Vehicle fuel consumption management method, system, equipment and product

Abstract

The invention discloses a vehicle fuel consumption management method, system, device and product. The method comprises the steps of obtaining vehicle dynamic data; abnormal monitoring and alarm processing are carried out on the vehicle dynamic data, and an abnormal alarm event is generated; performing driving behavior analysis processing on the vehicle dynamic data to obtain a driving behavior report; performing dynamic route and speed optimization analysis and vehicle condition and energy consumption correlation optimization analysis processing on the vehicle dynamic data to obtain an optimization suggestion; and carrying out visual display on the abnormal alarm event, the driving behavior report and the optimization suggestion, and receiving an interaction instruction to carry out oil consumption management on a target vehicle. The embodiment of the invention can improve the fuel consumption management of the vehicle, and can be widely applied to the technical field of vehicle management.

Description

Technical Field

[0001] This invention relates to the field of vehicle management technology, and in particular to a method, system, device and product for vehicle fuel consumption management. Background Technology

[0002] In related technologies, there are fixed-strategy methods for vehicle fuel consumption management, which involve acquiring single fuel consumption data or fixed operating condition parameters, combining them with fixed thresholds for monitoring, and then basing the results on the monitoring. However, in practical applications, it has been found that monitoring single fuel consumption data or fixed operating condition parameters can easily lead to a one-sided understanding of the factors affecting fuel consumption, thus affecting the efficiency of vehicle fuel consumption management.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to provide a vehicle fuel consumption management method, system, device, and product that can improve the efficiency of vehicle fuel consumption management.

[0005] To achieve the above objectives, one aspect of this application proposes a vehicle fuel consumption management method, the method comprising: Obtain vehicle dynamic data; The vehicle dynamic data is monitored for anomalies and alarms are processed to generate abnormal alarm events. The vehicle dynamic data is analyzed and processed to obtain a driving behavior report; The vehicle dynamic data is subjected to dynamic route and speed optimization analysis and vehicle condition and energy consumption correlation optimization analysis to obtain optimization suggestions. The abnormal alarm events, driving behavior reports, and optimization suggestions are visualized, and interactive commands are received to manage the fuel consumption of the target vehicle.

[0006] In some embodiments, acquiring vehicle dynamic data includes: Data is collected and processed from the target vehicle to obtain multimodal data; the multimodal data includes instantaneous fuel consumption, average fuel consumption, engine speed, water temperature, latitude and longitude, vehicle speed data and fuel level data; The multimodal data is subjected to outlier filtering and timestamp alignment to obtain standardized data; The standardized data is processed for protocol adaptation and uploaded to a time-series database for data partitioning and storage to obtain the vehicle dynamic data.

[0007] In some embodiments, the step of monitoring and alarming the vehicle dynamic data to generate an abnormal alarm event includes the following steps: The vehicle dynamic data is classified into types to obtain vehicle fuel consumption data, driving behavior data, and vehicle status data. The vehicle fuel consumption data is processed for fuel consumption anomaly monitoring according to multi-dimensional monitoring rules, the driving behavior data is processed for driving behavior monitoring, and the vehicle status data is processed for vehicle condition anomaly monitoring, generating the anomaly alarm event; the anomaly alarm event includes anomaly type, event, vehicle identification code and data value.

[0008] In some embodiments, the process of analyzing and processing the vehicle dynamic data to obtain a driving behavior report includes the following steps: The vehicle dynamic data is processed to extract driving behavior features to obtain a driving behavior feature set; the driving behavior feature set includes rapid acceleration events, emergency braking events, idling duration, and speeding duration. The driving behavior feature set is subjected to index calculation and historical comparison processing to obtain index data; The driving behavior report is obtained by performing correlation analysis on the indicator data based on the fuel consumption data in the vehicle dynamic data.

[0009] In some embodiments, the process of performing dynamic route and speed optimization analysis and vehicle condition and energy consumption correlation optimization analysis on the vehicle dynamic data to obtain optimization suggestions includes the following steps: Acquire external road conditions and environmental data; Fuel consumption baseline data is constructed based on the vehicle dynamic data; Based on the external road conditions and environmental data, route planning is performed to obtain a set of candidate routes; Based on the candidate route set, fuel consumption is estimated and route recommendation is performed on the vehicle dynamic data to obtain the target route; Based on the target route and the fuel consumption baseline data, the speed of the target vehicle is dynamically adjusted to obtain a recommended speed. The vehicle dynamic data is analyzed for correlation between component status and fuel consumption to obtain the fuel consumption increment under the current vehicle condition. Based on the increase in fuel consumption, anomaly analysis of vehicle condition is performed to obtain maintenance recommendations; The target route, the recommended speed, and the maintenance suggestions are processed in a structured manner to obtain the optimization suggestions.

[0010] In some embodiments, constructing fuel consumption benchmark data based on the vehicle dynamic data includes the following steps: Obtain vehicle historical data; The vehicle historical data is processed by operating condition classification to obtain operating condition data; The operating data is analyzed for fuel consumption mapping relationship to obtain initial baseline data; The initial baseline data is analyzed for deviation based on the vehicle dynamic data, and style correction is performed in combination with driving behavior characteristics to obtain the fuel consumption baseline data.

[0011] In some embodiments, the visualization of the abnormal alarm events, the driving behavior reports, and the optimization suggestions, and the receipt of interactive commands to manage fuel consumption of the target vehicle, includes the following steps: The abnormal alarm events, driving behavior reports, and optimization suggestions are visualized through a monitoring dashboard. The abnormal alarm events, driving behavior reports, and optimization suggestions are processed to generate a fuel consumption report. The monitoring dashboard obtains interactive commands, the fuel consumption report is processed by setting thresholds according to the interactive commands, and the target vehicle's fuel consumption is monitored and managed according to the set thresholds.

