Low-power-consumption traffic monitoring method, system and equipment based on edge computing

Abstract

The invention provides a low-power-consumption traffic monitoring method, system and device based on edge computing, and belongs to the technical field of traffic control systems. The method comprises the following steps: acquiring road traffic data from a multi-source traffic monitoring sensor; determining a corresponding preset range and one or more edge computing device groups corresponding to the preset range based on the road traffic data and the preset device communication distance; according to each edge computing device group and the corresponding road traffic data, switching a real-time operation detection model of each edge computing device in the edge computing device group, so that the switched real-time operation detection model processes the corresponding road traffic data; and based on a data processing result of the real-time operation detection model, determining whether a traffic monitoring alarm event exists in a monitoring position corresponding to each edge computing device, so as to generate monitoring alarm information under the condition that the traffic monitoring alarm event exists and send the monitoring alarm information to a user terminal. According to the scheme, low-power-consumption traffic monitoring with dynamic balance among energy consumption, efficiency and precision is realized.

Description

Technical Field

[0001] The present application relates to the technical field of traffic control systems, and in particular to a low-power traffic monitoring method, system and device based on edge computing. Background Art

[0002] With the rapid development of intelligent transportation systems, edge computing devices are increasingly used in traffic monitoring. Existing technologies usually deploy edge computing devices along the roads and use deep learning models such as convolutional neural networks (CNN) to analyze traffic data in real time to detect abnormal events.

[0003] However, traditional edge computing devices have been running a single, high-power CNN model (typical power consumption > 15 watts) for a long time, and are unable to dynamically adjust computing resources according to traffic flow, exacerbating the contradiction between energy consumption and computing power. For example, complex models continue to run during low-traffic periods at night, resulting in a large amount of ineffective energy consumption (accounting for more than 40% of the total daily energy consumption). At the same time, when an accident occurs on a certain road section, multiple edge devices repeatedly trigger high-precision models, which causes both computing redundancy and waste of computing resources.

[0004] Based on this, there is an urgent need for a traffic monitoring technology solution that can achieve a dynamic balance between energy consumption, efficiency and accuracy. Summary of the invention

[0005] The embodiments of the present application provide a low-power traffic monitoring method, system and device based on edge computing, which are used to solve the current traffic monitoring technology problems that road edge computing equipment has a single operating model, high power consumption, and inefficient computing redundancy, making it difficult to achieve a dynamic balance between energy consumption, efficiency and accuracy.

[0006] On the one hand, an embodiment of the present application provides a low-power traffic monitoring method based on edge computing, the method comprising: Obtain road traffic data from multi-source traffic monitoring sensors; Based on the road traffic data and the preset device communication distance, determine the corresponding preset range and its corresponding one or more edge computing device groups; wherein each edge computing device group executes different model switching rules respectively; the model switching rules are at least used for the edge computing device to dynamically switch the real-time running detection model to one of the multiple different traffic monitoring detection models deployed in advance; According to each edge computing device group and the corresponding road traffic data, switching the real-time operation detection model of each edge computing device in the edge computing device group, so that the switched real-time operation detection model processes the corresponding road traffic data; Based on the data processing results of the real-time running detection model, it is determined whether there is a traffic monitoring alarm event at the monitoring location corresponding to each edge computing device, so as to generate monitoring alarm information and send it to the user terminal when the traffic monitoring alarm event exists.

[0007] In one implementation of the present application, the multi-source traffic monitoring sensor includes at least one or more of the following: a lidar sensor, a microphone array, an infrared thermal imager, and a geomagnetic sensor.

[0008] In one implementation of the present application, based on the road traffic data and the preset device communication distance, determining a corresponding preset range and its corresponding one or more edge computing device groups specifically includes: Based on the traffic density value and average vehicle speed in the road traffic data and the preset congestion condition, the duration of continuous road congestion is determined; wherein the preset congestion condition is used to determine whether the traffic density value and the average vehicle speed are both within a corresponding preset congestion determination interval within a preset time window; the preset congestion determination interval includes a preset congestion density subinterval and a preset average vehicle speed subinterval; Calculating a traffic flow saturation coefficient based on the traffic density value, a preset maximum road load density value, and the duration of continuous congestion on the road; Determine the spatial neighborhood radius corresponding to the edge computing device according to the preset device communication distance and the traffic flow saturation coefficient, and determine the preset range with the location of the edge computing device as the center and the spatial neighborhood radius as the radius; Compare the current traffic density value corresponding to each edge computing device within the preset range with the preset traffic density threshold to obtain each first comparison result, and compare the data volume processed per unit time corresponding to each edge computing device with the preset data volume threshold to obtain each second comparison result; Each of the edge computing device groups is generated according to each of the first comparison results and each of the second comparison results.

