Intelligent OTA upgrading method and device, terminal and storage medium

Abstract

The invention discloses an intelligent OTA upgrading method and device, a terminal and a storage medium in the technical field of remote wireless upgrading, and aims to solve the problems that in the prior art, mode selection is carried out only according to three factors of upgrading package volume, function importance and memory space, and more flexible coping strategies are lacked for some special conditions; and the upgrading success rate is relatively low. The method comprises the steps of obtaining update information and an update type; inputting the update information into a corresponding preset first fuzzy set and a membership function, and determining the membership of the update information in the corresponding first fuzzy set; determining a second fuzzy set of each fuzzy rule activation degree by adopting a Mamdani reasoning method; according to the method, the selection of the upgrading mode and the control of the upgrading process are further optimized by introducing technical means such as vehicle real-time state monitoring and network condition evaluation and using a fuzzy rule strategy, and the method is suitable for different special conditions, has flexibility and improves the success rate of OTA upgrading and the user experience.

Description

Technical Field

[0001] The present invention relates to an intelligent OTA upgrade method, device, terminal and storage medium, belonging to the technical field of remote wireless upgrade. Background Art

[0002] With the wide application of intelligent devices, OTA upgrade technology has become an important means to improve device performance, repair vulnerabilities and add new functions. The existing OTA upgrade technology mainly relies on fixed upgrade strategies, usually using full upgrade or differential upgrade, but these methods have some limitations under different network conditions and device states.

[0003] In most of the existing solutions, a single full upgrade or differential upgrade strategy is adopted, and most of them do not combine the two to intelligently select the upgrade plan according to conditions to improve the upgrade efficiency. Patent document CN117724735A proposes a multi-mode OTA full upgrade method and system, aiming to solve the problems of poor compatibility and low security in the OTA upgrade process of commercial vehicle on-board controllers. The core of this solution is to select different upgrade modes according to the volume of the upgrade package, the functional importance of the control unit to be upgraded, and the hardware storage resources, so as to improve the compatibility and reliability of the upgrade.

[0004] As can be seen from the above, when the existing technology performs OTA upgrade, it only selects the mode according to three factors: the volume of the upgrade package, the functional importance and the memory space. For some special situations (such as poor network conditions, the vehicle is in a special operating state, etc.), there is a lack of more flexible coping strategies, resulting in a low upgrade success rate and affecting the user experience. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies in the existing technology, and provide an intelligent OTA upgrade method, device, terminal and storage medium. By introducing technical means such as vehicle real-time state monitoring and network condition evaluation, the fuzzy rule strategy is used to further optimize the selection of the upgrade mode and the control of the upgrade process, so as to improve the success rate of OTA upgrade and the user experience.

[0006] To solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0007] In the first aspect, the present invention provides an intelligent OTA upgrade method, including the following steps:

[0008] Obtain update information and update type;

[0009] Input the update information into the corresponding pre-set first fuzzy set and membership function to determine the membership degree of the update information in the corresponding first fuzzy set;

[0010] Using the Mamdani inference method, according to the membership degree of the update information in the corresponding first fuzzy set, the update type, and the preset fuzzy rule base, determine the activation degree of each fuzzy rule, and according to the activation degree of each fuzzy rule, determine the second fuzzy set of the activation degree of each fuzzy rule; wherein, the fuzzy rule base includes a plurality of fuzzy rules.

[0011] Defuzzify the second fuzzy set of the activation degree of each fuzzy rule to determine the output value.

[0012] Compare the output value with a preset threshold, and confirm the update type according to the comparison result.

[0013] Further, the update information includes the installation package size, network quality, estimated transmission time, and memory space.

[0014] Further, the activation degree of the fuzzy rule includes the minimum value in the membership degree of the update information in the fuzzy rule.

[0015] Input each update information into the corresponding preset first fuzzy set and membership function to determine the membership degree of each update information in the corresponding first fuzzy set.

[0016] Further, the membership function is a triangular membership function, and the expression of the membership function of the installation package size is as follows:

[0017]

[0018] In the formula: PS is the installation package size; is the membership function of the installation package size; is the preset minimum value of the installation package size; is the preset maximum value of the installation package size.

