Data processing method and device, electronic equipment and storage medium

Abstract

This application discloses a data processing method, apparatus, electronic device, and storage medium. The method includes: generating preset rules in a rule pool according to a rule orchestration package; receiving real-time vehicle-to-everything (V2X) intelligent driving data and monitoring the preset rules in the rule pool through a computing engine package; fusing the real-time V2X intelligent driving data and the preset rules in the computing engine package to obtain a vehicle data stream; and distributing the vehicle data stream to a distributed computing unit through the computing engine package to verify whether the real-time V2X intelligent driving data conforms to the corresponding preset rules. This application achieves real-time and efficient rule calculation for massive amounts of V2X data.

Description

Technical Field

[0001] This application relates to the field of vehicle networking big data processing technology, and in particular to a data processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] A rules engine is a component embedded in an application that separates business decisions from application code, allowing business decisions to be written using predefined semantic modules. It accepts data input, interprets business rules, and makes business decisions based on those rules.

[0003] Common rule engines include compiled ones such as DROOLS, and interpreted ones such as QLEXPRESS, GROOVY, and JEXL.

[0004] Compiled rule engines are mostly based on the RETE algorithm, which uses forward reasoning. It is a reasoning method that derives conclusions from conditions. Starting from a set of facts, it uses certain reasoning rules to prove the validity of the target facts or propositions. It is more suitable for business scenarios where there are more rules than facts.

[0005] Interpreted rule engines are mostly based on weakly typed scripting languages, which are more flexible than strongly typed scripting languages. However, with the same amount of computation, they are more suitable for business scenarios where real-time requirements are not high.

[0006] In related technologies, if the aforementioned rule engine is still used for big data processing in the Internet of Vehicles, it cannot meet the real-time requirements of data processing, nor can it match data processing scenarios with massive amounts of data exceeding the number of rules. Summary of the Invention

[0007] This application provides data processing methods, devices, electronic devices, and storage media to improve the efficiency of rule calculation in scenarios involving massive amounts of vehicle network intelligent driving data, while ensuring the timeliness of data processing.

[0008] The embodiments of this application adopt the following technical solutions:

[0009] In a first aspect, embodiments of this application provide a data processing method, wherein the method includes:

[0010] Pre-defined rules are generated in the rule pool based on the rule arrangement package;

[0011] The engine package receives real-time data on intelligent driving in the vehicle network and listens to preset rules in the rule pool.

[0012] The real-time data of intelligent driving in the Internet of Vehicles and the preset rule 5 are fused in the computing engine package to obtain the vehicle data stream;

[0013] The vehicle data stream is distributed to a distributed computing unit via the computing engine package to verify whether the real-time data of the vehicle-to-everything (V2X) intelligent driving conforms to the corresponding preset rules.

[0014] In some embodiments, the method further includes:

[0015] Each preset rule in the rule pool is distributed to a corresponding sub-real-time computing task; according to the sub-real-time computing task, real-time rule matching and judgment calculation is performed according to the various indicators and state conditions described by the preset rule;

[0016] Based on multiple sub-real-time computing tasks, real-time rule computing tasks are performed through the computing engine package.

[0017] In some embodiments, distributing each preset rule in the rule pool to a corresponding sub-real-time computing task includes: 5 distributing the fused vehicle data stream in groups to a sub-real-time computing task corresponding to a downstream distributed computing sub-node through a data distributor.

[0018] In some embodiments, the method further includes:

[0019] The data distributor dynamically adjusts the binding relationship between the preset rules and the real-time data of the vehicle-to-everything (V2X) intelligent driving system.

[0020] 0 In some embodiments, the step of generating preset rules in a rule pool beforehand according to the rule orchestration package includes:

[0021] According to the rule arrangement package, the corresponding rule template is called in the generation interface according to the preset generation mode to generate preset rules in the rule pool. The preset generation mode includes at least one of the following: number mode, order mode, and time period mode.

[0022] 5 and / or,

[0023] The rule orchestration package modifies or deletes the expressions, activation policies, and notification policies of the preset rules.