[0009] In one implementation of the present application, generating each of the edge computing device groups according to each of the first comparison results and each of the second comparison results specifically includes: When the current traffic density value is greater than or equal to the preset traffic density threshold and the amount of data processed per unit time is greater than or equal to the preset data amount threshold, adding the corresponding edge computing device to the first edge computing device group; When the current traffic density value is greater than or equal to the preset traffic density threshold or the amount of data processed per unit time is greater than or equal to the preset data amount threshold, adding the corresponding edge computing device to the second edge computing device group; When the current traffic density value is less than the preset traffic density threshold and the amount of data processed per unit time is less than the preset data amount threshold, the corresponding edge computing device is added to the third edge computing device group.

[0010] In one implementation of the present application, according to each edge computing device group and the corresponding road traffic data, switching the real-time operation detection model of each edge computing device in the edge computing device group specifically includes: Calculate the normalized traffic density index corresponding to each first edge computing device in the first edge computing device group according to the current traffic density value, the preset road maximum load density value, the preset road minimum load density value, the vehicle speed standard deviation and the average vehicle speed respectively corresponding to each first edge computing device in the first edge computing device group; According to the vehicle flow density index weights corresponding to the first edge computing devices, a weighted average value corresponding to each normalized vehicle flow density index is calculated; wherein the vehicle flow density index weight is positively correlated with the monitoring area corresponding to the first edge computing device; When the weighted average value is greater than a first preset threshold, switching the first traffic monitoring detection model to the real-time operation detection model of each of the first edge computing devices; otherwise, switching the second traffic monitoring detection model to the real-time operation detection model; When the first edge computing device group switches the real-time operation detection model, synchronously switching the corresponding real-time operation detection model of the second edge computing device group; Switch each third edge computing device of the third edge computing device group to a sleep mode to disable the corresponding real-time running detection model.

[0011] In one implementation of the present application, the method further includes: Before switching the real-time operation detection model of the first edge computing device group, respectively calculating the switching decision weights corresponding to each of the first edge computing devices according to the current traffic density value and the corresponding device interval distance of each of the first edge computing devices, and comparing the sum of the switching decision weights with a second preset threshold; When the sum is greater than the second preset threshold, it is determined that the first edge computing device group meets the switching condition.

[0012] In one implementation of the present application, the plurality of different traffic monitoring detection models include at least a spiking neural network SNN model and a convolutional neural network CNN model; The method further comprises: When the real-time operation detection model of the edge computing device is an SNN model, determining whether the edge computing device has the traffic monitoring alarm event; the traffic monitoring alarm event includes at least one or more of the following: vehicle emergency braking, speeding, traffic accidents, abnormal parking, and sudden congestion; If so, switch the real-time running detection model of the edge computing device to a CNN model, and continue to run the CNN model within a preset period of time.

[0013] In one implementation of the present application, the chip of the edge computing device is embedded with a microchannel cooling structure and an integrated Peltier effect temperature control module; the method further includes: When the chip temperature of the edge computing device is greater than a first temperature threshold, a first temperature control signal is generated to increase the coolant flow rate of the microchannel cooling structure and the driving voltage of the Peltier effect temperature control module through the first temperature control signal; When the chip temperature of the edge computing device is less than a second temperature threshold, a second temperature control signal is generated to reduce the coolant flow rate of the microchannel cooling structure and the driving voltage of the Peltier effect temperature control module through the second temperature control signal.

[0014] On the other hand, an embodiment of the present application further provides a low-power traffic monitoring system based on edge computing, the system comprising: An acquisition module, used to acquire road traffic data from multi-source traffic monitoring sensors; A first determination module is used to determine a corresponding preset range and one or more edge computing device groups corresponding thereto based on the road traffic data and the preset device communication distance; wherein each of the edge computing device groups respectively executes a different model switching rule; and the model switching rule is at least used for the edge computing device to dynamically switch the real-time running detection model to one of a plurality of different traffic monitoring detection models deployed in advance; A switching module, configured to switch the real-time operation detection model of each edge computing device in the edge computing device group according to each edge computing device group and the corresponding road traffic data, so that the switched real-time operation detection model processes the corresponding road traffic data; The second determination module is used to determine whether there is a traffic monitoring alarm event at the monitoring location corresponding to each edge computing device based on the data processing result of the real-time running detection model, so as to generate monitoring alarm information and send it to the user terminal when the traffic monitoring alarm event exists.

[0015] On the other hand, an embodiment of the present application further provides a low-power traffic monitoring device based on edge computing, the device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a low-power traffic monitoring method based on edge computing as described above.