[0019] Further, the defuzzification specifically includes:

[0020] Use the centroid method for defuzzification, and the specific expression of the centroid method is as follows:

[0021]

[0022] In the formula: is the output value; is the value of the update type, which is preset according to the actual situation; n is the number of rules; i is the rule number; is the activation degree of the i-th rule.

[0023] Further, the comparison of the output value with the preset threshold and the confirmation of the update type according to the comparison result specifically include:

[0024] The update types include differential update and full update;

[0025] If the output value is greater than the preset threshold, confirm that the update type is differential update;

[0026] If the output value is not greater than the preset threshold, confirm that the update type is full update.

[0027] Further, after confirming the update type according to the comparison result, it also includes checking the memory status of the device to be updated;

[0028] The specific steps of checking the memory status of the device to be updated include:

[0029] Step a: Obtain the memory of the device to be updated;

[0030] Step b: Compare the memory of the device to be updated with the size of the installation package. If the memory of the device to be updated is greater than the size of the installation package, the memory is sufficient and the device can be updated normally; if the memory of the device to be updated is not greater than the size of the installation package, the memory is insufficient and step c is executed;

[0031] Step c: Prompt the user to delete the useless files in the device, obtain the memory of the device after deletion, and compare the memory of the device after deletion with the size of the installation package. If the memory of the device after deletion is greater than the size of the installation package, the memory is sufficient and the device can be updated normally;

[0032] Step d: If the memory of the device after deletion is not greater than the size of the installation package, the memory is insufficient. Repeat step c and step d until the maximum number of repetitions is reached, and then the device stops updating.

[0033] In a second aspect, the present invention provides an intelligent OTA upgrade device, including:

[0034] Collection module: Obtain update information and update types;

[0035] First calculation module: Input the update information into the corresponding preset first fuzzy set and membership function to determine the membership degree of the update information in the corresponding first fuzzy set;

[0036] Inference module: Adopt the Mamdani inference method to determine the activation degree of each fuzzy rule according to the membership degree of the update information in the corresponding first fuzzy set, the update type, and the preset fuzzy rule base. According to the activation degree of each fuzzy rule, determine the second fuzzy set of the activation degree of each fuzzy rule; among them, the fuzzy rule base includes multiple fuzzy rules;

[0037] Second calculation module: Defuzzify the second fuzzy set of the activation degree of each fuzzy rule to determine the output value;

[0038] Output module: Compare the output value with a preset threshold, and confirm the update type according to the comparison result.

[0039] In a third aspect, the present invention provides a terminal, including a processor and a storage medium;

[0040] The storage medium is used to store instructions;

[0041] The processor is used to operate according to the instructions to execute the steps of the method according to the first aspect.

[0042] In a fourth aspect, a computer-readable storage medium stores a computer program, and when the program is executed by a processor, the steps of the method according to the first aspect are implemented.

[0043] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0044] The present invention proposes a more flexible and intelligent OTA full-scale upgrade method. By introducing technical means such as vehicle real-time status monitoring and network condition evaluation, the fuzzy rule strategy is used to further optimize the selection of the upgrade mode and the control of the upgrade process, and it is applicable to different special situations, with flexibility, improving the success rate of OTA upgrades and the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flowchart of an intelligent OTA upgrade method provided according to an embodiment of the present invention;

[0046] Figure 2 is a flowchart of checking the memory status of a device to be updated provided according to an embodiment of the present invention;

[0047] Figure 3 is a system diagram of an intelligent OTA upgrade device provided according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The technical solution of the present invention will be described in detail below with reference to the drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0049] The term "and / or" is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " generally represents an "or" relationship between the associated objects before and after.