[0012] To achieve the above objectives, another aspect of this application proposes a vehicle fuel consumption management system, the system comprising: The vehicle dynamic data acquisition module is used to acquire vehicle dynamic data; The dynamic monitoring and alarm module is used to monitor and alarm the vehicle's dynamic data for anomalies and generate abnormal alarm events. The driving behavior analysis module is used to perform driving behavior analysis and processing on the vehicle dynamic data to obtain a driving behavior report. The intelligent energy consumption collaborative scheduling module is used to perform dynamic route and speed optimization analysis and vehicle condition and energy consumption correlation optimization analysis on the vehicle dynamic data to obtain optimization suggestions. The user interaction module is used to visualize the abnormal alarm events, driving behavior reports and optimization suggestions, and to receive interactive commands to manage the fuel consumption of the target vehicle.

[0013] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0014] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0015] The embodiments of this application include at least the following beneficial effects: This application provides a vehicle fuel consumption management method, system, device, and product. This solution improves the comprehensive utilization efficiency of vehicle dynamic data and strengthens the comprehensive control over factors affecting fuel consumption by acquiring vehicle dynamic data, performing anomaly monitoring and alarm processing on the vehicle dynamic data, and generating anomaly alarm events. Furthermore, this solution performs driving behavior analysis on vehicle dynamic data to obtain driving behavior reports; it also performs dynamic route and speed optimization analysis and vehicle condition and energy consumption correlation optimization analysis on vehicle dynamic data to obtain optimization suggestions, enhancing the real-time perception capability of fuel consumption anomalies, driving behavior deviations, and potential vehicle condition hazards. By constructing a complete data link from data acquisition to anomaly warning and then to behavior analysis, the embodiments of this application provide accurate data support for fuel consumption management and effectively improve the dynamic grasp of vehicle energy consumption status. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of an implementation environment provided in the embodiments of this application; Figure 2 This is a flowchart of a vehicle fuel consumption management method provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a vehicle fuel consumption management system provided in an embodiment of this application; Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0018] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0019] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] In related technologies, there are fixed-strategy methods for vehicle fuel consumption management, which involve acquiring single fuel consumption data or fixed operating condition parameters, combining them with fixed thresholds for monitoring, and then basing the results on the monitoring. However, in practical applications, it has been found that monitoring single fuel consumption data or fixed operating condition parameters can easily lead to a one-sided understanding of the factors affecting fuel consumption, thus affecting the efficiency of vehicle fuel consumption management. These technologies struggle to prevent fuel consumption increases caused by component deterioration in advance, remaining largely at the stage of passive recording or post-event analysis, lacking the closed-loop management capability from data collection to proactive optimization.

[0022] In view of this, this application provides a vehicle fuel consumption management method, system, device, and product. This solution collects dynamic vehicle data, enabling real-time access and standardized processing of multi-dimensional data such as fuel consumption, location, and driving behavior. Furthermore, by monitoring and alerting on dynamic vehicle data, it enhances the real-time perception of abnormal fuel consumption, driving behavior deviations, and potential vehicle condition hazards. The driving behavior analysis module refines the quantitative identification of fuel consumption-sensitive operations such as rapid acceleration and idling. This application constructs a complete data link from data collection to anomaly warning and behavior analysis, providing precise data support for fuel consumption management. It effectively improves the weak link in dynamically understanding the vehicle's energy consumption status, providing improvement directions for vehicle design and manufacturing, thereby enhancing the overall quality and reliability of the vehicle.

[0023] This application's embodiments are based on IoT, big data, and cloud computing technologies, enabling an intelligent management system for real-time monitoring, analysis, and optimization of vehicle fuel consumption. This application's embodiments collect fuel consumption data through in-vehicle terminals, combining GPS positioning, driving behavior, and road condition information to achieve dynamic visualization of fuel consumption, anomaly warnings, and energy-saving suggestions. Managers can centrally monitor and statistically analyze multiple vehicles through a cloud platform, identifying high-fuel-consumption vehicles and poor driving habits, and assisting in the formulation of scientific scheduling and maintenance strategies. This system helps enterprises reduce operating costs, improve fuel efficiency, and promote green travel and low-carbon transportation development, making it an important component of modern fleet management and intelligent transportation construction.

[0024] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0025] Figure 1 This is a schematic diagram illustrating the implementation environment of a method provided in an embodiment of this application. (Refer to...) Figure 1 The main hardware and software components of this implementation environment include a terminal 101 and a server 102, which are communicatively connected. The method can be executed based on the interaction between the terminal 101 and the server 102. Furthermore, the terminal 101 and the server 102 can be nodes in a blockchain; this embodiment does not specifically limit this.

[0026] Figure 2 This is an optional flowchart of a vehicle fuel consumption management method provided in an embodiment of this application. Figure 2 The method may include, but is not limited to, steps S201 to S205.

[0027] Step S201: Obtain vehicle dynamic data; Step S202: Perform abnormal monitoring and alarm processing on the vehicle dynamic data to generate abnormal alarm events; Step S203: Perform driving behavior analysis and processing on the vehicle dynamic data to obtain a driving behavior report; Step S204: Perform dynamic route and speed optimization analysis and vehicle condition and energy consumption correlation optimization analysis on the vehicle dynamic data to obtain optimization suggestions; Step S205: Visualize the abnormal alarm events, driving behavior reports, and optimization suggestions, and receive interactive instructions to manage fuel consumption of the target vehicle.

