Remote upgrading method and system based on vehicle-side fota

Abstract

The application discloses a remote upgrading method and system based on vehicle end FOTA, and the method comprises the following steps: after receiving a remote upgrading instruction, current background data information of a vehicle is acquired; a condition judgment sequence is acquired from a condition pool, and whether the current background data information of the vehicle meets remote upgrading conditions is judged by using the condition judgment sequence, the condition pool is used for storing condition judgment combinations, and the condition judgment combination comprises at least one condition judgment sequence; a remote upgrading instruction is executed based on a judgment result of the condition judgment sequence; and the condition pool is updated based on the judgment result. The application guarantees the adaptability and safety in the process of remote upgrading of the automobile, and avoids the situation that the automobile cannot move and influences road traffic due to upgrading in a special scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicle end remote upgrading, and particularly relates to a remote upgrading method and system based on vehicle end FOTA. BACKGROUND

[0002] With the development of advanced auxiliary driving and the introduction of automatic driving, cars are becoming more and more intelligent, and the problem of updating and upgrading of a large amount of software programs is becoming more and more frequent and serious. When the traditional car software program is upgraded, the real state of the vehicle cannot be obtained, and the vehicle cannot move normally during the upgrading process, which may result in low success rate of vehicle upgrading and affect road traffic.

[0003] Therefore, the prior art still needs further development. SUMMARY

[0004] In view of the above technical problems, the present application provides a remote upgrading method and system based on vehicle end FOTA.

[0005] In a first aspect of the present application, a remote upgrading method based on vehicle end FOTA is provided, comprising:

[0006] After receiving the remote upgrading instruction, the current background data information of the vehicle is acquired;

[0007] The condition judgment sequence is acquired from the condition pool, and the condition judgment sequence is used to judge whether the current background data information of the vehicle meets the remote upgrading condition, the condition pool is used to store the condition judgment combination, and the condition judgment combination includes at least one condition judgment sequence;

[0008] Based on the judgment result of the condition judgment sequence, the remote upgrading instruction is executed;

[0009] The condition pool is updated based on the judgment result.

[0010] Further, the current background data information of the vehicle is acquired, comprising:

[0011] The background data information includes vehicle shadow data, service order information and vehicle login information;

[0012] The vehicle shadow data includes vehicle fault information, vehicle state information and vehicle position information;

[0013] The service order information includes work order processing information, after-sales store information and maintenance state information;

[0014] The vehicle login information includes vehicle user information.

[0015] Further, the remote upgrade instruction is executed based on the result of the condition judgment sequence, and the method comprises the following steps:

[0016] The remote upgrade instruction can be sent by the cloud or selected by the mobile terminal.

[0017] The condition pool can be set in the cloud.

[0018] Further, the current background data information of the vehicle is obtained, and the method comprises the following steps:

[0019] The background data scene information in different scenes of the vehicle is obtained.

[0020] The corresponding background data scene information mounting weight is determined based on the background data scene information.

[0021] The condition judgment combination is obtained based on the background data scene information and the background data scene information mounting weight.

[0022] Further, the background data scene information in different scenes of the vehicle is obtained, and the method comprises the following steps:

[0023] The background data scene information is represented as the background data information within the preset scene threshold range.

[0024] Further, the condition pool is updated based on the result of the judgment, and the method comprises the following steps:

[0025] The information gain is recalculated based on the result of the judgment.

[0026] The background data scene information mounting weight is updated based on the information gain, and the updated condition judgment combination is obtained.

[0027] Further, the information gain is recalculated based on the result of the judgment, and the method comprises the following steps:

[0028] The information gain is recalculated based on the result of the judgment by using the decision tree algorithm.

[0029] The background data scene information mounting weight is updated according to the information gain.

[0030] Further, the method further comprises the following steps:

[0031] If the result of the judgment is normal, the remote upgrade instruction is executed.

[0032] If the result of the judgment indicates to suspend the remote upgrade, the suspend remote upgrade instruction is executed.

[0033] If the result of the judgment indicates to stop the remote upgrade, the reject remote upgrade instruction is executed.

