Regional traffic energy scheduling method and system

Abstract

The invention provides a regional traffic energy scheduling method and system, and relates to the field of traffic energy scheduling, and the method comprises the steps: receiving a distress signal from an electric vehicle in a region; acquiring regional traffic condition information; on the basis of regional traffic condition information, according to the received distress signal and the position information of the electric vehicle, crisis distribution in the region is identified, and a crisis distribution region is obtained; identifying available energy supply resources around the crisis distribution area; matching the energy supply resource with the electric vehicle according to the crisis situation of the electric vehicle and the state of the energy supply resource; issuing an emergency power supply task instruction to the matched energy supply resource; and the operation state of the energy supply resource is obtained, and the emergency power supply task is adjusted according to the operation state, so that the basic stability of the whole traffic energy network under extreme disturbance is ensured, and the toughness and the emergency response capability of the urban traffic energy system are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of traffic energy dispatching technology, and more specifically, to a regional traffic energy dispatching method and system. Background Technology

[0002] In the field of urban transportation and energy management, the efficient scheduling of electric vehicle charging is crucial for maintaining urban operational efficiency and grid stability. Traditional regional transportation and energy dispatching systems typically optimize charging behavior based on predictable operational data to reduce operating costs. However, with the increasing presence of unstable renewable energy sources in the grid and the occurrence of sudden large-scale traffic disruptions, these conventional systems face significant challenges.

[0003] In existing regional transportation energy dispatching systems, dispatching strategies heavily rely on accurate predictions of vehicle routes, energy consumption, and grid conditions. When large-scale, sudden traffic events occur, such as severe traffic accidents leading to prolonged congestion, significant deviations can arise between the actual locations, energy consumption, and accessibility of numerous vehicles and system predictions. These deviations not only render existing vehicle charging plans ineffective but also trigger a chain reaction, making the routes and energy consumption of other vehicles in the region unpredictable, thereby impacting the pre-set charging and discharging plans for distributed energy storage units.

[0004] Therefore, the current technical problem is: when the underlying traffic prediction model on which the dispatch system relies fails on a large scale and continuously due to a sudden event, how to design an emergency energy dispatch mechanism that can respond quickly? This mechanism should be able to prioritize the power supply needs of high-risk units such as stranded vehicles in the absence of reliable prediction data, while avoiding energy shortages in other areas and ensuring the basic stability of the entire transportation energy network under extreme disturbances. Summary of the Invention

[0005] This application discloses a regional transportation energy dispatching method and system, which aims to solve the technical problem that large-scale sudden traffic events cause traditional prediction mechanisms to fail, thereby affecting the energy supply of key transportation units and even potentially causing grid instability.

[0006] The technical solution of this application is as follows: Firstly, this application discloses a regional traffic energy dispatching method to address the challenge of traditional prediction mechanisms failing due to large-scale sudden traffic events, including: It receives distress signals from electric vehicles within the area. These distress signals are generated by the electric vehicles based on their operating status data and preset reference energy consumption patterns, and are broadcast through various communication channels. Obtain regional traffic information; Based on regional traffic information, the received distress signal and the location information of the electric vehicle are used to identify the crisis distribution within the region and obtain the crisis distribution area. Identify available energy supply resources around the crisis distribution area, including stationary energy storage units and mobile energy relay vehicles; Based on the crisis situation of the electric vehicle and the status of the energy supply resource, match the energy supply resource with the electric vehicle; Issue emergency power supply command to the matched energy supply resources; and Obtain the operating status of the energy supply resource and adjust the emergency power supply task accordingly.

[0007] Secondly, this application also discloses a regional traffic energy dispatching system to address the challenge of traditional prediction mechanisms failing due to large-scale sudden traffic events. The system includes: The distress signal receiving module is used to receive distress signals from electric vehicles within the area. The distress signal is generated by the electric vehicle based on its operating status data and a preset reference energy consumption mode, and is broadcast through multiple communication channels. The traffic condition information acquisition module is used to acquire regional traffic condition information; The crisis distribution identification module is used to identify the crisis distribution within a region based on the received distress signal and the location information of the electric vehicle, and to obtain the crisis distribution area. An energy supply resource identification module is used to identify available energy supply resources around the crisis distribution area, including fixed energy storage units and mobile energy relay vehicles; An energy resource matching module is used to match the energy supply resource with the electric vehicle based on the emergency situation of the electric vehicle and the status of the energy supply resource. The task instruction issuance module is used to issue emergency power supply task instructions to the matched energy supply resource; and The task adjustment module is used to obtain the operating status of the energy supply resource and adjust the emergency power supply task according to the operating status. Beneficial effects

[0008] The regional transportation energy dispatching method disclosed in this application can identify the crisis distribution area in real time by receiving distress signals from electric vehicles within the region and combining them with regional traffic condition information. Based on this, the system can identify available fixed energy storage units and mobile energy relay vehicles in the vicinity, and intelligently match them according to the crisis situation of the electric vehicles and the status of energy supply resources, issuing emergency power supply task instructions. Furthermore, the system can also acquire the operating status of energy supply resources and adjust tasks accordingly, thus forming a dynamic and adaptive dispatching closed loop. This method effectively solves the problem in existing technologies where, when the underlying traffic prediction model on which the dispatching system relies fails on a large scale and continuously due to a sudden event, it cannot quickly respond to and coordinately adjust vehicle charging tasks and the energy release priority of distributed energy storage units. Through this method, the rescue power supply needs of high-risk units such as stranded vehicles can be prioritized, while avoiding energy shortages in other areas, ensuring the basic stability of the entire transportation energy network under extreme disturbances, and significantly improving the resilience and emergency response capability of the urban transportation energy system. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of a regional traffic energy dispatching method provided in this application.

[0010] Figure 2 This is a schematic diagram of a regional traffic energy dispatching system provided in this application. Detailed Implementation

[0011] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0012] Reference Figure 1 The diagram illustrates an embodiment of a regional traffic energy dispatching method according to the present invention, which may specifically include the following steps: S101, receiving distress signals from electric vehicles within the area. The distress signals are generated by the electric vehicles themselves based on their operating status data and preset reference energy consumption patterns, identifying a crisis situation, and are broadcast through various communication channels.

[0013] S102, obtain regional traffic information.

[0014] S103, Based on the regional traffic condition information, the crisis distribution within the region is identified according to the received distress signal and the location information of the electric vehicle, and the crisis distribution area is obtained.

[0015] S104, Identify available energy supply resources around the crisis distribution area, including fixed energy storage units and mobile energy relay vehicles.

[0016] S105, based on the crisis situation of the electric vehicle and the state of the energy supply resources, match the energy supply resources with the electric vehicle.

[0017] S106, issue an emergency power supply task instruction to the matched energy supply resource.

[0018] S107, Obtain the operating status of the energy supply resources, and adjust the emergency power supply task according to the operating status.

[0019] To better understand this application, some key terms and implementation environments involved will be explained below.

[0020] A distress signal is a signal sent by an electric vehicle requesting assistance when it detects that it is in an energy crisis. This crisis may be determined by a combination of factors, such as low remaining energy, prolonged entrapment, or high mission priority. A distress signal typically includes the vehicle's unique identifier, current location, remaining energy, duration of entrapment, and mission priority.

[0021] "Operating status data" refers to various real-time data generated by electric vehicles during operation, such as battery level, mileage, average energy consumption, vehicle speed, and geographical location. This data is the basis for determining whether a vehicle is in a critical state.

[0022] The "preset reference energy consumption mode" refers to the energy consumption standard preset based on factors such as vehicle type, driving conditions, and load. By comparing the vehicle's actual energy consumption with the reference mode, potential energy crises can be identified more accurately.

[0023] "Multiple communication methods" refers to the various communication methods that electric vehicles use to broadcast distress signals, such as cellular networks (4G / 5G), vehicle-to-everything (V2X) communication, and satellite communication. Using multiple methods can improve the reliability and coverage of distress signal transmission.

[0024] "Regional traffic information" refers to real-time traffic data within a specific area, including but not limited to road congestion, traffic accident reports, road closure information, traffic flow, and average vehicle speed. This information is crucial for assessing crisis distribution and planning rescue routes.