[0028] Steps S201 to S205 of this embodiment involve collecting multi-dimensional data such as fuel consumption, location, and driving behavior to obtain vehicle dynamic data. Then, anomaly monitoring and alarms are performed on the vehicle dynamic data. This embodiment can construct a multi-dimensional monitoring rule system to process the target vehicle's dynamic data for fuel consumption anomalies, driving behavior monitoring, and vehicle condition anomaly monitoring. The monitored data is compared based on preset thresholds; when the threshold is exceeded, alarm processing is triggered, and a corresponding anomaly alarm event is generated. This embodiment also performs driving behavior analysis on the vehicle dynamic data. Key driving behavior parameters are extracted from the vehicle dynamic data, and index calculations and correlation analyses are performed to obtain a driving behavior report. This embodiment performs dynamic route and speed optimization analysis and vehicle condition and energy consumption correlation optimization analysis on the vehicle dynamic data. It can integrate real-time data from the vehicle dynamic data acquisition module with external road and weather data, and generate route optimization suggestions and speed adjustment schemes through a route fuel consumption prediction model and a dynamic economic speed algorithm to obtain optimization suggestions. Finally, by visualizing abnormal alarm events, driving behavior reports, and optimization suggestions, and presenting them to users through the interface rendering unit, users can receive operation commands. This allows alarm thresholds to be set or abnormal alarm events to be marked, thereby enabling fuel consumption management for the target vehicle.

[0029] In step S201 of some embodiments, acquiring vehicle dynamic data includes: Data is collected and processed from the target vehicle to obtain multimodal data; the multimodal data includes instantaneous fuel consumption, average fuel consumption, engine speed, water temperature, latitude and longitude, vehicle speed data and fuel level data; The multimodal data is subjected to outlier filtering and timestamp alignment to obtain standardized data; The standardized data is processed for protocol adaptation and uploaded to a time-series database for data partitioning and storage to obtain the vehicle dynamic data.

[0030] Specifically, this application embodiment performs data acquisition and processing on the target vehicle to obtain multimodal data, which includes instantaneous fuel consumption, average fuel consumption, engine speed, coolant temperature, latitude and longitude, vehicle speed data, and fuel level data. This application embodiment continuously receives real-time information from various on-board devices. It can use the international standard On-Board Diagnostics (OBD) interface to provide parameters such as instantaneous fuel consumption, average fuel consumption, engine speed, and coolant temperature. The GPS module transmits latitude and longitude and vehicle speed data, and the ultrasonic fuel level sensor provides real-time fuel level information. All types of data are aggregated to the unit buffer through a dedicated interface channel.

[0031] Optionally, embodiments of this application perform outlier filtering and timestamp alignment on multimodal data. Specifically, this involves performing standardization operations on the aggregated data, first filtering outliers that exceed a reasonable range, such as marking data with instantaneous fuel consumption exceeding 50L / 100km as invalid, then unifying the time base of data from different sources through a timestamp alignment algorithm, and finally converting the structured data into JSON format to adapt to the parsing requirements of subsequent modules, thus obtaining standardized data.

[0032] In some embodiments, this application performs protocol adaptation processing on standardized data and uploads it to a time-series database for data partitioning and storage. Specifically, it implements long-term communication between the vehicle and the cloud by incorporating an MQTT protocol stack. A built-in protocol converter converts the private data protocols of different brands of vehicle devices into a system-wide protocol. Simultaneously, a heartbeat mechanism maintains connection stability, ensuring real-time and continuous data transmission. The data storage unit adopts a time-series database architecture, dividing the data into independent partitions based on the Vehicle Identification Number (VIN). It stores the raw dynamic data for the most recent three months in real time. Data exceeding the storage period undergoes an automatic archiving process, and archived data is retained on a cold backup storage medium to support historical traceability needs, thereby obtaining vehicle dynamic data.

[0033] One of the above technical solutions has the following advantages or beneficial effects: By acquiring multimodal data of the target vehicle, the embodiments of this application can collect real-time information of various devices on the vehicle, providing a data foundation for subsequent fuel consumption analysis.

[0034] In step S202 of some embodiments, the abnormal monitoring and alarm processing of the vehicle dynamic data to generate an abnormal alarm event includes the following steps: The vehicle dynamic data is classified into types to obtain vehicle fuel consumption data, driving behavior data, and vehicle status data. The vehicle fuel consumption data is processed for fuel consumption anomaly monitoring according to multi-dimensional monitoring rules, the driving behavior data is processed for driving behavior monitoring, and the vehicle status data is processed for vehicle condition anomaly monitoring, generating the anomaly alarm event; the anomaly alarm event includes anomaly type, event, vehicle identification code and data value.

[0035] Specifically, this application embodiment acquires real-time data streams from a data acquisition device to obtain vehicle dynamic data. Then, by classifying the vehicle dynamic data, it can be categorized according to vehicle fuel consumption, driving behavior, vehicle status, etc., thereby obtaining vehicle fuel consumption data, driving behavior data, and vehicle status data. Among these, vehicle fuel consumption data mainly includes average fuel consumption, instantaneous fuel consumption, idling fuel consumption, and total fuel consumption; driving behavior data covers the frequency and intensity of rapid acceleration, sudden braking, sharp turns, speeding, and prolonged idling, as well as operational details such as average vehicle speed and travel time; and vehicle status data involves real-time operating parameters such as engine speed, coolant temperature, fault codes (DTC), battery voltage, odometer reading, tire pressure, and various fluid levels (such as engine oil and coolant).

[0036] Optionally, this application embodiment uses multi-dimensional monitoring rules to monitor anomalies in the categorized data. Specifically, it performs fuel consumption anomaly monitoring on vehicle fuel consumption data, driving behavior monitoring on driving behavior data, and vehicle status anomaly monitoring on vehicle condition data. The multi-dimensional monitoring rules can be set as follows: Fuel consumption anomaly rules define a fuel level drop exceeding 5% within 10 minutes as a risk of fuel theft, and a slow fuel level drop exceeding 2% over 30 consecutive minutes as a potential fuel leak; driving behavior rules specify that idling time exceeding 10 minutes triggers an alarm, and three or more consecutive instances of rapid acceleration trigger a warning; vehicle condition anomaly rules are linked to the engine fault code database, automatically activating the monitoring process when fuel system-related fault codes such as P0171 are detected. This application embodiment compares data with rule thresholds in real time, generating alarm events containing the anomaly type, occurrence time, vehicle VIN code, and specific data values ​​when anomalies are detected, and simultaneously marking them as emergency or general alarm levels. It is conceivable that the embodiments of this application can distribute alarm information according to a preset strategy. Emergency alarms are delivered through a dual channel of system platform pop-up and APP push, while general alarms are only pushed through the APP. At the same time, the alarm events are synchronized to the alarm list of the user interaction module for subsequent processing.

