A vehicle traffic control method, system and device based on cloud customer service

Abstract

The present application provides a vehicle traffic control method, system and device based on cloud customer service, which belongs to the field of traffic control technology. In this method, the cloud server determines the corresponding special situation event data set based on the information of the vehicle to be passed from the lane edge device; the special situation event data set is input into the pre-trained special situation event classification model to determine the special situation category of the vehicle to be passed according to the model output result. The special situation category and its corresponding special situation event data set are used as special situation event information; based on the preset work order scheduling strategy and the location of the special situation event, the cloud customer service corresponding to the vehicle to be passed is matched, so that the special situation event information is sent to the customer service terminal of the cloud customer service, and a communication connection is established between the lane communication terminal and the customer service terminal; in response to the special situation event handling result from the customer service terminal, a traffic control instruction is generated to control the operation of the lane equipment through the traffic control instruction. Thereby, special situation processing is completed efficiently, standardized and intelligently, and traffic efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of traffic control technology, and in particular to a vehicle traffic control method, system and device based on cloud customer service. Background Art

[0002] In the field of vehicle traffic control, the traditional operation mode has long relied on basic hardware equipment detection and manual subjective judgment. Although the license plate recognition system in the past is a common means of vehicle identity verification, it lacks a multi-dimensional and intelligent response plan when faced with situations such as damaged license plates, blurred recognition due to weather conditions, or recognition errors caused by complex lighting conditions. It is difficult to accurately determine the true status of the vehicle, which often leads to obstruction of vehicle traffic and management chaos.

[0003] With the explosive growth of traffic volume, especially at key nodes such as highway toll stations, the operational efficiency under the traditional control model is stretched to its limits. Traditional toll stations require full-time personnel to man each lane, and the high cost of manual operation has become a heavy economic burden on the traffic management department. At the same time, the toll road network continues to expand, and special events in mixed lanes emerge in an endless stream, such as cumbersome vehicle information verification, complex special authorization process, missing entry information that is difficult to trace, and sudden lane requests for help that cannot be responded to in a timely manner. The difficulty of handling is increasing day by day, which seriously interferes with the normal traffic order of vehicles and greatly reduces the user's traffic experience.

[0004] In the process of handling special situations, it mainly relies on manual operation by on-site staff, lacking a systematic and intelligent dispatching mechanism. This makes the handling of special situations inefficient, and different staff members handle them in different ways, making it impossible to achieve unified and efficient control, which runs counter to the efficient, accurate and intelligent goals pursued by modern traffic management. Based on this, a vehicle traffic control method is urgently needed to break through the limitations of the traditional model and meet the growing traffic management needs. Summary of the invention

[0005] To solve the above problems, the embodiments of the present application provide a vehicle traffic control method, system and device based on cloud customer service.

[0006] On the one hand, an embodiment of the present application provides a vehicle traffic control method based on cloud customer service, the method comprising: The cloud server determines a corresponding special event data set based on the information of the vehicle to be passed from the lane edge device; wherein the special event data set is obtained based on the analysis of the multimodal data set corresponding to the information of the vehicle to be passed; The special situation event data set is input into a pre-trained special situation event classification model to determine the special situation category of the vehicle to be passed according to the model output result; the special situation category includes at least one of the following categories corresponding to multiple preset special situation handling processes: vehicle information verification, special situation authorization, entry information missing, lane assistance; The special situation category and the corresponding special situation event data set are used as special situation event information; Based on the preset work order scheduling strategy and the location of the special event, the cloud customer service corresponding to the vehicle to be passed is matched, so as to send the special event information to the customer service terminal of the cloud customer service, and establish a communication connection between the lane communication terminal and the customer service terminal; In response to the special event handling result from the customer service terminal, a traffic control instruction is generated to control the operation of the lane equipment through the traffic control instruction.

[0007] In one implementation of the present application, before determining the corresponding special event data set based on the information of the vehicle to be passed from the lane edge device, the method further includes: The lane edge device obtains initial multimodal data in the multimodal data set and determines the corresponding size of the initial multimodal data; wherein the initial multimodal data at least includes: image data, video data, audio data and radar data; The lane edge device calculates the cloud upload time, local processing time and cloud processing time corresponding to the initial multimodal data, respectively, based on the initial multimodal data size, predefined local processing capacity, cloud processing capacity and current network bandwidth; The lane edge device compares the sum of the cloud upload time and the cloud processing time with the local processing time to determine whether the initial multimodal data is analyzed locally at the lane edge device according to the comparison result; If yes, the lane edge device analyzes and processes the initial multimodal data to extract corresponding vehicle feature information and adds it to the information of the vehicle to be passed; Otherwise, the lane edge device compresses the initial multimodal data and adds it to the information of the vehicle to be passed.

[0008] In one implementation of the present application, the cloud server determines the corresponding special event data set based on the information of the vehicles to be passed from the lane edge device, specifically including: Extracting vehicle verification information from the to-be-passed vehicle information; wherein the vehicle verification information at least includes: highway composite pass card information, lane capture information and overload control weighing information; Comparing the vehicle verification information with a preset verification information list; When the comparison result is consistent, the vehicle to be passed is determined to be a passable vehicle, so as to control the lane equipment to release the passable vehicle; Otherwise, the vehicle to be passed is regarded as a special situation vehicle, and the vehicle verification information corresponding to each special situation category is added to the special situation event data set; wherein, the vehicle verification information corresponding to each special situation category is used for special situation event classification and special situation event handling.

[0009] In one implementation of the present application, the special event data set is input into a pre-trained special event classification model to determine the special event category of the vehicle to be passed according to the model output result, specifically including: Performing quantum state encoding on the special event data set to determine a number of special event data features; Inputting each of the special situation data features into the special situation event classification model to determine the quantum state probability distribution output by the model; wherein the quantum state probability distribution includes the probability value of the vehicle to be passed belonging to each of the special situation categories; The special situation category corresponding to the vehicle to be passed is determined according to the quantum state probability distribution and a preset threshold.