[0050] Embodiment 1:

[0051] A complete OTA upgrade system generally includes two parts: the cloud side and the device side. The cloud side mainly includes the upgrade package production, policy engine, and file service (CDN) module, while the device side mainly includes the version verification, installation execution, and upgrade result feedback module. The intelligent OTA upgrade method mainly includes parts such as device status monitoring, network condition monitoring, and intelligent selection of upgrade strategies; Device status monitoring: Before the upgrade, the system automatically monitors the device's power, storage space, system status, etc.; For example, monitor whether the device's power is lower than the set threshold), and whether the storage space is sufficient; Network condition monitoring: The system monitors the network bandwidth, signal strength, network speed, etc. in real time; For example, monitor whether the network bandwidth is lower than the set threshold and whether the signal strength is lower than the set threshold; Intelligent selection of upgrade strategies: Dynamically select full - scale upgrade or differential upgrade according to the device status and network conditions.

[0052] As Figure 1 shown, the present invention provides an intelligent OTA upgrade method, which includes the following steps:

[0053] Obtain update information and update type;

[0054] Input the update information into the corresponding pre - set first fuzzy set and membership function to determine the membership degree of the update information in the corresponding first fuzzy set;

[0055] Adopt the Mamdani inference method. According to the membership degree of the update information in the corresponding first fuzzy set, update type, and the pre - set fuzzy rule base, determine the activation degree of each fuzzy rule. According to the activation degree of each fuzzy rule, determine the second fuzzy set of the activation degree of each fuzzy rule; Among them, the fuzzy rule base includes multiple fuzzy rules;

[0056] Defuzzify the second fuzzy set of the activation degree of each fuzzy rule to determine the output value;

[0057] Compare the output value with a preset threshold, and confirm the update type according to the comparison result.

[0058] Specifically, a complete OTA upgrade system generally includes two parts: the cloud side and the device side. The cloud side mainly includes the upgrade package production, policy engine, and file service (CDN) module. The device side mainly includes the version verification, installation execution, and upgrade result feedback module. The intelligent OTA upgrade method mainly includes parts such as device status monitoring, network condition monitoring, and intelligent selection of upgrade strategies. Device status monitoring: Before the upgrade, the system automatically monitors the device's power, storage space, system status, etc. For example, it monitors whether the device's power is lower than the set threshold and whether the storage space is sufficient. Network condition monitoring: The system monitors the network bandwidth, signal strength, network speed, etc. in real time. For example, it monitors whether the network bandwidth is lower than the set threshold and whether the signal strength is lower than the set threshold. Intelligent selection of upgrade strategies: According to the device status and network conditions, it dynamically selects full upgrade or differential upgrade.

[0059] In this embodiment, first, update information and update type are obtained. Specifically, that is, input and output variables are determined. The input variable is the update information, including the installation package size (PackageSize, PS ), network quality (NetworkQuality, NQ ), estimated transmission time (EstimatedTransmissionTime, ETT ), and memory space (MemorySpace, MS ). The output variable is the update type (UpdateType, UT ), and its values are full update (FU) and differential update (DU).

[0060] The update information is input into the corresponding pre-set first fuzzy set and membership function to obtain the membership degree of the update information in the corresponding first fuzzy set. Specifically, for each input and output variable, its fuzzy set and membership function are determined. Among them, the first fuzzy set is a manually defined classification used to describe the fuzzy concepts of input / output. For example, the installation package size is divided into small ( S ), medium ( M ), and large ( L ), the network quality is divided into poor ( P ), average ( F ), and good ( G ), the estimated transmission time is divided into short ( ST ), medium ( MT ), and long ( LT ), and the memory space is divided into sufficient ( A ), average ( B ), and tight ( C), the update types are divided into full update (FU) and differential update (DU). For different input variables, a value range can be set for each case, and membership functions can be designed for different inputs. The membership functions can adopt function forms such as triangles and trapezoids;

[0061] In this embodiment, the membership function is a triangular membership function, and the expression of the membership function of the installation package size is as follows:

[0062]

[0063] In the formula: PS is the installation package size; is the membership function of the installation package size; is the preset minimum value of the installation package size; is the preset maximum value of the installation package size.