[0025] "Stationary energy storage units" refer to facilities located in specific geographical locations that have the capacity to store and release energy, such as charging stations, battery swapping stations, and large-scale energy storage battery packs. They typically have large energy capacities and high power outputs.

[0026] "Mobile energy relay vehicles" refer to vehicles with energy storage and mobility capabilities, such as mobile charging vehicles and mobile battery swapping vehicles. They can move flexibly on roads to provide on-site energy replenishment for stranded electric vehicles.

[0027] The implementation environment of this application is typically an urban area with well-developed communication infrastructure and traffic monitoring networks. The dispatch center, as the core, is responsible for receiving and processing information and issuing instructions. Electric vehicles, stationary energy storage units, and mobile energy relay vehicles all need to have corresponding communication and energy management functions.

[0028] The main features of this application will be described in detail below.

[0029] First, regarding the feature of "receiving distress signals from electric vehicles within the area": ​​Electric vehicles continuously monitor their operational status data during operation, such as battery level, estimated range, and current speed. Simultaneously, the vehicle has a preset reference energy consumption mode, which adjusts based on factors such as vehicle type, road conditions, and driving habits. When the vehicle's actual operational status data is compared with the reference energy consumption mode, if the remaining energy reserve is found to be below a preset threshold, or the estimated range is insufficient for the current mission requirements, the vehicle will autonomously identify a crisis state. Once a crisis state is identified, the vehicle immediately generates a distress signal. To ensure the distress signal is received promptly by the dispatch center or nearby rescue resources, the electric vehicle broadcasts the signal through various communication methods. For example, the vehicle can connect to a cellular network via its onboard communication unit to send the distress signal to a cloud-based dispatch platform; or, in a vehicle-to-everything (V2X) environment, the vehicle can broadcast distress information to surrounding vehicles and roadside units via direct communication (DSRC or C-V2X). Furthermore, in remote areas or communication dead zones, the vehicle can also send brief distress messages via satellite communication modules.

[0030] Secondly, regarding the feature of "acquiring regional traffic condition information," the dispatch center or regional traffic management system continuously collects traffic condition information within the region. This can be achieved in various ways. For example, it can acquire real-time video streams from traffic monitoring cameras and analyze road congestion and traffic flow using image recognition technology; it can receive real-time traffic data from in-vehicle navigation systems or third-party traffic information service providers to understand emergencies such as road closures and traffic accidents; and it can also acquire data such as vehicle density and average speed through sensors deployed on the roads, such as geomagnetic sensors and radar sensors. This information is aggregated at the dispatch center to form a comprehensive view of the regional traffic condition.

[0031] Next, regarding the feature of "identifying the crisis distribution within a region based on the received distress signals and the location information of electric vehicles, and obtaining the crisis distribution area," after receiving a distress signal from an electric vehicle, the dispatch center extracts the electric vehicle's location information contained in the signal. Combined with real-time acquired regional traffic information, the dispatch center performs a comprehensive analysis. For example, if multiple distress signals are concentrated on a certain road segment, and the traffic information for that segment indicates severe congestion or a traffic accident, then that area will be identified as a crisis distribution area. The dispatch center can use a Geographic Information System (GIS) to overlay the location of the distressed vehicles and traffic information, intuitively identifying the most severely affected areas. For example, when a section of highway is closed for an extended period due to a chain-reaction accident, and multiple electric vehicles within that segment send out distress signals, that segment and its surrounding area will be marked as a crisis distribution area.

[0032] Next, regarding the feature of "identifying available energy supply resources around the crisis distribution area," once the crisis distribution area is determined, the dispatch center immediately queries the available energy supply resources around that area. These resources mainly include fixed energy storage units and mobile energy relay vehicles. Fixed energy storage units can be nearby charging stations, battery swapping stations, or microgrid nodes with large-capacity energy storage batteries. The dispatch center queries the real-time available energy capacity, the number of charging / battery swapping interfaces, and whether these fixed units are currently in service. Mobile energy relay vehicles include dedicated mobile charging vehicles or electric rescue vehicles capable of supplying power to external systems. The dispatch center tracks the real-time location, remaining energy, maximum discharge power, and current mission status of these mobile vehicles. For example, the dispatch center can maintain an energy supply resource database containing information such as the geographical location, type, capacity, and status of each resource, and update it in real time.

[0033] Furthermore, regarding the feature of "matching the energy supply resources with the electric vehicles based on the crisis situation of the electric vehicles and the status of the energy supply resources," the dispatch center needs to perform intelligent matching after identifying the crisis distribution and available energy supply resources. The crisis situation of the electric vehicles includes their remaining energy, the duration of being stranded, and the urgency of the mission. The status of the energy supply resources includes their available energy capacity, maximum discharge power, distance from the distressed vehicles, and estimated arrival time. The dispatch center will comprehensively consider these factors. For example, for electric vehicles with extremely low remaining energy and a long stranding time, it may prioritize matching them with the nearest fixed energy storage unit with sufficient energy capacity; while for vehicles far from fixed units or requiring rapid, small-scale replenishment, it may match them with mobile energy relay vehicles. The matching process can employ optimization algorithms to maximize rescue efficiency and minimize rescue time.

[0034] Next, regarding the feature of "issuing emergency power supply task instructions to the matched energy supply resources," once matching is complete, the dispatch center will immediately issue specific emergency power supply task instructions to the selected energy supply resources. For fixed energy storage units, instructions may include reserving charging spots for specific electric vehicles, adjusting charging power, etc. For mobile energy relay vehicles, instructions will include detailed rescue routes, the location of the target electric vehicle, the estimated power supply, and the estimated arrival time. These instructions are sent to the corresponding energy supply resources via the communication network to ensure that rescue operations can be initiated quickly. For example, the dispatch center sends an instruction to a mobile energy relay vehicle, requesting it to go to a congested section of road to provide 30 minutes of emergency charging service to an electric taxi that has run out of power.

[0035] Finally, regarding the feature of "obtaining the operational status of the energy supply resources and adjusting the emergency power supply task based on the operational status," the dispatch center continuously acquires the real-time operational status of the energy supply resources during the execution of the emergency power supply task. For example, mobile energy relay vehicles will transmit information such as their location, remaining energy, and power supply progress in real time; fixed energy storage units will also report the usage status of their charging piles and energy release status. If an anomaly is detected in an energy supply resource during the task execution, such as a mobile energy relay vehicle encountering new traffic congestion and being unable to arrive on time, or a fixed energy storage unit malfunctioning, the dispatch center will adjust the emergency power supply task in a timely manner based on this new operational status information. Adjustments may include re-matching other available energy supply resources, modifying rescue routes, or adjusting the power supply volume to ensure that vehicles in crisis can receive timely energy replenishment.

[0036] The regional transportation energy dispatching method proposed in this application aims to address the problem of predictive mechanism failure in traditional dispatching systems when facing large-scale sudden traffic events. Its core innovation lies in constructing a real-time response and collaborative dispatching emergency energy management framework.

[0037] Compared with existing technologies, the advantages of this application are: Firstly, regarding information acquisition, this application significantly improves the efficiency and reliability of crisis information acquisition by receiving distress signals generated by electric vehicles autonomously recognizing crisis situations and broadcasting them through multiple communication channels. Traditional systems may rely on passive reporting or periodic data uploads, which are prone to information delays or omissions in the event of emergencies. The proactive distress mechanism of this application ensures that the dispatch center can obtain critical information at the very first moment of a crisis.

[0038] Secondly, in terms of crisis identification and resource allocation, this application, based on regional traffic information, distress signals, and electric vehicle location information, can quickly and accurately identify the crisis distribution area and the surrounding available fixed and mobile energy supply resources. This allows dispatch decisions to be more precisely focused on the most severely affected areas and to make full use of all available resources within the area. Traditional systems often lack the ability to quickly perceive the regional crisis distribution under emergencies, resulting in low resource allocation efficiency.

[0039] Furthermore, regarding task matching and adjustment, this application intelligently matches electric vehicles based on the crisis situation and the status of energy supply resources, and can adjust emergency power supply tasks in real time according to the operating status of energy supply resources. This dynamic adjustment capability enables the dispatch scheme to adapt to constantly changing on-site conditions, ensuring the flexibility and effectiveness of rescue missions. For example, when a mobile energy relay vehicle encounters new traffic obstacles on its way to rescue, the dispatch center can quickly replan the route or assign other resources to avoid delays in rescue. Traditional systems often lack this real-time feedback and adjustment mechanism during mission execution; once the initial plan is obstructed, the entire rescue operation may come to a standstill.

