of this application can be described in detail as follows:

[0035] First, obtain the ambient light intensity information and traffic flow information in real time.

[0036] Next, input the obtained data into a preset calculation algorithm. This algorithm considers the influence of light intensity on the difficulty of signal light recognition and the influence of traffic flow on the importance of monitoring. For example, when the light is dim, the sampling frequency needs to be increased to ensure accurate recognition of the signal light status; when the traffic flow is large, the sampling frequency needs to be increased to capture signal light changes in a timely manner.

[0037] The calculated sampling frequency is then applied to the monitoring system. The system will dynamically adjust its sampling time interval according to the calculation results. For example, when the light suddenly dims or the traffic flow increases sharply, the system can quickly increase the sampling frequency to ensure the monitoring quality.

[0038] Selecting the ambient light intensity and traffic flow as key parameters is because these two factors directly affect the importance of signal light monitoring. By dynamically adjusting the sampling frequency, the system can maximize energy savings while ensuring the monitoring effect, thereby extending the working time of the system and improving the overall efficiency.

[0039] As a preferred embodiment, this application can be implemented at a busy intersection. There are traffic signals in four directions at this intersection. A high-definition camera is installed in each direction for signal light status recognition, and a light sensor is used to measure the ambient light intensity. The traffic flow is counted using the camera.

[0040] The central processing unit of the system is set to obtain environmental data every 5 seconds. For example, at a certain moment, the light intensity measured by the light sensor is 500 lux, and the number of vehicles passing by within 5 minutes is 300. Based on a preset algorithm, considering historical data and current trends, the optimal sampling frequency can be calculated as 2 Hz.

[0041] Subsequently, the system adjusts the sampling frequency to 2 Hz, that is, samples the signal light state every 0.5 seconds. The camera captures images at this frequency and identifies the current state (red, yellow, green) of the signal light through an image processing algorithm. If a change in the signal light state is detected, the system will immediately record and report it.

[0042] During the subsequent monitoring process, if the light intensity suddenly drops to 100 lux (for example, the weather becomes cloudy), the system will quickly recalculate the sampling frequency. The new calculation result may increase the sampling frequency to 4 Hz to cope with the increased recognition difficulty caused by the dimming of the light.

[0043] In the solution of this application, calculating the sampling frequency based on environmental light intensity information and traffic flow information is mainly to reduce power consumption and optimize monitoring efficiency.

[0044] Among them, the relationship between environmental light intensity and sampling frequency is reflected in the visual characteristics of the human eye and the visibility of traffic signal lights. When the environmental light intensity is high, due to the strong environmental light, the brightness of the traffic signal light is relatively less prominent. To make the human eye clearly identify it, the signal light usually works at a higher brightness. At this time, even if the sampling frequency is slightly lower, due to the high brightness and good contrast of the signal light, the human eye is not likely to miss the change in the signal light state.

[0045] When the environmental light intensity is low, due to the weak environmental light, the traffic signal light is relatively more conspicuous even with a lower brightness. However, if the signal light fails (such as a sudden decrease in brightness, flashing, etc.), it may be more difficult to be detected by the human eye at low brightness. Therefore, when the environmental light is relatively dim, appropriately increasing the sampling frequency can capture the abnormal state of the signal light more timely.

[0046] Regarding traffic flow and monitoring requirements, usually, the traffic flow is larger during the day, and higher real-time and reliability requirements are placed on traffic signal lights. Although the environmental light intensity is high during the day, considering the traffic flow information, a relatively high sampling frequency may still be required to ensure timely detection of signal light failures and avoid traffic congestion or accidents.

[0047] The traffic flow is smaller at night, and the monitoring pressure on the signal lights is relatively small, so the sampling frequency can be appropriately reduced.

[0048] Through this dynamic adjustment, the system can reduce the sampling frequency to save energy when the light is sufficient and the traffic flow is small, and increase the sampling frequency to ensure the monitoring quality when the light is insufficient or the traffic is busy. This adaptive method not only improves the energy utilization efficiency but also enhances the system's adaptability to complex environmental changes.

[0049] In some of the above embodiments of the present application, a method is proposed to calculate the sampling frequency for monitoring the traffic signal state based on the environmental light intensity information and the traffic flow information to dynamically adjust the sampling frequency. However, in this process, there is still room for optimization in how to reasonably allocate the weights of the environmental light intensity information and the traffic flow information, and how to calculate the sampling frequency based on these two types of information.

[0050] In response to this, the present application further proposes that the steps of calculating the sampling frequency for monitoring the traffic signal state based on the environmental light intensity information and the traffic flow information include: making the calculation weight of the traffic flow information greater than the calculation weight of the environmental light intensity information; calculating the sampling frequency for monitoring the traffic signal state according to the traffic flow information, and the greater the traffic flow information, the higher the calculated sampling frequency; calculating the sampling frequency for monitoring the traffic signal state according to the environmental light intensity information, and the darker the environmental light intensity information, the higher the calculated sampling frequency.

[0051] The technical solution of the present application effectively solves the problem of how to optimize the calculation of the sampling frequency by reasonably allocating the weights of the environmental light intensity information and the traffic flow information and establishing the relationship between these two types of information and the sampling frequency. Specifically, this solution gives priority to the traffic flow information, which meets the requirements of actual traffic management because the traffic flow directly reflects the busyness of the intersection.

[0052] In practical applications, the calculation weight of the traffic flow information can be set to 0.7, while the calculation weight of the environmental light intensity information can be set to 0.3. This weight allocation ensures that the influence of the traffic flow information is greater when calculating the sampling frequency, thus better reflecting the needs of the actual traffic situation.

[0053] When calculating the sampling frequency according to the traffic flow information, a linear or non-linear mapping relationship can be adopted. For example, a basic sampling frequency can be set, and then the sampling frequency increases linearly with the increase in traffic flow. Specifically, the formula can be used: sampling frequency = basic frequency + k * traffic flow, where k is a preset coefficient. In this way, when the traffic flow increases, the sampling frequency will increase accordingly, ensuring that during peak traffic periods, the system can monitor the signal state more frequently, improving the real-time and accuracy of monitoring.