[0024] In some embodiments, the step of distributing the vehicle data stream to a distributed computing unit via the computing engine package to verify whether the real-time data of the vehicle-to-everything (V2X) intelligent driving conforms to the corresponding standardized processing rules includes:

[0025] In the rule calculator of the computing engine package, the indicators of real-time data of vehicle-to-everything (V2X) intelligent driving are pre-set as keys. At the same time, the operation on the indicators is functionalized and the corresponding function is set as value. The {key, value} pairs are stored in the indicator dictionary table.

[0026] By calling the indicator dictionary table, the corresponding operation for the indicator is obtained, which is used to verify whether the real-time data of the vehicle-to-everything (V2X) intelligent driving conforms to the corresponding standardized processing rules.

[0027] In some embodiments, the step of pre-generating preset rules in the rule pool according to the rule orchestration package further includes:

[0028] The rule validator deployed by the generation interface in the rule orchestration package verifies the legality of the generated preset rules.

[0029] Secondly, embodiments of this application also provide a data processing apparatus, wherein the apparatus includes:

[0030] The rule orchestration module is used to generate preset rules in the rule pool based on the rule orchestration package.

[0031] The computing engine module is used to receive real-time data of intelligent driving in the Internet of Vehicles through the computing engine package and to listen to the preset rules in the rule pool;

[0032] The real-time data of intelligent driving in the vehicle network and the preset rules are fused in the computing engine package to obtain a vehicle data stream;

[0033] The vehicle data stream is distributed to a distributed computing unit via the computing engine package to verify whether the real-time data of the vehicle-to-everything (V2X) intelligent driving conforms to the corresponding preset rules.

[0034] Thirdly, embodiments of this application also provide an electronic device, including: a processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the above-described method.

[0035] Fourthly, embodiments of this application also provide a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform the above-described method.

[0036] The at least one technical solution adopted in this application embodiment can achieve the following beneficial effects: First, preset rules are generated in the rule pool according to the rule orchestration package. Then, the computing engine package receives real-time data of vehicle-to-everything (V2X) intelligent driving and listens to the preset rules in the rule pool. Next, the computing engine package fuses the real-time V2X intelligent driving data and the preset rules to obtain a vehicle data stream. The computing engine package then distributes the vehicle data stream to a distributed computing unit to verify whether the real-time V2X intelligent driving data conforms to the corresponding preset rules. This improves the efficiency of rule calculation for real-time V2X intelligent driving data while ensuring timeliness in the data processing process. Attached Figure Description

[0037] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0038] Figure 1 This is a schematic diagram of the architecture of the rule calculation engine in the data processing method of this application embodiment;

[0039] Figure 2 This is a flowchart illustrating the data processing method in an embodiment of this application;

[0040] Figure 3 This is a schematic diagram of the data processing device in the embodiments of this application;

[0041] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0043] During their research, the inventors discovered that the main flaws in the related technologies were:

[0044] Compiled rule engines based on the RETE algorithm are suitable for business scenarios where rules outweigh facts, such as case hearings (where legal clauses usually far outweigh factual evidence) and insurance purchases (where product terms usually far outweigh information about the policyholder and insured). However, the volume of business rules for connected vehicle intelligent driving is far less than the massive amount of data reported by vehicles. If the method of statically verifying vehicle data one by one is still used, the efficiency will be low.

[0045] While existing interpreted rule engines offer greater flexibility than strongly typed rule engines, they are significantly slower than strongly typed scripting languages ​​for the same computational load. Since vehicle data calculations based on rule pools have extremely high real-time requirements, ensuring stable and real-time computation of massive amounts of vehicle data becomes an unavoidable problem.

[0046] To address the aforementioned issues of efficiency and stability in processing massive vehicle network data, the data processing method in this application embodiment can be used for efficient rule calculation in scenarios with massive vehicle data while ensuring the timeliness of data processing. Specifically, a rule calculation engine and corresponding rule calculation engine processing methods are employed to solve the above-mentioned shortcomings.

[0047] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0048] like Figure 1 The schematic diagram of the rule calculation engine 10 shown includes: a rule orchestration package 11, a calculation engine package 12, and real-time data 13 for intelligent driving in the Internet of Vehicles. The rule orchestration package 11 includes a generation interface 111, rule templates 112, and a rule pool 113. The calculation engine package 12 includes a data ingestor 121, a data distributor 122, and a rule calculator 123.

[0049] It can be understood that the rule arrangement package 11 and the computing engine package 12 refer to a combination package of multiple functional components or modules.