[0016] Compared with the prior art, the present invention has the following significant effects: Through the above scheme, this application completes the dynamic model switching strategy according to dynamic parameters such as traffic density and speed, which can effectively achieve a balance between high precision and low power consumption; at the same time, based on the preset device communication distance and road traffic data, an edge computing device group is constructed to optimize task allocation and resource scheduling, increasing equipment utilization and data processing efficiency; this application can also quickly identify traffic monitoring alarm events, ensuring event response time and alarm accuracy. Furthermore, through the above dynamic model switching, equipment collaborative optimization and real-time alarm generation, the problems of high energy consumption, low efficiency and lack of flexibility in traditional traffic monitoring methods are solved, and high-precision, low-power and high-efficiency traffic monitoring is achieved, providing reliable technical support for smart transportation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A schematic diagram of a flow chart of a low-power traffic monitoring method based on edge computing in an embodiment of the present application; Figure 2 This is a structural diagram of a low-power traffic monitoring system based on edge computing in an embodiment of the present application; Figure 3 This is a structural diagram of a low-power traffic monitoring device based on edge computing in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0019] The embodiments of the present application provide a low-power traffic monitoring method, system and device based on edge computing, which are used to solve the current traffic monitoring technology problems that the road edge computing equipment has a single operation model, high power consumption, and inefficient computing redundancy, and it is difficult to achieve a dynamic balance between energy consumption, efficiency and accuracy.

[0020] The following describes in detail various embodiments of the present application in conjunction with the accompanying drawings.

[0021] The present application embodiment provides a low-power traffic monitoring method based on edge computing, such as Figure 1 As shown, the method may include steps S101-S104: S101, the edge computing device cluster obtains road traffic data from multi-source traffic monitoring sensors.

[0022] It should be noted that the edge computing device cluster, as the executor of the low-power traffic monitoring method based on edge computing, is a distributed computing network composed of multiple edge computing devices set up on the road.

[0023] Multi-source traffic monitoring sensors include but are not limited to laser radar sensors, microphone arrays, infrared thermal imagers, and geomagnetic sensors. Among them, laser radar sensors can be used to generate three-dimensional vehicle contour features; microphone arrays can collect voiceprint features of road sounds to identify vehicle types and abnormal sounds; infrared thermal imagers can detect vehicle heat source distribution; geomagnetic sensors can obtain vehicle traffic frequency, vehicle speed, etc.

[0024] After each edge computing device in the edge computing device cluster receives the monitoring data from its corresponding multi-source traffic monitoring sensor through its sensor interface, it uses each monitoring data as road traffic data.

[0025] S102, the edge computing device cluster determines a corresponding preset range and its corresponding one or more edge computing device groups based on road traffic data and preset device communication distance.

[0026] Each edge computing device group executes different model switching rules respectively. The model switching rules are at least used for the edge computing device to dynamically switch the real-time running detection model to one of a plurality of different traffic monitoring detection models deployed in advance.

[0027] In the embodiment of the present application, the above-mentioned determination of the corresponding preset range and its corresponding one or more edge computing device groups based on the road traffic data and the preset device communication distance specifically includes: Based on the traffic density value and average speed in the road traffic data and the preset congestion conditions, the duration of continuous road congestion is determined. Among them, the preset congestion conditions are used to determine whether the traffic density value and the average speed are both in the corresponding preset congestion determination interval within the preset time window. The preset congestion determination interval includes a preset congestion density subinterval and a preset average speed subinterval. Based on the traffic density value, the preset road maximum load density value and the duration of continuous road congestion, the traffic flow saturation coefficient is calculated. According to the preset device communication distance and the traffic flow saturation coefficient, the spatial neighborhood radius corresponding to the corresponding edge computing device is determined, and the preset range is determined with the location of the edge computing device as the center and the spatial neighborhood radius as the radius. The current traffic density value corresponding to each edge computing device within the preset range is compared with the preset traffic density threshold to obtain each first comparison result, and the data volume processed per unit time corresponding to each edge computing device is compared with the preset data volume threshold to obtain each second comparison result. According to each first comparison result and each second comparison result, each edge computing device group is generated.

[0028] In other words, the edge computing device cluster extracts data sub-items such as traffic density and average speed from the road traffic data for each edge computing device, and calculates the duration of continuous road congestion in the area monitored by each edge computing device. The traffic density value can be obtained by dividing the number of vehicles detected by the device by the area monitored by the edge computing device.

