Battlefield distributed edge calculation method and device based on differential fault tolerance

Abstract

The invention provides a battlefield distributed edge calculation method and device based on differential fault tolerance, and relates to the technical field of battlefield distributed edge calculation. The method comprises the following steps: constructing an objective function and constraint parameters according to battlefield node equipment information and battlefield calculation task information; according to the battlefield node equipment information and the battlefield calculation task information, calculation task unloading is carried out through an initial allocation rule, and a preliminary task allocation scheme is obtained; according to the target function and the constraint parameters, a dynamic task allocation optimization algorithm is adopted to optimize and update the preliminary task allocation scheme, and an updated task allocation scheme is obtained; based on the initial task allocation scheme and a preset threshold value, performing update verification on the update task allocation scheme to obtain an update verification result; and when the update verification result is successful verification, determining the update task allocation scheme as an optimal task allocation scheme. The battlefield distributed edge calculation method disclosed by the invention is a battlefield distributed edge calculation method which gives consideration to real-time performance and continuity aiming at a battlefield environment.

Description

Technical Field

[0001] The present invention relates to the field of battlefield distributed edge computing technology, and in particular to a battlefield distributed edge computing method and device based on differentiated fault tolerance. Background Art

[0002] Edge computing is a distributed computing framework whose core concept is to offload computing, storage, and data processing functions from centralized data centers to edge nodes close to end devices. This approach can significantly reduce data transmission latency and provide greater data security and flexibility. The "Internet of Battlefield Things" (IoBT), centered around the application of machine learning, deep integration, and intelligent sensing technologies, aims to bring artificial intelligence applications to the forefront of future battlefields. This system design requires improving the performance, speed, and mobility of battlefield edge networking and computing, meeting the critical requirements of low latency, minimal energy consumption, high accuracy, and security and reliability.

[0003] In modern battlefield environments, drones, soldiers' smart devices, combat vehicles, battlefield command centers, and other devices generate massive amounts of real-time data, including images, videos, sensor information, geographic information, and friend-or-foe identification data. This data needs to be processed rapidly within the battlefield to support timely tactical decision-making.

[0004] However, traditional centralized computing models often transmit large amounts of data back to remote central data centers for processing, resulting in high latency and continuity pressures. This approach cannot meet real-time requirements, especially in the complex and dynamic battlefield environment. While existing edge computing solutions can partially achieve task offloading and data processing, they pay less attention to the challenges posed by the high dynamics and distributed deployment of edge computing nodes in battlefield environments. This is especially true in battlefield environments, where equipment failures and data loss are common, making it difficult to ensure the reliability of data processing and task execution.

[0005] In the existing technology, there is a lack of a battlefield distributed edge computing method that takes into account both real-time and continuity in the battlefield environment. Summary of the Invention

[0006] In order to solve the technical problems that exist in the existing technology, the traditional centralized computing model transmits a large amount of data back to a remote central data center for processing, resulting in high latency and continuity pressure; in the battlefield environment, equipment failures and data loss are frequent, making it difficult to ensure the reliability of data processing and task execution, the embodiment of the present invention provides a battlefield distributed edge computing method and device based on differentiated fault tolerance. The technical solution is as follows:

[0007] On the one hand, a battlefield distributed edge computing method based on differentiated fault tolerance is provided, which is implemented by a battlefield distributed edge computing device and includes:

[0008] Obtain battlefield node device information based on the basic parameters of battlefield node devices; collect information through sensors carried by battlefield node devices to obtain battlefield computing task information;

[0009] Constructing an objective function and constraint parameters according to the battlefield node device information and battlefield computing task information;

[0010] According to the battlefield node device information and the battlefield computing task information, computing task unloading is performed through an initial allocation rule to obtain a preliminary task allocation plan;

[0011] According to the objective function and the constraint parameters, the preliminary task allocation scheme is optimized and updated using a dynamic task allocation optimization algorithm to obtain an updated task allocation scheme;

[0012] Based on the initial task allocation plan and a preset threshold, the updated task allocation plan is updated and verified to obtain an updated verification result; when the updated verification result is verification success, the updated task allocation plan is determined as the optimal task allocation plan.