[0050] In the rule orchestration package 11, the various configuration information of the rules is encoded and formatted by calling the generation interface 111; the formatted rule configuration information is used to call the rule template 112 to generate standard rules that can be recognized by the computing engine; and the standard rules are written into the rule pool 113.

[0051] In the computing engine package 12, the data ingestor 121 is used to monitor changes in the rule pool in real time on the one hand, and to receive massive amounts of real-time vehicle data (using the real-time data 13 of vehicle-to-everything intelligent driving as the data source) on the other hand, and to merge the two data links.

[0052] Furthermore, the data distributor 122 is used to group and distribute the vehicle data stream after rule fusion to downstream distributed computing nodes; the rule calculator 123 calls the corresponding algorithm template according to the rule information in the data stream to perform actual rule judgment calculation.

[0053] Unlike the compiled rule engine based on the RETE algorithm used in related technologies, the rule calculation engine in this application embodiment is applicable to scenarios where the amount of real-time vehicle data is much larger than the preset rules in the rule pool. Through the distributed computing engine, the efficiency of the rule calculation engine in processing massive amounts of data can be improved while ensuring the timeliness of data processing.

[0054] To improve data processing efficiency, distributed real-time computing technology for big data is used. To ensure timely data processing, a streaming rule calculation engine is employed. Furthermore, the use of highly abstract rule templates 112 and rule calculators 123 allows for flexible scalability to handle other rule calculation scenarios that may arise later.

[0055] Specifically, for connected vehicle business scenarios, a rule pool is established for various vehicle operating condition indicators, and a rule calculation engine performs real-time matching and judgment of the operating conditions within each rule, providing real-time vehicle safety monitoring capabilities. Simultaneously, flexible rule configuration schemes provide real-time judgment and recognition capabilities for various vehicle operating conditions, offering computational matching support for vehicle development.

[0056] This application provides a data processing method, such as... Figure 2 As shown, a schematic flowchart of a data processing method in an embodiment of this application is provided. The method includes at least the following steps S210 to S240:

[0057] Step S210: Generate preset rules in the rule pool in advance according to the rule arrangement package.

[0058] In the rule calculation engine, preset rules are generated in the rule pool based on the rule orchestration package. These preset rules can be configured, deleted, added, or modified according to actual business needs.

[0059] It is understandable that the rule calculation engine is mainly used for backend services or middleware services, and the generated preset rules are stored in the database for later use.

[0060] Considering the unique nature of real-time data from vehicle-to-everything (V2X) intelligent driving, a large amount of diverse vehicle data and sensor data will be reported over a period of time. For each piece of data, rule calculation and matching need to be performed in the background service to monitor the vehicle's status.

[0061] Step S220: Receive real-time data on intelligent driving via the vehicle network through the computing engine package and listen to the preset rules in the rule pool.

[0062] In addition to the preset rules generated in the rule pool through the rule orchestration package, a separate computing engine package is also needed to receive the real-time data of the vehicle-to-everything (V2X) intelligent driving system.

[0063] It is understood that the real-time data for intelligent driving in the Internet of Vehicles includes, but is not limited to, vehicle-specific data, as well as a large amount of sensor data, which may include visual sensor, pose sensor, and speed sensor data.

[0064] Sensors, including height sensors, pressure sensors, and laser sensors, are used in vehicle-to-everything (V2X) intelligent driving systems. Because the real-time data from these systems is massive and diverse in type, big data distributed real-time computing technology can be used to solve the computational bottleneck problem that may arise while ensuring high availability and real-time performance in the context of massive vehicle data. This can be achieved by horizontally scaling hardware resources (adding physical servers to the backend server).

[0065] In addition to receiving real-time data, the computing engine package also monitors the preset rules in the rule pool. "Monitoring" can be understood as reading existing historical rule data and incrementally changed rule data from the rule pool.

[0066] The listening and receiving process is synchronous and real-time. If the rules are updated, the updated preset rules can be obtained by listening to the changes in the rules in the rule pool.

[0067] Step S230: The real-time data of intelligent driving in the Internet of Vehicles and the preset rules are fused in the computing engine package to obtain a vehicle data stream.

[0068] 5. Based on the monitoring results, the preset rules are fused with the real-time data of the vehicle network intelligent driving to obtain the vehicle data stream.