[0010] In one implementation of the present application, based on a preset work order scheduling strategy and the location where the special event occurs, a cloud customer service corresponding to the vehicle to be passed is matched, specifically including: Determine the cloud customer service group corresponding to the vehicle to be passed according to the location where the special event occurred and the preset cloud customer service group information; According to the cloud customer service group, the corresponding preset work order scheduling strategy is matched; wherein the preset work order scheduling strategy includes one of the following allocation mechanisms: a rotation allocation mechanism and an idleness allocation mechanism; the allocation mechanism includes the following allocation principles: an efficiency principle, a saturation principle, a balance principle, and an accuracy principle; According to the matched preset work order scheduling strategy, the cloud customer service corresponding to the vehicle to be passed in the cloud customer service group is determined.

[0011] In one implementation of the present application, determining the cloud customer service corresponding to the vehicle to be passed in the cloud customer service group specifically includes: Determine at least one first cloud customer service and its corresponding second cloud customer service in the cloud customer service group according to the matched preset work order scheduling strategy; Determine a first work order allocation score for the first cloud customer service based on the first current work order quantity, the first historical work order allocation frequency, and the first work order allocation peak period of the first cloud customer service; and determine a second work order allocation score for the second cloud customer service based on the second current work order quantity, the second historical work order allocation frequency, and the second work order allocation peak period of the second cloud customer service; wherein the work order allocation peak period is obtained based on historical traffic data of the preset planned responsible area of ​​the first cloud customer service; Calculating a score difference between the first work order allocation score and the second work order allocation score; When the score difference is greater than a predetermined value, the second cloud customer service is used as the cloud customer service corresponding to the vehicle to be passed; Otherwise, the first cloud customer service is used as the cloud customer service corresponding to the vehicle to pass.

[0012] In one implementation of the present application, the communication connection between the lane communication terminal and the customer service terminal includes at least lane real-time video and visual intercom.

[0013] In one implementation of the present application, the method further includes: Acquire a first voice signal obtained through the visual intercom; the first voice signal at least corresponds to the driving object of the vehicle to be passed; Inputting the first speech signal into a pre-trained speech recognition model to determine the corresponding first speech text according to the model output result; wherein the speech recognition model is trained based on speech data of several different regions and languages ​​and preset language labels; Sending the first voice text to the display interface of the customer service terminal and / or the lane communication terminal; The method further comprises: Acquire a second voice signal from the customer service terminal, and generate a corresponding second voice text according to the voice recognition model and the second voice signal; According to the language tag of the first voice signal and the corresponding preset TTS model, the second voice text is converted into a second voice and sent to the lane communication terminal to play the second voice to the driver of the vehicle to be passed.

[0014] On the other hand, the embodiment of the present application further provides a vehicle traffic control system based on cloud customer service, the system comprising: A determination module is used for the cloud server to determine a corresponding special event data set based on the information of the vehicle to be passed from the lane edge device; wherein the special event data set is obtained based on the analysis of the multimodal data set corresponding to the information of the vehicle to be passed; An input module is used to input the special situation event data set into a pre-trained special situation event classification model to determine the special situation category of the vehicle to be passed according to the model output result; the special situation category includes at least one of the following categories corresponding to multiple preset special situation handling processes: vehicle information verification, special situation authorization, entry information missing, lane assistance; As a module, used to use the special situation category and the corresponding special situation event data set as special situation event information; A matching module, used to match the cloud customer service corresponding to the vehicle to be passed based on a preset work order scheduling strategy and the location of the special event, so as to send the special event information to the customer service terminal of the cloud customer service, and establish a communication connection between the lane communication terminal and the customer service terminal; A generation module is used to generate a traffic control instruction in response to the special event handling result from the customer service terminal, so as to control the operation of the lane equipment through the traffic control instruction.

[0015] On the other hand, the embodiment of the present application further provides a vehicle traffic control device based on cloud customer service, 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 vehicle traffic control method based on cloud customer service as described above.

[0016] Compared with the prior art, the present invention has the following significant effects: This application constructs a special event dataset through in-depth analysis of multimodal datasets (such as vehicle images, sensor data, etc.), providing more comprehensive and accurate information for vehicle identity authentication. Even if the license plate is damaged or other abnormal conditions occur, the vehicle status can be accurately judged with the help of other dimensional data, greatly improving the reliability of identity authentication. The cloud customer service model is introduced to reduce the need for on-site manual duty, and remote control is achieved through intelligent systems to reduce manual operating costs. At the same time, efficient traffic control reduces vehicle congestion time and indirectly reduces hidden costs such as energy consumption. This application also uses a pre-trained special event classification model to quickly and accurately identify special situation categories, and intelligently matches cloud customer service to handle special situations based on preset work order scheduling strategies. This standardized and intelligent processing flow greatly improves the efficiency and standardization of special situation processing, and effectively improves traffic efficiency and user experience.

[0017] In addition, the coordinated operation of cloud servers, lane edge devices, cloud customer service and lane communication terminals is realized. Through data sharing and intelligent decision-making, a comprehensive and intelligent vehicle traffic control system is built, which can flexibly respond to changes in traffic flow and complex special situations and meet the diverse needs of modern traffic management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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 process of a vehicle traffic control method based on cloud customer service in an embodiment of the present application; Figure 2 A workflow diagram of a vehicle traffic control method based on cloud customer service in an embodiment of the present application; Figure 3 This is a schematic diagram of a special situation processing flow in a vehicle traffic control method based on cloud customer service in an embodiment of the present application; Figure 4 This is a structural diagram of a vehicle traffic control system based on cloud customer service in an embodiment of the present application; Figure 5 This is a structural diagram of a vehicle traffic control device based on cloud customer service in an embodiment of the present application. DETAILED DESCRIPTION

[0019] 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.

[0020] The embodiments of the present application provide a vehicle traffic control method, system and device based on cloud customer service, which are used to solve the current technical problems of low reliability of vehicle identity authentication, high operating costs, poor efficiency and standardization of special situation processing, and lack of intelligent control system for high-speed vehicle traffic.

[0021] The following describes in detail various embodiments of the present application in conjunction with the accompanying drawings.

[0022] The present application embodiment provides a vehicle traffic control method based on cloud customer service, such as Figure 1 As shown, the method may include steps S101-S105: S101, the cloud server determines a corresponding special event data set based on the information of vehicles to be passed from the lane edge device.