[0013] On the other hand, a battlefield distributed edge computing device based on differentiated fault tolerance is provided, which is applied to a battlefield distributed edge computing method based on differentiated fault tolerance, and the device includes:

[0014] The battlefield computing information acquisition module is used to obtain battlefield node equipment information based on the basic parameters of the battlefield node equipment; collect information through the sensors carried by the battlefield node equipment to obtain battlefield computing task information;

[0015] An objective function construction module is used to construct an objective function and constraint parameters according to the battlefield node device information and battlefield computing task information;

[0016] A preliminary task allocation module is used to perform computing task offloading according to the battlefield node device information and the battlefield computing task information through an initial allocation rule to obtain a preliminary task allocation plan;

[0017] A task allocation plan updating module is used to optimize and update the preliminary task allocation plan according to the objective function and constraint parameters using a dynamic task allocation optimization algorithm to obtain an updated task allocation plan;

[0018] The task allocation scheme verification module is used to perform update verification on the updated task allocation scheme based on the initial task allocation scheme and a preset threshold to obtain an update verification result; when the update verification result is verification success, the updated task allocation scheme is determined as the optimal task allocation scheme.

[0019] On the other hand, a battlefield distributed edge computing device is provided, which includes: a processor; a memory, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned battlefield distributed edge computing methods based on differentiated fault tolerance is implemented.

[0020] On the other hand, a computer-readable storage medium is provided, in which at least one instruction is stored. The at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned battlefield distributed edge computing methods based on differentiated fault tolerance.

[0021] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0022] This paper proposes a battlefield distributed edge computing method based on differentiated fault tolerance. By co-optimizing task offloading and fault tolerance mechanisms, the real-time and reliability of battlefield information processing are significantly improved. An objective function is constructed based on data transmission latency and transmission energy consumption; parameter optimization is performed using the objective function minimization as the optimization problem. Data is stored in replicas and an erasure code mechanism is used to recover data from failed nodes by integrating these data blocks and check blocks. This presents a battlefield distributed edge computing method that balances real-time and continuity for battlefield environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 This is a flow chart of a battlefield distributed edge computing method based on differentiated fault tolerance provided by an embodiment of the present invention;

[0025] Figure 2 This is a block diagram of a battlefield distributed edge computing device based on differentiated fault tolerance provided by an embodiment of the present invention;

[0026] Figure 3 It is a structural diagram of a battlefield distributed edge computing device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0028] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0029] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0030] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0031] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0032] The embodiment of the present invention provides a battlefield distributed edge computing method based on differentiated fault tolerance, which can be implemented by a battlefield distributed edge computing device, which can be a terminal or a server. Figure 1 The flowchart of the battlefield distributed edge computing method based on differentiated fault tolerance is shown. The processing flow of this method may include the following steps:

[0033] S1. Obtain battlefield node device information based on the basic parameters of the battlefield node device; collect information through the sensors carried by the battlefield node device to obtain battlefield computing task information.

[0034] Among them, battlefield node device information includes the upload rate of the terminal device, the download rate of the terminal device, the computing frequency of the terminal device, the computing frequency of the edge server and the computing task copy information;

[0035] The battlefield computing task information includes the task vector of the battlefield computing, the input data volume of the computing task, the output data volume of the computing task, the priority of the computing task and the importance of the task data.

[0036] In a feasible implementation, there are N terminal device tasks and M edge servers in the battlefield distributed edge computing system of the present invention. Task allocation matrix Used to represent the assignment of tasks, where Indicates that the task is completed on the terminal device. Indicates that the task is processed on the edge node, that is, Indicates data unloading tasks On the terminal, complete, This indicates a data offloading task. Assigned to edge nodes Completed.

[0037] Indicates the importance of task data, Indicates the priority of the task, the average upload rate of the system terminal is , the download rate is , the calculation frequency of the terminal device is , the input and output data volumes of the task are and .

[0038] S2. Construct the objective function and constraint parameters based on the battlefield node equipment information and battlefield computing task information.

[0039] Optionally, constructing an objective function and constraint parameters based on battlefield node device information and battlefield computing task information includes:

[0040] Build a task offloading delay model and task energy consumption model based on battlefield node equipment information and battlefield computing task information;

[0041] With the goal of minimizing the sum of task offloading delay and energy consumption, the objective function and constraint parameters are constructed according to the task offloading delay model and the task energy consumption model.