[0040] In summary, this application constructs a regional traffic energy dispatching method that can maintain efficient operation even under extreme disturbances by introducing mechanisms such as active distress signals, real-time traffic condition perception, intelligent resource matching, and dynamic task adjustment. This not only prioritizes the power supply needs of high-risk units such as stranded vehicles, effectively avoiding energy shortages, but also ensures the basic stability of the entire traffic energy network under emergencies, significantly improving the resilience of urban traffic and energy management.

[0041] Specifically, the step of matching the energy supply resources with the electric vehicle based on the crisis situation of the electric vehicle and the status of the energy supply resources includes: Electric vehicles in crisis are prioritized and ranked to obtain a priority ranking result. The priority ranking is based on the remaining energy reserves of the electric vehicles, the criticality of the operational tasks, and the duration of being stranded. The priority ranking result includes a classification of high-priority crisis vehicles, medium-priority crisis vehicles, and low-priority crisis vehicles. Assess the rescue capability of available energy supply resources, including the available energy capacity, maximum discharge power, and time required to reach the emergency electric vehicle. A rescue task allocation matrix is ​​constructed based on the priority ranking results and rescue capabilities. The rows of the rescue task allocation matrix represent the electric vehicles in crisis, and the columns represent the available energy supply resources. Each element in the rescue task allocation matrix represents the expected effect of a specific energy supply resource on rescuing a specific electric vehicle in crisis. Based on the rescue mission allocation matrix, a multi-stage resource allocation process is executed; the multi-stage resource allocation process includes: In the first phase, high-priority emergency vehicles will be matched with energy supply resources that can provide rapid, high-capacity energy replenishment. In the second phase, mobile energy relay vehicles are matched for medium-priority and low-priority emergency vehicles. At the same time, the path planning of the mobile energy relay vehicles is considered so that the mobile energy relay vehicles can provide energy to multiple emergency electric vehicles on their way to rescue, or can return to replenish energy after completing the rescue. Continuously monitor the energy release of the energy supply resources and the changes in the energy reserves of the emergency electric vehicles; and The rescue mission allocation matrix is ​​adjusted based on the energy release status of the energy supply resources and the changes in the energy reserves of the emergency electric vehicles.

[0042] Prioritizing electric vehicles in crisis aims to ensure that, given limited resources, the most urgent and critical vehicles receive energy replenishment first. Specifically, prioritization can be based on a comprehensive assessment of multiple indicators. For example, remaining energy reserves reflect the immediate level of danger, the criticality of the operational task considers the vehicle's social function or economic value (e.g., ambulances, police cars, buses), and the duration of being stranded reflects the duration of the vehicle's entrapment and its potential secondary impacts. These indicators can be assigned different weights, and through weighted summation or other multi-criteria decision-making methods, electric vehicles can be categorized into high-priority, medium-priority, and low-priority crisis vehicles.

[0043] Furthermore, a rescue task allocation matrix is ​​constructed based on the priority ranking results and rescue capabilities. The purpose is to quantitatively correlate the needs of emergency electric vehicles with the capacity of energy supply resources. This rescue task allocation matrix can be a two-dimensional table, where rows represent emergency electric vehicles and columns represent available energy supply resources. Each element in the matrix can represent the expected effect of a specific energy supply resource on rescuing a specific emergency electric vehicle; for example, it can be a comprehensive score considering factors such as rescue speed, energy matching degree, and cost-effectiveness.

[0044] Based on this, a multi-stage resource allocation process is implemented, aiming to adopt differentiated allocation strategies according to the characteristics of vehicles with different priorities and resource types. In the first stage, high-priority emergency vehicles typically require rapid, high-capacity energy replenishment and are therefore prioritized for allocation to energy supply resources capable of meeting these needs, such as large fixed energy storage units or mobile energy relay vehicles with fast-charging capabilities. In the second stage, for medium-priority and low-priority emergency vehicles, the focus is more on optimizing the utilization efficiency of mobile energy relay vehicles. At this time, the route planning of mobile energy relay vehicles is taken into consideration, enabling them to provide energy to multiple emergency electric vehicles en route to rescue, or to return for recharging after completing rescue operations, thereby maximizing their service range and resource utilization.

[0045] Ultimately, the rescue task allocation matrix is ​​adjusted based on the energy release status of the energy supply resources and the changes in the energy reserves of the emergency electric vehicles. This adjustment aims to address unforeseen circumstances and dynamic changes. For example, if the energy release rate of a certain energy supply resource is lower than expected, or if the energy reserves of an emergency electric vehicle recover slowly, the dispatch center can reassess the rescue task allocation matrix based on the latest monitoring data and adjust the already issued emergency power supply tasks to ensure the effectiveness and adaptability of the rescue operation.

[0046] This application further proposes the following steps for continuously monitoring the energy release of the energy supply resources and the changes in the energy reserves of the emergency electric vehicles: The key contextual information perceived locally is analyzed and abstracted, and compressed and encoded into a contextual fingerprint short code through a preset semantic mapping rule; the contextual fingerprint short code is then subjected to forward error correction encoding to obtain the core beacon data packet; On the V2V / V2I channel, multiple retransmissions are performed at preset intervals, and the core beacon data packets are broadcast using frequency hopping spread spectrum technology. After receiving the core beacon data packets, FEC decoding and data verification are performed. The scenario parsing engine performs reverse mapping of the scenario fingerprint short code according to the preset scenario fingerprint dictionary to obtain the core nature of the electric vehicle crisis and obtain the electric vehicle crisis scenario. If the dispatch center or nearby rescue resources require information that meets preset conditions, a situation details request instruction is sent to the emergency electric vehicle; after receiving the situation details request instruction, the emergency electric vehicle extracts detailed data from local storage, divides the detailed data into multiple data blocks, and prioritizes the data blocks; it monitors the real-time link quality of all available communication channels and dynamically selects the channel with the best current performance for transmission; If the performance of all channels does not meet the preset transmission conditions, high-priority data blocks are sent through the V2V / V2I channel at a low rate and with a high number of retransmissions, and the surrounding mobile energy relay vehicles are used as temporary relay nodes for relay forwarding.

[0047] Specifically, analyzing and abstracting key contextual information perceived locally refers to the continuous collection of vehicle operating status data, environmental data, and communication status information by sensors and onboard systems within electric vehicles. This raw data is further processed, and through pre-defined semantic mapping rules, the complex and diverse contextual information is compressed and encoded into concise contextual fingerprint codes. For example, key elements such as the vehicle's remaining battery power, fault type, location of being stranded, and surrounding traffic conditions are mapped into a numerical or character sequence with specific meaning. The purpose is to efficiently summarize the core characteristics of the current crisis situation and reduce the amount of data transmitted.

[0048] The process of applying forward error correction (FEC) encoding to the context fingerprint short code to obtain the core beacon data packet refers to the application of FEC encoding to the context fingerprint short code before transmission to enhance data transmission reliability. FEC encoding technology enables the receiver to recover the original data using redundant information even if some data is lost or corrupted during transmission, aiming to improve data integrity in unstable communication environments. Therefore, the generated core beacon data packet contains the context fingerprint short code and its error correction code.

[0049] In practical applications, on V2V / V2I channels, multiple retransmissions are performed at preset intervals, and frequency-hopping spread spectrum technology is used to broadcast the core beacon data packets. This means that the core beacon data packets are broadcast through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication channels. To ensure that the information can be effectively received, the data packets are retransmitted multiple times at preset time intervals. Simultaneously, the use of frequency-hopping spread spectrum technology can effectively combat channel interference and eavesdropping, improving the anti-interference capability and security of communication. Its purpose is to ensure the reliable transmission of critical information in complex electromagnetic environments. After receiving the core beacon data packets, FEC decoding and data verification are performed. The context analysis engine reverse maps the context fingerprint short code according to a preset context fingerprint dictionary, thereby obtaining the core properties of the electric vehicle crisis and deriving the electric vehicle crisis situation.