[0037] One of the above technical solutions has the following advantages or beneficial effects: The embodiments of this application monitor the classified data for anomalies through multi-dimensional monitoring rules, which can enhance the real-time perception of abnormal fuel consumption, driving behavior deviations and vehicle condition hazards, and improve the efficiency of vehicle fuel consumption monitoring.

[0038] In step S203 of some embodiments, the process of analyzing and processing the vehicle dynamic data to obtain a driving behavior report includes the following steps: The vehicle dynamic data is processed to extract driving behavior features to obtain a driving behavior feature set; the driving behavior feature set includes rapid acceleration events, emergency braking events, idling duration, and speeding duration. The driving behavior feature set is subjected to index calculation and historical comparison processing to obtain index data; The driving behavior report is obtained by performing correlation analysis on the indicator data based on the fuel consumption data in the vehicle dynamic data.

[0039] Optionally, in this embodiment of the application, driving behavior feature extraction processing is performed on vehicle dynamic data. A neural network model can be used for feature extraction processing to obtain a driving behavior feature set, which may include rapid acceleration events, emergency braking events, idling duration, and speeding duration.

[0040] In one feasible embodiment, the system identifies rapid acceleration events with acceleration exceeding 0.3g and sudden braking events with deceleration exceeding 0.4g using acceleration sensor data, counts the duration of idling when the engine speed is 0 and the vehicle speed is 0, and records the duration of speeding when the vehicle speed exceeds the road speed limit, thus forming a structured behavioral feature set.

[0041] Specifically, this application embodiment performs index calculation and historical comparison processing on the driving behavior feature set. Core indicators are calculated based on the feature set to obtain indicator data, which may include idling percentage, number of rapid accelerations, and speeding percentage. The idling percentage is calculated as the ratio of idling time to total driving time. The number of rapid accelerations is counted in units of 10 kilometers. The speeding percentage is calculated as the ratio of speeding mileage to total mileage. Each indicator is directly related to the fuel consumption impact model. This fuel consumption impact model can be constructed using a neural network or statistically based on relevant fuel consumption indicators.

[0042] Optionally, embodiments of this application perform correlation analysis on indicator data based on fuel consumption data in vehicle dynamic data, coupling driving behavior indicators with fuel consumption data to establish a mapping relationship between the number of rapid accelerations and the increase in fuel consumption, quantifying the correlation ratio between idling time and additional fuel consumption, and providing data support for driving behavior optimization. It is conceivable that embodiments of this application can also summarize indicator data daily to generate a daily driver report, covering specific values ​​such as daily idling time, number of rapid accelerations, and speeding percentage, comparisons with historical averages, and explanations of the fuel consumption impact of each indicator, presented in text format for easy reading.

[0043] One of the above technical solutions has the following advantages or beneficial effects: This application embodiment, by analyzing the driver's driving behavior, can obtain fuel consumption-related indicator data, providing a data foundation for subsequent fuel consumption management. Furthermore, it can provide corresponding driving behavior suggestions based on driving behavior analysis, improving the user experience.

[0044] In step S204 of some embodiments, the process of performing dynamic route and speed optimization analysis and vehicle condition and energy consumption correlation optimization analysis on the vehicle dynamic data to obtain optimization suggestions includes the following steps: Acquire external road conditions and environmental data; Fuel consumption baseline data is constructed based on the vehicle dynamic data; Based on the external road conditions and environmental data, route planning is performed to obtain a set of candidate routes; Based on the candidate route set, fuel consumption is estimated and route recommendation is performed on the vehicle dynamic data to obtain the target route; Based on the target route and the fuel consumption baseline data, the speed of the target vehicle is dynamically adjusted to obtain a recommended speed. The vehicle dynamic data is analyzed for correlation between component status and fuel consumption to obtain the fuel consumption increment under the current vehicle condition. Based on the increase in fuel consumption, anomaly analysis of vehicle condition is performed to obtain maintenance recommendations; The target route, the recommended speed, and the maintenance suggestions are processed in a structured manner to obtain the optimization suggestions.

[0045] In some embodiments, external road conditions and environmental data can be acquired by installing various sensors on the vehicle, or by accessing the internet, map applications, etc. External road conditions may include real-time traffic flow data, such as traffic volume, average vehicle speed, and congestion levels, as well as road infrastructure data, such as road grade, gradient, curvature, and the location of tunnels and bridges. Environmental data may include dynamic event data, such as traffic accidents, road construction, and traffic control, and may also include detailed environmental information, such as real-time weather conditions, temperature, humidity, visibility, wind speed, and road surface conditions—whether it is slippery or icy.

[0046] In some embodiments, constructing fuel consumption benchmark data based on the vehicle dynamic data includes the following steps: Obtain vehicle historical data; The vehicle historical data is processed by operating condition classification to obtain operating condition data; The operating data is analyzed for fuel consumption mapping relationship to obtain initial baseline data; The initial baseline data is analyzed for deviation based on the vehicle dynamic data, and style correction is performed in combination with driving behavior characteristics to obtain the fuel consumption baseline data.