[0023] Among them, the special event data set is obtained based on the analysis of the multimodal data set corresponding to the information of the vehicles to be passed.

[0024] It should be noted that cloud charging software can be run on the cloud server to determine whether to issue a card or charge a fee to the vehicle to be passed. The cloud server can be connected to the cloud customer service platform, which can analyze special event information and dispatch work orders.

[0025] Lane edge devices can be understood as lane devices installed in and around manual semi-automatic toll lanes (MTC), such as license plate recognition cameras, vehicle detectors, ultrasonic sensors, lidar sensors, lane controllers, etc. This application does not specifically limit the type of lane edge devices. This application adopts a "cloud-edge-end" collaborative architecture to achieve a centralized, remote, one-stop special situation handling method through cloud computing, the Internet of Things, and audio and video interaction technology. The system consists of a cloud customer service platform, cloud toll software, and lane equipment to achieve real-time monitoring, rapid response, and efficient handling of vehicle special situations. Lane equipment includes antennas, cameras, sensors, self-service card machines, and other equipment installed on toll lanes. Cloud customer service platform: responsible for handling toll lane special situations and communicating with drivers in real time through voice and video. Cloud toll software: used to collect and transmit special situation information and complete toll services.

[0026] In the embodiment of the present application, the lane edge device of the present application has a certain data processing capability. For example, after collecting a video of a vehicle passing through, the lane edge device can extract key features related to the vehicle from the video. However, the computing power of the lane edge device is limited. In order to ensure the efficiency of vehicle passage, the present application adopts the following embodiments. Before determining the corresponding special event data set based on the information of the vehicle to be passed from the lane edge device, the method also includes: The lane edge device obtains the initial multimodal data in the multimodal data set and determines the corresponding initial multimodal data size. The initial multimodal data includes at least: image data, video data, audio data and radar data. Based on the initial multimodal data size, the predefined local processing capacity, the cloud processing capacity and the current network bandwidth, the lane edge device calculates the cloud upload time, local processing time and cloud processing time corresponding to the initial multimodal data. The lane edge device compares the sum of the cloud upload time and the cloud processing time with the local processing time to determine whether the initial multimodal data is analyzed locally at the lane edge device according to the comparison result. When it is determined that the initial multimodal data is analyzed locally at the lane edge device, the lane edge device analyzes and processes the initial multimodal data to extract the corresponding vehicle feature information and add it to the vehicle information to be passed. When it is determined that the initial multimodal data is not analyzed locally at the lane edge device, the lane edge device compresses the initial multimodal data and adds it to the vehicle information to be passed.

[0027] In other words, each lane edge device can collect the initial multimodal data corresponding to the vehicle to be passed and construct a multimodal data set. The initial multimodal data can be understood as lane snapshot photos, license plate numbers, license plate colors, etc. The lane edge device can preliminarily determine the size of the initial multimodal data D, which can be in megabytes (MB). At the same time, the lane edge device records its predefined local processing capacity C local , which can be expressed in MB per second. Lane edge devices also store cloud processing capacity C cloud , expressed in MB per second, which is much larger than the local processing capacity. The lane edge device can also obtain the current network bandwidth B. Then, the upload time to the cloud can be calculated by The calculation shows that 1MB = 8Mbps. The local processing time is calculated by The cloud processing time is calculated by Calculated.

[0028] Lane edge computing devices will then + and Compare, if + , the initial multimodal data is compressed and added to the information of the vehicle to be passed, so that the cloud server processes the initial multimodal data, otherwise it is processed locally at the lane edge device.

[0029] In addition, the present application may also preset a delay requirement L, which indicates the maximum acceptable time required from data collection to data processing completion. + and + ,At this time, the cloud server processes the initial multimodal data.

[0030] In the embodiment of the present application, the cloud server determines the corresponding special event data set based on the information of the vehicles to be passed from the lane edge device, specifically including: Extract the vehicle verification information from the information of the vehicle to be passed. Among them, the vehicle verification information at least includes: highway composite pass card information, lane capture information and overweight control weighing information. Compare the vehicle verification information with the preset verification information list. If the comparison result is consistent, determine that the vehicle to be passed is a passable vehicle to control the lane equipment to release the passable vehicle. Otherwise, the vehicle to be passed is regarded as a special situation vehicle, and the vehicle verification information corresponding to each special situation category is added to the special situation event data set. Among them, the vehicle verification information corresponding to each special situation category is used for special situation event classification and special situation event handling.

[0031] That is to say, the cloud server can obtain vehicle inspection information for vehicle verification from the information of the vehicles to be passed, including but not limited to highway composite pass card (CPC card) information, lane capture information, and overweight control weighing information. The cloud server can be pre-set with a preset verification information list, which can verify and check the vehicle verification information. If the verification passes, the vehicle is released, such as for card issuance or toll collection. If the verification fails, the vehicle is regarded as a special situation vehicle, and the vehicle verification information associated with the special situation category is further extracted and added to the special situation event data set.

[0032] Among them, the special situation categories include four categories, namely vehicle information verification, special situation authorization, entry information missing, and lane assistance. The four categories of special situation categories are obtained by classifying and summarizing the handling methods of 24 small categories of special situations. The corresponding relationship table of special situation categories is shown in Table 1 below.

[0033] Table 1 Special situation category correspondence table

[0034] That is, the vehicle information check corresponds to the special situation subcategory of vehicle information check, the lane assistance includes the special situation subcategories of exit lane assistance and entrance lane assistance, the special situation authorization includes the special situation subcategories of vehicle blacklist, recovery list, vehicle gray list, bottom-line billing, free authorization on holidays, weighing abnormalities, ETC discounts, inconsistent entry and exit license plates, inconsistent entry and exit vehicle models, and self-service payment failure, and the entry information abnormality includes the special situation subcategories of unreadable on-board unit (OBU), disassembled OBU, no entry information, invalid entry information, inconsistent entry and exit license plates, no card, bad card, adjacent road interference, entry equipment failure, paper card, and inconsistency between the card and the query.