[0050] Furthermore, if the dispatch center or nearby rescue resources require information that meets preset conditions—for example, if they need more detailed data after a preliminary understanding of the crisis situation to formulate a precise rescue plan—they will send a situation details request instruction to the emergency electric vehicle. Upon receiving the situation details request instruction, the emergency electric vehicle will retrieve detailed data from its local storage, such as more specific fault diagnosis reports, battery health status, and passenger numbers. This detailed data will be divided into multiple data blocks and prioritized according to its importance and timeliness, with the aim of prioritizing the transmission of the most critical information within limited communication bandwidth.

[0051] Furthermore, monitoring the real-time link quality of all available communication channels and dynamically selecting the best-performing channel for transmission means that the vehicle continuously evaluates the real-time link quality of all available communication channels, including cellular networks, Wi-Fi, and DSRC, considering metrics such as bandwidth, latency, and packet loss rate. Based on these evaluation results, the system dynamically selects the best-performing channel for data transmission, aiming to maximize the efficiency and reliability of data transmission.

[0052] As a preferred implementation, if the performance of all channels fails to meet the preset transmission conditions, such as in signal blind spots or severely congested areas, the system will transmit high-priority data blocks at a low rate and with a high retransmission count via V2V / V2I channels. Simultaneously, nearby mobile energy relay vehicles will be used as temporary relay nodes for relay forwarding. This mechanism effectively extends the communication range and overcomes the limitations of single-hop communication, aiming to ensure that high-priority crisis information can still be reliably transmitted to the dispatch center or rescue resources even in extremely harsh communication environments.

[0053] The steps described above for monitoring the real-time link quality of all available communication channels and dynamically selecting the channel with the best current performance for transmission include: Time series smoothing is performed on the real-time link quality data of all available communication channels to obtain the stationary link quality assessment value of the communication channels; Set channel switching cost parameters for each communication channel; Based on the stable link quality assessment value of the communication channel and the channel switching cost parameter, calculate the comprehensive selection score for each available communication channel; Prioritize the communication channel with the highest overall score; and When the difference between the overall selection score of the current communication channel and the overall selection score of the second-best communication channel is less than a preset threshold, the connection of the current communication channel is maintained.

[0054] Specifically, real-time link quality data can include, but is not limited to, metrics such as signal-to-noise ratio (SNR), bit error rate (BER), throughput, and latency. Time-series smoothing processing is applied to this data, using algorithms such as moving averages, exponential smoothing, or Kalman filtering, to eliminate instantaneous noise and short-term fluctuations, thereby obtaining a more representative and stable link quality assessment value. This stable link quality assessment value can more accurately reflect the long-term performance trend of the channel, avoiding unnecessary channel switching decisions due to temporary performance degradation or enhancement.

[0055] The channel switching cost parameter refers to the expense incurred when switching between different communication channels. It can be understood as the additional time required for the switching operation, the consumption of computing resources, network reconfiguration overhead, and the potential packet loss rate. This parameter is designed to quantify the negative impact of switching behavior, thereby introducing consideration of switching stability into the channel selection process. For example, switching from an established V2V channel to another V2I channel may involve more complex handshake protocols and authentication processes, resulting in a higher switching cost than fine-tuning within the same type of channel.

[0056] Furthermore, when the difference between the overall selection score of the current communication channel and the overall selection score of the suboptimal communication channel is less than a preset threshold, the system will maintain the connection of the current communication channel. This preset threshold is a key parameter, set to define a "tolerance" range. If the performance improvement of the suboptimal channel is insufficient to significantly offset the switching cost, no switching will occur, thus avoiding the "ping-pong effect" and unnecessary resource consumption.

[0057] The solution proposed in this application effectively solves the problems of frequent switching and insufficient stability that may occur in traditional channel selection strategies when facing dynamically changing communication environments by introducing time series smoothing processing, channel switching cost parameters, and a threshold-based connection maintenance mechanism.

[0058] This application further proposes an optimization of the steps described above: analyzing and abstracting the key contextual information perceived locally, and compressing and encoding it into short contextual fingerprint codes using predefined semantic mapping rules, to enhance the system's adaptability to unknown contexts. This optimization step includes: Identify discrete contextual elements contained in the current key contextual information; The identified contextual elements are compared with the preset semantic mapping rules stored locally; When at least one novel context element is identified that cannot be found in the preset semantic mapping rules, or when the identified combination of context elements does not match any preset composite context rule, an unknown context marker is triggered. After the unknown situation marker is triggered, a combination analysis is performed on all currently identified discrete situation elements to generate a temporary situation descriptor describing the combination of current situation elements. A new scenario discovery report containing the temporary scenario descriptor is sent via an encrypted channel; Upon receiving the new context discovery report, a new semantic mapping rule is generated or an existing rule is updated, and a temporary context fingerprint short code is assigned to the new context. Rule update instructions containing newly generated or updated semantic mapping rules and temporary context fingerprint shortcodes are issued through encrypted channels; Upon receiving the rule update instruction, the semantic mapping rules and context fingerprint dictionary stored locally are updated; and the updated rules are used to re-encode the current context, generate a core beacon containing the temporary context fingerprint short code, and broadcast it through multiple channels.

[0059] Specifically, when processing locally perceived critical context information, the first step is to identify the discrete context elements contained within the current critical context information. These discrete context elements can be specific descriptions of the vehicle's physical state (e.g., battery level, vehicle speed, fault codes), environmental conditions (e.g., weather, road conditions, obstacles), and communication status (e.g., signal strength, network congestion). Subsequently, the identified context elements are compared with locally stored preset semantic mapping rules. These preset semantic mapping rules are predefined and used to map specific context elements or combinations thereof into standardized context fingerprint short codes.

[0060] Furthermore, when the system identifies at least one novel contextual element that cannot be encoded in the preset semantic mapping rules, or when the identified combination of contextual elements does not match any preset composite contextual rules, an unknown contextual marker will be triggered. This marker indicates that the current context exceeds the scope of the system's preset knowledge and requires special handling. After triggering the unknown contextual marker, the system will perform a combined analysis on all currently identified discrete contextual elements to gain a deeper understanding of their inherent relationships and overall meaning, and generate a temporary contextual descriptor describing the combination of current contextual elements. This descriptor typically contains more detailed text or structured data than the contextual fingerprint shortcode, aiming to comprehensively characterize the essence of the unknown context.

[0061] Subsequently, a new scenario discovery report containing the temporary scenario descriptor is sent to the dispatch center or other central management entity via an encrypted channel. The use of an encrypted channel ensures the security and integrity of data transmission. Upon receiving the new scenario discovery report, the dispatch center analyzes and processes it, generating new semantic mapping rules or updating existing rules to incorporate the new scenario into the system's knowledge base. Simultaneously, a temporary scenario fingerprint short code is assigned to the new scenario for rapid subsequent identification and communication.

[0062] In a preferred implementation, the dispatch center sends a rule update instruction, containing newly generated or updated semantic mapping rules and temporary context fingerprint short codes, to the electric vehicles via an encrypted channel. Upon receiving the rule update instruction, the electric vehicles immediately update their locally stored semantic mapping rules and context fingerprint dictionaries. Finally, using the updated rules, the electric vehicles re-encode the current context, generate a core beacon containing the temporary context fingerprint short codes, and broadcast it through multiple channels, thereby ensuring that even new contexts are accurately and promptly communicated.

[0063] This application further proposes the following steps for analyzing and abstracting key contextual information perceived locally, and compressing and encoding it into contextual fingerprint short codes through preset semantic mapping rules: Receive raw sensing data from multiple source sensors; Perform a data quality assessment on the raw sensing data to identify noisy data, missing values, and outliers in the raw sensing data; For the identified noise data, an adaptive filtering mechanism is applied to adjust the filtering parameters according to the spectral characteristics of the noise and the temporal correlation of the data in order to suppress the noise and obtain denoised data. For the identified missing values, the multimodal data fusion unit is activated to combine redundant information from different types of sensors or to utilize the temporal correlation in the vehicle's historical operating data to complete the missing data and obtain the completed data. For the identified outliers, anomaly pattern recognition is performed, comparing the outliers with a preset normal operating range and historical data distribution. If the outlier deviates from the normal operating range, the outlier is marked. Based on a preset rule set and pattern matching logic, discrete context elements are identified from the denoised data, the completed data, and the marked outliers. The discrete context elements include the vehicle's physical state, environmental conditions, and communication status. The identified discrete context elements are subjected to consistency verification. If logical conflicts or inconsistencies are found among the discrete context elements, the process is backtracked to the data quality assessment stage, and the filtering parameters, the completion parameters, or the anomaly handling parameters are reassessed and adjusted until the context elements meet the preset conditions.