[0034] The second aspect of the application provides a remote upgrading system based on vehicle end FOTA, comprising:

[0035] A triggering module is configured to trigger acquisition of current background data information of the vehicle after receiving the remote upgrading instruction;

[0036] A processing module is configured to acquire a condition judgment sequence from a condition pool, and judge whether the current background data information of the vehicle meets the remote upgrading condition by using the condition judgment sequence, wherein the condition pool is configured to store a condition judgment combination, and the condition judgment combination comprises at least one condition judgment sequence;

[0037] An upgrading module is configured to execute the remote upgrading instruction based on a judgment result of the condition judgment sequence;

[0038] An updating module is configured to update the condition pool based on the judgment result.

[0039] The third aspect of the application provides a computing-based device, comprising:

[0040] A processor; and

[0041] A memory arranged to store computer executable instructions, which, when executed, cause the processor to:

[0042] Trigger acquisition of current background data information of the vehicle after receiving a remote upgrading instruction;

[0043] Acquire a condition judgment sequence from a condition pool, and judge whether the current background data information of the vehicle meets the remote upgrading condition by using the condition judgment sequence, wherein the condition pool is configured to store a condition judgment combination, and the condition judgment combination comprises at least one condition judgment sequence;

[0044] Execute the remote upgrading instruction based on a judgment result of the condition judgment sequence;

[0045] Update the condition pool based on the judgment result.

[0046] The fourth aspect of the application provides a computer readable storage medium having computer executable instructions stored thereon, wherein the computer executable instructions, when executed by a processor, cause the processor to:

[0047] Trigger acquisition of current background data information of the vehicle after receiving a remote upgrading instruction;

[0048] Acquire a condition judgment sequence from a condition pool, and judge whether the current background data information of the vehicle meets the remote upgrading condition by using the condition judgment sequence, wherein the condition pool is configured to store a condition judgment combination, and the condition judgment combination comprises at least one condition judgment sequence;

[0049] Execute the remote upgrade instruction based on the judgment result of the condition judgment sequence;

[0050] Update the condition pool based on the judgment result.

[0051] In the technical solution provided by the application, a condition pool for storing a condition judgment sequence is constructed, whether the vehicle meets the remote upgrade condition is judged by using the condition judgment sequence, and the remote upgrade instruction is executed based on the judgment result of the condition judgment sequence, so that the adaptability and safety in the process of remote upgrade of the automobile are ensured, and the situation that the automobile cannot move and affects road traffic due to upgrade in a special scene is avoided.

[0052] And according to the judgment result, the decision tree algorithm is introduced, the information gain is calculated, the optimal split condition is automatically selected, the overhead and calculation time can be greatly shortened, and the efficiency and accuracy of the judgment result are improved. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 It is a flowchart of a remote upgrade method based on vehicle-side FOTA in an embodiment of the application;

[0054] Figure 2 It is another flowchart of a remote upgrade method based on vehicle-side FOTA in an embodiment of the application;

[0055] Figure 3 It is another flowchart of a remote upgrade method based on vehicle-side FOTA in an embodiment of the application;

[0056] Figure 4 It is a schematic diagram of updating the condition pool weight ranking by using the decision tree algorithm in an embodiment of the application;

[0057] Figure 5 It is a module diagram of a remote upgrade system based on vehicle-side FOTA in an embodiment of the application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0059] With the development of science and technology and the vigorous development of the intelligent automobile industry, the application functions of automobiles are more and more, and the iteration and upgrade of automobile performance are faster and faster. The wireless transmission of updated software to the automobile and automatic completion of the update effectively solve the disadvantages of difficult and slow upgrade of traditional automobiles.

[0060] With the deepening of the demand for software in the automotive industry, in order to change the disadvantages brought by the existing remote upgrade method, make the software upgrade rapid and accurate, and the remote upgrade software technology OTA (Over the Air) is used and paid more attention to, wherein, OTA refers to the technology for remotely managing firmware, data and applications on the terminal of the parts of the vehicle through the mobile communication network (2G / 3G / 4G / 5G or Wifi), and the OTA technology is realized in three steps: first, upload the updated software to the OTA center, then the OTA center wirelessly transmits the updated software to the vehicle end, and finally the vehicle end automatically updates the software.