[0035] S102, the cloud server inputs the special situation event data set into the pre-trained special situation event classification model to determine the special situation category of the vehicle to be passed according to the model output result.

[0036] The above-mentioned special situation categories include at least one of the following categories corresponding to multiple preset special situation handling processes: vehicle information verification, special situation authorization, missing entry information, and lane assistance.

[0037] In the embodiment of the present application, the above-mentioned input of the special situation event data set into the pre-trained special situation event classification model to determine the special situation category of the vehicle to be passed according to the model output result specifically includes: The special event data set is quantum-encoded to determine several special event data features. Each special event data feature is input into the special event classification model to determine the quantum state probability distribution output by the model. The quantum state probability distribution includes the probability value of the vehicle to be passed belonging to each special event category. According to the quantum state probability distribution and the preset threshold, the special event category corresponding to the vehicle to be passed is determined.

[0038] In other words, the special event classification model of the present application is a quantum neural network model to process multimodal data. Specifically, the cloud server extracts the data features in the special event data set, and then performs quantum state encoding. Taking image data as an example, it is assumed that the image has m features after feature extraction, corresponding to the m quantum bits of the input layer of the quantum neural network (QNN). According to the size of the image pixel value, it can be mapped to different states of the quantum bit. For example, when the pixel value is greater than a preset pixel threshold, the corresponding quantum bit is in state, and when it is less than the pixel threshold, it is in In the model training stage, several special event data sets with accurate special event category labels (such as vehicle information verification, special event authorization, entrance information missing, lane assistance, etc.) can be accurately obtained, and the QNN model can be trained using quantum computing equipment. During the training process, the coefficients of the quantum bits are continuously adjusted to minimize the error between the model prediction results and the accurate special event category labels until the error is less than the predetermined error threshold, and a trained special event classification model is obtained. After the training is completed, the QNN model is deployed on the cloud server.

[0039] After the special situation data features are input into the special situation event classification model, the model output layer outputs the quantum state probability distribution of each special situation category, and the cloud server compares the probability value corresponding to each special situation category with the preset threshold value, and selects the special situation category greater than the preset threshold value. For example, if the probability of the output layer quantum bit corresponding to the special situation category "vehicle information verification" is 0.6, the probability of "special situation authorization" is 0.2, the probability of "entry information missing" is 0.1, and the probability of "lane help" is 0.1, and the preset threshold is 0.5, then the special situation category of the vehicle is determined to be "vehicle information verification".

[0040] In addition, when there are multiple special situation categories greater than a preset threshold, the present application can introduce an additional judgment mechanism, such as screening the special situation category corresponding to the maximum probability value, or combining the vehicle's historical traffic data and the real-time status of the current lane to determine whether the vehicle has experienced one of the special situation categories greater than the preset threshold. If so, the special situation category that has occurred will be screened.

[0041] After the special situation category is determined, the special situation category corresponds to multiple preset special situation handling processes, as follows: Vehicle information verification: Cloud customer service personnel confirm vehicle information through vehicle identification information, lane snapshot information, cloud toll software prompt information, and other information, as well as through real-time lane video and video intercom conversations with the driver. They correct vehicle information by manually modifying the vehicle information and using automatic license plate recognition technology, and finally transmit the license plate number, license plate color, vehicle model, vehicle type, weighing information, etc. to the cloud toll software.

[0042] Special situation authorization: The cloud customer service staff uses vehicle information, lane capture information, cloud toll software prompt information, vehicle path restoration information, and other information, as well as real-time lane video and video intercom to talk to the driver to confirm the vehicle information, and finally transmit the authorization instruction to the cloud toll software through authorization by the squad leader.

[0043] Missing entrance information: The cloud customer service staff uses vehicle information, lane capture information, vehicle path restoration information, vehicle entrance information, and other information, as well as real-time lane video and video intercom to talk to the driver to confirm the entrance toll station, and finally transmits the entrance toll station, lane number, entry time, traffic medium and other information to the cloud toll collection software.

[0044] Lane assistance: The cloud customer service staff uses the lane real-time video and video intercom to communicate with the driver to confirm vehicle information, entrance toll station, and pass medium, and finally transmits the license plate number, license plate color, vehicle model, vehicle type, weighing information, entrance toll station, lane number, entrance event, pass medium, special situation type and other information to the cloud toll collection software.

[0045] In addition, this application uses video intercom technology and optical character recognition (OCR) technology to quickly capture the license plate number of temporary vehicles, reducing the workload of manual input of license plate numbers. At the same time, this application supports the full process recording and traceability management of special events, including basic information of special events, handling process information, video information, and handling result information. All data is stored in the cloud database for subsequent analysis and optimization.

[0046] S103, the cloud server uses the special situation category and its corresponding special situation event data set as special situation event information.

[0047] That is to say, this application can compress the special situation category and the special situation event data set related to the special situation category, and then send it to the cloud customer service platform as special situation event information, and the cloud customer service platform will dispatch it to the selected cloud customer service personnel and send it to the customer service terminal of the cloud customer service personnel. The cloud customer service personnel can view the special situation category, and view the relevant data of the special situation event data set according to the special situation handling method corresponding to the special situation category, communicate with the driver, and complete the handling of the special situation event.

[0048] S104, the cloud server matches the cloud customer service corresponding to the vehicle to be passed based on the preset work order scheduling strategy and the location of the special event, so as to send the special event information to the customer service terminal of the cloud customer service, and establish a communication connection between the lane communication terminal and the customer service terminal.

[0049] In the embodiment of the present application, the above-mentioned matching of the cloud customer service corresponding to the vehicle to be passed based on the preset work order scheduling strategy and the location of the special event specifically includes: According to the location of the special event and the preset cloud customer service group information, determine the cloud customer service group corresponding to the vehicle to be passed. According to the cloud customer service group, match the corresponding preset work order scheduling strategy. Among them, the preset work order scheduling strategy includes the following allocation mechanism: rotation allocation mechanism, idleness allocation mechanism. The allocation mechanism includes the following allocation principles: efficiency principle, saturation principle, balance principle, and accuracy principle. According to the matched preset work order scheduling strategy, determine the cloud customer service corresponding to the vehicle to be passed in the cloud customer service group.