[0029] The calculation of the duration of continuous road congestion is performed using a preset congestion condition, which can be specifically: by presetting a time window length, such as a preset time window of 1 minute, it is determined whether the traffic density value is continuously in the preset congestion density sub-interval and whether the average vehicle speed is continuously in the preset average vehicle speed sub-interval within the preset time window. If both conditions are met, the preset time window meets the preset congestion condition, and the duration of continuous road congestion is accumulated as a preset time window length, i.e., 1 minute. Subsequently, the number of time windows that continuously meet the preset congestion condition is accumulated. If the preset congestion condition is not met in the next preset time window, the duration of continuous road congestion is obtained according to the product value of the number of time windows that meet the preset congestion condition and the length of the time window accumulated before.

[0030] For example, if the preset time window length is 1 minute, the preset congestion density sub-interval is ( ), the preset average speed sub-interval is ( ),in Indicates the preset maximum road load density value 80% Indicates the preset road speed If within the preset time window, the continuous traffic density value and average speed meet the following conditions: And the average speed , it is determined that the road corresponding to the area monitored by the corresponding edge computing device is in a congested state, and the congestion duration is accumulated by a time window length, that is, 1 minute; if the next preset time window still meets the above preset congestion condition, another time window length is accumulated on the basis of the above 1 minute, and the road is continuously congested for 2 minutes at this time. The judgment process of the preset congestion condition continues until the next preset time window does not meet the above preset congestion condition, and the previously accumulated congestion duration is used as the road continuous congestion duration. In addition, if the edge computing device cluster calculates that a certain edge computing device still meets the preset congestion condition until the current time, the accumulated congestion duration at this time will be used as the road continuous congestion duration.

[0031] After obtaining the duration of continuous road congestion, this application also calculates the traffic flow saturation coefficient through the traffic density value, the preset maximum road load density value and the duration of continuous road congestion. The traffic flow saturation coefficient is used to characterize the traffic flow saturation degree of the current road and realize dynamic evaluation of the traffic status. The formula is as follows:

[0032] in, Indicates Traffic flow saturation coefficient of edge computing devices, Indicates The traffic density value of the edge computing device, Indicates The preset maximum road load density value of the edge computing device, Indicates The duration of road congestion corresponding to each edge computing device. Used to assess the real-time congestion level of roads, It is used to reflect the impact of the duration of continuous road congestion on the traffic status, and the duration is positively correlated with the impact. Among them, the preset maximum road load density value and the preset minimum road load density value can be preset by the user, and this application does not make specific restrictions on this.

[0033] Furthermore, the present application will also calculate the spatial neighborhood radius based on the preset device communication distance and the traffic flow saturation coefficient calculated above, so as to cluster the edge computing devices. The spatial neighborhood radius is as follows: ,in Indicates The preset device communication distance of each edge computing device can be stored and shared in the edge computing device cluster; Indicates The spatial neighborhood radius of the edge computing device is used to generate a preset range through the spatial neighborhood radius and the location of the edge computing device.

[0034] Next, this application compares the current traffic density value of each edge computing device within the preset range with the preset traffic density threshold, and compares the amount of data processed per unit time with the preset data volume threshold. Among them, the preset traffic density threshold and the preset data volume threshold can be set by the user according to the actual usage scenario, and this application does not make specific restrictions on this. The amount of data processed per unit time can be obtained by the ratio of the sum of the sizes of each data packet processed by the edge computing device per unit time to the unit time. The data packet size can be megabytes or gigabytes.

[0035] In one embodiment of the present application, the generation of each edge computing device group according to each first comparison result and each second comparison result specifically includes: When the current traffic density value is greater than or equal to the preset traffic density threshold and the amount of data processed per unit time is greater than or equal to the preset data amount threshold, the corresponding edge computing device is added to the first edge computing device group.

[0036] When the current traffic density value is greater than or equal to the preset traffic density threshold or the amount of data processed per unit time is greater than or equal to the preset data amount threshold, the corresponding edge computing device is added to the second edge computing device group.

[0037] When the current traffic density value is less than the preset traffic density threshold and the amount of data processed per unit time is less than the preset data amount threshold, the corresponding edge computing device is added to the third edge computing device group.

[0038] That is to say, this application clusters and groups edge computing devices according to the traffic density value and the amount of data processed per unit time, divides them into three different edge computing device groups, and executes different model switching rules. By grouping edge computing devices, the edge computing devices can be uniformly scheduled to complete model switching. On the one hand, it avoids the consumption of computing resources by controlling each edge computing device separately, increasing system energy consumption and communication overhead; on the other hand, it enables edge computing devices to form a unified monitoring network during traffic monitoring, enhance the linkage between devices, and provide equipment utilization while ensuring the accuracy of event detection.

[0039] S103, the edge computing device cluster switches the real-time operation detection model of each edge computing device in the edge computing device group according to each edge computing device group and its corresponding road traffic data, so that the switched real-time operation detection model processes the corresponding road traffic data.

