Method, system, device and medium for monitoring activation rate of intelligent driving function

Abstract

The application discloses a kind of intelligent driving function activation rate monitoring method, system, device and medium.Intelligent driving function activation rate monitoring method includes: obtaining the intelligent driving function activation event stream related to intelligent driving function activation reported by vehicle;According to the intelligent driving function activation event stream, obtain activation rate index set, wherein the activation rate index set includes multiple activation rate indexes;According to the preset dimension system, the activation rate index set is aggregated, and multidimensional activation rate monitoring data is obtained;From multiple dimensions, the activation rate monitoring data is visually displayed.Using the present application, the problems of data lag, single analysis dimension and low visualization of traditional monitoring methods are solved, real-time, fine, intuitive and intelligent monitoring of intelligent driving function activation is realized, and strong data support is provided for product optimization and operation decision.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, system, device and medium for monitoring the activation rate of intelligent driving functions. Background Technology

[0002] The success of intelligent driving functions highly depends on the actual acceptance and usage by users. Among these, the function activation rate is a core key performance indicator (KPI) for measuring market acceptance, product fit, and user experience. Currently, the industry faces many technical bottlenecks in monitoring and managing this indicator.

[0003] Data timeliness is severely delayed: Traditional technologies rely on batch data warehouses with T+1 or longer cycles (such as offline computing based on Hadoop), generating historical reports. Product managers and operations personnel cannot perceive the real-time changes in user activation behavior after new feature releases, marketing campaigns, or negative public opinion events, leading to delayed decision-making and missing the best window for product iteration and operational intervention.

[0004] The analysis is coarse-grained, making root cause identification difficult: Existing solutions mostly provide macro-level indicators such as "ACC activation rate of the entire fleet." When the activation rate fluctuates, there is a lack of effective technical means to conduct multi-dimensional, drill-down root cause analysis. It is impossible to quickly distinguish whether the problem stems from compatibility with a specific vehicle model, road condition adaptability in a certain region, bugs in a new software version, or cognitive barriers for a specific user group.

[0005] The data presentation is not intuitive, and the decision-making threshold is high: the data is mostly presented in static tables or simple charts, requiring decision-makers to have a high level of data interpretation ability, and it is difficult to quickly form business insights. There is a lack of interactive visualization capabilities based on geographic information, time series and multi-dimensional filtering, which makes it impossible to intuitively show "where", "when" and "who" the problem occurred.

[0006] Passive response mode, lacking proactive perception capabilities: Existing systems generally lack real-time early warning mechanisms. They cannot automatically monitor and issue real-time alerts for key risks such as abnormally sharp drops in activation rates or conversion rates in the new user activation funnel falling below thresholds. They can only rely on manual inspection data, which is a passive response mode and is prone to causing business losses. Summary of the Invention

[0007] Based on this, it is necessary to provide a monitoring method, system, device, and medium for the activation rate of intelligent driving functions to address the aforementioned technical issues. This solves the problems of data lag, single analysis dimension, and low visualization in traditional monitoring methods, and enables real-time, precise, intuitive, and intelligent monitoring of the activation status of intelligent driving functions, providing strong data support for product optimization and operational decision-making.

[0008] Firstly, a method for monitoring the activation rate of intelligent driving functions is provided, including: Obtain the intelligent driving function activation event stream reported by the vehicle related to the activation of intelligent driving functions; Based on the intelligent driving function activation event stream, an activation rate indicator set is obtained, wherein the activation rate indicator set includes multiple activation rate indicators; The activation rate indicator set is aggregated according to a preset dimensional system to obtain multi-dimensional activation rate monitoring data. The monitoring data of the activation rate is visualized from multiple dimensions.

[0009] In some examples, the intelligent driving function activation event stream includes at least the anonymized hash ID of the vehicle VIN, a function identifier, an event type, an event timestamp, GPS latitude and longitude, a vehicle model code, and a software version number.