[0054] For the environmental light intensity information, an inverse proportional relationship can be used to calculate the sampling frequency. For example, the formula: sampling frequency = c / (light intensity + ε) can be used, where c is a constant and ε is a very small positive number used to avoid division by zero. This calculation method ensures that in low-light conditions, the sampling frequency will increase accordingly to cope with possible recognition difficulties and improve the monitoring quality.

[0055] These two calculation methods can be combined by weighted average to form the final sampling frequency calculation formula: final sampling frequency = 0.7 * (base frequency + k * traffic flow) + 0.3 * (c / (light intensity + ε)).

[0056] In this way, the technical solution of this application realizes the dynamic adjustment of the sampling frequency. During peak traffic periods, the system will increase the sampling frequency to improve the monitoring accuracy and real-time performance. In poor light conditions, the system will also appropriately increase the sampling frequency to ensure the monitoring quality. In other cases, the system can reduce the sampling frequency to save energy. This dynamic adjustment mechanism achieves a balance among monitoring accuracy, real-time response ability, and energy efficiency.

[0057] Specifically, by combining the ambient light intensity and traffic flow information, this method can intelligently adjust the sampling frequency:

[0058] In low light and high traffic: Maintain a high sampling frequency to ensure timely detection of faults.

[0059] In high light and high traffic: Appropriately increase the sampling frequency.

[0060] In low light and low traffic: The sampling frequency can be appropriately reduced because the real-time requirement for signal lights is not high at this time, and power consumption reduction is prioritized. However, the reduction amplitude cannot be too large, and a certain monitoring frequency still needs to be ensured to prevent relatively hidden faults (such as slight flashing) of the signal lights from not being detected in time.

[0061] In high light and low traffic: The sampling frequency can be reduced because the real-time requirement for signal lights is not high at this time, and the visibility of signal lights is high under high light, making faults easy to be detected.

[0062] That is, in some preferred embodiments, among various situations of the calculated sampling frequency, low light and high traffic > high light and high traffic > low light and low traffic.

[0063] In some of the above embodiments of the present application, a method is proposed to dynamically calculate the sampling frequency of traffic signal states based on environmental light intensity information and traffic flow information to meet the monitoring requirements under different environmental conditions. However, in this process, there may be a problem of insufficiently timely response to environmental changes. Since environmental conditions may change suddenly, such as a sudden change in weather or a sudden increase in traffic flow, relying solely on the environmental information at the current moment to calculate the sampling frequency may cause the monitoring system to fail to adjust in time, thus affecting the monitoring effect.

[0064] In response to this, the present application further proposes that the steps of calculating the sampling frequency for monitoring traffic signal states based on environmental light intensity information and traffic flow information include: obtaining the environmental light intensity information and traffic flow information within a preset time period, and predicting the environmental light intensity and traffic flow in the next time period; calculating the sampling frequency of traffic signal states in the next time period according to the predicted environmental light intensity and traffic flow in the next time period, so as to achieve an early response to environmental changes.

[0065] The present application introduces a prediction mechanism. By obtaining the environmental light intensity information and traffic flow information within a preset time period and predicting the environmental conditions in the next time period, the sampling frequency in the next time period is calculated. This prediction mechanism can proactively respond to possible environmental changes, making the monitoring system more flexible and efficient.

[0066] Specifically, first, environmental data including light intensity and traffic flow within a period of time are obtained. These historical data provide a basis for predicting future environmental changes. Then, these data are used to predict the environmental conditions in the next time period.

[0067] The prediction mechanism can be implemented in various ways. For example, the moving average method can be used, which predicts the value in the next time period by calculating the average value of the past several time periods. Another method is the exponential smoothing method, which gives higher weights to the most recent data, thus better capturing the latest trends.

[0068] After obtaining the prediction results, the system calculates the corresponding sampling frequency according to the predicted environmental light intensity and traffic flow in the next time period. This step can be achieved by setting a series of thresholds and corresponding frequencies. For example, when the predicted light intensity is lower than a certain threshold or the traffic flow is higher than a certain threshold, the system will correspondingly increase the sampling frequency.

[0069] Some of the above embodiments provide the ability to dynamically adjust the sampling frequency according to the current environmental conditions, while the prediction mechanism of this embodiment further enhances the forward-looking nature of the system. By combining these two aspects, it is possible to maintain the response to the current environment while also preparing for upcoming changes. This synergistic effect makes the system show stronger adaptability and stability in the face of complex and changing environments.

[0070] As a preferred embodiment, the technical solution of the present application can be implemented at an intersection. The intersection is equipped with multiple traffic lights and sensors for monitoring environmental light intensity and traffic flow. The system first sets a preset time period, for example, 30 minutes. Within these 30 minutes, the system records the environmental light intensity and traffic flow data every 5 minutes.

[0071] Specifically, the light intensity can be measured in lumens (lm), and the range may be from 100 lm (at dusk) to 100,000 lm (at noon on a sunny day). The traffic flow can be measured in vehicles per hour, and the range may be from 50 vehicles per hour (late at night) to 2000 vehicles per hour (during peak hours).

[0072] These data are used to predict the environmental conditions for the next 30 minutes. The prediction algorithm can adopt the exponential smoothing method, giving higher weights to the most recent data. For example, if the average light intensity within the current 30 minutes is 5000 lm and the traffic flow is 1000 vehicles per hour, and the prediction algorithm takes into account the upcoming dusk period, it may predict that the average light intensity for the next 30 minutes will be 3000 lm and the traffic flow will be 1200 vehicles per hour (considering the possible evening rush hour).

[0073] Based on these predicted values, the system calculates the sampling frequency for the next time period. Assume the system sets the following rules:

[0074] When the predicted light intensity < 2000 lm or the traffic flow > 1500 vehicles per hour, the sampling frequency is set to 2 times per second.