[0064] Specifically, receiving raw sensing data from multiple sensors refers to the unprocessed raw data streams collected from various sensors inside the electric vehicle and external environmental sensors. This data reflects the electric vehicle's operating status, surrounding environmental conditions, and communication status, among other things.

[0065] The data quality assessment of the raw sensor data, identifying noisy data, missing values, and outliers, can be understood as a preliminary check of the completeness, accuracy, and consistency of the received raw data. Noisy data refers to random or non-random interference signals present in the data; missing values ​​are data points that were not acquired during the data acquisition process; outliers are data points that significantly deviate from the overall data distribution or normal operating range. Identifying these issues is fundamental to subsequent data processing.

[0066] In practical applications, adaptive filtering mechanisms, such as Kalman filtering, wavelet filtering, or adaptive mean filtering, are applied to the identified noisy data. The filtering parameters (such as filter order, cutoff frequency, and weighting coefficients) are dynamically adjusted based on the real-time spectral characteristics of the noise (e.g., frequency components and intensity variations) and the temporal correlation of the data (e.g., correlation and trends between adjacent data points). The aim is to effectively suppress noise while preserving the maximum amount of valid information from the original data, thus obtaining denoised data.

[0067] Furthermore, for the identified missing values, a multimodal data fusion unit is activated. The purpose of this unit is to complete the missing data by combining redundant information from different types of sensors (e.g., when GPS signals are lost, location estimation can be performed using data from the inertial measurement unit (IMU)) or by utilizing temporal correlations in the vehicle's historical operating data (e.g., estimating the currently missing battery charge based on historical energy consumption data under similar operating conditions). This ensures the integrity of the contextual information.

[0068] Furthermore, for identified outliers, anomaly pattern recognition is performed, which can employ statistical methods (such as Z-score, IQR), machine learning methods (such as Isolation Forest, One-Class SVM), or rule-based methods. The outliers are compared with preset normal operating ranges (e.g., normal battery temperature range, reasonable vehicle speed range) and historical data distributions. If an outlier deviates from the normal operating range, it is flagged.

[0069] Therefore, based on a preset rule set and pattern matching logic, discrete situational elements are identified from denoised data, completed data, and labeled outliers. The rule set may include predefined thresholds, logical judgment conditions, and state transition rules. The pattern matching logic is used to identify specific patterns in the data; for example, when the battery level is below a certain threshold and the vehicle speed is zero, the situational element of "low battery trapped" may be identified. These discrete situational elements specifically include the vehicle's physical state (e.g., battery level, vehicle speed, fault codes), environmental conditions (e.g., traffic congestion level, weather conditions), and communication conditions (e.g., signal strength, network latency).

[0070] In some preferred embodiments, a specific example is given below. Suppose an electric vehicle is traveling on a congested section of urban road and becomes stranded due to a sudden traffic incident, with its battery power continuously decreasing. The vehicle's local perception system receives raw perception data from multiple sensors (e.g., battery management system, GPS module, onboard camera, communication module).

[0071] First, the data quality assessment module analyzes this raw data. For example, the GPS module may generate noisy data due to tall buildings blocking the signal or signal interference; the battery management system may experience temporary loss of power data due to instantaneous voltage fluctuations; and the vehicle camera may capture blurry images when the vehicle is bumpy, which may be identified as outliers.

[0072] For GPS noise data, an adaptive filtering mechanism is applied. Based on the spectral characteristics of the GPS signal and the vehicle's historical trajectory, filtering parameters are dynamically adjusted to smooth the location data, resulting in denoised location information. For missing battery power data, a multimodal data fusion unit is activated. Combining redundant information such as the vehicle's motor current data, mileage, and historical energy consumption models, the missing power data is estimated and completed, yielding the completed battery power data. For blurry images captured by the onboard camera, the anomaly pattern recognition module marks them as anomalies, but they are not directly used for contextual element recognition; instead, they serve as auxiliary information.

[0073] Subsequently, based on the denoised location information, the completed battery power data, and other sensor data, combined with a preset set of rules (e.g., "battery power is below 20% and vehicle speed is 0 for more than 5 minutes"), discrete situational elements are identified, such as "low battery power", "vehicle stationary", "traffic congestion" and "weak communication signal".

[0074] Finally, consistency checks are performed on these identified discrete contextual elements. For example, if a vehicle reports "low battery" but also reports "driving at high speed," this is clearly a logical inconsistency. In this case, the system will go back to the data quality assessment stage and re-evaluate the processing parameters of the battery power data and vehicle speed data. For example, it will adjust the weight of the battery power completion algorithm or the threshold of the vehicle speed filtering until these contextual elements are logically consistent, ensuring that the finally identified contextual elements are accurate and reliable, thus providing high-quality input for generating contextual fingerprint short codes.

[0075] The above-mentioned steps for performing consistency checks on the identified discrete context elements, and if logical conflicts or inconsistencies are found among the discrete context elements, will backtrack to the data quality assessment stage to re-evaluate and adjust the filtering parameters, completion parameters, or anomaly handling parameters until the context elements meet the preset conditions. These steps include: When receiving raw sensing data from multiple sensors, timestamps and spatial coordinate information are appended to each sensor's data. After assessing the data quality of the raw sensing data, the spatiotemporal alignment processing unit is activated. Based on the timestamps and spatial coordinates of each sensor's data, time synchronization and spatial registration operations are performed to eliminate or reduce spatiotemporal alignment deviations and obtain spatiotemporally aligned data. Based on a pre-defined set of rules and pattern matching logic, discrete context elements are identified from denoised data, completed data, and marked outliers. Then, preliminary logical verification is performed on the identified discrete context elements to identify potential logical conflicts. When a potential logical conflict is identified, the spatiotemporal correlation of the discrete contextual elements that caused the logical conflict is reassessed based on the spatiotemporally aligned data to determine whether the conflict stems from the logical inconsistency of the real context or from the residual spatiotemporal alignment deviation. If the conflict is determined to be due to residual spatiotemporal alignment deviation, the scenario analysis module adjusts the time synchronization parameters or spatial registration parameters, re-executes the spatiotemporal alignment process, and performs scenario element extraction and logical verification again until the scenario elements meet the preset conditions.

[0076] Specifically, when receiving raw sensing data from multiple sensors, each sensor's data is appended with a timestamp and spatial coordinate information. The timestamp records the precise moment of data acquisition, while the spatial coordinates identify the geographical location of the data acquisition or the sensor's relative position on the vehicle. This information forms the basis for subsequent spatiotemporal alignment. Further, after assessing the data quality of the raw sensing data, a spatiotemporal alignment processing unit is activated. This unit performs time synchronization and spatial registration operations based on the timestamps and spatial coordinates of each sensor's data. Time synchronization aims to eliminate differences in the acquisition times of different sensors, ensuring consistency across all data along the timeline; spatial registration corrects for deviations in the spatial positions of different sensors, ensuring that data from different sensors are accurately mapped to the same reference coordinate system. Through these operations, spatiotemporal alignment biases can be eliminated or reduced, resulting in spatiotemporally aligned data that provides a more accurate and consistent data foundation for subsequent contextual analysis.

[0077] Based on this, and using a pre-defined set of rules and pattern matching logic, discrete context elements are identified from denoised data, completed data, and labeled outliers. Preliminary logical checks are then performed on these identified discrete context elements to identify potential logical conflicts. For example, if a vehicle's accelerometer data shows the vehicle is traveling at high speed, while GPS data shows the vehicle is stationary, this constitutes a potential logical conflict.

[0078] When a potential logical conflict is identified, the proposed solution does not directly revert to the data quality assessment stage. Instead, it reassesses the spatiotemporal correlation of the discrete contextual elements causing the logical conflict based on the spatiotemporally aligned data. This reassessment process aims to deeply analyze the temporal and spatial relationships between conflicting elements to determine whether the conflict is an inherent logical inconsistency in the real-world context or due to residual spatiotemporal alignment biases that were not completely eliminated during data acquisition or processing. For example, if a significant delay is found in the GPS data, causing its timestamp to differ from the actual location, the conflict may stem from spatiotemporal alignment bias.