[0064] Using the Mamdani inference method, according to the membership degree of the update information in the corresponding first fuzzy set, the update type, and the preset fuzzy rule base, determine the activation degree of each fuzzy rule, and determine the second fuzzy set of the activation degree of each fuzzy rule; among them, the fuzzy rule base includes multiple fuzzy rules; specifically, establish a fuzzy rule base: formulate fuzzy rules according to experience and actual situations, such as "if PS is small and NQ is good and ETT is short and MS is sufficient, then UT is a full update", etc.; perform inference according to the membership degree of the input variable and the first fuzzy rule to obtain the second fuzzy set belonging to the activation degree of different rules (the second fuzzy set consists of multiple values, representing the activation degree of different rules);

[0065] Defuzzify the second fuzzy set of the activation degree of each fuzzy rule to obtain the output value; specifically, convert the second fuzzy set obtained by inference into a specific update type, such as full update or differential update. Commonly used defuzzification methods include the centroid method, the maximum membership degree method, etc.; the centroid method of defuzzification is to convert the fuzzy result obtained by fuzzy inference into an accurate numerical value, and this numerical value will be used as a control signal to be input into the system to perform specific operations;

[0066] The defuzzification specifically includes:

[0067] Perform defuzzification using the centroid method, and the specific expression of the centroid method is as follows:

[0068]

[0069] In the formula: is the output value; The value of the update type is preset according to the actual situation. (The domain of the defined update type can be [0, 1], where a full update is 0 and a differential update is 1); n is the number of rules; i is the rule number; is the activation degree of the i-th rule;

[0070] Compare the output value with a preset threshold, and confirm the update type according to the comparison result; specifically, based on the result of defuzzification, make a fuzzy decision to select the most appropriate update type; for example, compare the value obtained from defuzzification with the preset threshold to select the update strategy; Fuzzy decision-making allows for choices to be made in situations of incomplete or imprecise information, reflecting the complexity of the real world;

[0071] It also includes evaluating the generated update plan and optimizing and adjusting the fuzzy rules, membership functions, etc. according to the actual effect and feedback information.

[0072] An embodiment, the activation degree of the fuzzy rule includes the minimum value in the membership degree of the update information in the fuzzy rule;

[0073] Input each update information into the corresponding preset first fuzzy set and membership function to determine the membership degree of each update information in the corresponding first fuzzy set.

[0074] An embodiment, the comparison of the output value with the preset threshold and the confirmation of the update type according to the comparison result specifically include:

[0075] The update type includes differential update and full update;

[0076] If the output value is greater than the preset threshold, confirm that the update type is a differential update;

[0077] If the output value is not greater than the preset threshold, confirm that the update type is a full update.

[0078] An embodiment, obtain the update information and the update type, that is, confirm the input and output variables:

[0079] Input variables: installation package size, network quality, estimated transmission time, and memory space.

[0080] Output variable: update type, with values of full update and differential update.

[0081] The preset first fuzzy set and membership function are as follows:

[0082] Installation package size ( PS )

[0083] Small ( S )

[0084] Medium ( M ):

[0085] Large ( L ):

[0086] Where: is the membership function of the installation package size; S, M, L are all subscripts;

[0087] Network quality ( NQ ):

[0088] Poor ( P ):

[0089] Average ( F ):

[0090] Good ( G ):

[0091] Where: is the membership function of the network quality; P, F, G are all subscripts;

[0092] Estimated transmission time ( ETT ):

[0093] Short ( ST ):

[0094] Medium ( MT ):

[0095] Long ( LT ):

[0096] Where: is the membership function of the estimated transmission time; ST, MT, LT are all subscripts;

[0097] Memory space ( MS ):

[0098] Adequate ( A ):

[0099] Average ( B ):

[0100] Tense ( C ):

[0101] Where: Membership function for memory space; A, B, C Both are subscripts;

[0102] Update type (UT):

[0103]

[0104] In the formula: Value of the update type;

[0105] The pre-set fuzzy rule base is shown in Table 1:

[0106] Table 1

[0107] Rule Number Installation Package Size () Network Quality () Estimated Transmission Time () Memory Space () Update Type (UT) Rule 1 Small () Good () Short () Adequate () Full Update (FU) Rule 2 Large () Poor () Long () Tense () Differential Update (DU) Rule 3 Medium () Average () Medium () Average () Differential Update (DU) Rule 4 Small () Average () Short () Average () Full Update (FU) Rule 5 Large () Good () Long () Adequate () Full Update (FU)

[0108] For each rule, calculate its activation degree, which is the minimum of the membership degrees of the input variables; for example, for Rule 1, the activation degree is ;

[0109] In the formula: Represents the activation degree of Rule 1.