[0010] In some examples, the multiple activation rate metrics include at least the global and functional real-time activation rate, activation failure rate, reasons for activation failure, distribution of reasons for exiting the function, and the first-week activation rate of new users. The real-time activation rate includes the activation rate based on available time and the activation rate based on available mileage.

[0011] In some examples, the dimensional system includes spatial, temporal, vehicle attribute, and user behavior dimensions. The activation rate metric set is aggregated according to the preset dimensional system to obtain multi-dimensional activation rate monitoring data, including: The activation rate metric set is aggregated according to the spatial dimension, time dimension, vehicle attribute dimension, and user behavior dimension respectively to obtain activation rate monitoring data corresponding to the spatial dimension, time dimension, vehicle attribute dimension, and user behavior dimension.

[0012] In some examples, the visualization of the activation rate monitoring data from multiple dimensions includes: The monitoring data of the activation rate is visualized from multiple dimensions through multiple view components. The view components include real-time indicator cards, spatiotemporal heat maps, multi-dimensional drill-down analyzers, and early warning notification centers. The early warning notification centers have built-in early warning engines, which are pre-configured with rule-based or machine learning-based anomaly detection models to capture abnormal indicators and notify users through the early warning notification centers.

[0013] In some examples, the rules include: threshold rules, year-on-year / month-on-month anomaly rules, and statistical process control rules, wherein: The threshold rule refers to the rule that is triggered when the activation rate under a specified dimension decreases by more than an absolute threshold or a relative percentage within two consecutive time windows. The year-on-year / month-on-month anomaly rule refers to the rule that the current value is compared with the value of the same period in history or the previous period, and is triggered when the deviation exceeds the set range. Statistical process control rules refer to identifying the causes of variation that exceed control limits based on the historical mean and standard deviation of indicators.

[0014] In some examples, before obtaining the activation rate metric based on the intelligent driving function activation event stream, the process further includes filtering and standardizing the intelligent driving function activation event stream.

[0015] Secondly, a monitoring system for the activation rate of intelligent driving functions is provided, including: The acquisition module is used to obtain the intelligent driving function activation event stream reported by the vehicle that is related to the activation of the intelligent driving function; The processing module is used to obtain an activation rate indicator set based on the intelligent driving function activation event stream, wherein the activation rate indicator set includes multiple activation rate indicators. The analysis module is used to aggregate the activation rate indicator set according to a preset dimensional system to obtain multi-dimensional activation rate monitoring data. The display module is used to visualize the monitoring data of the activation rate from multiple dimensions.

[0016] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the method for monitoring the activation rate of intelligent driving functions as described in the first aspect and any possible implementation of the first aspect.

[0017] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method for monitoring the activation rate of intelligent driving functions as described in the first aspect and any possible implementation thereof.

[0018] Fifthly, a computer program product is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method for monitoring the activation rate of intelligent driving functions as described in the first aspect and any possible implementation of the first aspect.

[0019] The embodiments of this application receive activation event streams reported by vehicles, perform real-time indicator calculations on the event streams, and then aggregate the indicators in multiple dimensions such as space, time, and vehicle attributes. Finally, the data is displayed in multiple dimensions, such as providing an interactive dashboard that includes a geographic heat map, a multi-dimensional drill-down analyzer, and an early warning center. This solves the problems of data lag, single analysis dimensions, and low visualization in traditional monitoring methods, and achieves real-time, detailed, intuitive, and intelligent monitoring of the activation status of intelligent driving functions, providing strong data support for product optimization and operational decision-making. Attached Figure Description

[0020] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 A flowchart illustrating the method for monitoring the activation rate of intelligent driving functions provided in this application embodiment; Figure 2 A visual diagram illustrating the activation rate monitoring results of the intelligent driving function activation rate monitoring method provided in this application embodiment; Figure 3 This is a structural block diagram of the intelligent driving function activation rate monitoring system provided in the embodiments of this application; Figure 4 This is a structural block diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0021] The present application will now be described in further detail with reference to the embodiments and accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the application. Furthermore, it should be noted that, for ease of description, only the parts relevant to the application are shown in the accompanying drawings.