[0047] In this embodiment, historical vehicle data is collected over three months, covering more than 100,000 samples under different speeds, loads, and weather conditions. This historical data is then categorized by operating condition: "basic operating condition (flat road, standard tire pressure) + variable operating condition (slope, low temperature)." Fuel consumption mapping analysis is then performed on this data, based on theoretical fuel consumption and operating condition parameters. For example, with operating parameters of 80 km / h vehicle speed, 2000 rpm engine speed, and 25°C air temperature, the baseline fuel consumption can be mapped to 7.2 L / 100km, thus obtaining initial baseline data. Subsequently, dynamic vehicle data, including current vehicle speed, engine status, and ambient temperature, is acquired. By analyzing the deviation between the actual data and the baseline, model weights are dynamically adjusted. For example, a "temperature factor" weight is added during low-temperature winter conditions. Furthermore, driving behavior characteristics, such as the frequency of rapid acceleration, are combined to construct an "individual driving style correction term," ultimately forming a unique energy consumption baseline for each vehicle that adaptively updates over time and operating conditions. The embodiments of this application can construct a corresponding dynamic fuel consumption baseline, and the expression of the dynamic fuel consumption baseline calculation model is as follows: ; in, Represents the real-time dynamic fuel consumption baseline, measured in liters per 100 kilometers (L / 100km). It is the theoretical fuel consumption value of the vehicle under the current operating conditions output by the model, reflecting the expected fuel consumption level under specific vehicle speed, idling time, and environmental conditions.

[0048] This indicates the vehicle's base fuel consumption, measured in liters per 100 kilometers (L / 100km). It refers to the constant-speed fuel consumption specified by the manufacturer when the vehicle leaves the factory (such as the Ministry of Industry and Information Technology's comprehensive fuel consumption standard), and is a fixed reference value that does not change with real-time operating conditions.

[0049] The weighting of vehicle speed is dimensionless and ranges from 0.1 to 0.3. It is obtained by fitting historical data and is used to quantify the sensitivity of vehicle speed to fuel consumption (e.g., higher weighting at high speeds).

[0050] It is the vehicle speed deviation factor, which is dimensionless. The calculation formula is (current vehicle speed - economic speed) / economic speed, where economic speed refers to the optimal fuel consumption speed designed for the vehicle (usually 60-90 km / h), and is used to measure the degree of deviation between the current vehicle speed and the ideal operating conditions.

[0051] The weight of idling influence is dimensionless and fixed at 0.25, representing the contribution of idling time to fuel consumption fluctuations (idling is one of the main factors contributing to increased fuel consumption in urban driving conditions).

[0052] It is the idling time factor, which is dimensionless and is calculated as idling time / total travel time. For example, if idling time is 10 minutes and the total travel time is 60 minutes, then the factor is 0.17, which directly reflects the proportion of the impact of idling behavior on fuel consumption.

[0053] The environmental weight is dimensionless and dynamically adjusted: it is increased to 0.2 in low temperature (<5℃) or high temperature (>35℃) environments, and 0.1 in standard environment (20-30℃) environments, to adapt to the different impacts of different environments on fuel consumption.

[0054] It is an environmental correction factor, dimensionless, calculated by superimposing the temperature factor and the slope factor: the temperature factor is |current temperature - 25℃| / 25℃ (25℃ is the baseline ambient temperature), and the slope factor is the slope percentage × 0.02 (for example, the factor is 0.1 when the slope is 5%), which comprehensively reflects the superimposed effect of temperature and road slope on fuel consumption.

[0055] One of the above technical solutions has the following advantages or beneficial effects: The embodiments of this application perform collaborative calculation of parameters to dynamically calculate the fuel consumption baseline that fits the real-time operating conditions, providing a quantitative basis for subsequent optimization strategies.

[0056] In some embodiments, route planning is performed based on the external road conditions and the environmental data to obtain a set of candidate routes; Specifically, this embodiment of the application uses data fusion and map matching to overlay real-time traffic flow, accidents, construction, and dynamic information such as weather and slope onto the basic road network model, forming an enhanced road network with multi-dimensional cost weights (such as travel time, safety risk, and energy consumption). Subsequently, the system employs a multi-objective optimization algorithm to simultaneously calculate paths based on different preferences (such as shortest time, lowest fuel consumption, and safest), generating multiple non-dominated solutions with varying advantages and disadvantages in terms of travel time, distance, safety, and economy. Finally, these paths are filtered and ranked to form a diverse and comparable set of candidate routes for a specific travel objective.

[0057] In some embodiments, fuel consumption prediction and route recommendation are performed on vehicle dynamic data based on the candidate route set to obtain the target route; Specifically, this embodiment of the application calculates three core fuel consumption influencing factors for each route in the candidate route set: congestion coefficient, gradient integral, and traffic light density. Finally, a weighted summation is performed to obtain the "estimated total fuel consumption" for each route, and the route with the lowest value is recommended to obtain the target route. The estimated total fuel consumption can be calculated using a route fuel consumption prediction model, the expression of which is shown below: ; in, The estimated total fuel consumption for the route is expressed in liters (L) and is a core indicator for measuring the fuel economy of the route.

[0058] This represents the total route distance in kilometers (km), directly taken from the route length data returned by the map API.

[0059] This is the vehicle's base fuel consumption, expressed in liters per 100 kilometers (L / 100km), which is the standard fuel consumption of a vehicle under flat, smooth, and traffic-free conditions (e.g., 6L / 100km for a compact sedan).

[0060] It represents the weight of congestion impact, is dimensionless, and has a fixed value of 0.4, corresponding to the increase in fuel consumption in congested road sections compared to smoother road sections.

[0061] The congestion coefficient is dimensionless and is calculated as "length of congested section / total route length". For example, if 3 kilometers of a 10-kilometer route are congested, then c = 0.3.

[0062] It is the gradient influence weight, with the unit being L / (km·%), and a fixed value of 0.015. Its physical meaning is the fuel consumption increment corresponding to each 1% gradient in each kilometer of road segment.

[0063] The average slope of the route is expressed as a percentage. It is calculated by dividing the elevation difference between the start and end points of the route by the distance. For example, if the elevation rises by 250 meters in a 5-kilometer section, then s = 5%.

[0064] It represents the weighting of traffic light impact, measured in liters (L) per light, with a fixed value of 0.02. This indicates the extra fuel consumption caused by idling at each traffic light intersection.