[0061] Among them, the technology used in the present application is FOTA (Firmware Over the Air), which means that under the premise of not changing the original parts of the vehicle, by writing new firmware program, the device with networking function is upgraded, including the control system of the engine, motor, gearbox, chassis, etc. of the vehicle, such as adding automatic driving function of the vehicle, increasing battery capacity and improving brake distance through FOTA.

[0062] Please refer to Figure 1 , Figure 1 is a flowchart of a remote upgrade method based on vehicle end FOTA in an embodiment of the present application, and the remote upgrade method based on vehicle end FOTA comprises the following steps:

[0063] Step S1000: After receiving the remote upgrade instruction, trigger to obtain the current background data information of the vehicle.

[0064] After the vehicle dealer operates batch release of remote upgrade task, the upgrade task includes whole vehicle control system upgrade or local function upgrade of the vehicle. The vehicle end receives the remote upgrade instruction in the form of mobile terminal such as mobile phone APP, or automatically sent by the cloud.

[0065] The user can choose automatic triggering or manual triggering mode. The manual triggering mode can select to click the upgrade instruction button on the mobile phone APP end or the vehicle display screen, and the automatic triggering mode can send the remote upgrade instruction from the mobile terminal to the vehicle end or from the cloud to the vehicle end. At this time, the sent upgrade instruction can be reflected in the display pop-up window of the mobile terminal and the vehicle end.

[0066] Specifically, when the vehicle end receives the remote upgrade instruction, the current background data information of the vehicle is triggered to be acquired. At this time, the triggering mode can be based on the vehicle communication transmission to the control unit of the vehicle end. The control unit of the vehicle end acquires the current background data information of the vehicle. The background data information shows the current vehicle state in a quantitative form, such as the vehicle being in a waiting red light state, the vehicle being in a high-speed driving state, etc. Through this way, the remote upgrade instruction and the actual state of the vehicle can be associated, and the adaptability in the transmission of the remote upgrade instruction is effectively guaranteed.

[0067] Step S2000: acquiring a condition judgment sequence from a condition pool, judging whether the current background data information of the vehicle meets the remote upgrade condition by using the condition judgment sequence, the condition pool being used to store a condition judgment combination, the condition judgment combination including at least one set of condition judgment sequences.

[0068] When the current background data information of the vehicle is acquired, the condition judgment sequence can judge whether the current background data information of the vehicle meets the remote upgrade condition. Here, it can also be said that the condition pool is used to store a condition judgment combination, and the condition judgment combination includes at least one set of condition judgment sequences.

[0069] It should be noted that the condition pool also stores a lot of background data information of the vehicle in different states. The background data information is calculated to obtain different sequences, and the set of condition judgment sequences is a condition judgment combination. For example, when receiving a remote upgrade instruction, the current background data information of the vehicle is triggered to be acquired, and whether the current background data information of the vehicle meets the remote upgrade condition is determined by the judgment result of the condition judgment sequence.

[0070] Step S3000: executing the remote upgrade instruction based on the judgment result of the condition judgment sequence.

[0071] The current background data information of the vehicle represents the state of the vehicle when it needs to be upgraded. The condition judgment sequence in the condition pool is used to judge the current background data information of the vehicle, which can qualitatively divide the state of the vehicle when it needs to be upgraded, and can avoid the downtime phenomenon caused by the remote upgrade instruction of the vehicle being started but not allowed due to the state of the vehicle, or the occurrence of dangerous events such as traffic accidents caused by forcibly remote upgrading due to the state of the vehicle not being allowed.

[0072] Step S4000: updating the condition pool based on the judgment result.

[0073] Specifically, in order to ensure the accuracy of the background data information of the vehicle and the stability of the determination result, the determination result is updated each time, for example, the information gain is recalculated by the decision tree algorithm, the weight ranking of the condition pool is updated, and the weight ranking is associated with the actual remote upgrade state of the vehicle.

[0074] Here, by using the determination result, introducing the decision tree algorithm, regularly updating the weight ranking, the accuracy of the condition judgment order in the condition pool can be improved, and the efficiency of vehicle data processing can be ensured, the calculation time is reduced, the weight priority is automatically adjusted, the flexibility of vehicle remote upgrade is improved, and the condition state demand of vehicle update is adapted.