[0110] Suppose the parameters of a certain intelligent device are as follows:

[0111] (1) The installation package size is 150 MB;

[0112] (2) The network quality is average, and the speed is 3 MB / s;

[0113] (3) The estimated transmission time is 4 minutes;

[0114] (4) The remaining memory space is 30%;

[0115] Input the update information into the corresponding pre-set first fuzzy set and membership function to determine the membership degree of the update information in the corresponding first fuzzy set: According to the above fuzzy rules and scheme design, the fuzzy reasoning process is as follows:

[0116] The membership degree that the installation package size of 150 MB belongs to small ( S ) is , the membership degree that it belongs to medium ( M ) is , and the membership degree that it belongs to large ( L ) is .

[0117] The membership degree that the network quality is average and the speed is 3 MB / s belongs to poor ( P ) is , the membership degree that it belongs to average ( F ) is , and the membership degree that it belongs to good ( G ) is .

[0118] The estimated transmission time is 4 minutes, which belongs to the short ( ST ), and the membership degree is ), which belongs to the medium ( MT ), and the membership degree is ), which belongs to the long ( LT ), and the membership degree is .

[0119] The remaining memory space is 30%, which belongs to the sufficient ( A ), and the membership degree is ), which belongs to the general ( B ), and the membership degree is ), which belongs to the tense ( C ), and the membership degree is .

[0120] According to the membership degree in the corresponding first fuzzy set of the update information, the update type, and the preset fuzzy rule base, determine the activation degree of each fuzzy rule:

[0121] According to Rule 1, the activation degree is .

[0122] According to Rule 2, the activation degree is .

[0123] According to Rule 3, the activation degree is .

[0124] According to Rule 4, the activation degree is .

[0125] According to Rule 5, the activation degree is .

[0126] Then the second fuzzy set of the activation degree of each fuzzy rule is ;

[0127] If the differential update is used as a reference (i.e., when the differential update is , and the full update is ), then the centroid method is used for defuzzification to obtain:

[0128]

[0129] Compare the output value with the preset threshold, and confirm the update type according to the comparison result: If the differential update threshold S' is set to 0.5, and 0.57 > 0.5, then the differential update is finally selected;

[0130] Through the above fuzzy reasoning process, the intelligent device can intelligently select full update or differential update according to the actual situation, improving the success rate of the upgrade and the user experience.

[0131] In this solution, the activation degree indicates the degree to which each fuzzy rule is triggered under the current input conditions, which reflects the matching degree between the input variables and the rule premise conditions. The value range of the activation degree is usually between 0 and 1. The larger the value, the higher the degree of rule activation and the greater the impact on the final output. The result after defuzzification is compared with the set threshold. A higher threshold tends to perform a full update, while a lower threshold is more inclined to perform a differential update. In practical applications, the threshold can be reasonably adjusted according to the device situation and user requirements.

[0132] The process of full upgrade is as follows: After meeting the full update conditions, check the device memory. If the memory is sufficient, update normally. If the device memory is insufficient, prompt the user to delete the useless memory and then continue the update. If the update fails, roll back to the previous program version. The process of differential upgrade: After meeting the differential update conditions, check the device memory. If the memory is sufficient, update normally. If the device memory is insufficient, prompt the user to delete the useless memory and then continue the update. If the update fails, roll back to the previous program version.