[0042] In a feasible implementation, a delay model and an energy consumption model of the task are constructed based on the received offload computing task.

[0043] Among them, the task offloading delay model includes the battlefield task delay model and the redundant data generation delay model;

[0044] The battlefield mission delay model includes the terminal computing delay model and upload delay model for offloading on the terminal device of the battlefield node device; the container computing delay model for offloading on the edge server of the battlefield node device; and the return delay model after offloading is completed on the edge server of the battlefield node device.

[0045] The redundant data generation delay model includes the erasure code redundant data delay model and the replica redundant data delay model;

[0046] The task energy consumption model includes the terminal device energy consumption model and the edge server energy consumption model.

[0047] In a feasible embodiment, the present invention is Indicates data unloading tasks The computational delay model when executed on the terminal device is as follows (1):

[0048] (1);

[0049] by Indicates data unloading tasks The upload delay model when executed on the terminal device is as follows (2):

[0050] (2);

[0051] For edge containers The calculation frequency of Indicates data unloading tasks In edge container The container computing latency model during execution is as follows (3):

[0052] (3);

[0053] by Indicates data unloading tasks After the execution is completed, the delay model is returned, as shown in the following formula (4):

[0054] (4);

[0055] The energy consumption model of terminal equipment is as follows (5):

[0056] (5);

[0057] in, is the computing power of the battlefield terminal.

[0058] The transmission energy consumption model of the terminal device is as follows (6):

[0059] (6);

[0060] in, and are the transmit power and receive power of the terminal device respectively.

[0061] The energy consumption model of edge server computing is as follows (7):

[0062] (7);

[0063] in, is the computing power of the edge server device, To generate the delay of erasure code redundant data, The delay in generating redundant data copies.

[0064] by 、 、 、 、 and They represent the total calculation delay of battlefield terminal equipment, total upload delay, total calculation delay of edge server, total return delay and delay of generating redundant data for all data offloading tasks, as shown in the following equations (8), (9), (10), (11), (12) and (13):

[0065] (8);

[0066] (9);

[0067] (10);

[0068] (11);

[0069] (12);

[0070] (13);

[0071] in, is the erasure code factor, is the replication factor, when replica fault tolerance is used ; When using erasure code fault tolerance .

[0072] Therefore, according to the above formula, the task offloading delay model is as follows (14):

[0073] (14);

[0074] Among them, the delay factor .

[0075] The task energy consumption model is as follows (15):

[0076] (15);

[0077] The objective function is as follows (16):

[0078] (16);

[0079] Where X is the allocation rule; The total cost of using the X allocation rule for the system;

[0080] The parameter constraints are as follows (17):

[0081] (17);

[0082] Where T is the task offloading delay; E is the task transmission energy consumption; is the delay weight, is the energy consumption weight, and ; N is the number of terminal device tasks; M is the number of edge servers.

[0083] In a feasible implementation method, in order to achieve optimal task allocation, the minimization of offloading task delay and battlefield terminal equipment energy consumption is used as the objective function, and corresponding parameter constraints are designed.

[0084] S3. Based on the battlefield node equipment information and battlefield computing task information, computing tasks are unloaded through the initial allocation rules to obtain a preliminary task allocation plan.

[0085] In a feasible implementation, the battlefield distributed edge computing system of the present invention has task allocation vectors, and generate an initial task allocation matrix for each task allocation vector according to the initial allocation rule , , the mathematical expression of the initial allocation rule is as follows (18):

[0086] (18);

[0087] in, Indicates the parameter value when the function reaches its minimum value.

[0088] S4. According to the objective function and constraint parameters, a dynamic task allocation optimization algorithm is used to optimize and update the preliminary task allocation plan to obtain an updated task allocation plan.

[0089] Optionally, according to the objective function and the constraint parameters, a dynamic task allocation optimization algorithm is used to optimize and update the preliminary task allocation plan to obtain an updated task allocation plan, including:

[0090] Based on the objective function and the constraint parameters, according to the preliminary task allocation plan, the single task allocation matrix set is updated by a first updating rule;

[0091] Based on the objective function and the constraint parameters, according to the preliminary task allocation plan, the global allocation matrix set is updated by the second updating rule;

[0092] Calculate based on the individual task allocation matrix set and the global allocation matrix set to obtain the allocation adjustment trend;

[0093] According to the allocation adjustment trend, the preliminary task allocation plan is updated to obtain an updated task allocation plan.