[0075] When 2000 lm ≤ predicted light intensity < 5000 lm and 1000 vehicles per hour < traffic flow ≤ 1500 vehicles per hour, the sampling frequency is set to 1 time per second.

[0076] In other cases, the sampling frequency is set to 1 time per 2 seconds.

[0077] In this example, based on the predicted light intensity of 3000 lm and traffic flow of 1200 vehicles per hour, the system will set the sampling frequency for the next 30 minutes to 1 time per second. In this way, the system can adjust to a higher sampling frequency in advance before the light starts to dim and the traffic flow increases, ensuring accurate monitoring when the environmental conditions change.

[0078] In this way, while ensuring the monitoring quality, the energy use is optimized, providing an efficient solution for scenarios with unstable or limited power supply.

[0079] In some of the above embodiments of the present application, a method is proposed to calculate the sampling frequency of traffic signal states in the next time period based on the predicted environmental light intensity and traffic flow in the next time period, so as to achieve an early response to environmental changes. However, during this process, if the environment changes suddenly, it may affect the accuracy and real-time performance of monitoring.

[0080] In response to this, the present application further proposes to obtain the difference values between the predicted environmental light intensity and traffic flow in the next time period and the environmental light intensity and traffic flow in the current time period; determine whether the difference values exceed a preset threshold; when the preset threshold is exceeded, increase the buffer sampling frequency on the basis of the calculated sampling frequency in the next time period, so as to avoid monitoring blind spots caused by sudden environmental changes.

[0081] The present application identifies possible sudden environmental changes by obtaining the difference values between the predicted environmental light intensity and traffic flow in the next time period and the current time period, and determining whether the difference values exceed the preset threshold. When the difference value exceeds the preset threshold, the system will increase the buffer sampling frequency on the basis of the originally calculated sampling frequency in the next time period. This method can increase the sampling frequency when the environment changes significantly, thus avoiding monitoring blind spots caused by sudden environmental changes.

[0082] Specifically, the difference values can be calculated in various ways. For example, the differences in environmental light intensity and traffic flow can be calculated separately, and then the two differences can be added together or the maximum value can be taken as the final difference value. The preset threshold can be set based on historical data analysis or expert experience.

[0083] The step of determining whether the difference value exceeds the preset threshold can be further refined. For example, multiple threshold levels can be set, and different degrees of frequency adjustment can be taken according to the interval in which the difference value falls. This hierarchical processing can achieve more refined control of the sampling frequency.

[0084] The buffer sampling frequency increased when the preset threshold is exceeded can be proportional to the degree of the difference value. For example, a basic increment can be set, and then the buffer sampling frequency can be increased linearly or non-linearly according to the degree to which the difference value exceeds the threshold. This dynamic adjustment method can better adapt to environmental changes of different degrees.

[0085] Through this method of dynamically adjusting the sampling frequency, the system can respond to environmental changes in a timely manner while maintaining low-power operation, improving the accuracy and real-time performance of monitoring. When the environment is relatively stable, the system maintains a low sampling frequency to save energy; while when it is predicted that the environment may change significantly, the system will increase the sampling frequency in advance to ensure that important signal light state changes are not missed.

[0086] As a preferred implementation manner, the technical solution of the present application can be implemented in the following specific scenarios:

[0087] Suppose at an intersection, the system predicts that the average ambient light intensity for the next 10 - minute period is 500 lux and the average traffic flow is 200 vehicles per hour, while the actual data for the current period are 450 lux and 180 vehicles per hour respectively.

[0088] The system first calculates the difference value. Suppose a simple linear weighting method is adopted, and the weights of the ambient light intensity and traffic flow are 0.4 and 0.6 respectively. Then the difference value is calculated as follows:

[0089] Difference value = 0.4 * |500 - 450| / 450 + 0.6 * |200 - 180| / 180 ≈ 0.16

[0090] Suppose the preset threshold is 0.15, then the system determines that the difference value exceeds the preset threshold.

[0091] Next, the system calculates the buffered sampling frequency. Suppose a linear increase method is adopted, with a base increment of 10%, increasing by 1% for every 1% exceeding the threshold, and the maximum not exceeding 50%. In this example, the difference value exceeds the threshold by about 6.7%. Suppose the predicted sampling frequency for the next time period is 1 time per second, so the system decides to increase the sampling frequency by 16.7%.

[0092] Therefore, the system adjusts the sampling frequency for the next time period to 1.167 times per second (116.7% of the original frequency). This adjustment can increase the sampling frequency in advance when the expected environmental change is large, effectively avoiding possible monitoring blind spots, and at the same time not over - burdening the system.

[0093] In this way, the technical solution of this application can effectively solve the problem of monitoring blind spots caused by environmental mutations, improve the accuracy and real - time performance of the traffic signal monitoring system, and at the same time maintain the low - power consumption characteristics of the system. This adaptive mechanism enables the system to operate efficiently and stably in various complex environments, providing more reliable technical support for traffic management.

[0094] In some of the above - mentioned embodiments of this application, it is proposed to calculate the sampling frequency of the traffic signal state for the next time period according to the predicted ambient light intensity and traffic flow for the next time period to achieve an early response to environmental changes. However, in this process, calculating the sampling frequency only based on the predicted values may not fully consider the rate and trend of environmental changes, resulting in inaccurate and inflexible adjustment of the sampling frequency. In addition, the lack of a rapid response mechanism for sudden environmental changes may cause monitoring blind spots when the environment changes rapidly.