[0065] The number of traffic lights along the route is expressed in units, and is calculated by obtaining the number of intersection coordinates through the map API.

[0066] In some embodiments, the speed of the target vehicle is dynamically adjusted based on the target route and the fuel consumption baseline data to obtain a recommended speed. Specifically, in this embodiment, the economic speed benchmark can be dynamically adjusted within the safe speed limit range based on the current road segment's speed limit threshold, combined with real-time gradient and wind direction data: maintaining the factory-set economic speed range of 60-90 km / h on flat roads, reducing the speed by 5 km / h for every 1% increase in gradient on uphill sections, and increasing the speed by 5-10 km / h on downwind sections, thus forming a "route-speed" linked fuel consumption optimization strategy to obtain the corresponding recommended speed.

[0067] In some embodiments, the vehicle dynamic data is subjected to component status and fuel consumption correlation analysis to obtain the fuel consumption increment under the current vehicle condition; In this embodiment, by real-time monitoring of the status parameters of key vehicle components and combining historical energy consumption data, a "component health - fuel consumption impact" correlation model is constructed to accurately identify the risk of increased fuel consumption due to abnormal vehicle conditions and generate targeted maintenance suggestions. Specifically, the module focuses on three core components: tires, engine, and engine oil, collecting real-time data through the OBD interface and dedicated sensors. Based on this data, the module first establishes a "component health benchmark library"—for example, defining a state with tire pressure of 2.5 bar, no fault codes, and an oil change interval of <5000km as a "health benchmark," and recording the average fuel consumption under this state. Subsequently, by comparing the deviation between the current component state and the benchmark value, and combining the fuel consumption increase corresponding to similar deviations in historical data, the "potential fuel consumption increment" under the current vehicle condition is calculated. When the increment exceeds a threshold, the system automatically generates maintenance suggestions, clearly pointing out specific measures such as "inflating to 2.5 bar to reduce fuel consumption by 1.2%" and "checking the fuel injectors can reduce fuel consumption by 7-9%", and estimating the fuel consumption improvement effect after maintenance. The formula for calculating the fuel consumption increment under the influence of vehicle conditions is as follows: ; in, This represents the increase in fuel consumption caused by abnormal vehicle conditions, measured in liters per 100 kilometers (L / 100km), directly reflecting the additional fuel consumption due to the current condition of the components.

[0068] The baseline fuel consumption for healthy components is expressed in liters per 100 kilometers (L / 100km). It refers to the average fuel consumption of a vehicle when all key components are in good condition. It is calculated based on the average fuel consumption over the past three months without any abnormal data (e.g., the average fuel consumption of a vehicle in a healthy condition is 7.2L / 100km).

[0069] It is the tire pressure influence coefficient, with the unit being L / (100km·bar), and a fixed value of 0.03. Its physical meaning is the rate of change in fuel consumption when the tire pressure deviates from the reference value by 1 bar (based on experimental data: for every 0.1 bar decrease in tire pressure, fuel consumption increases by 0.3%).

[0070] This is the tire pressure deviation value, in bar, calculated using the formula "reference tire pressure - current tire pressure".

[0071] It is the engine fault impact coefficient, with the unit being L / (100km·code), and a fixed value of 0.015. It represents the average impact of a single critical fault code on fuel consumption (derived from historical data of 10 types of high-impact fault codes such as P0171 and P0300).

[0072] This represents the number of currently active critical fault codes, expressed in units of individual codes. Only fault codes related to the fuel system and ignition system are counted (e.g., engine misfire code P0301 is recorded as 1, and e=0 when there are no fault codes).

[0073] It is the engine oil aging coefficient, with the unit being L / (100km·thousand kilometers), and a fixed value of 0.002. It refers to the impact on fuel consumption for every 1000km that the engine oil exceeds its service life (based on experiments: when the engine oil exceeds its service life by 5000km, the average fuel consumption increases by 1%).

[0074] The oil change mileage is the distance beyond the recommended oil change interval, expressed in thousands of kilometers (10³km). The formula is "current mileage - last oil change mileage - recommended oil change interval" (e.g., if the recommended interval is 5000km and the current oil change interval is 2000km beyond the recommended interval, then t=2).

[0075] In some embodiments, vehicle condition anomaly analysis is performed based on the fuel consumption increment to obtain maintenance recommendations; Specifically, this application embodiment performs abnormal vehicle condition analysis based on incremental fuel consumption data. First, through threshold comparison and trend analysis, it identifies abnormal fuel consumption patterns that exceed the normal fluctuation range. These patterns are then correlated with vehicle status data (such as engine load, oxygen sensor readings, and ignition timing) and driving behavior data in a multi-dimensional manner to pinpoint potential fault sources. The system then uses a fault tree model and expert knowledge base to map specific abnormal fuel consumption characteristics to possible root causes (such as clogged air filters, carbon deposits on fuel injectors, aging spark plugs, or insufficient tire pressure), and assesses the urgency and safety risks of each fault. Finally, based on the diagnostic results, it generates prioritized maintenance recommendations, including specific repair items (such as cleaning the throttle body and replacing spark plugs), processing time (immediate handling or planned maintenance), and cost estimates, forming an executable maintenance plan.

[0076] In some embodiments, the target route, the recommended speed, and the maintenance suggestions are subjected to structured generation processing to obtain the optimization suggestions; Specifically, embodiments of this application can integrate these three elements into a unified decision-making framework through information fusion and contextual association. For example, high-priority items in maintenance recommendations can be matched with the length and complexity of the route to determine the urgency of execution. Subsequently, the system employs a rule-based template engine and natural language generation technology to transform the processing logic into hierarchical and easily understandable natural language instructions. Finally, through structured output, these elements are integrated into a comprehensive optimization recommendation document that includes a trip overview, segmented driving guidelines, and contextualized maintenance reminders.