[0079] As a preferred implementation, if the conflict is determined to stem from residual spatiotemporal alignment deviations, the context analysis module will no longer adjust the filtering parameters, completion parameters, or anomaly handling parameters from the data quality assessment phase. Instead, it will selectively adjust the time synchronization parameters or spatial registration parameters. After adjustment, the system will re-execute the spatiotemporal alignment process and perform context element extraction and logical verification again. This iterative process will continue until the logical conflicts between discrete context elements are effectively resolved and the context elements meet the preset logical consistency conditions.

[0080] In some preferred embodiments, a specific example is given below. Suppose an electric vehicle, while navigating a complex urban intersection, suddenly experiences a lateral collision, causing it to lose power and issue a distress signal. The vehicle is equipped with multiple sensors, including a Global Positioning System (GPS) module, an Inertial Measurement Unit (IMU), millimeter-wave radar, and multiple cameras.

[0081] When receiving raw sensing data from multiple sources, such as GPS data, IMU acceleration and angular velocity data, radar distance and velocity data, and camera image data, each data packet is appended with a precise timestamp and the vehicle's real-time spatial coordinates.

[0082] Subsequently, after assessing the data quality of the raw sensing data, the spatiotemporal alignment processing unit is activated. This unit performs time synchronization operations based on the timestamps and spatial coordinates, such as correcting time drift from different sensors using Kalman filtering or extended Kalman filtering to ensure all data are on the same time reference. Simultaneously, it performs spatial registration operations, such as transforming all sensor data to the vehicle's unified coordinate system using sensor fusion algorithms to eliminate spatial deviations caused by differences in sensor installation locations. This yields the spatiotemporally aligned data.

[0083] Based on the spatiotemporally aligned denoised data, completed data, and labeled outliers, the context analysis module identifies discrete context elements. For example, IMU data shows that the vehicle decelerated and rotated drastically within a short period, radar data shows that an obstacle rapidly approached and made contact with it from the side, and camera images show obvious deformation on the side of the vehicle. At this point, preliminary logic verification reveals a potential logical conflict: GPS data shows that the vehicle was still traveling in a straight line at a low speed when the collision occurred, while IMU and radar data show that the vehicle had already undergone a violent collision and come to a stop.

[0084] When this potential logical conflict is identified, the system does not immediately adjust the filtering parameters of the IMU or radar data. Instead, it reassesses the spatiotemporal correlation between the GPS data and the IMU / radar data based on the spatiotemporally aligned data. The scenario analysis module discovers that at the moment of collision, due to the drastic change in vehicle attitude, the GPS signal may be affected by temporary obstruction or multipath effects, causing a brief delay or drift in its position update. This results in a residual spatiotemporal alignment deviation between the timestamp of the GPS data and the actual location information.

[0085] If the conflict is determined to stem from residual spatiotemporal alignment deviations in GPS data, the scenario analysis module will adjust the time synchronization or spatial registration parameters of the GPS data. For example, it might increase the weight attenuation factor of the GPS data in the short term or adjust its covariance matrix when fused with IMU data. After adjustment, the system re-executes the spatiotemporal alignment process and performs scenario element extraction and logical verification again. This adjustment achieves more precise temporal and spatial alignment between GPS data and IMU / radar data, eliminating the logical conflict. The system ultimately identifies that the vehicle has indeed experienced a side collision and is in a critical state. This targeted adjustment avoids unnecessary "corrections" to IMU or radar data, ensuring accurate judgment of the real-world scenario.

[0086] The steps described above, after assessing the data quality of the raw sensing data, include activating the spatiotemporal alignment processing unit to perform time synchronization and spatial registration operations based on the timestamps and spatial coordinates of each sensor's data. This process aims to eliminate or reduce spatiotemporal alignment deviations and obtain spatiotemporally aligned data. Real-time monitoring of the vehicle's speed and bump level; Based on the monitored driving speed and bump level, the sampling rate of the time synchronization operation and the number of iterations of the spatial registration operation are dynamically adjusted. Based on the real-time quality of the sensor data after the addition of timestamps and spatial coordinate information, and the motion state of the vehicle, the fusion weights of the sensor data are dynamically allocated to fuse the spatiotemporal information from different sensors to obtain the spatiotemporally aligned data.

[0087] Specifically, real-time monitoring of vehicle speed and bump level refers to continuously acquiring information about the vehicle's motion status at the current moment through inertial measurement units (IMU), global positioning systems (GPS), or other onboard sensors. Speed ​​reflects the overall movement trend of the vehicle and the required data update frequency, while bump level indicates road conditions and potential instantaneous interference to sensor data.

[0088] The dynamic adjustment of the sampling rate for time synchronization and the number of iterations for spatial registration based on the monitored driving speed and bump level can be understood as adaptively optimizing the granularity of data processing and the allocation of computational resources according to the vehicle's real-time motion state. For example, when the vehicle is traveling at high speed or on bumpy roads, the sensor data changes drastically, which may require a higher sampling rate to capture details and an increased number of spatial registration iterations to achieve more accurate alignment; while when traveling at low speeds and on smooth roads, the sampling rate and number of iterations can be appropriately reduced to save computational resources.

[0089] In practical applications, the fusion weights of each sensor's data are dynamically allocated based on the real-time quality of the data after adding timestamps and spatial coordinate information, and the vehicle's motion state. This involves fusing spatiotemporal information from different sensors by assigning different weights to each sensor's data based on its current reliability, accuracy, and the vehicle's motion context. For example, in a tunnel where GPS signals are blocked, the weight of the inertial navigation system (INS) data may be increased; while during severe vehicle vibrations, the weight of vibration-sensitive sensor data may be appropriately reduced to ensure the accuracy and robustness of the fusion result. The aim is to fully utilize the advantages of each sensor and effectively suppress its disadvantages during multi-source data fusion, thereby obtaining more reliable and accurate spatiotemporally aligned data.

[0090] In some preferred embodiments, a specific example is given below. Assume an electric vehicle is traveling on an urban road, equipped with a GPS module, an inertial measurement unit (IMU), and multiple vision sensors. When the vehicle is traveling at a moderate speed on a flat urban main road, the system detects a stable speed and low bumpiness. In this case, the spatiotemporal alignment processing unit may use a relatively low sampling rate for time synchronization and perform spatial registration with fewer iterations. Meanwhile, GPS data and vision sensor data are given high fusion weights because they typically have high accuracy and reliability in such environments.

[0091] In some of the above implementations, after identifying discrete situational elements from denoised data, completed data, and marked outliers based on a preset rule set and pattern matching logic, preliminary logical verification of the identified discrete situational elements is required to identify potential logical conflicts. However, preliminary logical verification alone may not be sufficient to accurately distinguish between real logical inconsistencies and temporary ambiguities caused by dynamic situation evolution or data uncertainty. This limitation may lead to misjudgment of the situation by the system, thereby affecting the accuracy and efficiency of emergency power supply task adjustments.

[0092] In response, this application further proposes the above-mentioned steps for preliminary logical verification of the identified discrete context elements and identification of potential logical conflicts, including: Extract the discrete context elements from the output; compare the discrete context elements with a preset logical conflict rule base, which includes various conflict modes such as mutual exclusion, causal contradiction and temporal inconsistency, in order to identify potential logical conflicts. When the potential logical conflict is identified, based on the currently identified discrete situational elements and the evolution trajectory of similar situations in the vehicle's historical operation data, the evolution trend of the discrete situational elements within a preset time period is predicted. Based on the quality of the source sensor data of the discrete situation elements, the uncertainty in the data processing process, and the degree of matching with the preset pattern, a confidence score is assigned to each discrete situation element; a dynamic situation graph is constructed by discrete situation elements, evolution trends, logical conflicts, and assigned confidence scores, where nodes represent the discrete situation elements, edges represent the logical relationships between the discrete situation elements and the predicted evolution trend, and the weight of the edges is determined by the confidence score; The dynamic scenario graph is subjected to graph structure analysis and path deduction to identify whether there are loops or disconnected branches in the graph that do not conform to the preset logical rules; and by combining the scenario evolution trend and the confidence score, it is determined whether the potential logical conflict is real and needs to be backtracked, or is a temporary ambiguity caused by the dynamic evolution of the scenario or the uncertainty of the discrete scenario elements.