[0075] For example, because there are many condition combinations in the condition pool, the condition judgment order can be filtered out, and some special places such as the roadside of the car and the charging station can be selected not to be executed or delayed execution, for example, when the car is parked on the roadside, it can be directly informed that the precondition of the car machine is not met and the upgrade cannot be completed, otherwise, if the condition is met and other conditions are also met, the remote upgrade instruction can be executed.

[0076] In an embodiment, the current background data information of the vehicle is obtained, including:

[0077] The background data information includes vehicle shadow data, service order information, and vehicle login information.

[0078] The vehicle shadow data includes vehicle fault information, vehicle state information, and vehicle location information.

[0079] The service order information includes work order processing information, after-sales store information, and maintenance state information.

[0080] The vehicle login information includes vehicle user information.

[0081] The condition judgment order can be customized by the user or can be configured according to the system recommendation, and the weight is generated, for example, the user can select the geographical location based on the vehicle location information in the vehicle shadow data, when the vehicle is located in Shanghai, the weight is 0.9, and when the vehicle is located in Beijing, the weight is 0.2; the user can select the maintenance state information based on the vehicle condition in the service order information, when the vehicle is in the maintenance state, the vehicle weight is 1, and the vehicle remote upgrade can be suspended or refused, when the vehicle is in the maintenance completion state or the state without maintenance, the vehicle weight is much smaller than 1, such as 0.1; the user can view the current user information based on the vehicle user information in the vehicle login information, and can define the weight based on the user login state and user login information, such as setting a sliding box or a sliding virtual knob to adjust the weight.

[0082] Further, the weight design can be based on external environment information, such as setting the weight based on weather information, with the highest weight value in sunny weather, and setting the weight value as 1 in heavy rain, heavy snow, thunder and lightning, and heavy fog, so as to suspend or reject the remote upgrade of the vehicle; the weight design can be based on user emotional information or physiological information, which can have a veto power to suspend or reject the remote upgrade of the vehicle.

[0083] Here, the background data information of the vehicle and the external information or personal emotional information of the user that can affect the upgrade of the vehicle can be used as the weight index of whether to execute the remote upgrade instruction of the vehicle, based on which, the customized configuration of the remote upgrade of the vehicle is realized, which is beneficial to improve the happiness and participation of the user, and also effectively avoids the adverse consequences of the remote upgrade of the vehicle in adverse environment.

[0084] In an embodiment, the remote upgrade instruction is executed based on the judgment result of the condition judgment sequence, including:

[0085] The remote upgrade instruction can be actively sent by the cloud or selected to be sent by the mobile terminal.

[0086] The condition pool can be set in the cloud.

[0087] The first mode of the remote upgrade instruction is to actively send by the cloud, and the second mode is to select to send by the user in the mobile terminal. The above two modes can be switched based on the user's selection, which is the result of considering humanization and high efficiency. For example, the user's selection right is placed in the mobile terminal, and the user can select automatic upgrade or upgrade reminder in the APP, so that the upgrade is more humanized.

[0088] The main reason for setting the condition pool in the cloud includes that the cloud condition is flexible to configure and has strong expansion ability and high flexibility. For weight adjustment, the adjustment space is wide. Based on the increase and iteration of background data information or other information, the memory space requirement is relatively large, while the cloud can ensure the memory space and be flexible to configure. If the condition pool is opened, it is also beneficial to other users to learn or operate statistical data and avoid risks.

[0089] In an embodiment, referring to Figure 2 The condition judgment sequence is obtained from the condition pool, including:

[0090] Step S2100: Obtain the background data scene information of the vehicle in different scenes.

[0091] Specifically, acquiring background data information for vehicles in different scenarios includes acquiring vehicle shadow data, service order information, and vehicle login information. The vehicle shadow data includes vehicle fault information, vehicle status information, and vehicle location information. The service order information includes work order processing information, after-sales store information, and repair status information. The vehicle login information includes vehicle user information. In addition, external environmental information such as weather information and personal emotional information can also be combined. The maximum dimension of vehicle information included in different scenarios, i.e., the maximum scope that remote vehicle upgrades may cover, is primarily aimed at constructing a condition judgment order in the condition pool.