[0069] The vehicle data stream obtained here is designed to facilitate distributed computing after distribution. Furthermore, the vehicle data stream carries real-time data on intelligent driving in the Internet of Vehicles (IoV) and corresponding preset rules.

[0070] Step S240: The vehicle data stream is distributed to a distributed computing unit through the computing engine package to verify whether the real-time data of the vehicle-to-everything (V2X) intelligent driving conforms to the corresponding preset rules.

[0071] In the rule calculation engine, the vehicle data stream is distributed to the distributed computing unit through the calculation engine package, and the real-time data of the vehicle network intelligent driving is verified in the corresponding distributed computing unit to see if it conforms to the corresponding preset rule.

[0072] Because the rule orchestration package employs highly abstract rule templates and the calculation engine 5 package incorporates a rule calculator, it provides flexibility to handle other rule calculation scenarios that may arise later.

[0073] Lively scalability.

[0074] The above method differs from the interpretive rule engines used in related technologies, which, under the same computational load, cannot guarantee real-time performance during rule calculation. Furthermore, unlike the compiled rule engines based on the RETE algorithm used in related technologies, which are mostly suitable for business scenarios where rules outweigh facts, this method is less efficient when the volume of business rules for connected vehicle intelligent driving is far less than the massive amount of data reported by vehicles. Staticly verifying vehicle data one by one would result in low processing efficiency.

[0075] The above methods, targeting the efficiency of massive data processing, utilize distributed real-time computing technology to improve data processing efficiency. Regarding ensuring the timeliness of massive data processing, a streaming processing rule-based computing engine is used to guarantee data processing timeliness.

[0076] In one embodiment of this application, the method further includes: distributing each preset rule in the rule pool to a corresponding sub-real-time computing task; performing real-time rule matching and judgment calculations according to the sub-real-time computing task and the various indicators and state conditions described by the preset rule; and performing real-time rule calculation tasks based on multiple sub-real-time computing tasks through the computing engine package.

[0077] The computing engine package is primarily responsible for the core real-time rule computation tasks. Furthermore, each rule in the rule pool corresponds to a sub-real-time computation task, which includes real-time rule matching and judgment calculations based on the various indicators and state conditions described in the rule.

[0078] In other words, the data in the data ingestor is distributed by the data distributor in the computing engine package to multiple distributed computing units for processing into sub-real-time computing tasks. After distribution, the distributed computing units perform real-time rule calculation tasks based on multiple sub-real-time computing tasks.

[0079] It is understandable that the rule calculation task mainly includes determining whether there is a match between preset rules and real-time data of vehicle-to-everything (V2X) intelligent driving, or whether the data conforms to preset rules.

[0080] In one embodiment of this application, distributing each preset rule in the rule pool to a corresponding sub-real-time computing task includes: distributing the fused vehicle data stream in groups to a sub-real-time computing task corresponding to a downstream distributed computing sub-node through a data distributor.

[0081] The data distributor in the computing engine package groups and distributes the fused vehicle data stream to a corresponding sub-real-time computing task of the downstream distributed computing sub-node. In other words, the distributed computing task used to process the vehicle data stream completes the computation through multiple sub-real-time computing tasks.

[0082] In one embodiment of this application, the method further includes: dynamically adjusting the binding relationship between the preset rules and the real-time data of the vehicle-to-everything (V2X) intelligent driving through the data distributor.

[0083] By dynamically adjusting the binding relationship between rules and vehicle data through the data distributor, the amount of invalid data transmission and computation is effectively reduced in scenarios involving massive data processing, thereby further saving computing resources.

[0084] For example, the original real-time data from vehicle-to-everything (V2X) intelligent driving contained 10 fields. However, not all fields required rule matching calculations. By adjusting the binding relationship between the two, the original 10 fields could be reduced to 5 fields after binding processing. This reduces invalid data transmission and decreases the computational load per data entry.

[0085] In one embodiment of this application, the step of generating preset rules in the rule pool according to the rule orchestration package includes: according to the rule orchestration package and a preset generation mode, calling the corresponding rule template in the generation interface to generate preset rules in the rule pool, wherein the preset generation mode includes at least one of the following: frequency mode, order mode, time period mode; and / or, modifying or deleting the expression, activation policy, and notification policy of the preset rules according to the rule orchestration package.