[0077] One of the above technical solutions has the following advantages or beneficial effects: This application embodiment dynamically plans the fuel-efficient route and adjusts the economical speed by integrating external data such as real-time road conditions, slope, and wind direction, thus avoiding the fuel waste of traditional fixed route navigation; at the same time, it generates preventive maintenance suggestions by combining vehicle condition data such as tire pressure and engine fault codes, thus eliminating the risk of increased fuel consumption caused by abnormal components in advance.

[0078] In step S205 of some embodiments, the visualization of the abnormal alarm event, the driving behavior report, and the optimization suggestions, and the receiving of interactive instructions to manage fuel consumption of the target vehicle, includes the following steps: The abnormal alarm events, driving behavior reports, and optimization suggestions are visualized through a monitoring dashboard. The abnormal alarm events, driving behavior reports, and optimization suggestions are processed to generate a fuel consumption report. The monitoring dashboard obtains interactive commands, the fuel consumption report is processed by setting thresholds according to the interactive commands, and the target vehicle's fuel consumption is monitored and managed according to the set thresholds.

[0079] Specifically, in this embodiment of the application, the interface rendering unit can be responsible for the visual presentation of the system's functional interface, the real-time monitoring dashboard displays the current fuel consumption, cumulative mileage and abnormal status of a single vehicle, the driving behavior analysis interface displays the driver's daily report and historical indicator trend curves, the alarm list interface arranges unprocessed and processed alarm events in chronological order, and the report export interface provides data filtering conditions and format selection options.

[0080] It is conceivable that this application embodiment can also respond to user operation requests to query data, supporting queries for historical fuel consumption data of specific vehicles by VIN code, retrieval of driving behavior indicators by date range, and filtering of historical alarm records by alarm type. The query results are displayed in real time through the interface rendering unit. This application embodiment can also generate statistical reports according to the user-selected period and data type. Daily reports include daily fuel consumption, mileage, and driving behavior summaries; weekly reports summarize weekly average fuel consumption and indicator change trends; and monthly reports provide monthly fuel consumption analysis and optimization suggestions, supporting export to Excel format files. This application embodiment captures user interface operation commands, including setting alarm thresholds, adjusting idling time alarm parameters, marking alarm status to mark emergency alarms as processed, confirming report export commands, and providing real-time feedback of processing results to the interface display.

[0081] Please see Figure 3 This application also provides a vehicle fuel consumption management system that can implement the above-mentioned vehicle fuel consumption management method. The system includes: The vehicle dynamic data acquisition module 301 is used to acquire vehicle dynamic data. The dynamic monitoring and alarm module 302 is used to perform abnormal monitoring and alarm processing on the vehicle dynamic data and generate abnormal alarm events. The driving behavior analysis module 303 is used to perform driving behavior analysis and processing on the vehicle dynamic data to obtain a driving behavior report. The intelligent energy consumption collaborative scheduling module 304 is used to perform dynamic route and speed optimization analysis and vehicle condition and energy consumption correlation optimization analysis on the vehicle dynamic data to obtain optimization suggestions. The user interaction module 305 is used to visualize the abnormal alarm events, the driving behavior reports and the optimization suggestions, and to receive interactive commands to manage the fuel consumption of the target vehicle.

[0082] Optionally, this application embodiment can also include a system management module, which internally includes a user permission unit, a data security unit, a system configuration unit, and a log auditing unit. The user permission unit manages system roles and operational permissions. Administrators have full module operation permissions, drivers can only view their own vehicle's driving behavior reports and fuel consumption data, and visitors are restricted to browsing system function introductions. A permission matrix controls the data access and function usage scope of different roles. The data security unit implements multi-level security protection. The transmission layer uses the TLS 1.2 protocol to encrypt data transmission, and the storage layer uses the AES-256 algorithm to encrypt sensitive data such as fuel level and fuel consumption in the database. The encryption key is updated periodically to strengthen security. The system configuration unit supports user-defined system parameters. The data acquisition frequency can be set with the OBD data upload interval, the alarm threshold can be configured with the upper limit of fuel consumption per 100 kilometers and the idling time standard, and the data storage period can be set with the historical data retention time. Configuration results take effect in real time and are saved to the system configuration file. The log auditing unit records all system operation logs, covering user login and logout times, data query and modification records, alarm triggering and handling processes. The logs are stored in chronological order and can be retrieved by user, operation type, and time range, providing a basis for troubleshooting and security auditing.

[0083] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0084] Please see Figure 4 , Figure 4 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 401 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 402 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 402 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 402 and is called and executed by the processor 401. Input / output interface 403 is used to implement information input and output; The communication interface 404 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 405 transmits information between various components of the device (e.g., processor 401, memory 402, input / output interface 403, and communication interface 404); The processor 401, memory 402, input / output interface 403 and communication interface 404 are connected to each other within the device via bus 405.

[0085] The vehicle fuel consumption management device provided in this embodiment of the invention includes a memory, a processor, and a program stored in the memory and executable on the processor. When the program is executed by the processor, it implements the vehicle fuel consumption management method of the above embodiment.

[0086] Taking the example of a processor and memory in a vehicle controller being connected via a bus, the memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the control processor, and these remote memories can be connected to the control device via a network.

[0087] The non-transient software program and instructions required to implement the control method of the above embodiments are stored in memory. When executed by the processor, the vehicle fuel consumption management method in the above embodiments is executed.

[0088] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0089] This invention also provides a vehicle including the vehicle fuel consumption management device described in the above embodiments.

[0090] The vehicle can be a private car, such as a sedan, SUV, MPV, or pickup truck. It can also be a commercial vehicle, such as a van, bus, small truck, or large semi-trailer. The vehicle must have an electric motor capable of outputting power or acting as a generator to store mechanical energy. When the vehicle is a new energy vehicle, it can be a hybrid or a pure electric vehicle.

[0091] Since the vehicle applies all the technical solutions of the above-mentioned vehicle fuel consumption management device or vehicle fuel consumption management controller, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.

[0092] Furthermore, one embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions for performing the control method described above. Exemplarily, the method steps described above are performed.