[0050] That is to say, the cloud server may store preset cloud customer service group information, which includes the correspondence between several cloud customer service groups and the locations where different special events occur. The locations where special events occur may correspond to the names of toll stations corresponding to the MTC lanes and be replaced by the names of toll stations. This application may preset different preset work order scheduling strategies for different cloud customer service groups, wherein the preset work order scheduling strategies may include allocation mechanisms and allocation principles.

[0051] Specifically, the allocation mechanism includes round-robin allocation and idleness allocation. Round-robin allocation is to assign work orders to any cloud customer service personnel, while idleness allocation is to selectively assign work orders according to the number of work orders of cloud customer service personnel. There are four types of allocation principles: efficiency principle, saturation principle, balance principle and accuracy principle: 1. Efficiency principle: Prioritize assigning orders to cloud customer service personnel with the least number of current work orders.

[0052] 2. Saturation principle: Prioritize assigning orders to cloud customer service personnel with the least number of historical work orders.

[0053] 3. Balance principle: Dispatch orders evenly according to the current number of work orders and the historical number of work orders of cloud customer service personnel.

[0054] 4. Accuracy principle: Orders are given priority to cloud customer service personnel with the highest handling accuracy.

[0055] The default work order scheduling strategy is the idleness allocation plus balance principle. The specific scheduling process is: when a special event occurs, first find the scheduling strategy configured for the toll station to which the special event belongs, then find the appropriate cloud customer service personnel based on the scheduling strategy (the cloud customer service personnel can control the current order status through the order acceptance button on the cloud customer service platform), and finally complete the scheduling of the work order.

[0056] Furthermore, the allocation principle under the rotation allocation mechanism is: Efficiency principle: Establish a circular list of cloud customer service personnel. Each time an order is assigned, select the cloud customer service personnel with the least current work orders from the list in order. For example, there are customer service personnel A, B, and C, and their current work orders are 5, 3, and 7 respectively. Customer service B is next in turn. Since customer service B has the least work orders, the work order is assigned to customer service B. Saturation principle: Also in the order of the circular list, give priority to the cloud customer service personnel with the least historical work orders. Assume that customer service A has 50 historical work orders, customer service B has 30, and customer service C has 70. When customer service B is next in turn, since customer service B has the least historical work orders, the work order is assigned to customer service B. Balance principle: Calculate the ratio of the current work order number to the historical work order number of each cloud customer service personnel. For example, customer service A has 5 current work orders and 50 historical work orders, with a ratio of 0.1; customer service B has 3 current work orders and 30 historical work orders, with a ratio of 0.1; customer service C has 7 current work orders and 70 historical work orders, with a ratio of 0.1. According to the order of the circular list, the cloud customer service personnel with the ratio closest to the overall average ratio (0.1 in this example) are selected. If there are multiple customers with the same ratio, the one with the highest order is selected first. For example, when it is customer service B's turn, the work order is assigned to customer service B. Accuracy principle: In the circular list, the cloud customer service personnel with the highest handling accuracy are selected first. Assuming that customer service A has an accuracy rate of 80%, customer service B has an accuracy rate of 90%, and customer service C has an accuracy rate of 85%, when it is customer service B's turn, the work order is assigned to customer service B because of his highest accuracy rate.

[0057] Allocation principles under the idleness allocation mechanism: Efficiency principle: directly assign the work order to the cloud customer service staff with the least number of work orders. By real-time statistics of the number of work orders of each customer service, for example, if customer service A currently has 5 work orders, customer service B has 3 work orders, and customer service C has 7 work orders, the work order will be assigned to customer service B.

[0058] Saturation principle: Based on historical work order data, find the cloud customer service staff with the least number of historical work orders and assign them to the staff. For example, if customer service A has 50 historical work orders, customer service B has 30, and customer service C has 70, assign the work order to customer service B.

[0059] Balance principle: Build a comprehensive evaluation model and set the current work order weight as , the weight of the number of historical work orders is ( ), can be adjusted according to actual conditions, such as , , which can be set by the user or developer, and this application does not make any specific restrictions on this. Calculate the comprehensive score of each cloud customer service staff :

[0060] in, For the The current number of work orders for customer service personnel. The sum of the current work orders of all customer service groups for all cloud customer service groups. For the The number of historical work orders for a customer service representative.

[0061] After calculating the comprehensive score, the work order is assigned to the cloud customer service representative with the lowest comprehensive score (i.e., the idlest and with low historical saturation).

[0062] Accuracy principle: Based on the accuracy of the cloud customer service personnel, among the customers with high availability (small number of current work orders), the customer service with the highest accuracy is selected for dispatching. For example, customer service A currently has 3 work orders and an accuracy rate of 80%; customer service B currently has 4 work orders and an accuracy rate of 90%. The work orders are preferentially assigned to customer service B.

[0063] In the embodiment of the present application, the above-mentioned determination of the cloud customer service corresponding to the vehicle to be passed in the cloud customer service group specifically includes: According to the matched preset work order scheduling strategy, at least one first cloud customer service and its corresponding second cloud customer service in the cloud customer service group are determined. Based on the first current work order quantity, the first historical work order allocation frequency and the first work order allocation peak period of the first cloud customer service, the first work order allocation score of the first cloud customer service is determined, and based on the second current work order quantity, the second historical work order allocation frequency and the second work order allocation peak period of the second cloud customer service, the second work order allocation score of the second cloud customer service is determined. Among them, the work order allocation peak period is obtained based on the historical traffic data of the preset planned responsible area of ​​the first cloud customer service. Calculate the score difference between the first work order allocation score and the second work order allocation score. When the score difference is greater than the predetermined value, the second cloud customer service is used as the cloud customer service corresponding to the vehicle to be passed. Otherwise, the first cloud customer service is used as the cloud customer service corresponding to the vehicle to be passed.