[0086] After the corresponding rule module is called in the generation interface according to the preset generation mode, preset rules will be generated in the rule pool. Considering that the real-time data of vehicle-to-everything (V2X) intelligent driving has many types and different verification methods, it is necessary to complete the verification of specified data types in the preset generation mode:

[0087] The count-based mode is based on the number of times a data transmission A is reported within a certain period of time, which determines how many data transmissions A meet the occurrence count criteria.

[0088] Sequence mode, based on order verification, that is, how many transmitted data B that meet the occurrence order conditions are reported within a certain period of time.

[0089] The time-period mode is based on time-based verification, which measures how many qualified data transmissions (C) are reported within a time period.

[0090] Furthermore, the expressions (e.g., A = B * C), activation strategies (e.g., when the rule takes effect), and notification strategies (e.g., whether to notify backend users) of the preset rules can be modified or deleted according to the rule orchestration package.

[0091] In addition, different rule patterns can be extended to call different templates and generate corresponding rules in the rule pool.

[0092] In one embodiment of this application, the step of distributing the vehicle data stream to a distributed computing unit through the computing engine package to verify whether the real-time data of the connected vehicle intelligent driving conforms to the corresponding standardized processing rules includes: pre-setting the indicators of the real-time data of the connected vehicle intelligent driving as keys in the rule calculator of the computing engine package, and functionalizing the operations on the indicators and setting the corresponding functions as values, and storing the {key, value} pairs in an indicator dictionary table; and obtaining the operation corresponding to the indicator by calling the indicator dictionary table to verify whether the real-time data of the connected vehicle intelligent driving conforms to the corresponding standardized processing rules.

[0093] Rule expressions typically consist of one or more vehicle indicators processed through arithmetic, comparison, and logical operations. These vehicle indicators are usually dynamically changing. The industry practice generally involves using Java reflection to retrieve indicator values ​​from vehicle event objects based on the indicator name. This step is typically costly, and the performance overhead can be amplified exponentially with massive amounts of vehicle data, requiring significantly more hardware resources to ensure computational performance. Therefore, we optimize this process by introducing an indicator dictionary table into the rule calculator.

[0094] Specifically, the indicator name is used as the key, the operation of retrieving the indicator value is functionalized, and this function is stored as the value in the indicator dictionary table. When it is necessary to dynamically retrieve the corresponding value based on the indicator name, this table is called, achieving indicator value retrieval with a time complexity of O(1), simplifying the computation and reducing the overhead of computing resources.

[0095] In one embodiment of this application, the step of generating preset rules in the rule pool in advance according to the rule orchestration package further includes: verifying the legality of the generated preset rules according to the rule validator deployed by the generation interface in the rule orchestration package.

[0096] A rule validator can be used in the generation interface 111 to perform test execution on each generated rule locally first. Since the test execution is performed in a local environment, illegal rules are prevented from falling into the rule pool at a lower cost, thus avoiding unnecessary occupation of computing resources.

[0097] This application embodiment also provides a data processing device 300, such as... Figure 3 As shown, a schematic diagram of the data processing device in an embodiment of this application is provided. The data processing device 300 includes at least: a rule arrangement module 310 and a computing engine module 320, wherein:

[0098] In one embodiment of this application, the acquisition module 310 is specifically used to: generate preset rules in the rule pool in advance according to the rule arrangement package.

[0099] In the rule calculation engine, preset rules are generated in the rule pool based on the rule orchestration package. These preset rules can be configured, deleted, added, or modified according to actual business needs.

[0100] It is understandable that the rule calculation engine is mainly used for backend services or middleware services, and the generated preset rules are stored in the database for later use.

[0101] Considering the unique nature of real-time data from vehicle-to-everything (V2X) intelligent driving, a large amount of diverse vehicle data and sensor data will be reported over a period of time. For each piece of data, rule calculation and matching need to be performed in the background service to monitor the vehicle's status.

[0102] In one embodiment of this application, the computing engine module 320 is specifically used to: receive real-time data of intelligent driving in the Internet of Vehicles through the computing engine package and listen to the preset rules in the rule pool.

[0103] In addition to the preset rules generated in the rule pool through the rule orchestration package, a separate computing engine package is also needed to receive the real-time data of the vehicle-to-everything (V2X) intelligent driving system.