[0022] It should be noted that, unless otherwise specified, the embodiments and features of the embodiments in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] The following describes in detail, with reference to the accompanying drawings, a method, system, device, and medium for monitoring the activation rate of intelligent driving functions according to embodiments of this application.

[0024] Figure 1 This is a flowchart of a method for monitoring the activation rate of intelligent driving functions according to an embodiment of this application. Figure 1 As shown, the method for monitoring the activation rate of intelligent driving functions according to an embodiment of this application includes the following steps: S101: Obtain the intelligent driving function activation event stream reported by the vehicle related to the activation of the intelligent driving function.

[0025] The intelligent driving function activation event stream includes, but is not limited to, the anonymized hash ID of the vehicle VIN code, function identifier, event type, event timestamp, GPS latitude and longitude, vehicle model code, and software version number.

[0026] Specifically, the data acquisition and access module can receive in real time anonymized intelligent driving function activation event streams reported by the vehicle terminal that are related to the activation of intelligent driving functions. In a specific example, the data structure of the activation event stream includes at least the following: the anonymized hash ID of the vehicle VIN code, function identifier, event type, event timestamp, GPS latitude and longitude, vehicle model code, and software version number.

[0027] The data acquisition and access module is implemented using Apache Kafka or RocketMQ as message middleware, for example.

[0028] S102: Based on the intelligent driving function activation event stream, obtain an activation rate indicator set, wherein the activation rate indicator set includes multiple activation rate indicators.

[0029] In one embodiment of this application, multiple activation rate metrics include at least the global and functional real-time activation rate, activation failure rate, reasons for activation failure, distribution of reasons for function exit, and the first-week activation rate for new users. The real-time activation rate includes the activation rate based on available time and the activation rate based on available mileage. It should be noted that before obtaining the activation rate metrics based on the intelligent driving function activation event stream, the process further includes filtering and standardizing the intelligent driving function activation event stream.

[0030] Specifically, the streaming processing and computing engine can be configured to receive real-time indicator results output by the streaming processing and computing engine, perform real-time parsing, filtering and data standardization on the intelligent driving function activation event stream reported by the vehicle related to intelligent driving function activation, and dynamically calculate the core indicator set (i.e., the activation rate indicator set) based on a sliding time window. The streaming processing and computing engine can be implemented using Apache Flink or Spark Streaming.

[0031] In the above example, real-time activation rate includes, but is not limited to, activation rate based on available time and activation rate based on available mileage. Taking the IPC function as an example: The activation rate of IPC usage time is calculated as follows: IPC Activation Rate = Total Activation Time of IPC per Vehicle / (Total Activation Time of IPC per Vehicle + Total Activable Time of IPC per Vehicle) * 100%.

[0032] The activation rate of available IPC miles is calculated as follows: IPC Activation Rate = Total Activated Mileage of IPC per Vehicle / (Total Activated Mileage of IPC per Vehicle + Total Activable Mileage of IPC per Vehicle) * 100%.

[0033] Taking the LDP LDWFCW function, which does not require active driver activation, as an example, the number of activations / alarms per 100 kilometers is calculated.

[0034] The number of LDW alarms per 100 kilometers is: total number of LDW alarms / (total mileage driven with LDW switch on / 100).

[0035] S103: Aggregate the activation rate indicator set according to the preset dimension system to obtain multi-dimensional activation rate monitoring data.

[0036] As a specific example, the dimension system includes four dimensions: spatial dimension, time dimension, vehicle attribute dimension, and user behavior dimension. Based on this, the activation rate indicator set is aggregated according to the preset dimension system to obtain multi-dimensional activation rate monitoring data, including: aggregating the activation rate indicator set according to the spatial dimension, time dimension, vehicle attribute dimension, and user behavior dimension respectively to obtain activation rate monitoring data corresponding to the spatial dimension, time dimension, vehicle attribute dimension, and user behavior dimension.