[0092] The condition pool contains a lot of background data. As the intelligence of automobiles is upgraded and improved, the quantity and types of data will also increase. For example, geolocation information is one type of information that participates in and constructs the order of condition judgments. This includes whether the vehicle is located in a residential area, whether the road is open, and whether the road segment is marked. This can improve the security level that the vehicle faces when remotely upgrading. Account information may contain multiple user information and is also a key factor to consider. In addition, there are the vehicle's charging status and battery status. When the vehicle is at a charging station, the judgment result can be to postpone the remote upgrade. Similarly, when the vehicle's battery status is detected to be low on power or there is a potential risk of power failure, the judgment result can be to postpone the remote upgrade.

[0093] Step S2200: Determine the corresponding background data scenario information mounting weight based on the background data scenario information.

[0094] Once the background data scenario information is obtained, the corresponding background data scenario information attachment weight is determined based on each piece of background data information. Specifically, this includes attaching an initial weight value to the background data information in the condition pool, and obtaining a condition judgment combination based on the background data scenario information and the background data scenario information attachment weight.

[0095] Step S2300: Obtain a condition judgment combination based on the background data scenario information and the weight attached to the background data scenario information.

[0096] Here, in the remote vehicle upgrade, configuration can be based on the actual situation. For example, when the vehicle is parked on the side of the road, at a charging station, or when the vehicle's power is low, the corresponding vehicle location information - the weight of the roadside and charging station is the highest, which can be the first priority condition judgment order. Since there are safety hazards in remote upgrades at the roadside and charging stations, the judgment result can be to postpone or stop the remote upgrade.

[0097] In one embodiment, executing the remote upgrade command based on the judgment result of the condition judgment order includes:

[0098] If the result of the judgment is normal, execute the remote upgrade instruction;

[0099] If the result of the judgment is to suspend the remote upgrade, execute the remote upgrade instruction.

[0100] If the result of the judgment is to stop the remote upgrade, execute the remote upgrade instruction.

[0101] Here, the result of the judgment based on the condition judgment sequence is divided into three parts to meet the judgment needs of the vehicle in the scene with the remote upgrade task instruction. In a normal, reasonable and safe scene, the result of the judgment is normal, the remote upgrade instruction can be executed, and in some special scenes or unreasonable scenes, the remote upgrade instruction is executed. The special scene and the unreasonable scene can be based on user customization or system determination, such as traffic congestion, network congestion, bad weather, etc. In the case of high-speed driving, abnormal account login, etc. The remote upgrade instruction can be executed to protect the safety of the vehicle and the user and the information security.

[0102] In an embodiment, as shown in Figure 3 The updating of the condition pool based on the result of the judgment includes:

[0103] Step S4100: Recalculating information gain based on the result of the judgment;

[0104] Based on the actual situation, the information gain is calculated based on the result of the judgment. The information gain is an important indicator representing the result of the judgment. The information gain can be defined as how much information a judgment result can bring to the condition judgment combination in the condition pool. The more information brought, the more important the feature, and the greater the corresponding information gain.

[0105] Step S4200: Based on the information gain, update the background data scene information mounting weight to obtain the updated condition judgment combination.

[0106] Here, the information gain can be calculated based on the result of the judgment, the background data scene information mounting weight is updated, the updated condition judgment sequence is obtained, and the updated condition judgment combination is obtained, so that the update of the condition pool is more timely and comprehensive.

[0107] In an embodiment, the recalculating of the information gain based on the result of the judgment includes:

[0108] The information gain is recalculated based on the result of the judgment by using the decision tree algorithm.

[0109] In the embodiment, the decision tree is a tree structure, each non-leaf node of which represents a test on a characteristic attribute, each branch represents an output of the characteristic attribute on a value range, and each leaf node stores a class. The process of decision making using the decision tree is to start from the root node, test the corresponding characteristic attribute in the item to be classified, select the output branch according to the value, and reach the leaf node. The class stored in the leaf node is taken as the decision result, where the information gain can be taken as the selection basis of the root node, represents an output of a judgment result, and the leaf node is a final classification result.