[0093] It is worth noting that, since the computer-readable storage medium of the present invention is capable of executing the control method of any of the above embodiments, the specific implementation and technical effects of the computer-readable storage medium of the present invention can be referred to the specific implementation and technical effects of the control method of any of the above embodiments.

[0094] Furthermore, one embodiment of the present invention provides a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer program or computer instructions from the computer-readable storage medium and executes the computer program or computer instructions, causing the computer device to perform the robot control method described above. Exemplarily, the method steps described above are performed.

[0095] It is worth noting that, since the computer program product of the present invention can execute the control method of any of the above embodiments, the specific implementation method and technical effect of the computer program product of the present invention can be referred to the specific implementation method and technical effect of the control method of any of the above embodiments.

[0096] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0097] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

Claims

1. A method for managing vehicle fuel consumption, characterized in that, The method includes: Obtain vehicle dynamic data; The vehicle dynamic data is monitored for anomalies and alarms are processed to generate abnormal alarm events. The vehicle dynamic data is analyzed and processed to obtain a driving behavior report; The vehicle dynamic data is subjected to dynamic route and speed optimization analysis and vehicle condition and energy consumption correlation optimization analysis to obtain optimization suggestions. The abnormal alarm events, driving behavior reports, and optimization suggestions are visualized, and interactive commands are received to manage the fuel consumption of the target vehicle.

2. The method according to claim 1, characterized in that, The acquisition of vehicle dynamic data includes: Data is collected and processed from the target vehicle to obtain multimodal data; the multimodal data includes instantaneous fuel consumption, average fuel consumption, engine speed, water temperature, latitude and longitude, vehicle speed data and fuel level data; The multimodal data is subjected to outlier filtering and timestamp alignment to obtain standardized data; The standardized data is processed for protocol adaptation and uploaded to a time-series database for data partitioning and storage to obtain the vehicle dynamic data.

3. The method according to claim 1, characterized in that, The process of monitoring and alarming the vehicle dynamic data to generate abnormal alarm events includes the following steps: The vehicle dynamic data is classified into types to obtain vehicle fuel consumption data, driving behavior data, and vehicle status data. The vehicle fuel consumption data is processed for fuel consumption anomaly monitoring according to multi-dimensional monitoring rules, the driving behavior data is processed for driving behavior monitoring, and the vehicle status data is processed for vehicle condition anomaly monitoring, generating the anomaly alarm event; the anomaly alarm event includes anomaly type, event, vehicle identification code and data value.

4. The method according to claim 1, characterized in that, The process of analyzing and processing the vehicle dynamic data to obtain a driving behavior report includes the following steps: The vehicle dynamic data is processed to extract driving behavior features to obtain a driving behavior feature set; the driving behavior feature set includes rapid acceleration events, emergency braking events, idling duration, and speeding duration. The driving behavior feature set is subjected to index calculation and historical comparison processing to obtain index data; The driving behavior report is obtained by performing correlation analysis on the indicator data based on the fuel consumption data in the vehicle dynamic data.

5. The method according to claim 1, characterized in that, The process of performing dynamic route and speed optimization analysis and vehicle condition and energy consumption correlation optimization analysis on the vehicle dynamic data to obtain optimization suggestions includes the following steps: Acquire external road conditions and environmental data; Fuel consumption baseline data is constructed based on the vehicle dynamic data; Based on the external road conditions and environmental data, route planning is performed to obtain a set of candidate routes; Based on the candidate route set, fuel consumption is estimated and route recommendation is performed on the vehicle dynamic data to obtain the target route; Based on the target route and the fuel consumption baseline data, the speed of the target vehicle is dynamically adjusted to obtain a recommended speed. The vehicle dynamic data is analyzed for correlation between component status and fuel consumption to obtain the fuel consumption increment under the current vehicle condition. Based on the increase in fuel consumption, anomaly analysis of vehicle condition is performed to obtain maintenance recommendations; The target route, the recommended speed, and the maintenance suggestions are processed in a structured manner to obtain the optimization suggestions.

6. The method according to claim 5, characterized in that, The process of constructing fuel consumption benchmark data based on the vehicle dynamic data includes the following steps: Obtain vehicle historical data; The vehicle historical data is processed by operating condition classification to obtain operating condition data; The operating data is analyzed for fuel consumption mapping relationship to obtain initial baseline data; The initial baseline data is analyzed for deviation based on the vehicle dynamic data, and style correction is performed in combination with driving behavior characteristics to obtain the fuel consumption baseline data.

7. The method according to any one of claims 1 to 6, characterized in that, The process of visually displaying the abnormal alarm events, driving behavior reports, and optimization suggestions, and receiving interactive commands to manage fuel consumption for the target vehicle, includes the following steps: The abnormal alarm events, driving behavior reports, and optimization suggestions are visualized through a monitoring dashboard. The abnormal alarm events, driving behavior reports, and optimization suggestions are processed to generate a fuel consumption report. The monitoring dashboard obtains interactive commands, the fuel consumption report is processed by setting thresholds according to the interactive commands, and the target vehicle's fuel consumption is monitored and managed according to the set thresholds.

8. A vehicle fuel consumption management system, characterized in that, The system includes: The vehicle dynamic data acquisition module is used to acquire vehicle dynamic data; The dynamic monitoring and alarm module is used to monitor and alarm the vehicle's dynamic data for anomalies and generate abnormal alarm events. The driving behavior analysis module is used to perform driving behavior analysis and processing on the vehicle dynamic data to obtain a driving behavior report; The intelligent energy consumption collaborative scheduling module is used to perform dynamic route and speed optimization analysis and vehicle condition and energy consumption correlation optimization analysis on the vehicle dynamic data to obtain optimization suggestions. The user interaction module is used to visualize the abnormal alarm events, driving behavior reports and optimization suggestions, and to receive interactive commands to manage the fuel consumption of the target vehicle.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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