[0133] Specifically, as Figure 2 shown, after confirming the update type according to the comparison result, it also includes checking the memory status of the device to be updated;

[0134] The specific steps of checking the memory status of the device to be updated include:

[0135] Step a: Obtain the memory of the device to be updated;

[0136] Step b: Compare the memory of the device to be updated with the size of the installation package. If the memory of the device to be updated is greater than the size of the installation package, the memory is sufficient and the device updates normally. If the update fails, roll back to the old version. If the memory of the device to be updated is not greater than the size of the installation package, the memory is insufficient and go to step c;

[0137] Step c: Prompt the user to delete the useless files in the device, obtain the memory of the device after deletion, and compare the memory of the device after deletion with the size of the installation package. If the memory of the device after deletion is greater than the size of the installation package, the memory is sufficient and the device updates normally. If the update fails, roll back to the old version;

[0138] Step d: If the memory of the device after deletion is not greater than the size of the installation package, the memory is insufficient. Repeat steps c and d until the maximum number of repetitions is reached, and then the device stops updating.

[0139] The present invention has flexibility and can dynamically select an appropriate upgrade method (differential upgrade or full upgrade) according to different device states, network conditions, and update contents;

[0140] Can improve the success rate of upgrading: By dynamically selecting the upgrade strategy, it ensures successful upgrading under different device states and network conditions.

[0141] For example: When the device has sufficient power and good network conditions, full-scale upgrading can be completed quickly; when the device has low power or poor network conditions, differential upgrading can save time and resources and improve the success rate of upgrading.

[0142] Can optimize the user experience: Reduce the device downtime caused by upgrade failures, improve the availability and reliability of the device. Through user interaction and custom selection, give users more control and options, and at the same time provide clear upgrade feedback.

[0143] For example: Users can perform full-scale upgrading under the best conditions, and perform differential upgrading when conditions are limited, avoiding the trouble of repeated attempts due to upgrade failures.

[0144] Can save resources: Differential upgrading reduces the data transmission volume, saving network bandwidth and storage space.

[0145] For example: When the network bandwidth is low or the storage space is insufficient, differential upgrading can effectively utilize limited resources and reduce the burden on the device and network.

[0146] The present invention proposes a more flexible and intelligent OTA full-scale upgrading method. By introducing technical means such as vehicle real-time state monitoring and network condition assessment, the fuzzy rule strategy is used to further optimize the selection of the upgrade mode and the control of the upgrade process, and it is applicable to different special situations, with flexibility, improving the success rate of OTA upgrading and the user experience; at the same time, the present invention can also explore the expansion of more upgrade modes (such as intelligent recommended upgrade modes) to meet the upgrade requirements in different scenarios and further enhance the compatibility and reliability of OTA upgrading.

[0147] Embodiment 2:

[0148] As Figure 3 shown, the present invention provides an intelligent OTA upgrading device, including:

[0149] Acquisition module: Obtain update information and update type;

[0150] First calculation module: Input the update information into the corresponding preset first fuzzy set and membership function to determine the membership degree of the update information in the corresponding first fuzzy set;

[0151] Inference module: Adopt the Mamdani inference method. According to the membership degree of the updated information in the corresponding first fuzzy set, the update type, and the preset fuzzy rule base, determine the activation degree of each fuzzy rule. According to the activation degree of each fuzzy rule, determine the second fuzzy set of the activation degree of each fuzzy rule; wherein, the fuzzy rule base includes multiple fuzzy rules.

[0152] Second calculation module: Defuzzify the second fuzzy set of the activation degree of each fuzzy rule to determine the output value.

[0153] Output module: Compare the output value with a preset threshold, and confirm the update type according to the comparison result.

[0154] The present invention is applicable to intelligent vehicles: applicable to the OTA upgrade of intelligent vehicles to improve the performance and safety of vehicles; industrial equipment: applicable to the OTA upgrade of various industrial equipment to improve the operating efficiency and reliability of equipment; smart home: applicable to the OTA upgrade of smart home devices to improve the intelligent level and user experience of devices.

[0155] Embodiment III:

[0156] The embodiment of the present invention further provides a terminal, including a processor and a storage medium;

[0157] The storage medium is used to store instructions;

[0158] The processor is used to operate according to the instructions to execute the steps of the method described in Embodiment I.

[0159] Embodiment IV:

[0160] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the steps of the method described in Embodiment I.

[0161] Since the storage medium provided by the embodiment of the present invention can execute the method provided by Embodiment I of the present invention, therefore, it has the corresponding functional modules and beneficial effects of the execution method.