[0104] It is understood that the real-time data for connected vehicle intelligent driving includes, but is not limited to, vehicle-specific data, as well as a large amount of sensor data, including visual sensors, pose sensors, speed sensors, height sensors, pressure sensors, laser sensors, and so on. Because the real-time data for connected vehicle intelligent driving involves massive amounts of data and various data types, the use of big data distributed real-time computing technology, through horizontal scaling of hardware resources (adding physical servers to the backend server), solves the computational bottleneck problem that may be encountered while ensuring high availability and real-time performance in the context of massive vehicle data.

[0105] In addition to receiving real-time data, the computing engine package also monitors the preset rules in the rule pool. "Monitoring" can be understood as reading existing historical rule data and incrementally changed rule data from the rule pool.

[0106] The listening and receiving process is synchronous and real-time. If the rules are updated, the updated preset rules can be obtained by listening to the changes in the rules in the rule pool.

[0107] The computing engine package integrates the real-time data of vehicle-to-everything (V2X) intelligent driving with the preset rules to obtain a vehicle data stream.

[0108] The vehicle data stream is obtained by fusing the preset rules with the real-time data of intelligent driving in the Internet of Vehicles based on the monitoring results.

[0109] The vehicle data stream obtained here is designed to facilitate distributed computing after distribution. Furthermore, the vehicle data stream carries real-time data on intelligent driving in the Internet of Vehicles (IoV) and corresponding preset rules.

[0110] The vehicle data stream is distributed to a distributed computing unit via the computing engine package to verify whether the real-time data of the vehicle-to-everything (V2X) intelligent driving conforms to the corresponding preset rules.

[0111] In the rule calculation engine, the vehicle data stream is distributed to the distributed computing unit through the calculation engine package, and the real-time data of the vehicle network intelligent driving is verified in the corresponding distributed computing unit to see if it conforms to the corresponding preset rule.

[0112] Because the rule orchestration package uses highly abstract rule templates and the calculation engine package uses a rule calculator, it provides flexible scalability to cope with other rule calculation scenarios that may arise in the future.

[0113] It is understood that the above-described data processing device can implement each step of the data processing method provided in the foregoing embodiments. The relevant explanations of the data processing method are applicable to the data processing device and will not be repeated here.

[0114] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 4 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0115] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0116] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0117] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, forming a data processing device at the logical level. The processor executes the program stored in memory and specifically performs the following operations:

[0118] Pre-defined rules are generated in the rule pool based on the rule arrangement package;

[0119] The engine package receives real-time data on intelligent driving in the vehicle network and listens to preset rules in the rule pool.

[0120] The real-time data of intelligent driving in the vehicle network and the preset rules are fused in the computing engine package to obtain a vehicle data stream;

[0121] The vehicle data stream is distributed to a distributed computing unit via the computing engine package to verify whether the real-time data of the vehicle-to-everything (V2X) intelligent driving conforms to the corresponding preset rules.

[0122] The above is as stated in this application. Figure 2The methods executed by the data processing apparatus disclosed in the illustrated embodiments can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0123] The electronic device can also perform Figure 2 The method executed by the data processing device, and the implementation of the data processing device in Figure 2 The functions of the embodiments shown are not described in detail here.

[0124] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform... Figure 2 The method executed by the data processing device in the illustrated embodiment is specifically used to perform:

[0125] Pre-defined rules are generated in the rule pool based on the rule arrangement package;

[0126] The engine package receives real-time data on intelligent driving in the vehicle network and listens to preset rules in the rule pool.

[0127] The real-time data of intelligent driving in the vehicle network and the preset rules are fused in the computing engine package to obtain a vehicle data stream;

[0128] The vehicle data stream is distributed to a distributed computing unit via the computing engine package to verify whether the real-time data of the vehicle-to-everything (V2X) intelligent driving conforms to the corresponding preset rules.

[0129] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0130] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0131] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0132] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented processing, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0133] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0134] Memory may include non-permanent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM.

[0135] Memory is an example of a computer-readable medium.

[0136] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic cassettes, and magnetic tape.

[0137] Disk storage or other magnetic storage devices, or any other non-transfer media, can be used to store information that can be accessed by a computer device. As defined herein, computer-readable media does not include transient media, such as modulated data signals and carrier waves.