[0110] Here, in the learning process of the decision tree algorithm, the information gain is an important index for decision tree feature selection and attribute judgment, which is defined as how much information a characteristic can bring to the classification system. The more information brought, the more important the characteristic, and the greater the corresponding information gain.

[0111] As shown in Figure 4 The main steps of establishing the decision tree include (taking ID3 as an example):

[0112] Step S4110: A window is established, which is used to input part of the background data information in the condition pool. Since the overall content of the background data information is large, in order to improve the running efficiency, part of the background data information can be selected. The basis for selection can be equal division of the overall data information, and a certain part of the background data information is selected, which can ensure the integrity and comprehensiveness of the establishment of the decision tree, and also can ensure the integrity and stability of the data test in the later period.

[0113] Step S4120: A tree is built, which can be understood as building a decision tree or an improved algorithm thereof; taking the ID3 (Iterative Dichotomiser 3, 3 generation of iterative binary tree) algorithm as an example, step S4121: selecting a characteristic, the maximum information gain is taken as the node selection basis, and the judgment result is taken as the basis of the information gain. For the ID3 algorithm, the entropy principle can be used to determine which root node needs to be split. Wherein, the entropy represents the physical quantity representing the degree of disorder of the system in the macroscopic view of a large number of microscopic particles. The greater the disorder degree of the system, the greater the entropy, and vice versa, which means the better the classification result.

[0114] Step S4122: Dividing the data set, the data set is divided according to the size of the information gain, that is, after obtaining the leaf node, the data set from the root node to the leaf node is indexed out;

[0115] Step S4123: verifying whether the divided data set meets the termination condition, for the judgment of whether the termination condition is met, the entropy value or the Gini value can be calculated to determine whether the entropy value or the Gini value is less than a preset threshold, the size of the entropy and the Gini value can represent the uncertainty degree of the data, when the entropy or the Gini value is too small, it means that the purity of the data is relatively large, that is, if the entropy or the Gini value is less than the set threshold, it means that a good classification effect has been achieved, at this time, the node division can be stopped;

[0116] Step S4124: if yes, the tree building is completed, and if no, returning to step 4120.

[0117] Step S4130: testing other data outside the window, that is, the remaining background data information in the test condition pool, the background data information is traversed into the decision tree model to serve as a test;

[0118] Step S4140: verifying whether there is an error classification, the background data information is divided into two parts, the data resources can be fully utilized and are not repeatedly used, and the establishment of the decision tree and the test accuracy can be improved;

[0119] Step S4150: if there is no error, generating a decision tree, and if there is an error, expanding the window, that is, returning to step S4120, the data with an error classification and part of the background data information in the initial selected area are input into step S4120 again for training.

[0120] Here, by introducing the decision tree algorithm, the optimal split condition is automatically selected by calculating the information gain, the overhead and the calculation time can be greatly shortened, the result of the introduced decision tree algorithm is updated to the condition pool, the background data information is re-ranked by weight, and the accuracy and stability of the condition judgment order are improved.

[0121] As shown in FIG. Figure 5 The second aspect of the present application provides a remote upgrade system based on vehicle-side FOTA, which comprises:

[0122] A triggering module 51 is configured to trigger acquisition of current background data information of a vehicle after receiving a remote upgrade instruction;

[0123] A processing module 52 is configured to acquire a condition judgment order from a condition pool, and determine whether the current background data information of the vehicle meets a remote upgrade condition by using the condition judgment order, wherein the condition pool is configured to store a condition judgment combination, and the condition judgment combination comprises at least one condition judgment order.

[0124] An upgrade module 53 is configured to execute a remote upgrade instruction based on a judgment result of the condition judgment order.

[0125] An updating module 54 is configured to update the condition pool based on the judgment result.

[0126] Through the above module, the upgrade success rate of the vehicle end in the remote upgrade process can be improved, and the traffic and other normal behaviors of the vehicle end itself will not be affected.