[0093] Specifically, when performing preliminary logical verification on discrete context elements, it is first necessary to extract the discrete context elements output after preprocessing (such as denoising, completion, and outlier labeling). These elements may include the vehicle's physical state (such as speed, battery level, and location), environmental conditions (such as traffic density and weather), and communication status (such as signal strength and connection stability).

[0094] Furthermore, the extracted discrete situational elements are compared with a pre-defined logical conflict rule base. This logical conflict rule base is designed to include various conflict patterns, such as mutual exclusion (e.g., a vehicle cannot be simultaneously "stationary" and "high-speed driving"), causal contradictions (e.g., a battery with extremely low charge reporting continuous high power output), and temporal inconsistencies (e.g., an accident occurring before emergency braking). Through this comparison, potential logical conflicts between situational elements can be preliminarily identified.

[0095] When a potential logical conflict is identified, the system predicts the evolution trend of the identified discrete situational elements within a preset time period based on the currently identified discrete situational elements and the evolution trajectory of similar situations in the vehicle's historical operating data. For example, if a communication signal is temporarily interrupted, historical data may indicate that such interruptions usually recover on their own within a short period of time, rather than being a persistent fault.

[0096] Meanwhile, to quantify the reliability of contextual elements, a confidence score is assigned to each discrete contextual element based on the quality of the source sensor data, the uncertainties in the data processing, and the degree of matching with a preset pattern. For example, raw data from high-precision, well-calibrated sensors may receive higher confidence scores for their corresponding contextual elements; while data that has undergone extensive interpolation or heavily relies on model predictions may have lower confidence scores.

[0097] Based on this, a dynamic scenario graph is constructed using discrete scenario elements, evolutionary trends, logical conflicts, and configuration confidence scores. In this graph, nodes represent various discrete scenario elements, and edges represent the logical relationships between these scenario elements and the predicted evolutionary trends. The weight of the edges is determined by the confidence scores; that is, scenario elements with higher confidence scores are likely to have greater relevance or influence in the graph.

[0098] Subsequently, graph structure analysis and path deduction are performed on the dynamic scenario graph. This analysis aims to identify whether there are loops (e.g., contradictory causal chains) or disconnected branches (e.g., key scenario elements are completely disconnected from other related elements) in the graph that do not conform to preset logical rules. These structural anomalies may indicate deeper logical problems.

[0099] Ultimately, by combining the situational evolution trend and confidence score, the system determines that the potential logical conflict is real and requires backtracking. The dispatch center will receive an alert and may instruct vehicle A to resend more detailed operational data or adjust data quality assessment parameters to further verify the vehicle's true status, thereby avoiding emergency dispatch based on erroneous information. For example, it might discover that a navigation system malfunction caused a false alarm of "low speed driving" when the vehicle has actually stopped, or that there is indeed some special circumstance causing the vehicle to need to move a short distance with extremely low battery power. In this way, the system can avoid misjudgments caused by simple rule comparisons, ensuring the accuracy of dispatch decisions.

[0100] Secondly, referring to Figure 2 This application further proposes a regional transportation energy dispatching system, which includes: The distress signal receiving module 201 is used to receive distress signals from electric vehicles within the area. The distress signal is generated by the electric vehicle based on its operating status data and a preset reference energy consumption mode, and is broadcast through multiple communication channels. Traffic condition information acquisition module 202 is used to acquire regional traffic condition information; The crisis distribution identification module 203 is used to identify the crisis distribution within the area based on the received distress signal and the location information of the electric vehicle, and obtain the crisis distribution area. The energy supply resource identification module 204 is used to identify available energy supply resources around the crisis distribution area, including fixed energy storage units and mobile energy relay vehicles. The energy resource matching module 205 is used to match the energy supply resources with the electric vehicle based on the crisis situation of the electric vehicle and the status of the energy supply resources; The task instruction issuing module 206 is used to issue emergency power supply task instructions to the matched energy supply resources; The task adjustment module 207 is used to obtain the operating status of the energy supply resources and adjust the emergency power supply task according to the operating status.

[0101] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A regional traffic energy dispatching method to address the challenge of traditional prediction mechanisms failing due to large-scale sudden traffic events, characterized in that, include: The system receives distress signals from electric vehicles within the area. These distress signals are generated by the electric vehicles based on their operating status data and preset reference energy consumption patterns, and are broadcast through various communication channels. Obtain regional traffic information; Based on regional traffic information, the crisis distribution within the region is identified by the received distress signals and the location information of electric vehicles, thus obtaining the crisis distribution area. Identify available energy supply resources around the crisis distribution area, including fixed energy storage units and mobile energy relay vehicles; Based on the crisis situation of the electric vehicle and the status of the energy supply resources, match the energy supply resources with the electric vehicle; Issue emergency power supply task instructions to the matching energy supply resources; as well as The operating status of the energy supply resources is obtained, and the emergency power supply task is adjusted according to the operating status.

2. The regional transportation energy dispatching method according to claim 1, characterized in that, The step of matching the energy supply resources with the electric vehicle based on the crisis situation of the electric vehicle and the status of the energy supply resources includes: Electric vehicles in crisis are prioritized and ranked to obtain a priority ranking result. The priority ranking is based on the remaining energy reserves of the electric vehicles, the criticality of the operational tasks, and the duration of being stranded. The priority ranking result includes a classification of high-priority crisis vehicles, medium-priority crisis vehicles, and low-priority crisis vehicles. Assess the rescue capability of available energy supply resources, including the available energy capacity, maximum discharge power, and time required to reach the emergency electric vehicle. A rescue task allocation matrix is ​​constructed based on the priority ranking results and rescue capabilities. The rows of the rescue task allocation matrix represent the electric vehicles in crisis, and the columns represent the available energy supply resources. Each element in the rescue task allocation matrix represents the expected effect of a specific energy supply resource on rescuing a specific electric vehicle in crisis. Based on the rescue mission allocation matrix, a multi-stage resource allocation process is executed; the multi-stage resource allocation process includes: In the first phase, high-priority emergency vehicles will be matched with energy supply resources that can provide rapid, high-capacity energy replenishment. In the second phase, mobile energy relay vehicles are matched for medium-priority and low-priority emergency vehicles. The route planning of the mobile energy relay vehicles is also taken into consideration so that they can provide energy to multiple emergency electric vehicles on their way to rescue, or return to replenish energy after completing the rescue. Continuously monitor the energy release of the energy supply resources and the changes in the energy reserves of the emergency electric vehicles; and The rescue mission allocation matrix is ​​adjusted based on the energy release status of the energy supply resources and the changes in the energy reserves of the emergency electric vehicles.

3. The regional transportation energy dispatching method according to claim 2, characterized in that, The steps for continuously monitoring the energy release of the energy supply resources and the changes in the energy reserves of the emergency electric vehicles include: The key contextual information perceived locally is analyzed and abstracted, and compressed and encoded into a contextual fingerprint short code through a preset semantic mapping rule; the contextual fingerprint short code is then subjected to forward error correction encoding to obtain the core beacon data packet; On the V2V / V2I channel, multiple retransmissions are performed at preset intervals, and the core beacon data packets are broadcast using frequency hopping spread spectrum technology. After receiving the core beacon data packets, FEC decoding and data verification are performed. The scenario parsing engine performs reverse mapping on the scenario fingerprint short code according to the preset scenario fingerprint dictionary to obtain the core nature of the electric vehicle crisis and obtain the electric vehicle crisis scenario. If the dispatch center or nearby rescue resources require information that meets preset conditions, a situation details request instruction is sent to the emergency electric vehicle; after receiving the situation details request instruction, the emergency electric vehicle extracts detailed data from local storage, divides the detailed data into multiple data blocks, and prioritizes the data blocks; it monitors the real-time link quality of all available communication channels and dynamically selects the channel with the best current performance for transmission; If the performance of all channels does not meet the preset transmission conditions, high-priority data blocks are sent through the V2V / V2I channel at a low rate and with a high number of retransmissions, and the surrounding mobile energy relay vehicles are used as temporary relay nodes for relay forwarding.