[0040] In the embodiment of the present application, the real-time operation detection model of each edge computing device in the edge computing device group is switched according to each edge computing device group and its corresponding road traffic data, specifically including: According to the current traffic density value, the preset road maximum load density value, the preset road minimum load density value, the speed standard deviation and the average speed corresponding to each first edge computing device in the first edge computing device group, the normalized traffic density index corresponding to each first edge computing device is calculated. According to the weight of the traffic density index corresponding to each first edge computing device, the weighted average value corresponding to each normalized traffic density index is calculated. Among them, the traffic density index weight is positively correlated with the monitoring area corresponding to the first edge computing device. When the weighted average value is greater than the first preset threshold, the first traffic monitoring detection model is switched to the real-time operation detection model of each first edge computing device, otherwise, the second traffic monitoring detection model is switched to the real-time operation detection model. When the first edge computing device group switches the real-time operation detection model, the corresponding real-time operation detection model of the second edge computing device group is switched synchronously. Each third edge computing device of the third edge computing device group is switched to sleep mode to disable the corresponding real-time operation detection model.

[0041] In other words, after obtaining the above-mentioned different edge computing device groups, for the first edge computing device group, the edge computing device cluster calculates the normalized traffic density index by the following formula:

[0042] in, Indicates Normalized traffic density index of edge computing devices, Indicates The preset minimum road load density value of the edge computing device, Indicates The standard deviation of vehicle speed for each edge computing device, Indicates The average vehicle speed monitored by an edge computing device.

[0043] Then, the weighted average of the normalized traffic density indexes corresponding to the first edge computing device group is calculated. :

[0044] in, For the The traffic density index weight of each edge computing device, , For the The monitoring area of ​​the edge computing device is is the total number of edge computing devices in the first edge computing device group. , .

[0045] Subsequently, the edge computing device cluster compares the weighted average value with the first preset threshold value. The first preset threshold value is set according to the actual usage scenario and is not specifically limited here. If the weighted average value is greater than the first preset threshold value, then the real-time operation detection model of each first edge computing device will be switched to the first traffic monitoring detection model, otherwise it will be switched to the second traffic monitoring detection model. Among them, the multiple different traffic monitoring detection models include at least a spiking neural network (Spiking Neural Network, SNN) model and a convolutional neural network CNN model. The first traffic monitoring detection model is a CNN model, and the second traffic monitoring detection model is an SNN model. The CNN model consumes more power than the SNN model, but the CNN model can perform real-time and high-precision data processing and analyze more complex events. Through the flexible switching between the CNN model and the SNN model, it can not only ensure effective traffic monitoring, but also reduce equipment energy consumption.

[0046] For the second edge computing device group, it follows the first edge computing device group to switch models. If the first edge computing device group completes the switching of the real-time operation detection model, the second edge computing device group will synchronously switch to the same real-time operation detection model as the first edge computing device group, and maintain consistency with the real-time operation detection model of the first edge computing device group.

[0047] For the third edge computing device group, each of its third edge computing devices will enter sleep mode, that is, low-power standby mode, and no SNN model or CNN model will be executed.

[0048] Through the above solution, different model switching rules can be used to manage different edge computing device groups to achieve more energy-saving and effective traffic monitoring.

[0049] In addition, when the real-time operation detection model of each of the above-mentioned edge computing devices is an SNN model, determine whether the edge computing device has a traffic monitoring alarm event. Traffic monitoring alarm events include at least one or more of the following: vehicle emergency braking, speeding, traffic accidents, abnormal parking, and sudden congestion. If so, switch the real-time operation detection model of the edge computing device to a CNN model, and continue to run the CNN model within a preset period of time.

[0050] That is to say, if a traffic monitoring alarm event occurs in the monitoring area monitored by the edge computing device, in order to better achieve monitoring, the edge computing device will maintain the CNN model within a preset period of time to monitor traffic anomalies with high precision. The preset period of time can be set by the user according to the actual usage scenario, and this application does not make specific restrictions on this.

[0051] In another embodiment of the present application, for the third edge computing device group, when it maintains the sleep mode, it can be determined whether to execute the SNN model according to the real-time vehicle density value change rate of the third edge computing device. If the real-time vehicle density value change rate within 30 minutes is 0.5, which is greater than a preset change rate threshold of 0.3, the real-time operation detection model of the third edge computing device will be switched to the SNN model to detect traffic monitoring alarm events.

[0052] In another embodiment of the present application, in order to avoid frequent model switching of the first edge computing device group and balance reasonable power consumption and monitoring accuracy, the following embodiments are also included: Before switching the real-time operation detection model of the first edge computing device group, the switching decision weights corresponding to each first edge computing device are calculated according to the current traffic density value of each first edge computing device and the corresponding device interval distance, and the sum of the switching decision weights is compared with the second preset threshold. When the sum is greater than the second preset threshold, it is determined that the first edge computing device group meets the switching condition.