[0095] In response to this, the present application further proposes to obtain the ambient light intensities L(t) and L(t-1), and traffic flows F(t) and F(t-1) in the current time period t and the previous time period t-1, as well as the predicted ambient light intensity L(t+1) and traffic flow F(t+1) in the next time period t+1;

[0096] Calculate the environmental change difference value D, where

[0097] D = |L(t+1) - L(t)| + |F(t+1) - F(t)|;

[0098] Calculate the environmental change trend T, where

[0099] T = sign((L(t+1) - L(t)) * (L(t) - L(t-1)) + (F(t+1) - F(t)) * (F(t) - F(t-1)));

[0100] Calculate the environmental change rate R, where

[0101] R = (|L(t+1) - L(t)| / |L(t) - L(t-1)| + |F(t+1) - F(t)| / |F(t) - F(t-1)|) / 2;

[0102] Determine the gain coefficient f(R) according to the environmental change rate R. When R > R1, f(R) = α; when R2 < R ≤ R1, f(R) = β; when R ≤ R2, f(R) = γ; where R1 and R2 are preset change rate thresholds, and R1 > R2, and α, β, γ are preset gain coefficients, and α > β > γ;

[0103] Calculate the traffic signal state sampling frequency S in the next time period, where

[0104] S = S_base + k1 * max(0, D - D_threshold) * (1 + k2 * T) * f(R);

[0105] Where, S_base is the base sampling frequency, k1 is the first adjustment coefficient, k2 is the second adjustment coefficient, and D_threshold is the difference value threshold; ensure that the calculated sampling frequency S satisfies the constraint condition S_min ≤ S ≤ S_max, where S_min is the minimum sampling frequency and S_max is the maximum sampling frequency; use the calculated sampling frequency S as the traffic signal state sampling frequency in the next time period.

[0106] The technical solution proposed by the present application introduces multiple key features, and each feature has its specific implementation method and function.

[0107] The calculation of the environmental change difference value D adopts the method of the sum of absolute values. There can be various variants of this method. For example, the Euclidean distance or Manhattan distance can be used to calculate the difference value. The advantage of choosing the sum of absolute values is that the calculation is simple and it can reflect the overall amplitude of environmental changes at the same time.

[0108] The calculation of the environmental change trend T uses the sign function. Other methods can be considered here, such as using the slope or weighted average to calculate the trend. The advantage of the sign function is that it can clearly distinguish the three situations of positive direction, negative direction, and no obvious trend, which is beneficial for subsequent sampling frequency adjustment.

[0109] The calculation of the environmental change rate R adopts the average value of the relative change rate. Methods such as weighted average or exponential smoothing can be considered to calculate the rate. The advantage of the current method is that it can consider the change rates of both light intensity and traffic flow at the same time, and the averaging process reduces the impact of extreme values of a single factor on the results.

[0110] The determination of the gain coefficient f(R) adopts the form of a piecewise function. This method can set more thresholds and corresponding gain coefficients according to actual needs. The advantage of the piecewise function is that it can flexibly adjust the gain coefficient according to different degrees of the environmental change rate, so as to achieve refined control of the sampling frequency.

[0111] The calculation formula of the sampling frequency S comprehensively considers the base frequency, environmental change difference, trend, and rate. This formula can be further optimized, for example, introducing a non-linear function or adaptive weights. The advantage of the current formula is that the relationship between various factors is clear, which is convenient for understanding and adjustment.

[0112] The settings of the constraint conditions S_min and S_max ensure that the sampling frequency is within a reasonable range. It can be considered to dynamically adjust these two thresholds according to the system hardware performance and actual application scenarios. The benefit of this constraint mechanism is that it can prevent the system load from being too heavy due to too high sampling frequency, or the monitoring accuracy from being insufficient due to too low sampling frequency.

[0113] The environmental change difference value D reflects the amplitude of environmental changes, the trend T indicates the direction of changes, and the rate R represents the speed of changes. These three factors jointly determine the adjustment direction and amplitude of the sampling frequency. The gain coefficient f(R) provides different degrees of adjustment intensity according to different rates, enabling the system to respond more sensitively to the rapidly changing environment. Finally, the calculation formula of the sampling frequency integrates these factors to achieve the comprehensive adjustment of the sampling frequency.

[0114] In practical applications, appropriate parameter values can be set according to specific scenarios. For example, at intersections with large light changes, the weight of light intensity can be set higher; at intersections with frequent traffic flow fluctuations, the thresholds of R1 and R2 can be reduced to make the system more sensitive to rate changes. The specific parameter settings need to be determined through actual testing and optimization.

[0115] The technical solution of this application realizes the refined dynamic adjustment of the sampling frequency by introducing three dimensions: the environmental change difference value, the change trend, and the change rate. This method can capture the characteristics of environmental changes more accurately, thereby achieving precise control of the sampling frequency.

[0116] Specifically, the environmental change difference value D reflects the amplitude of environmental changes, enabling the system to respond to significant environmental changes. The change trend T indicates the direction of environmental changes, enabling the system to predict the persistence of environmental changes and thus adjust the sampling strategy in advance. The change rate R further refines the characteristics of environmental changes, enabling the system to distinguish between gradual and sudden changes and make corresponding adjustments.

[0117] The introduction of the gain coefficient f(R) enables the system to flexibly adjust the change amplitude of the sampling frequency according to the rate of environmental changes. This mechanism can improve the system's response speed when the environment changes rapidly and avoid over-adjustment when the environment is relatively stable.

[0118] The sampling frequency calculation formula S = S_base + k1 * max(0, D - D_threshold) * (1 + k2 * T) * f(R) comprehensively considers all the above factors. The basic sampling frequency S_base ensures the basic monitoring ability of the system. Adjustment is only triggered when the difference value exceeds the threshold D_threshold, avoiding over-response to minor changes. The trend T affects the adjustment amplitude through the term (1 + k2 * T), enabling the system to make forward-looking adjustments according to the change trend. Finally, the gain coefficient f(R) further adjusts the sampling frequency according to the change rate, achieving an accurate response to the speed of environmental changes.

[0119] The constraint condition S_min ≤ S ≤ S_max ensures that the sampling frequency always remains within the acceptable range of the system, avoiding system problems caused by too high or too low sampling frequencies.

[0120] This method of comprehensively considering multiple factors can capture the characteristics of environmental changes more comprehensively compared to simple predicted value calculations, thereby achieving more accurate and flexible sampling frequency adjustment. The system can quickly increase the sampling frequency when the environment changes sharply, reducing the occurrence of monitoring blind spots; at the same time, it can appropriately reduce the sampling frequency when the environment is relatively stable, saving system resources.