[0037] Specifically, the multi-dimensional data analysis module can receive real-time indicator results output by the streaming processing and computing engine, and associate them with dimension tables from the data storage cluster. Aggregation calculations are then performed according to a preset dimension system. As a concrete example, the dimension system is constructed to include at least four levels: spatial dimension, temporal dimension, vehicle attribute dimension, and user behavior dimension. Wherein: Spatial dimension: supports drill-down from global, national, provincial, and city levels down to specific road levels; In terms of time dimension, it supports rolling statistics by minute, hour, day, week, and month; Vehicle attribute dimensions include vehicle model, hardware sensor configuration, and software version; User behavior dimensions include user lifecycle stages and historical driving style classifications.

[0038] S104: Visualize the monitoring data of the activation rate from multiple dimensions.

[0039] In one embodiment of this application, the monitoring data of the activation rate is visualized from multiple dimensions, including: the monitoring data of the activation rate is visualized from multiple dimensions through multiple view components, wherein the view components include a real-time indicator card, a spatiotemporal heat map, a multi-dimensional drill-down analyzer, and an early warning notification center. The early warning notification center has a built-in early warning engine, which is pre-configured with an anomaly detection model based on rules or machine learning to capture abnormal indicators and notify them through the early warning notification center.

[0040] In the above example, the rules include: threshold rules, year-on-year / month-on-month anomaly rules, and statistical process control rules, wherein: the threshold rule refers to the triggering of the activation rate under a specified dimension when it decreases by more than an absolute threshold or relative percentage within two consecutive time windows; the year-on-year / month-on-month anomaly rule refers to the triggering of the comparison between the current value and the value of the same period in history or the previous period when the deviation exceeds a set range; the statistical process control rule refers to the identification of the causes of variation that exceed the control limits based on the historical mean and standard deviation of the indicator.

[0041] Specifically, the function of visualizing the activation rate monitoring data from multiple dimensions can be achieved through a visualization and early warning module. This module is configured to provide an interactive visualization dashboard based on a web technology stack. This dashboard integrates multiple view components, including: real-time indicator cards, spatiotemporal heatmaps, a multi-dimensional drill-down analyzer, and an early warning notification center. It also has a built-in early warning engine, allowing users to configure rule-based or machine learning-based anomaly detection models to capture and notify of indicator anomalies in real time. Figure 2 As shown, the spatiotemporal heatmap in the visualization and early warning module maps GPS latitude and longitude data onto a base map, dynamically reflecting the real-time activation density of different geographical areas through color variations. The multi-dimensional drill-down analyzer provides interactive functionality: when a user clicks on any area on the spatiotemporal heatmap, the system automatically filters and refreshes other view components, displaying only the vehicle attribute distribution and time trend chart related to that area.

[0042] The intelligent driving function activation rate monitoring method of this application embodiment receives activation event streams reported by vehicles, performs real-time indicator calculations on the event streams, and then aggregates the indicators in multiple dimensions such as space, time, and vehicle attributes. Finally, it provides multi-dimensional display, such as providing an interactive dashboard that includes a geographic heat map, a multi-dimensional drill-down analyzer, and an early warning center. This solves the problems of data lag, single analysis dimensions, and low visualization in traditional monitoring methods, and realizes real-time, precise, intuitive, and intelligent monitoring of intelligent driving function activation, providing strong data support for product optimization and operational decision-making.

[0043] Compared with existing technologies, the method for monitoring the activation rate of intelligent driving functions provided in this application has the following advantages: It enables real-time business monitoring. By introducing a streaming computing engine, the latency of metric calculation has been reduced from hours to seconds, enabling business teams to perceive changes in user behavior in near real-time, providing a technical foundation for agile products and operations.

[0044] It provides sophisticated root cause identification capabilities. By constructing a complex multi-dimensional analysis system, the system can quickly pinpoint the micro-level causes of a macro-level business problem, achieving a technological leap from "discovering the problem" to "locating the problem".

[0045] It lowers the barrier to data consumption and decision-making, transforming complex data into intuitive business insights through highly interactive and intuitive visualization designs (such as geographic heatmaps and drill-down links), enabling decision-makers without technical backgrounds to quickly understand the business landscape and drive a data-driven decision-making culture.