[0053] In other words, before executing the model switching, the edge computing device cluster will calculate the switching decision weight for each first edge computing device, and the formula is as follows:

[0054] in, For the The switching decision weight of the first edge computing device, is a node set consisting of each first edge computing device in the first edge computing device group, Indicates The traffic density value of the first edge computing device, For the The first edge computing device and the The actual physical distance between the first edge computing devices. The actual physical distance can be preset by the user, or can be obtained by calculating the Euclidean distance according to the location coordinates of the edge computing devices, and this application does not make specific limitations on this. It is the distance attenuation factor, which reflects the attenuation degree of influence between nodes affected by the actual physical distance.

[0055] After obtaining the switching decision weights of each of the above-mentioned edge computing devices, the switching decision weights are added together to calculate the sum, and the sum is compared with the preset second preset threshold. If the sum is greater than the second preset threshold, it means that the switching condition is met, and the above-mentioned model switching of the first edge computing device group can be performed, otherwise the model switching is not performed. Through the above scheme, the regional-level model switching of the first edge computing device group can be realized, and the high-precision and low-power optimization of traffic monitoring can be better realized.

[0056] S104, the edge computing device cluster determines whether there is a traffic monitoring alarm event at the monitoring location corresponding to each edge computing device based on the data processing results of the real-time running detection model, so as to generate monitoring alarm information and send it to the user terminal when there is a traffic monitoring alarm event.

[0057] That is to say, the edge computing device can monitor the traffic in the monitoring area by running the detection model in real time, and when a traffic monitoring alarm event is detected, a notification of the existence of a traffic monitoring alarm event is generated and sent to the edge computing device cluster, and the edge computing device cluster further generates monitoring alarm information and sends it to the user terminal. The user terminal can be understood as a mobile phone, computer or other device of the traffic management department or traffic police, and this application does not make specific restrictions on this.

[0058] In one embodiment of the present application, the chip of the above-mentioned edge computing device is also embedded with a microchannel cooling structure and an integrated Peltier effect temperature control module.

[0059] Specifically, a microfluidic cooling structure is embedded in the chip packaging layer of the edge computing device. The microfluidic channel includes: an inlet channel connected to the coolant supply system for introducing coolant; a branch channel distributed in the chip heat source area for uniform heat dissipation; and an outlet channel connected to the coolant recovery system for exporting coolant. The heat source area of ​​the chip packaging layer integrates a Peltier effect temperature control module, which may include a thermocouple pair formed by alternating P-type semiconductors and N-type semiconductors; a cold end in contact with the chip heat source area for absorbing heat; and a hot end in contact with the microfluidic cooling structure for releasing heat.

[0060] In addition, the edge computing device may also include a coolant circulation system, including: a coolant pump for driving the coolant to circulate in the microchannel; a radiator for cooling the coolant flowing through the outlet channel; and a temperature sensor for real-time monitoring of the coolant temperature and feedback control of the coolant flow rate.

[0061] In an embodiment of the present application, when the chip temperature of the edge computing device is greater than the first temperature threshold, a first temperature control signal is generated to increase the coolant flow rate of the microchannel cooling structure and the driving voltage of the Peltier effect temperature control module through the first temperature control signal. When the chip temperature of the edge computing device is less than the second temperature threshold, a second temperature control signal is generated to reduce the coolant flow rate of the microchannel cooling structure and the driving voltage of the Peltier effect temperature control module through the second temperature control signal.

[0062] Through the cooperation of the above-mentioned microfluidic cooling structure and the Peltier effect temperature control module, the chip can be efficiently cooled. Based on the above-mentioned active temperature control algorithm of real-time temperature, precise temperature control of the chip can be achieved. When combined with solar power supply, the chip computing power can be more effectively maintained to achieve low-power traffic monitoring.

[0063] Through the above scheme, this application completes the dynamic model switching strategy according to dynamic parameters such as traffic density and speed, which can effectively achieve a balance between high precision and low power consumption; at the same time, based on the preset device communication distance and road traffic data, an edge computing device group is constructed to optimize task allocation and resource scheduling, increasing equipment utilization and data processing efficiency; this application can also quickly identify traffic monitoring alarm events, ensuring event response time and alarm accuracy. Furthermore, through the above dynamic model switching, equipment collaborative optimization and real-time alarm generation, the problems of high energy consumption, low efficiency and lack of flexibility in traditional traffic monitoring methods are solved, and high-precision, low-power and high-efficiency traffic monitoring is achieved, providing reliable technical support for smart transportation systems.