[0121] As a preferred embodiment, the following specific parameter settings can be considered:

[0122] Set the base sampling frequency S_base = 1 Hz, the minimum sampling frequency S_min = 0.5 Hz, and the maximum sampling frequency S_max = 10 Hz.

[0123] The environmental light intensity L uses lux as the unit, and the traffic flow F uses the number of vehicles passing through per minute as the unit.

[0124] Set the difference value threshold D_threshold = 100. This value indicates that when the environmental light intensity changes by 100 lux or the traffic flow changes by 100 vehicles / minute, the sampling frequency adjustment is triggered.

[0125] Set the first adjustment coefficient k1 = 0.01 and the second adjustment coefficient k2 = 0.5.

[0126] For the gain coefficient f(R), set R1 = 2, R2 = 1, α = 2, β = 1.5, γ = 1. This means that when the environmental change rate R > 2, the maximum gain coefficient 2 is adopted; when 1 < R ≤ 2, the medium gain coefficient 1.5 is adopted; when R ≤ 1, the minimum gain coefficient 1 is adopted.

[0127] Suppose at a certain moment, the system obtains the following data:

[0128] L(t - 1) = 1000 lux, F(t - 1) = 200 vehicles / minute;

[0129] L(t) = 1200 lux, F(t) = 250 vehicles / minute;

[0130] The predicted L(t + 1) = 1500 lux, F(t + 1) = 320 vehicles / minute.

[0131] Based on these data, the system performs the following calculations:

[0132] Calculate the environmental change difference value D = |1500 - 1200| + |320 - 250| = 370;

[0133] Calculate the environmental change trend

[0134] T = sign((1500 - 1200)*(1200 - 1000) + (320 - 250)*(250 - 200)) = 1;

[0135] Calculate the environmental change rate

[0136] R = (|1500 - 1200| / |1200 - 1000| + |320 - 250| / |250 - 200|) / 2 = 1.75;

[0137] Since 1 < R ≤ 2, the gain coefficient f(R) = β = 1.5 is determined.

[0138] Calculate the sampling frequency S = 1 + 0.01 * max(0, 370 - 100) * (1 + 0.5 * 1) * 1.5 = 5.55 Hz;

[0139] The final sampling frequency S = 5.55 Hz, which satisfies the constraint condition of S_min ≤ S ≤ S_max.

[0140] Through this example, it can be seen how the system dynamically adjusts the sampling frequency according to the difference value, trend, and rate of environmental changes. In this scenario, due to the significant environmental changes (the difference value far exceeds the threshold), and showing a positive trend, and the change rate is relatively fast, the system increases the sampling frequency from the basic 1 Hz to 5.55 Hz to better capture the rapid environmental changes.

[0141] This dynamic adjustment mechanism can effectively reduce the average power consumption of the system while ensuring the monitoring accuracy. When the environment is relatively stable, the system can maintain a lower sampling frequency to save energy; while when the environment changes rapidly, the system can promptly increase the sampling frequency to ensure that important environmental changes will not be missed. This adaptive ability makes the system particularly suitable for applications in scenarios with limited power supply, such as traffic signal monitoring systems powered by solar energy. At the same time, by precisely controlling the sampling frequency, the system can quickly respond in case of emergencies and meet the requirements as an important emergency passage in the city.

[0142] In some of the above embodiments of the present application, it is proposed to obtain the environmental light intensity information and traffic flow information within a preset time period, and predict the environmental light intensity and traffic flow in the next time period to calculate the traffic signal state sampling frequency in the next time period, so as to achieve an early response to environmental changes. However, in this process, relying solely on the current and predicted environmental information may not accurately reflect the regularity and periodicity of environmental changes, thus affecting the accuracy of prediction and the rationality of the sampling frequency.

[0143] In response to this, the present application further proposes to obtain the historical environmental light intensity information and historical traffic flow information corresponding to the next time period to be predicted within a preset time period according to the preset time period; obtain the mutation points based on the historical environmental light intensity information and historical traffic flow information and their corresponding time information; predict the environmental light intensity and traffic flow in the next time period according to the time information of the detected light intensity and traffic flow mutation points.

[0144] This technical solution improves the accuracy of environmental change prediction by introducing historical data analysis and mutation point detection. Specifically, the solution first obtains historical data according to a preset time period, which helps to capture the periodic patterns of environmental changes. The preset time period can be one day, one week, one month, etc., depending on the characteristics of the intersection where the traffic signal is located. For example, for an intersection with obvious differences in traffic patterns between weekdays and weekends, one week can be selected as the preset time period.

[0145] When obtaining historical data, the system can adopt various methods. One method is to directly extract data from the database for the same past time period. Another method is to use the sliding window technique to dynamically obtain data for the most recent several cycles. This can ensure that the historical data used can reflect both long-term trends and capture recent changes.

[0146] Next, analyze the mutation points and their time information in the historical data. Mutation point detection can adopt various algorithms, such as statistical-based methods (such as the CUSUM algorithm), which can identify the key nodes and abnormal situations of environmental changes. For example, the system may detect a mutation point in traffic flow between 7 am and 9 am every day, which may correspond to the start of the morning rush hour.

[0147] Mutation point detection is not limited to a single variable, and the joint distribution of environmental light intensity and traffic flow can also be considered. This can capture the mutual influence between the two factors, such as the combined effect of reduced light intensity and traffic flow caused by rain.

[0148] Based on the detected mutation point time information, the system can more accurately predict the environmental conditions in the next time period. The prediction method can adopt time series analysis techniques, through the patterns in the historical data, and combine the mutation point information for prediction.

[0149] Through this method, it is possible to better cope with periodic changes and emergencies. For example, if the historical data shows that traffic flow mutations often occur between 4 pm and 6 pm every Friday, the system can adjust the sampling frequency in advance to cope with possible traffic congestion. Similarly, if a mutation pattern in light intensity is detected under specific weather conditions, the system can adjust the prediction strategy accordingly.