[0162] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented 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.

[0163] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0164] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction means that implements the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0166] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. An intelligent OTA upgrade method, characterized in that, It includes the following steps: Obtain update information and update type; Input the update information into the corresponding pre-set first fuzzy set and membership function to determine the membership degree of the update information in the corresponding first fuzzy set; Adopt the Mamdani inference method. According to the membership degree of the update information in the corresponding first fuzzy set, the update type, and the pre-set fuzzy rule base, determine the activation degree of each fuzzy rule. According to the activation degree of each fuzzy rule, determine the second fuzzy set of the activation degree of each fuzzy rule; wherein, the fuzzy rule base includes multiple fuzzy rules; Defuzzify the second fuzzy set of the activation degree of each fuzzy rule to determine the output value; Compare the output value with a preset threshold, and confirm the update type according to the comparison result.

2. The intelligent OTA upgrade method according to claim 1, wherein The update information includes the installation package size, network quality, estimated transmission time, and memory space.

3. The intelligent OTA upgrade method according to claim 2, wherein, The activation degree of the fuzzy rule includes the minimum value in the membership degree of the update information in the fuzzy rule; Input each update information into the corresponding pre-set first fuzzy set and membership function to determine the membership degree of each update information in the corresponding first fuzzy set.

4. The intelligent OTA upgrade method according to claim 2, wherein The membership function is a triangular membership function, and the expression of the membership function of the installation package size is as follows: ； In the formula: PS is the installation package size; is the membership function of the installation package size; is the minimum value of the pre-set installation package size; is the maximum value of the pre-set installation package size.

5. The intelligent OTA upgrade method according to claim 1, wherein, The defuzzification specifically includes: Adopt the centroid method for defuzzification, and the specific expression of the centroid method is as follows: ； In the formula: is the output value; is the value of the update type, which is preset according to the actual situation; n is the number of rules; i is the rule number; is the activation degree of the i-th rule.

6. The intelligent OTA upgrade method according to claim 1, wherein The comparison of the output value with the preset threshold and the confirmation of the update type according to the comparison result specifically include: The update type includes differential update and full update; If the output value is greater than the preset threshold, confirm that the update type is differential update; If the output value is not greater than the preset threshold, confirm that the update type is full update.

7. The intelligent OTA upgrade method according to claim 2, wherein After confirming the update type according to the comparison result, it also includes checking the memory status of the device to be updated; The checking of the memory status of the device to be updated specifically includes: Step a: Obtain the memory of the device to be updated; Step b: Compare the memory of the device to be updated with the installation package size. If the memory of the device to be updated is greater than the installation package size, the memory is sufficient, and the device can be updated normally; if the memory of the device to be updated is not greater than the installation package size, the memory is insufficient, and execute step c; Step c: Prompt the user to delete the useless files in the device, obtain the memory of the device after deletion, and compare the memory of the device after deletion with the installation package size. If the memory of the device after deletion is greater than the installation package size, the memory is sufficient, and the device can be updated normally; Step d: If the memory of the device after deletion is not greater than the installation package size, the memory is insufficient, and repeat steps c and d until the maximum number of repetitions is reached, and the device stops updating.

8. An intelligent OTA upgrade device, characterized in that, It includes: Collection module: Obtain update information and update type; First calculation module: Input the update information into the corresponding pre-set first fuzzy set and membership function to determine the membership degree of the update information in the corresponding first fuzzy set; Inference module: Using the Mamdani inference method, based on the membership degree of the updated information in the corresponding first fuzzy set, the update type, and a preset fuzzy rule base, determine the activation degree of each fuzzy rule. According to the activation degree of each fuzzy rule, determine the second fuzzy set of the activation degree of each fuzzy rule; wherein, the fuzzy rule base includes multiple fuzzy rules. Second calculation module: Defuzzify the second fuzzy set of the activation degree of each fuzzy rule to determine the output value. Output module: Compare the output value with a preset threshold, and confirm the update type according to the comparison result.

9. A terminal, characterized in that, It includes a processor and a storage medium. The storage medium is used to store instructions. The processor is used to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 7.

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