[0064] Figure 2 A schematic diagram of a low-power traffic monitoring system based on edge computing provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the low-power traffic monitoring system 200 based on edge computing includes: The acquisition module 201 is used to obtain road traffic data from multi-source traffic monitoring sensors. The first determination module 202 is used to determine the corresponding preset range and its corresponding one or more edge computing device groups based on the road traffic data and the preset device communication distance. Among them, each edge computing device group executes different model switching rules respectively. The model switching rule is at least used for the edge computing device to dynamically switch the real-time operation detection model to one of multiple different traffic monitoring detection models deployed in advance. The switching module 203 is used to switch the real-time operation detection model of each edge computing device in the edge computing device group according to each edge computing device group and its corresponding road traffic data, so that the switched real-time operation detection model processes the corresponding road traffic data. The second determination module 204 is used to determine whether there is a traffic monitoring alarm event at the corresponding monitoring position of each edge computing device based on the data processing result of the real-time operation detection model, so as to generate monitoring alarm information and send it to the user terminal when there is a traffic monitoring alarm event.

[0065] Figure 3 A schematic diagram of a low-power traffic monitoring device based on edge computing provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the device includes: At least one processor; and a memory in communication with the at least one processor. The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Obtain road traffic data from multi-source traffic monitoring sensors. Based on the road traffic data and the preset device communication distance, determine the corresponding preset range and its corresponding one or more edge computing device groups. Among them, each edge computing device group executes different model switching rules respectively. The model switching rule is at least used for the edge computing device to dynamically switch the real-time operation detection model to one of multiple different traffic monitoring detection models deployed in advance. According to each edge computing device group and its corresponding road traffic data, switch the real-time operation detection model of each edge computing device in the edge computing device group so that the switched real-time operation detection model processes the corresponding road traffic data. Based on the data processing results of the real-time operation detection model, determine whether there is a traffic monitoring alarm event at the corresponding monitoring position of each edge computing device, so as to generate monitoring alarm information and send it to the user terminal when there is a traffic monitoring alarm event.

[0066] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system and device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0067] The system and equipment provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and equipment also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and equipment will not be repeated here.

[0068] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0069] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A low-power traffic monitoring method based on edge computing, characterized in that: The method comprises: Obtain road traffic data from multi-source traffic monitoring sensors; Based on the road traffic data and the preset device communication distance, determine the corresponding preset range and its corresponding one or more edge computing device groups; wherein each edge computing device group executes different model switching rules respectively; the model switching rules are at least used for the edge computing device to dynamically switch the real-time running detection model to one of the multiple different traffic monitoring detection models deployed in advance; According to each edge computing device group and the corresponding road traffic data, switching the real-time operation detection model of each edge computing device in the edge computing device group, so that the switched real-time operation detection model processes the corresponding road traffic data; Based on the data processing results of the real-time running detection model, it is determined whether there is a traffic monitoring alarm event at the monitoring location corresponding to each edge computing device, so as to generate monitoring alarm information and send it to the user terminal when the traffic monitoring alarm event exists.

2. A low-power traffic monitoring method based on edge computing according to claim 1, characterized in that: The multi-source traffic monitoring sensor includes at least one or more of the following: a lidar sensor, a microphone array, an infrared thermal imager, and a geomagnetic sensor.

3. A low-power traffic monitoring method based on edge computing according to claim 1, characterized in that: Based on the road traffic data and the preset device communication distance, determining a corresponding preset range and its corresponding one or more edge computing device groups specifically includes: Based on the traffic density value and average vehicle speed in the road traffic data and the preset congestion condition, the duration of continuous road congestion is determined; wherein the preset congestion condition is used to determine whether the traffic density value and the average vehicle speed are both within a corresponding preset congestion determination interval within a preset time window; the preset congestion determination interval includes a preset congestion density subinterval and a preset average vehicle speed subinterval; Calculating a traffic flow saturation coefficient based on the traffic density value, a preset maximum road load density value, and the duration of continuous congestion on the road; Determine the spatial neighborhood radius corresponding to the edge computing device according to the preset device communication distance and the traffic flow saturation coefficient, and determine the preset range with the location of the edge computing device as the center and the spatial neighborhood radius as the radius; Compare the current traffic density value corresponding to each edge computing device within the preset range with the preset traffic density threshold to obtain each first comparison result, and compare the data volume processed per unit time corresponding to each edge computing device with the preset data volume threshold to obtain each second comparison result; Each of the edge computing device groups is generated according to each of the first comparison results and each of the second comparison results.