[0150] In addition, this method can also be combined with real-time data to further improve the accuracy of prediction. For example, the system can compare the differences between real-time observed data and historical patterns, and if significant deviations are found, the prediction results can be adjusted in a timely manner.

[0151] By adopting this prediction method based on historical data and breakpoint analysis, this technical solution can more accurately predict the environmental light intensity and traffic flow in the next time period. This improvement enables the system to adjust the sampling frequency more precisely, thereby optimizing energy usage while ensuring monitoring accuracy. Especially in the face of periodic changes and emergencies, this method can provide more reliable prediction results, which in turn supports the system to make more intelligent decisions, improving the adaptability and efficiency of the entire traffic signal monitoring system.

[0152] To better understand the technical solution of this application, a specific embodiment is provided below:

[0153] Suppose a traffic signal monitoring system is deployed at a busy urban intersection. The system sets a preset time period of one week because the traffic patterns at this intersection are significantly different between weekdays and weekends. The system collects environmental light intensity and traffic flow data every 15 minutes.

[0154] First, the system obtains historical data. For example, if it is currently 3 pm on Wednesday, the system will extract the data from 3 pm to 4 pm on Wednesday in the past 4 weeks. This data includes the environmental light intensity (in lux) and traffic flow (in vehicles per hour) every 15 minutes.

[0155] Next, breakpoint detection is performed. Suppose the system detects that there are often breakpoints in traffic flow between 3:30 pm and 3:45 pm in the data of the past 4 weeks, with an average increase of about 30%. At the same time, the system also notices that on cloudy days, the light intensity during this time period will suddenly drop by about 20%.

[0156] Based on this information, predict the environmental conditions in the next hour. Consider the periodic pattern of historical data, the detected breakpoints, and the current weather forecast.

[0157] Suppose the current time is 3 pm on Wednesday and the weather forecast shows that it may be cloudy. The system predicts that between 3:30 pm and 3:45 pm, the traffic flow may increase from 1000 vehicles per hour to 1300 vehicles per hour, while the light intensity may decrease from 5000 lux to 4000 lux.

[0158] Based on this prediction, it is decided to gradually increase the sampling frequency starting at 3:25, from once per minute to once every 15 seconds, to cope with the upcoming traffic peak and light changes. In this way, the system can timely capture the state changes of traffic signals when environmental changes occur, while maintaining a lower sampling frequency in other time periods to save energy.

[0159] In this way, the technical solution of the present application can significantly reduce the average power consumption of the system while ensuring the monitoring accuracy, and still be able to respond to emergencies and environmental changes in a timely manner. This not only extends the battery life of the system, but also improves its adaptability and reliability in complex environments.

[0160] In some of the above embodiments of the present application, it is proposed to predict the ambient light intensity and traffic flow in the next time period based on the detected light intensity and the time information of the traffic flow mutation point to calculate the traffic signal state sampling frequency in the next time period. However, in this process, relying solely on the mutation point information in a single time period may lead to inaccurate and unstable prediction results. The changes in ambient light intensity and traffic flow may have certain periodicity and regularity, and the data in a single time period cannot fully reflect these long-term change trends. In addition, emergencies or abnormal situations may cause abnormal fluctuations in the data in a certain time period. If the prediction is based only on the data in this time period, it may cause unnecessary fluctuations in the sampling frequency, affecting the stability and energy efficiency of the system.

[0161] In response to this, the present application further proposes to count the occurrence frequency of the detected light intensity and traffic flow mutation points in multiple historical time periods within a preset time period; based on the occurrence frequency of the mutation points within the time period, calculate the probability of the occurrence of light intensity and traffic flow mutations in the next time period; and adjust the predicted ambient light intensity and traffic flow values in the next time period according to the probability of the occurrence of light intensity and traffic flow mutations in the next time period.

[0162] The present application introduces methods of statistical analysis and probability prediction, and predicts future environmental changes by analyzing data in multiple historical time periods. Specifically, first, count the data in multiple historical time periods within a preset time period, and count the occurrence frequency of the light intensity and traffic flow mutation points. This step can capture the long-term trends and periodic patterns of environmental changes. Different time periods can be selected for statistics, such as 24 hours, 7 days, or 30 days, to adapt to environmental change laws at different scales.

[0163] Furthermore, based on the counted occurrence frequency of the mutation points, calculate the probability of the occurrence of mutations in the next time period. For example, use simple frequency statistics or more complex time series analysis.

[0164] Finally, adjust the predicted ambient light intensity and traffic flow values in the next time period according to the calculated mutation probability. This step can correct the prediction results according to the magnitude of the mutation possibility. The adjustment can be made in a linear or non-linear manner. For example, when the mutation probability is high, the predicted value can be adjusted towards the historical mutation value, and when the mutation probability is low, the original predicted value can be kept unchanged or only slightly adjusted.

[0165] By introducing statistical analysis and probability prediction, this method effectively solves the limitations of single-time-period data prediction. It can better capture the long-term trends and periodic patterns of environmental changes, improving the accuracy and stability of predictions. At the same time, it can better handle emergencies and abnormal situations, avoiding prediction biases caused by abnormal data in a single time period.

[0166] By combining historical data analysis and real-time data collection, it is possible to more accurately predict environmental changes, thereby more reasonably adjusting the sampling frequency. This not only improves the monitoring accuracy but also optimizes the energy usage efficiency. For example, in the case where a mutation is predicted to occur in the next time period, the system can increase the sampling frequency in advance to ensure that important traffic signal state changes are not missed. Conversely, during periods when the environment is predicted to be relatively stable, the system can appropriately reduce the sampling frequency to save energy.

[0167] In the specific implementation process, the method of sliding time window can be adopted to process historical data. For example, set a sliding window of 30 days and update the statistical data daily, which can ensure that the latest historical data is always used while capturing the change rules over a longer time span. In addition, a weight factor can be introduced so that more recent data has a greater impact on the prediction result, thereby increasing the sensitivity of the model to recent changes.