[0046] A proactive risk prevention and control system has been built. Through a flexibly configurable intelligent early warning engine, the system transforms from passive to proactive, and can automatically monitor business health 24 / 7. It provides timely warnings before potential problems cause significant impact, effectively reducing business risks.

[0047] Figure 3 This is a structural block diagram of a monitoring system for the activation rate of intelligent driving functions according to an embodiment of this application. Figure 3 As shown, the intelligent driving function activation rate monitoring system according to an embodiment of this application includes: an acquisition module 310, a processing module 320, an analysis module 330, and a display module 340, wherein: The acquisition module 310 is used to acquire the intelligent driving function activation event stream reported by the vehicle that is related to the activation of the intelligent driving function; The processing module 320 is used to obtain an activation rate indicator set based on the intelligent driving function activation event stream, wherein the activation rate indicator set includes multiple activation rate indicators. Analysis module 330 is used to aggregate the activation rate indicator set according to a preset dimension system to obtain multi-dimensional activation rate monitoring data. The display module 340 is used to visualize the monitoring data of the activation rate from multiple dimensions.

[0048] The intelligent driving function activation rate monitoring system according to the embodiments of this application receives activation event streams reported by vehicles, performs real-time indicator calculations on the event streams, and then aggregates the indicators in multiple dimensions such as space, time, and vehicle attributes. Finally, it provides multi-dimensional display, such as providing an interactive dashboard that includes a geographic heat map, a multi-dimensional drill-down analyzer, and an early warning center. This solves the problems of data lag, single analysis dimensions, and low visualization in traditional monitoring methods, and realizes real-time, precise, intuitive, and intelligent monitoring of intelligent driving function activation, providing strong data support for product optimization and operational decision-making.

[0049] Specific limitations regarding the monitoring system for the activation rate of intelligent driving functions can be found in the limitations of the monitoring method for the activation rate of intelligent driving functions described above, and will not be repeated here. Each module of the aforementioned monitoring system for the activation rate of intelligent driving functions can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0050] In one embodiment, a computer device is provided. Figure 4 This is a structural block diagram of the computer device provided in the embodiments of this application, with reference to... Figure 4 The computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned embodiment of the intelligent driving function activation rate monitoring method. For example, it executes: obtaining the intelligent driving function activation event stream reported by the vehicle related to intelligent driving function activation; Based on the intelligent driving function activation event stream, an activation rate indicator set is obtained, wherein the activation rate indicator set includes multiple activation rate indicators; The activation rate indicator set is aggregated according to a preset dimensional system to obtain multi-dimensional activation rate monitoring data. The monitoring data of the activation rate is visualized from multiple dimensions.

[0051] This application also provides a computer-readable storage medium storing a computer program. When the processor executes the computer program, it implements the aforementioned embodiment of the method for monitoring the activation rate of intelligent driving functions. For example, it executes: obtaining intelligent driving function activation event streams reported by vehicles related to intelligent driving function activation; Based on the intelligent driving function activation event stream, an activation rate indicator set is obtained, wherein the activation rate indicator set includes multiple activation rate indicators; The activation rate indicator set is aggregated according to a preset dimensional system to obtain multi-dimensional activation rate monitoring data. The monitoring data of the activation rate is visualized from multiple dimensions.

[0052] This application provides a computer program product including instructions that, when executed, cause the method described in this application embodiment to be performed. For example, it can execute... Figure 1 The steps of the method for monitoring the activation rate of intelligent driving functions shown include, for example, obtaining the intelligent driving function activation event stream reported by the vehicle related to the activation of intelligent driving functions; Based on the intelligent driving function activation event stream, an activation rate indicator set is obtained, wherein the activation rate indicator set includes multiple activation rate indicators; The activation rate indicator set is aggregated according to a preset dimensional system to obtain multi-dimensional activation rate monitoring data. The monitoring data of the activation rate is visualized from multiple dimensions.