[0127] In a third aspect, the present application provides a computing-based device, comprising:

[0128] a processor; and

[0129] a memory arranged to store computer-executable instructions that, when executed, cause the processor to:

[0130] after receiving the remote upgrade instruction, trigger acquisition of current background data information of the vehicle;

[0131] acquire a condition judgment sequence from a condition pool, and use the condition judgment sequence to judge whether the current background data information of the vehicle meets the remote upgrade condition, the condition pool being used to store a condition judgment combination, the condition judgment combination including at least one condition judgment sequence;

[0132] based on a judgment result of the condition judgment sequence, execute the remote upgrade instruction;

[0133] based on the judgment result, update the condition pool.

[0134] In a third aspect, the present application provides a computer-readable storage medium having stored thereon computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, cause the processor to:

[0135] after receiving the remote upgrade instruction, trigger acquisition of current background data information of the vehicle;

[0136] acquire a condition judgment sequence from a condition pool, and use the condition judgment sequence to judge whether the current background data information of the vehicle meets the remote upgrade condition, the condition pool being used to store a condition judgment combination, the condition judgment combination including at least one condition judgment sequence;

[0137] based on a judgment result of the condition judgment sequence, execute the remote upgrade instruction;

[0138] based on the judgment result, update the condition pool.

[0139] The specific implementation process can refer to the contents of the embodiments shown in the accompanying drawings, which will not be described here.

[0140] Those skilled in the art can appreciate that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been described in the above description in general terms. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0141] The present application also provides a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program is executed by a processor to realize the steps of the method of any one of the above embodiments.

[0142] It can be understood that the computer readable storage medium can include any entity or device capable of carrying the computer program, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory) and software distribution medium, etc. The computer program includes computer program code. The computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer readable storage medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory) and software distribution medium, etc.

[0143] In some embodiments of the present application, the vehicle-side FOTA-based remote upgrading system can include a controller, which is a single-chip microcontroller integrating a processor, a memory, a communication module, etc. The processor can refer to the processor included in the controller. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.

[0144] Any procedural or methodological descriptions in flow charts or otherwise described herein can be understood as representing modules, segments or portions of code that include one or more executable instructions for implementing the specified logical function or process and that the scope of the preferred embodiments of the present application encompasses additional implementations that can not be in the order shown or discussed, including performing functions in substantially simultaneous fashion or in reverse order according to the involved functionality, as will be understood by those skilled in the art to which embodiments of the present application pertain.

[0145] The logic or steps represented in flow charts or otherwise described herein, for example, can be considered as a list of executable instructions for implementing the logic function, which can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or a combination of these.

[0146] The above embodiments are only used to illustrate the technical solutions of the present application, not limit it; although the above-mentioned embodiments of the present application are described in detail, those skilled in the art should understand: it can still modify the technical solutions recorded in the above-mentioned embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A remote upgrade method based on vehicle-side FOTA, characterized in that, include: Upon receiving the remote upgrade command, the system triggers the acquisition of the vehicle's current background data. The condition judgment order is obtained from the condition pool, and the condition judgment order is used to determine whether the current background data information of the vehicle meets the conditions for remote upgrade. The condition pool is used to store condition judgment combinations, and the condition judgment combination includes at least one condition judgment order. The condition judgment order is customized by the user or configured according to the system recommendation, and weights are generated. Based on the judgment result of the aforementioned condition judgment order, execute the remote upgrade command; The condition pool is updated based on the judgment result; wherein: The acquisition of the vehicle's current background data information includes: Obtain background data scenario information for vehicles in different scenarios; Determine the corresponding background data scenario information mounting weight based on the background data scenario information. Based on the background data scenario information and the weights attached to the background data scenario information, a combination of conditional judgments is obtained. Updating the condition pool based on the judgment result includes: The information gain is recalculated based on the decision tree algorithm using the judgment results. Based on the information gain, update the background data scenario information mounting weight and obtain the updated condition judgment combination.

2. The remote upgrade method according to claim 1, characterized in that, The acquisition of the vehicle's current background data information includes: The background data includes vehicle shadow data, service order information, and vehicle login information. The vehicle shadow data includes: vehicle fault information, vehicle status information, and vehicle location information; The service order information includes: work order processing information, after-sales store information, and repair status information; The vehicle login information includes: vehicle user information.