[0168] To further improve the prediction accuracy, the environmental light intensity and traffic flow can be modeled separately. Since these two factors may have different change rules and periodicities, separate modeling can more precisely capture their respective characteristics. For example, the environmental light intensity may be mainly affected by the natural sunlight cycle, while the traffic flow may be more influenced by factors such as weekdays and holidays.

[0169] In practical applications, multiple thresholds can be set to refine the impact of the mutation probability. For example, when the mutation probability is below 20%, the original predicted value remains unchanged; when the mutation probability is between 20% and 50%, a small adjustment is made to the predicted value; when the mutation probability is between 50% and 80%, a medium adjustment is made; when the mutation probability is above 80%, a large adjustment is made. This hierarchical adjustment strategy enables the system to more flexibly respond to different degrees of environmental change possibilities.

[0170] As a preferred implementation method, a feedback mechanism can be set in the system. By comparing the differences between the actually observed environmental changes and the prediction results, the system can dynamically adjust the parameters of the prediction model, such as the mutation point determination threshold, probability calculation method, etc. This adaptive mechanism can enable the system to maintain an efficient operating state under different environmental conditions.

[0171] In a specific embodiment, assume that in an urban intersection, the preset time period is 7 days. The system collects ambient light intensity and traffic flow data every 10 minutes and stores this data in a local database. At midnight every day, the system conducts a comprehensive analysis of the data from the past 7 days and counts the occurrence frequency of mutation points.

[0172] The determination criteria for mutation points can be set as follows: the change in ambient light intensity exceeds 50 lux, or the change in traffic flow exceeds 100 vehicles per hour. The system records the specific time when each mutation point occurs. For example, in the past 7 days, the system may observe the following patterns:

[0173] Mutation in ambient light intensity:

[0174] Between 5:30 and 6:00 in the early morning, there are 6 mutations (corresponding to sunrise);

[0175] Between 18:00 and 18:30 in the evening, there are 5 mutations (corresponding to sunset);

[0176] Mutation in traffic flow:

[0177] Between 7:30 and 8:00 in the morning on weekdays, there are 5 mutations (corresponding to the morning rush hour);

[0178] Between 17:30 and 18:00 in the afternoon on weekdays, there are 5 mutations (corresponding to the evening rush hour);

[0179] Between 11:30 and 12:00 at noon on weekends, there are 2 mutations (corresponding to the weekend shopping peak);

[0180] Based on these statistical data, the system can calculate the probability of a mutation occurring in the next time period. For example, if the next time period is 7:45 in the morning on a weekday, the system calculates that the probability of a traffic flow mutation in this time period is 71.4% (5 times / 7 days).

[0181] According to this probability, the predicted traffic flow value is adjusted accordingly. Assume that the originally predicted traffic flow is 300 vehicles per hour. Considering the mutation probability of 71.4%, the system may adjust the predicted value to 450 vehicles per hour. This adjusted predicted value will be used to calculate the sampling frequency of the traffic signal state in the next time period.

[0182] Through this method, the system can more accurately predict environmental changes, thereby more reasonably allocate energy resources. In the time period when a mutation is predicted to occur, the system will increase the sampling frequency to ensure that important traffic signal changes are not missed. In the period when the environment is predicted to be relatively stable, the system can reduce the sampling frequency to save energy.

[0183] In the second aspect, referring toFigure 2 , this application further proposes a traffic signal monitoring device, including:

[0184] An acquisition module 210, configured to acquire the ambient light intensity information and traffic flow information of the intersection where the traffic signal is located;

[0185] A calculation module 220, configured to calculate the sampling frequency for monitoring the traffic signal status according to the ambient light intensity information and traffic flow information;

[0186] A monitoring module 230, configured to monitor the traffic signal according to the calculated sampling frequency.

[0187] By dynamically adjusting the sampling frequency according to the ambient light intensity and traffic flow, the energy consumption is reduced while ensuring the monitoring effect, and it has the advantage of being able to dynamically adjust the sampling frequency according to environmental changes, thereby reducing the energy consumption while ensuring the monitoring effect.

[0188] In addition, in some preferred embodiments, a traffic signal monitoring device proposed by this application can execute any one of the steps in the above method.

[0189] In a third aspect, this application also provides an electronic device, including a processor and a memory. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the above method are run.

[0190] Through the above technical solution, the processor and the memory are interconnected and communicate with each other through a communication bus and / or other forms of connection mechanisms (not marked). The memory stores computer-readable instructions executable by the processor. When the electronic device runs, the processor executes the computer-readable instructions to execute the method in any optional implementation manner of the above embodiments to achieve the following functions: acquiring the ambient light intensity information and traffic flow information of the intersection where the traffic signal is located; calculating the sampling frequency for monitoring the traffic signal status according to the ambient light intensity information and traffic flow information; monitoring the traffic signal according to the calculated sampling frequency.

[0191] In a fourth aspect, this application also provides a storage medium, on which a computer program is stored. When the computer program is executed by the processor, the steps in the above method are run.

[0192] Through the above technical solution, when the computer program is executed by the processor, the method in any optional implementation manner of the above embodiments is executed to achieve the following functions: acquiring the ambient light intensity information and traffic flow information of the intersection where the traffic signal is located; calculating the sampling frequency for monitoring the traffic signal status according to the ambient light intensity information and traffic flow information; monitoring the traffic signal according to the calculated sampling frequency.

[0193] Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM for short), electrically erasable programmable read-only memory (EEPROM for short), erasable programmable read-only memory (EPROM for short), programmable read-only memory (PROM for short), read-only memory (ROM for short), magnetic memory, flash memory, magnetic disk or optical disk.