[0053] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0054] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0055] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for monitoring the activation rate of intelligent driving functions, characterized in that, include: Obtain the intelligent driving function activation event stream reported by the vehicle related to the activation of intelligent driving functions; Based on the intelligent driving function activation event stream, an activation rate indicator set is obtained, wherein the activation rate indicator set includes multiple activation rate indicators; The activation rate indicator set is aggregated according to a preset dimensional system to obtain multi-dimensional activation rate monitoring data. The monitoring data of the activation rate is visualized from multiple dimensions.

2. The method for monitoring the activation rate of intelligent driving functions according to claim 1, characterized in that, The intelligent driving function activation event stream includes at least the anonymized hash ID of the vehicle's VIN code, the function identifier, the event type, the event timestamp, the GPS latitude and longitude, the vehicle model code, and the software version number.

3. The method for monitoring the activation rate of intelligent driving functions according to claim 1 or 2, characterized in that, The multiple activation rate metrics include at least the global and functional real-time activation rate, activation failure rate, reasons for activation failure, distribution of reasons for exiting the function, and the activation rate of new users in the first week. The real-time activation rate includes the activation rate based on available time and the activation rate based on available mileage.

4. The method for monitoring the activation rate of intelligent driving functions according to claim 1, characterized in that, The dimensional system includes spatial, temporal, vehicle attribute, and user behavior dimensions. The activation rate indicator set is aggregated according to the preset dimensional system to obtain multi-dimensional activation rate monitoring data, including: The activation rate metric set is aggregated according to the spatial dimension, time dimension, vehicle attribute dimension, and user behavior dimension respectively to obtain activation rate monitoring data corresponding to the spatial dimension, time dimension, vehicle attribute dimension, and user behavior dimension.

5. The method for monitoring the activation rate of intelligent driving functions according to claim 1, characterized in that, The visualization of the activation rate monitoring data from multiple dimensions includes: The monitoring data of the activation rate is visualized from multiple dimensions through multiple view components. The view components include real-time indicator cards, spatiotemporal heat maps, multi-dimensional drill-down analyzers, and early warning notification centers. The early warning notification centers have built-in early warning engines, which are pre-configured with rule-based or machine learning-based anomaly detection models to capture abnormal indicators and notify users through the early warning notification centers.

6. The method for monitoring the activation rate of intelligent driving functions according to claim 5, characterized in that, The rules include: threshold rules, year-on-year / month-on-month anomaly rules, and statistical process control rules, wherein: The threshold rule refers to the rule that is triggered when the activation rate under a specified dimension decreases by more than an absolute threshold or a relative percentage within two consecutive time windows. The year-on-year / month-on-month anomaly rule refers to the rule that the current value is compared with the value of the same period in history or the previous period, and is triggered when the deviation exceeds the set range. Statistical process control rules refer to identifying the causes of variation that exceed control limits based on the historical mean and standard deviation of indicators.

7. The method for monitoring the activation rate of intelligent driving functions according to claim 1, characterized in that, Before obtaining the activation rate index based on the intelligent driving function activation event stream, the process further includes filtering and standardizing the intelligent driving function activation event stream.

8. A monitoring system for the activation rate of intelligent driving functions, characterized in that, include: The acquisition module is used to obtain the intelligent driving function activation event stream reported by the vehicle that is related to the activation of the intelligent driving function; The processing module is used to obtain an activation rate indicator set based on the intelligent driving function activation event stream, wherein the activation rate indicator set includes multiple activation rate indicators. The analysis module is used to aggregate the activation rate indicator set according to a preset dimensional system to obtain multi-dimensional activation rate monitoring data. The display module is used to visualize the monitoring data of the activation rate from multiple dimensions.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for monitoring the activation rate of intelligent driving functions according to any one of claims 1-7.

10. A computationally readable storage medium, comprising a memory and a computer program stored on the memory and executable on a processor, characterized in that, When the program is executed by the processor, it implements the method for monitoring the activation rate of intelligent driving functions according to any one of claims 1-7.

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