3. The remote upgrade method according to claim 1, wherein the remote upgrade instruction is executed based on the judgment result of the condition judgment order, characterized in that, include: The remote upgrade command is sent proactively by the cloud or selectively by the mobile terminal. The condition pool is set up in the cloud.

4. The remote upgrade method according to claim 1, characterized in that, The execution of the remote upgrade command based on the judgment result of the condition judgment order includes: If the judgment result is normal, then execute the remote upgrade command; If the judgment result indicates that the remote upgrade is to be postponed, then the command to postpone the remote upgrade is executed. If the judgment result indicates that remote upgrades should be stopped, then the command to refuse remote upgrades will be executed.

5. A remote upgrade system based on vehicle-side FOTA, characterized in that, include: The trigger module is used to obtain the vehicle's current background data information after receiving a remote upgrade command; The processing module is used to obtain the condition judgment order from the condition pool, and use the condition judgment order to determine whether the current background data information of the vehicle meets the remote upgrade conditions. The condition pool is used to store condition judgment combinations, and the condition judgment combination includes at least one condition judgment order. The condition judgment order is configured by the user or according to the system recommendation, and weights are generated. The upgrade module is used to execute remote upgrade commands based on the judgment results of the judgment order of the conditions. The update module is used to update the condition pool based on the judgment result; wherein: The acquisition of the vehicle's current background data information includes: Obtain background data scenario information for vehicles in different scenarios; Determine the corresponding background data scenario information mounting weight based on the background data scenario information. Based on the background data scenario information and the weights attached to the background data scenario information, a combination of conditional judgments is obtained. Updating the condition pool based on the judgment result includes: The information gain is recalculated based on the decision tree algorithm using the judgment results. Based on the information gain, update the background data scenario information mounting weight and obtain the updated condition judgment combination.

6. A computing-based device, wherein, include: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to: Upon receiving the remote upgrade command, the system triggers the acquisition of the vehicle's current background data. The condition judgment order is obtained from the condition pool, and the condition judgment order is used to determine whether the current background data information of the vehicle meets the conditions for remote upgrade. The condition pool is used to store condition judgment combinations, and the condition judgment combination includes at least one condition judgment order. The condition judgment order is customized by the user or configured according to the system recommendation, and weights are generated. Based on the judgment result of the aforementioned condition judgment order, execute the remote upgrade command; The condition pool is updated based on the judgment result; wherein: The acquisition of the vehicle's current background data information includes: Obtain background data scenario information for vehicles in different scenarios; Determine the corresponding background data scenario information mounting weight based on the background data scenario information. Based on the background data scenario information and the weights attached to the background data scenario information, a combination of conditional judgments is obtained. Updating the condition pool based on the judgment result includes: The information gain is recalculated based on the decision tree algorithm using the judgment results. Based on the information gain, update the background data scenario information mounting weight and obtain the updated condition judgment combination.

7. A computer-readable storage medium having stored thereon computer-executable instructions, wherein, When the computer-executable instruction is executed by the processor, it causes the processor to: Upon receiving the remote upgrade command, the system triggers the acquisition of the vehicle's current background data. The condition judgment order is obtained from the condition pool, and the condition judgment order is used to determine whether the current background data information of the vehicle meets the conditions for remote upgrade. The condition pool is used to store condition judgment combinations, and the condition judgment combination includes at least one condition judgment order. The condition judgment order is customized by the user or configured according to the system recommendation, and weights are generated. Based on the judgment result of the aforementioned condition judgment order, execute the remote upgrade command; The condition pool is updated based on the judgment result; wherein: The acquisition of the vehicle's current background data information includes: Obtain background data scenario information for vehicles in different scenarios; Determine the corresponding background data scenario information mounting weight based on the background data scenario information. Based on the background data scenario information and the weights attached to the background data scenario information, a combination of conditional judgments is obtained. Updating the condition pool based on the judgment result includes: The information gain is recalculated based on the decision tree algorithm using the judgment results. Based on the information gain, update the background data scenario information mounting weight and obtain the updated condition judgment combination.

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