[0064] That is to say, when the present application dispatches the work order to the cloud customer service through the above-mentioned preset work order dispatching strategy, a first cloud customer service and a corresponding second cloud customer service will be determined. For example, the cloud customer service with the lowest comprehensive score mentioned above will be found as the first cloud customer service, and the cloud customer service with a higher comprehensive score than the minimum will be used as the second cloud customer service. Customer services different from the first cloud customer service can also be randomly selected as the second cloud customer service, and this application does not make specific restrictions on this. Subsequently, the cloud server will retrieve the current number of work orders, historical work order allocation frequency and peak work order allocation period corresponding to the first cloud customer service and the second cloud customer service from the cloud customer service platform, wherein the first cloud customer service and the second cloud customer service may be in the same cloud customer service group, or the first cloud customer service and the second cloud customer service may correspond to multiple cloud customer service groups respectively, and each cloud customer service group is responsible for special events at different locations where special events occur, that is, corresponding to different preset planning responsible areas. There are different peak work order allocation periods obtained by analyzing the historical traffic data of different preset planning responsible areas.

[0065] Then, the cloud server calculates the current number of work orders of the first cloud customer service. And the maximum number of current work orders of each cloud customer service in the cloud customer service group corresponding to the vehicle to be passed and minimum value , calculate the first work order quantity score , the formula is as follows: ; For example, , , , .

[0066] Similarly, the score of the second work order quantity of the second cloud customer service can be calculated .

[0067] Further allocate the frequency according to the first historical work order of the first cloud customer service And the maximum value of the frequency of all corresponding cloud customer service historical work orders and minimum value , calculate the frequency score of the first work order assignment , the formula is as follows:

[0068] Similarly, the frequency score of the second work order allocation of the second cloud customer service can be calculated .

[0069] According to the peak time period assigned by the first work order of the first cloud customer service, determine whether the current time is in this period. If so, the score of the first assigned peak time period is , that is, the current time is in the peak period of the first work order allocation, ,otherwise Similarly, the second peak hour score of the second cloud customer service can be obtained .

[0070] Furthermore, the first work order allocation score is calculated by the following formula: and the second work order score :

[0071]

[0072] in, , This is a pre-set weight, and this application does not make any specific limitation on this.

[0073] The cloud server calculates the score difference , and With a predetermined value For comparison, if , the second cloud customer service is used as the cloud customer service corresponding to the vehicle to be passed. , the first cloud customer service will be used as the cloud customer service for the vehicle to pass.

[0074] Through the above technical solution, the flexibility of scheduling cloud customer service can be improved. After determining the first cloud customer service, a second cloud customer service that can be replaced can be found to comprehensively and accurately determine the cloud customer service used to serve the vehicles waiting to pass, making work order scheduling more intelligent.

[0075] In the embodiment of the present application, the communication connection between the lane communication terminal and the customer service terminal at least includes real-time lane video and visual intercom. That is, the customer terminal can see the real-time lane video and conduct visual intercom with the driving object of the vehicle to be passed, such as the driver.

[0076] In one embodiment of the present application, in order to more effectively improve the efficiency of special situation handling by cloud customer service personnel, the method further includes: A first voice signal obtained through video intercom is obtained. The first voice signal corresponds to at least a driving object of a vehicle to be passed. The first voice signal is input into a pre-trained voice recognition model to determine a corresponding first voice text according to the output result of the model. The voice recognition model is trained based on voice data of several different regions and languages ​​and preset language labels. The first voice text is sent to a display interface of a customer service terminal and / or a lane communication terminal.

[0077] That is to say, the cloud server can obtain the conversation between the cloud customer service and the driver through the video intercom in real time, extract the driver's first voice signal, and recognize the first voice signal through a pre-trained speech recognition model. The speech recognition model can be a recurrent neural network or a convolutional neural network, which is not specifically limited in this application. It is trained by voice data from several different regions and languages ​​marked with language tags, and preset language tags such as Mandarin, Sichuanese, Cantonese, etc. After the speech recognition model recognizes the first voice signal, it converts the voice into text format and displays it on the display interface of the customer service terminal and / or the lane communication terminal, so that the customer service can view the text content displayed on the display interface and understand the expression of the driving object, or the driving object can confirm whether the content of his expression is accurate. In addition, the above scheme can also be used to process the second voice signal of the customer service terminal, which is not specifically limited in this application.

[0078] In another embodiment of the present application, the method further includes: A second voice signal is obtained from the customer service terminal, and a corresponding second voice text is generated according to the voice recognition model and the second voice signal; according to the language label of the first voice signal and the corresponding preset TTS model, the second voice text is converted into a second voice, and sent to the lane communication terminal to play the second voice to the driver of the vehicle to be passed.

[0079] That is to say, the cloud server can also generate a second voice text for the second voice signal of the customer service terminal, and then, based on the language label of the first voice signal, use a preset text-to-speech (TTS) model to convert the second voice text into a second voice with the same voice label as the first voice signal, and then send it to the lane communication terminal, and play the second voice to the driver through the audio playback device, so that the driver can understand the customer service expression more effectively.

[0080] S105, the cloud server generates a traffic control instruction in response to the special event handling result from the customer service terminal to control the operation of the lane equipment through the traffic control instruction.

[0081] After the customer service terminal completes the special situation handling, the special situation handling result can be sent to the cloud server. The cloud server verifies the special situation handling result through the cloud charging software, determines whether to issue a card or charge, release, etc., and generates a traffic control instruction. The traffic control instruction can be to control the lane equipment to lift the barrier to release, prompt payment, etc.

[0082] The traditional special situation handling time is about 20 seconds. This application reduces the special situation handling time to about 14 seconds through the above solution. The above intelligent remote special situation handling improves the special situation handling efficiency by about 30%. It improves the operating efficiency of toll stations and user experience, has wide promotion value, and provides strong support for the intelligent management of highways.

[0083] Through the above technical solution, a deep analysis of multimodal data sets (such as vehicle images, sensor data, etc.) is carried out to construct a special event data set, providing more comprehensive and accurate information for vehicle identity authentication. Even if the license plate is damaged or other abnormal conditions occur, the vehicle status can be accurately judged with the help of other dimensional data, greatly improving the reliability of identity authentication. The cloud customer service model is introduced to reduce the need for on-site manual duty, and remote control is achieved through an intelligent system to reduce labor operation costs. At the same time, efficient traffic control reduces vehicle congestion time and indirectly reduces hidden costs such as energy consumption. This application also uses a pre-trained special event classification model to quickly and accurately identify special situation categories, and intelligently matches cloud customer service to handle special situations based on preset work order scheduling strategies. This standardized and intelligent processing flow greatly improves the efficiency and standardization of special situation processing, and effectively improves traffic efficiency and user experience. In addition, the coordinated operation of cloud servers, lane edge devices, cloud customer service and lane communication terminals is realized. Through data sharing and intelligent decision-making, a comprehensive and intelligent vehicle traffic control system is built, which can flexibly respond to changes in traffic flow and complex special situations, and meet the diverse needs of modern traffic management.