4. A low-power traffic monitoring method based on edge computing according to claim 3, characterized in that: Generating each of the edge computing device groups according to each of the first comparison results and each of the second comparison results specifically includes: When the current traffic density value is greater than or equal to the preset traffic density threshold and the amount of data processed per unit time is greater than or equal to the preset data amount threshold, adding the corresponding edge computing device to the first edge computing device group; When the current traffic density value is greater than or equal to the preset traffic density threshold or the amount of data processed per unit time is greater than or equal to the preset data amount threshold, adding the corresponding edge computing device to the second edge computing device group; When the current traffic density value is less than the preset traffic density threshold and the amount of data processed per unit time is less than the preset data amount threshold, the corresponding edge computing device is added to the third edge computing device group.

5. A low-power traffic monitoring method based on edge computing according to claim 4, characterized in that: According to each edge computing device group and the corresponding road traffic data, switching the real-time operation detection model of each edge computing device in the edge computing device group specifically includes: Calculate the normalized traffic density index corresponding to each first edge computing device in the first edge computing device group according to the current traffic density value, the preset road maximum load density value, the preset road minimum load density value, the vehicle speed standard deviation and the average vehicle speed respectively corresponding to each first edge computing device in the first edge computing device group; According to the vehicle flow density index weights corresponding to the first edge computing devices, a weighted average value corresponding to each normalized vehicle flow density index is calculated; wherein the vehicle flow density index weight is positively correlated with the monitoring area corresponding to the first edge computing device; When the weighted average value is greater than a first preset threshold, switching the first traffic monitoring detection model to the real-time operation detection model of each of the first edge computing devices; otherwise, switching the second traffic monitoring detection model to the real-time operation detection model; When the first edge computing device group switches the real-time operation detection model, synchronously switching the corresponding real-time operation detection model of the second edge computing device group; Switch each third edge computing device of the third edge computing device group to a sleep mode to disable the corresponding real-time running detection model.

6. A low-power traffic monitoring method based on edge computing according to claim 5, characterized in that: The method further comprises: Before switching the real-time operation detection model of the first edge computing device group, respectively calculating the switching decision weights corresponding to each of the first edge computing devices according to the current traffic density value and the corresponding device interval distance of each of the first edge computing devices, and comparing the sum of the switching decision weights with a second preset threshold; When the sum is greater than the second preset threshold, it is determined that the first edge computing device group meets the switching condition.

7. A low-power traffic monitoring method based on edge computing according to any one of claims 1 to 6, characterized in that: The multiple different traffic monitoring detection models include at least a spike neural network SNN model and a convolutional neural network CNN model; The method further comprises: When the real-time operation detection model of the edge computing device is an SNN model, determining whether the edge computing device has the traffic monitoring alarm event; the traffic monitoring alarm event includes at least one or more of the following: vehicle emergency braking, speeding, traffic accidents, abnormal parking, and sudden congestion; If so, switch the real-time running detection model of the edge computing device to a CNN model, and continue to run the CNN model within a preset period of time.

8. The low-power traffic monitoring method based on edge computing according to claim 1, characterized in that: The chip of the edge computing device is embedded with a microchannel cooling structure and an integrated Peltier effect temperature control module; the method also includes: When the chip temperature of the edge computing device is greater than a first temperature threshold, a first temperature control signal is generated to increase the coolant flow rate of the microchannel cooling structure and the driving voltage of the Peltier effect temperature control module through the first temperature control signal; When the chip temperature of the edge computing device is less than a second temperature threshold, a second temperature control signal is generated to reduce the coolant flow rate of the microchannel cooling structure and the driving voltage of the Peltier effect temperature control module through the second temperature control signal.

9. A low-power traffic monitoring system based on edge computing, characterized in that: The system comprises: An acquisition module, used to acquire road traffic data from multi-source traffic monitoring sensors; A first determination module is used to determine a corresponding preset range and one or more edge computing device groups corresponding thereto based on the road traffic data and the preset device communication distance; wherein each of the edge computing device groups respectively executes a different model switching rule; and the model switching rule is at least used for the edge computing device to dynamically switch the real-time running detection model to one of a plurality of different traffic monitoring detection models deployed in advance; A switching module, configured to switch the real-time operation detection model of each edge computing device in the edge computing device group according to each edge computing device group and the corresponding road traffic data, so that the switched real-time operation detection model processes the corresponding road traffic data; The second determination module is used to determine whether there is a traffic monitoring alarm event at the monitoring location corresponding to each edge computing device based on the data processing result of the real-time running detection model, so as to generate monitoring alarm information and send it to the user terminal when the traffic monitoring alarm event exists.

10. A low-power traffic monitoring device based on edge computing, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a low-power traffic monitoring method based on edge computing as described in any one of claims 1-8 above.

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