4. The regional transportation energy dispatching method according to claim 3, characterized in that, The steps of monitoring the real-time link quality of all available communication channels and dynamically selecting the channel with the best current performance for transmission include: Time series smoothing is performed on the real-time link quality data of all available communication channels to obtain the stable link quality assessment value of the communication channels; Set channel switching cost parameters for each communication channel; Based on the stable link quality assessment value of the communication channel and the channel switching cost parameter, calculate the comprehensive selection score for each available communication channel; Prioritize the communication channel with the highest comprehensive selection score; and When the difference between the overall selection score of the current communication channel and the overall selection score of the suboptimal communication channel is less than a preset threshold, the connection of the current communication channel is maintained.

5. A regional transportation energy dispatching method according to claim 3, characterized in that, The steps of analyzing and abstracting key contextual information perceived locally, and compressing and encoding it into contextual fingerprint short codes through preset semantic mapping rules, include: Identify discrete contextual elements contained in the current key contextual information; The identified contextual elements are compared with the preset semantic mapping rules stored locally; When at least one novel context element is identified that cannot be found in the preset semantic mapping rules, or when the identified combination of context elements does not match any preset composite context rule, an unknown context marker is triggered. After the unknown situation marker is triggered, a combination analysis is performed on all currently identified discrete situation elements to generate a temporary situation descriptor describing the combination of current situation elements. A new scenario discovery report containing the temporary scenario descriptor is sent via an encrypted channel; Upon receiving the new context discovery report, generate new semantic mapping rules or update existing rules, and assign a temporary context fingerprint short code to the new context; Rule update instructions containing newly generated or updated semantic mapping rules and temporary context fingerprint shortcodes are issued through encrypted channels; Upon receiving the rule update instruction, the semantic mapping rules and context fingerprint dictionary stored locally are updated; and the updated rules are used to re-encode the current context, generate a core beacon containing the temporary context fingerprint short code, and broadcast it through multiple channels.

6. A regional transportation energy dispatching method according to claim 5, characterized in that, The step of identifying discrete context elements contained in the current key context information includes: receiving raw sensing data from multiple source sensors; Perform a data quality assessment on the raw sensing data to identify noisy data, missing values, and outliers in the raw sensing data; For the identified noise data, an adaptive filtering mechanism is applied to adjust the filtering parameters according to the spectral characteristics of the noise and the temporal correlation of the data in order to suppress the noise and obtain denoised data. For the identified missing values, the multimodal data fusion unit is activated to combine redundant information from different types of sensors or to use the temporal correlation in the vehicle's historical operating data to complete the missing data and obtain the completed data. For the identified outliers, anomaly pattern recognition is performed, comparing the outliers with the preset normal operating range and historical data distribution. If the outlier deviates from the normal operating range, the outlier is marked. Based on the preset rule set and pattern matching logic, discrete context elements are identified from the denoised data, the completed data, and the marked outliers. Discrete context elements include the vehicle's physical state, environmental conditions, and communication status. Consistency checks are performed on the identified discrete context elements. If logical conflicts or inconsistencies are found among the discrete context elements, the process is backtracked to the data quality assessment stage, and the filtering parameters, the completion parameters, or the anomaly handling parameters are re-evaluated and adjusted until the context elements meet the preset conditions.

7. A regional transportation energy dispatching method according to claim 6, characterized in that, The step of performing consistency verification on the identified discrete context elements, and if logical conflicts or inconsistencies are found among the discrete context elements, then backtracking to the data quality assessment stage to re-evaluate and adjust the filtering parameters, the completion parameters, or the anomaly handling parameters until the context elements meet the preset conditions, includes: When receiving raw sensing data from multiple sensors, timestamps and spatial coordinate information are appended to each sensor's data. After assessing the data quality of the raw sensing data, the spatiotemporal alignment processing unit is activated. Based on the timestamps of each sensor data and the spatial coordinate information, time synchronization and spatial registration operations are performed to eliminate or reduce spatiotemporal alignment deviations and obtain spatiotemporally aligned data. Based on a preset set of rules and pattern matching logic, discrete context elements are identified from the denoised data, the completed data, and the marked outliers. Then, a preliminary logical check is performed on the identified discrete context elements to identify potential logical conflicts. When a potential logical conflict is identified, the spatiotemporal correlation of the discrete contextual elements that cause the logical conflict is reassessed based on the spatiotemporally aligned data, and it is determined whether the conflict stems from a logical inconsistency in the real context or from a residual spatiotemporal alignment deviation. If the conflict is determined to originate from the residual spatiotemporal alignment deviation, the scenario analysis module adjusts the time synchronization parameters or spatial registration parameters, re-executes the spatiotemporal alignment process, and performs scenario element extraction and logic verification again until the scenario elements meet the preset conditions.

8. A regional transportation energy dispatching method according to claim 7, characterized in that, The step of activating the spatiotemporal alignment processing unit after evaluating the data quality of the original sensing data, and performing time synchronization and spatial registration operations based on the timestamps of the data from each sensor and the spatial coordinate information to eliminate or reduce spatiotemporal alignment deviations and obtain spatiotemporally aligned data includes: real-time monitoring of the vehicle's driving speed and bump level. Based on the monitored driving speed and bump level, the sampling rate of the time synchronization operation and the number of iterations of the spatial registration operation are dynamically adjusted. Based on the real-time quality of the sensor data after the addition of timestamps and spatial coordinate information, and the motion state of the vehicle, the fusion weights of the sensor data are dynamically allocated to fuse the spatiotemporal information from different sensors to obtain the spatiotemporally aligned data.

9. A regional transportation energy dispatching method according to claim 8, characterized in that, The steps of identifying discrete context elements from the denoised data, the completed data, and the marked outliers based on a preset rule set and pattern matching logic, and then performing preliminary logical verification on the identified discrete context elements to identify potential logical conflicts include: Extract the discrete context elements from the output; compare the discrete context elements with a preset logical conflict rule base, which includes various conflict modes such as mutual exclusion, causal contradiction and temporal inconsistency, in order to identify potential logical conflicts; When the potential logical conflict is identified, based on the currently identified discrete situational elements and the evolution trajectory of similar situations in the vehicle's historical operation data, the evolution trend of the discrete situational elements within a preset time period is predicted. Based on the quality of the source sensor data of the discrete situation element, the uncertainty in the data processing process, and the degree of matching with the preset pattern, a confidence score is assigned to each discrete situation element. A dynamic scenario graph is constructed by using discrete scenario elements, evolutionary trends, logical conflicts, and configuration confidence scores. Nodes represent the discrete scenario elements, edges represent the logical relationships between the discrete scenario elements and the predicted evolutionary trends, and the weights of the edges are determined by the confidence scores. The dynamic scenario graph is subjected to graph structure analysis and path deduction to identify whether there are loops or disconnected branches in the graph that do not conform to the preset logical rules; and by combining the scenario evolution trend and the confidence score, it is determined whether the potential logical conflict is real and needs to be backtracked, or is a temporary ambiguity caused by the dynamic evolution of the scenario or the uncertainty of the discrete scenario elements.

10. A regional traffic energy dispatching system for addressing the challenge of traditional prediction mechanisms failing due to large-scale sudden traffic events, characterized in that, The system includes: The distress signal receiving module is used to receive distress signals from electric vehicles within the area. The distress signal is generated by the electric vehicle based on its operating status data and a preset reference energy consumption mode, and is broadcast through multiple communication channels. The traffic condition information acquisition module is used to acquire regional traffic condition information; The crisis distribution identification module is used to identify the crisis distribution within a region based on regional traffic conditions, received distress signals, and the location information of electric vehicles, thereby obtaining the crisis distribution area. An energy supply resource identification module is used to identify available energy supply resources around the crisis distribution area, including fixed energy storage units and mobile energy relay vehicles; An energy resource matching module is used to match the energy supply resources with the electric vehicle based on the emergency situation of the electric vehicle and the status of the energy supply resources; The task instruction issuance module is used to issue emergency power supply task instructions to the matched energy supply resources; The task adjustment module is used to obtain the operating status of the energy supply resources and adjust the emergency power supply task according to the operating status.

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