[0084] Figure 2 This application is a workflow diagram of a vehicle traffic control method based on cloud customer service, such as Figure 2 As shown, after the vehicle arrives at the MTC lane, the lane equipment detects and operates the vehicle, such as the Road Side Unit (RSU), license plate recognition, vehicle model recognition, card machine, guardrail, etc., and sends the vehicle information, CPC card information, license plate recognition information, etc. to the cloud charging software of the cloud server for charging rule verification, that is, to determine whether the vehicle is a special situation vehicle. If not, it means that the vehicle is a normal vehicle and the card is issued / charged; if so, the cloud customer service platform performs intelligent work order scheduling according to the special situation event information, and the cloud customer service personnel handle the special situation, and then send the special situation handling results to the cloud charging software to generate control instructions for card issuance / charging and other operations.

[0085] Figure 3 Schematic diagram of special situation processing in a vehicle traffic control method based on cloud customer service in an embodiment of the present application, such as Figure 3 As shown, the cloud charging software prompt information can include special situation categories. During the special situation handling process, vehicle identification information, lane capture information, cloud charging software prompt information and weighing information can be obtained, and lane real-time video, video intercom, path restoration related information and entrance information query can also be called.

[0086] On the cloud customer service platform corresponding to this application, cloud customer service personnel can independently control whether to accept orders. Cloud customer service personnel can actively establish a video intercom connection with the lane driver. When a special situation occurs in the lane, the driver can also establish a video intercom connection with the cloud customer service personnel, which can more conveniently handle special events on the lane. Develop lane keyboard software to facilitate cloud customer service personnel to complete the input of license plate numbers by clicking the mouse, enrich the license plate number input method, and meet the needs of different cloud customer service personnel. Access OCR recognition technology to realize automatic filling of temporary license plate vehicle license plate numbers. The device control function on the self-service card machine can also be connected to the cloud customer service platform, which is convenient for cloud customer service personnel to remotely control the self-service card machine equipment when handling special situations.

[0087] Figure 4 A schematic diagram of the structure of a vehicle traffic control system based on cloud customer service provided in an embodiment of the present application, such as Figure 4 As shown, the vehicle traffic control system 400 based on cloud customer service includes: The determination module 401 is used for the cloud server to determine the corresponding special situation event data set based on the information of the vehicle to be passed from the lane edge device. The special situation event data set is obtained based on the analysis of the multimodal data set corresponding to the information of the vehicle to be passed. The input module 402 is used to input the special situation event data set into the pre-trained special situation event classification model to determine the special situation category of the vehicle to be passed according to the model output result. The special situation category includes at least one of the following categories corresponding to multiple preset special situation handling processes: vehicle information verification, special situation authorization, entry information missing, lane help. As module 403, it is used to use the special situation category and its corresponding special situation event data set as special situation event information. The matching module 404 is used to match the cloud customer service corresponding to the vehicle to be passed based on the preset work order scheduling strategy and the location where the special situation event occurs, so as to send the special situation event information to the customer service terminal of the cloud customer service, and establish a communication connection between the lane communication terminal and the customer service terminal. The generation module 405 is used to generate a traffic control instruction in response to the special situation event handling result from the customer service terminal, so as to control the operation of the lane equipment through the traffic control instruction.

[0088] Figure 5 A schematic diagram of the structure of a vehicle traffic control device based on cloud customer service provided in an embodiment of the present application, such as Figure 5 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: The cloud server determines the corresponding special situation event data set based on the information of the vehicle to be passed from the lane edge device. Among them, the special situation event data set is obtained based on the analysis of the multimodal data set corresponding to the information of the vehicle to be passed. The special situation event data set is input into the pre-trained special situation event classification model to determine the special situation category of the vehicle to be passed according to the model output result. The special situation category includes at least one of the following categories corresponding to multiple preset special situation handling processes: vehicle information verification, special situation authorization, missing entry information, lane assistance. The special situation category and its corresponding special situation event data set are used as special situation event information. Based on the preset work order scheduling strategy and the location where the special situation event occurs, the cloud customer service corresponding to the vehicle to be passed is matched so that the special situation event information is sent to the customer service terminal of the cloud customer service, and a communication connection between the lane communication terminal and the customer service terminal is established. In response to the special situation event handling result from the customer service terminal, a traffic control instruction is generated to control the operation of the lane equipment through the traffic control instruction.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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 vehicle traffic control method based on cloud customer service, characterized in that: The method comprises: The cloud server determines a corresponding special event data set based on the information of the vehicle to be passed from the lane edge device; wherein the special event data set is obtained based on the analysis of the multimodal data set corresponding to the information of the vehicle to be passed; The special situation event data set is input into a pre-trained special situation event classification model to determine the special situation category of the vehicle to be passed according to the model output result; the special situation category includes at least one of the following categories corresponding to multiple preset special situation handling processes: vehicle information verification, special situation authorization, entry information missing, lane assistance; The special situation category and the corresponding special situation event data set are used as special situation event information; Based on the preset work order scheduling strategy and the location of the special event, the cloud customer service corresponding to the vehicle to be passed is matched, so as to send the special event information to the customer service terminal of the cloud customer service, and establish a communication connection between the lane communication terminal and the customer service terminal; In response to the special event handling result from the customer service terminal, a traffic control instruction is generated to control the operation of the lane equipment through the traffic control instruction.