[0138] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0139] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0140] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A data processing method, wherein, The method includes: Pre-defined rules are generated in the rule pool based on the rule arrangement package; The step of generating preset rules in the rule pool according to the rule orchestration package includes: According to the rule arrangement package, the corresponding rule template is called in the generation interface according to the preset generation mode to generate preset rules in the rule pool. The preset generation mode includes at least the number mode, the order mode, and the time period mode. The rule arrangement package modifies or deletes the expressions, activation policies, and notification policies of the preset rules. The engine package receives real-time data on intelligent driving in the vehicle network and listens to preset rules in the rule pool. The computing engine package distributes vehicle data streams to distributed computing units to verify whether the real-time data of the vehicle-to-everything (V2X) intelligent driving conforms to the corresponding standardized processing rules, including: In the rule calculator of the computing engine package, the indicators of real-time data of vehicle-to-everything (V2X) intelligent driving are pre-set as keys. At the same time, the operation on the indicators is functionalized and the corresponding function is set as value. The {key, value} pairs are stored in the indicator dictionary table. By calling the indicator dictionary table, the operation corresponding to the indicator is obtained, which is used to verify whether the real-time data of the vehicle network intelligent driving conforms to the corresponding standardized processing rules. The real-time data of intelligent driving in the vehicle network and the preset rules are fused in the computing engine package to obtain a vehicle data stream; The vehicle data stream is distributed to a distributed computing unit via the computing engine package to verify whether the real-time data of the vehicle-to-everything (V2X) intelligent driving conforms to the corresponding preset rules.

2. The method as described in claim 1, wherein, The method further includes: Each preset rule in the rule pool is distributed to a corresponding sub-real-time computing task; Based on the sub-real-time computing task, real-time rule matching and judgment calculations are performed according to the various indicators and state conditions described in the preset rules. Based on multiple sub-real-time computing tasks, real-time rule computing tasks are performed through the computing engine package.

3. The method as described in claim 2, wherein, The step of distributing each preset rule in the rule pool to a corresponding sub-real-time computing task includes: The data distributor groups and distributes the fused vehicle data stream to a corresponding sub-real-time computing task of the downstream distributed computing sub-node.

4. The method as described in claim 3, wherein, The method further includes: The data distributor dynamically adjusts the binding relationship between the preset rules and the real-time data of the vehicle-to-everything (V2X) intelligent driving system.

5. The method as described in claim 1, wherein, The step of generating preset rules in the rule pool according to the rule orchestration package also includes: The rule validator deployed by the generation interface in the rule orchestration package verifies the legality of the generated preset rules.

6. A data processing apparatus, wherein, The device includes: The rule orchestration module is used to generate preset rules in the rule pool based on the rule orchestration package; The step of generating preset rules in the rule pool according to the rule orchestration package includes: According to the rule arrangement package, the corresponding rule template is called in the generation interface according to the preset generation mode to generate preset rules in the rule pool. The preset generation mode includes at least the number mode, the order mode, and the time period mode. The rule arrangement package modifies or deletes the expressions, activation policies, and notification policies of the preset rules. The computing engine module is used to receive real-time data of intelligent driving in the Internet of Vehicles through the computing engine package and to listen to the preset rules in the rule pool; The computing engine package distributes vehicle data streams to distributed computing units to verify whether the real-time data of the vehicle-to-everything (V2X) intelligent driving conforms to the corresponding standardized processing rules, including: In the rule calculator of the computing engine package, the indicators of real-time data of vehicle-to-everything (V2X) intelligent driving are pre-set as keys. At the same time, the operation on the indicators is functionalized and the corresponding function is set as value. The {key, value} pairs are stored in the indicator dictionary table. By calling the indicator dictionary table, the operation corresponding to the indicator is obtained, which is used to verify whether the real-time data of the vehicle network intelligent driving conforms to the corresponding standardized processing rules. The real-time data of intelligent driving in the vehicle network and the preset rules are fused in the computing engine package to obtain a vehicle data stream; The vehicle data stream is distributed to a distributed computing unit via the computing engine package to verify whether the real-time data of the vehicle-to-everything (V2X) intelligent driving conforms to the corresponding preset rules.

7. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the method of any one of claims 1 to 4.

8. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the method of any one of claims 1 to 4.

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