[0194] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces. The indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0195] In addition, the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0196] Furthermore, in each embodiment of the present application, the various functional modules can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0197] The above are only the embodiments of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A traffic light monitoring method, characterized in that: include: Obtain the ambient light intensity information and traffic flow information at the intersection where the traffic light is located; Calculating a sampling frequency for monitoring the state of a traffic light according to the ambient light intensity information and the traffic flow information; Monitoring the traffic light according to the calculated sampling frequency; The step of calculating the sampling frequency for monitoring the state of the traffic light according to the ambient light intensity information and the traffic flow information comprises: Obtaining ambient light intensity information and traffic flow information within a preset time period, and predicting ambient light intensity and traffic flow in the next time period; According to the predicted ambient light intensity and traffic flow in the next time period, the traffic light status sampling frequency in the next time period is calculated to achieve early response to environmental changes; The step of calculating the traffic light state sampling frequency for the next time period according to the predicted ambient light intensity and traffic flow for the next time period comprises: Obtain the difference between the predicted ambient light intensity and traffic flow in the next time period and the ambient light intensity and traffic flow in the current time period; Determining whether the difference value exceeds a preset threshold; When the preset threshold is exceeded, the buffer sampling frequency is increased based on the calculated sampling frequency for the next time period to avoid monitoring blind spots caused by sudden changes in the environment.

2. A traffic signal light monitoring method according to claim 1, characterized in that: The step of calculating the sampling frequency for monitoring the state of the traffic light according to the ambient light intensity information and the traffic flow information comprises: Making the calculation weight of the traffic flow information greater than the calculation weight of the ambient light intensity information; Calculate the sampling frequency for monitoring the state of the traffic light according to the traffic flow information, the greater the traffic flow information, the higher the calculated sampling frequency; The sampling frequency for monitoring the state of the traffic light is calculated according to the ambient light intensity information. The darker the ambient light intensity information is, the higher the calculated sampling frequency is.

3. A traffic signal light monitoring method according to claim 1, characterized in that: The step of calculating the traffic light state sampling frequency for the next time period according to the predicted ambient light intensity and traffic flow for the next time period comprises: Obtain the ambient light intensity L(t), L(t-1) and traffic flow F(t), F(t-1) of the current time period t and the previous time period t-1, as well as the predicted ambient light intensity L(t+1) and traffic flow F(t+1) of the next time period t+1; Calculate the environmental change difference value D, where D = |L(t+1)-L(t)|+|F(t+1)-F(t)|; Calculate the environmental change trend T, where T=sign((L(t+1)-L(t))*(L(t)-L(t-1))+(F(t+1)-F(t))*(F(t)-F(t-1))); Calculate the environmental change rate R, where R=(|L(t+1)-L(t)| / |L(t)-L(t-1)|+|F(t+1)-F(t)| / |F(t)-F(t-1)|) / 2; Determine the gain coefficient f(R) according to the environmental change rate R. When R > R1, f(R) = α; when R2 < R ≤ R1, f(R) = β; when R ≤ R2, f(R) = γ; where R1 and R2 are preset change rate thresholds, and R1 > R2, and α, β, and γ are preset gain coefficients, and α > β > γ; Calculate the sampling frequency S of the traffic signal light state in the next time period, where S = S_base + k1 * max(0, D - D_threshold) * (1 + k2 * T) * f(R); where S_base is the basic sampling frequency, k1 is the first adjustment coefficient, k2 is the second adjustment coefficient, and D_threshold is the difference value threshold; Ensure that the calculated sampling frequency S satisfies the constraint condition S_min ≤ S ≤ S_max, where S_min is the minimum sampling frequency and S_max is the maximum sampling frequency; Use the calculated sampling frequency S as the sampling frequency of the traffic signal light state in the next time period.

4. A traffic signal light monitoring method according to claim 1, characterized in that: The steps of obtaining the environmental light intensity information and traffic flow information within a preset time period and predicting the environmental light intensity and traffic flow in the next time period include: According to the preset time period, obtain the historical environmental light intensity information and historical traffic flow information corresponding to the next time period to be predicted in the preset time period; Obtain the mutation points and their corresponding time information based on the historical environmental light intensity information and historical traffic flow information; Predict the environmental light intensity and traffic flow in the next time period according to the time information of the detected light intensity and traffic flow mutation points.

5. A traffic signal light monitoring method according to claim 4, characterized in that: The steps of predicting the environmental light intensity and traffic flow in the next time period according to the time information of the detected light intensity and traffic flow mutation points include: Statistically calculate the occurrence frequency of the detected light intensity and traffic flow mutation points within the time period in multiple historical time periods within the preset time period; Based on the occurrence frequency of the mutation points within the time period, calculate the probability of light intensity and traffic flow mutation in the next time period; According to the probability of light intensity and traffic flow mutation in the next time period, adjust the predicted environmental light intensity and traffic flow values in the next time period.

6. A traffic light monitoring device, characterized in that: Include: An acquisition module for acquiring the environmental light intensity information and traffic flow information of the intersection where the traffic signal light is located; A calculation module for calculating the sampling frequency of monitoring the traffic signal light state according to the environmental light intensity information and traffic flow information; A monitoring module for monitoring the traffic signal light according to the calculated sampling frequency; The steps of calculating the sampling frequency of monitoring the traffic signal light state according to the environmental light intensity information and traffic flow information include: Obtain the environmental light intensity information and traffic flow information within a preset time period and predict the environmental light intensity and traffic flow in the next time period; Calculate the sampling frequency of the traffic signal light state in the next time period according to the predicted environmental light intensity and traffic flow in the next time period to achieve an early response to environmental changes; The step of calculating the traffic light state sampling frequency for the next time period according to the predicted ambient light intensity and traffic flow for the next time period comprises: Obtain the difference between the predicted ambient light intensity and traffic flow in the next time period and the ambient light intensity and traffic flow in the current time period; Determining whether the difference value exceeds a preset threshold; When the preset threshold is exceeded, the buffer sampling frequency is increased based on the calculated sampling frequency for the next time period to avoid monitoring blind spots caused by sudden changes in the environment.

7. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the method according to any one of claims 1 to 5 are executed.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are executed.

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