2. A vehicle traffic control method based on cloud customer service according to claim 1, characterized in that: Before determining the corresponding special event data set based on the information of the vehicle to be passed from the lane edge device, the method further includes: The lane edge device obtains initial multimodal data in the multimodal data set and determines the corresponding size of the initial multimodal data; wherein the initial multimodal data at least includes: image data, video data, audio data and radar data; The lane edge device calculates the cloud upload time, local processing time and cloud processing time corresponding to the initial multimodal data, respectively, based on the initial multimodal data size, predefined local processing capacity, cloud processing capacity and current network bandwidth; The lane edge device compares the sum of the cloud upload time and the cloud processing time with the local processing time to determine whether the initial multimodal data is analyzed locally at the lane edge device according to the comparison result; If yes, the lane edge device analyzes and processes the initial multimodal data to extract corresponding vehicle feature information and adds it to the information of the vehicle to be passed; Otherwise, the lane edge device compresses the initial multimodal data and adds it to the information of the vehicle to be passed.

3. A vehicle traffic control method based on cloud customer service according to claim 1, characterized in that: The cloud server determines the corresponding special event data set based on the information of the vehicles to be passed from the lane edge device, including: Extracting vehicle verification information from the to-be-passed vehicle information; wherein the vehicle verification information at least includes: highway composite pass card information, lane capture information and overload control weighing information; Comparing the vehicle verification information with a preset verification information list; When the comparison result is consistent, the vehicle to be passed is determined to be a passable vehicle, so as to control the lane equipment to release the passable vehicle; Otherwise, the vehicle to be passed is regarded as a special situation vehicle, and the vehicle verification information corresponding to each special situation category is added to the special situation event data set; wherein, the vehicle verification information corresponding to each special situation category is used for special situation event classification and special situation event handling.

4. The vehicle traffic control method based on cloud customer service according to claim 1 is characterized in that: The special event data set is input into a pre-trained special event classification model to determine the special event category of the vehicle to be passed according to the model output result, specifically including: Performing quantum state encoding on the special event data set to determine a number of special event data features; Inputting each of the special situation data features into the special situation event classification model to determine the quantum state probability distribution output by the model; wherein the quantum state probability distribution includes the probability value of the vehicle to be passed belonging to each of the special situation categories; The special situation category corresponding to the vehicle to be passed is determined according to the quantum state probability distribution and a preset threshold.

5. The vehicle traffic control method based on cloud customer service according to claim 1 is characterized in that: Based on the preset work order dispatch strategy and the location of the special event, the cloud customer service corresponding to the vehicle to be passed is matched, specifically including: Determine the cloud customer service group corresponding to the vehicle to be passed according to the location where the special event occurred and the preset cloud customer service group information; According to the cloud customer service group, the corresponding preset work order scheduling strategy is matched; wherein the preset work order scheduling strategy includes one of the following allocation mechanisms: a rotation allocation mechanism and an idleness allocation mechanism; the allocation mechanism includes the following allocation principles: an efficiency principle, a saturation principle, a balance principle, and an accuracy principle; According to the matched preset work order scheduling strategy, the cloud customer service corresponding to the vehicle to be passed in the cloud customer service group is determined.

6. A vehicle traffic control method based on cloud customer service according to claim 5, characterized in that: Determining the cloud customer service corresponding to the vehicle to be passed in the cloud customer service group specifically includes: Determine at least one first cloud customer service and its corresponding second cloud customer service in the cloud customer service group according to the matched preset work order scheduling strategy; Determine a first work order allocation score for the first cloud customer service based on the first current work order quantity, the first historical work order allocation frequency, and the first work order allocation peak period of the first cloud customer service; and determine a second work order allocation score for the second cloud customer service based on the second current work order quantity, the second historical work order allocation frequency, and the second work order allocation peak period of the second cloud customer service; wherein the work order allocation peak period is obtained based on historical traffic data of the preset planned responsible area of ​​the first cloud customer service; Calculating a score difference between the first work order allocation score and the second work order allocation score; When the score difference is greater than a predetermined value, the second cloud customer service is used as the cloud customer service corresponding to the vehicle to be passed; Otherwise, the first cloud customer service is used as the cloud customer service corresponding to the vehicle to pass.

7. The vehicle traffic control method based on cloud customer service according to claim 1 is characterized in that: The communication connection between the lane communication terminal and the customer service terminal includes at least lane real-time video and visual intercom.

8. A vehicle traffic control method based on cloud customer service according to claim 7, characterized in that: The method further comprises: Acquire a first voice signal obtained through the visual intercom; the first voice signal at least corresponds to the driving object of the vehicle to be passed; Inputting the first speech signal into a pre-trained speech recognition model to determine the corresponding first speech text according to the model output result; wherein the speech recognition model is trained based on speech data of several different regions and languages ​​and preset language labels; Sending the first voice text to the display interface of the customer service terminal and / or the lane communication terminal; The method further comprises: Acquire a second voice signal from the customer service terminal, and generate a corresponding second voice text according to the voice recognition model and the second voice signal; According to the language tag of the first voice signal and the corresponding preset TTS model, the second voice text is converted into a second voice and sent to the lane communication terminal to play the second voice to the driver of the vehicle to be passed.

9. A vehicle traffic control system based on cloud customer service, characterized in that: The system comprises: A determination module is used for the cloud server to determine a corresponding special event data set based on the information of the vehicle to be passed from the lane edge device; wherein the special event data set is obtained based on the analysis of the multimodal data set corresponding to the information of the vehicle to be passed; An input module is used to input the special situation event data set into a pre-trained special situation event classification model to determine the special situation category of the vehicle to be passed according to the model output result; the special situation category includes at least one of the following categories corresponding to multiple preset special situation handling processes: vehicle information verification, special situation authorization, entry information missing, lane assistance; As a module, used to use the special situation category and the corresponding special situation event data set as special situation event information; A matching module, used to match the cloud customer service corresponding to the vehicle to be passed based on a preset work order scheduling strategy and the location of the special event, so as to send the special event information to the customer service terminal of the cloud customer service, and establish a communication connection between the lane communication terminal and the customer service terminal; A generation module is used to generate a traffic control instruction in response to the special event handling result from the customer service terminal, so as to control the operation of the lane equipment through the traffic control instruction.

10. A vehicle traffic control device based on cloud customer service, 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 vehicle traffic control method based on cloud customer service as described in any one of claims 1-8 above.

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