[0094] In a feasible implementation, in the preliminary allocation plan Then, the task allocation scheme of each task allocation vector is updated according to the objective function. The update rule is as follows (19):

[0095] (19);

[0096] in, for The task allocation plan for the iteration, for The task allocation plan for the iteration, The distribution adjustment trend is calculated as follows (20):

[0097] (20);

[0098] in, Indicates in The adjustment trend of , , is the inertia weight, , is a random number, . Represents the optimal solution for a single task allocation vector. represents the global optimal allocation solution.

[0099] The update rule of a single task allocation matrix is ​​as follows (21):

[0100] (twenty one);

[0101] The global allocation matrix update rule is as follows (22):

[0102] (twenty two);

[0103] S5. Based on the initial task allocation plan and the preset threshold, the updated task allocation plan is updated and verified to obtain an updated verification result; when the updated verification result is verification success, the updated task allocation plan is determined as the optimal task allocation plan.

[0104] In a feasible implementation, when the total cost change is less than a preset threshold , then the optimal task allocation solution is obtained .

[0105] Optionally, when the verification result is successful, after determining the updated task allocation scheme as the optimal task allocation scheme, the method further includes:

[0106] Based on the optimal task allocation scheme and battlefield computing task information, the corresponding computing task priority and the importance of the corresponding task data under the optimal task allocation are obtained.

[0107] Calculate the task importance based on the priority of the corresponding computing task and the importance of the corresponding task data;

[0108] Verify the importance of tasks based on preset importance thresholds;

[0109] When the task importance is greater than or equal to the preset importance threshold, data fault-tolerant recovery is performed on the computing task information corresponding to the optimal task allocation solution based on the computing task replica information;

[0110] When the importance of the task is less than the preset importance threshold, the computing task information corresponding to the optimal task allocation plan is restored through erasure code fault tolerance through the data blocks and check blocks built into the battlefield node device.

[0111] In a feasible implementation, the present invention uses task weights to dynamically measure the importance of tasks, as shown in the following formula (23):

[0112] (twenty three)

[0113] in, , is the adjustment coefficient; The preset importance threshold.

[0114] when , using replica fault tolerance , For replica data ;when , using erasure codes for fault tolerance , To group the data, is the check code of the corresponding group, .

[0115] If the data of the failed node belongs to the replica storage data, the system will quickly read the complete data directly from other nodes with replicas. If the data of the failed node uses the erasure code mechanism, the surviving block and encoding matrix Complete the recovery process .

[0116] This paper proposes a battlefield distributed edge computing method based on differentiated fault tolerance. By co-optimizing task offloading and fault tolerance mechanisms, the real-time and reliability of battlefield information processing are significantly improved. An objective function is constructed based on data transmission latency and transmission energy consumption; parameter optimization is performed using the objective function minimization as the optimization problem. Data is stored in replicas and an erasure code mechanism is used to recover data from failed nodes by integrating these data blocks and check blocks. This presents a battlefield distributed edge computing method that balances real-time and continuity for battlefield environments.

[0117] Figure 2 This is a block diagram of a battlefield distributed edge computing device based on differentiated fault tolerance according to an exemplary embodiment, which is used in a battlefield distributed edge computing method based on differentiated fault tolerance. Figure 2 The device includes a battlefield calculation information acquisition module 210, an objective function construction module 220, a preliminary task allocation module 230, a task allocation plan update module 240, and a task allocation plan verification module 250. Among them:

[0118] The battlefield computing information acquisition module 210 is used to obtain battlefield node device information based on the basic parameters of the battlefield node devices; collect information through sensors carried by the battlefield node devices to obtain battlefield computing task information;

[0119] An objective function construction module 220 is used to construct an objective function and constraint parameters based on battlefield node device information and battlefield computing task information;

[0120] The preliminary task allocation module 230 is used to offload computing tasks according to the battlefield node device information and battlefield computing task information using the initial allocation rules to obtain a preliminary task allocation plan;

[0121] The task allocation plan updating module 240 is used to optimize and update the preliminary task allocation plan according to the objective function and the constraint parameters using a dynamic task allocation optimization algorithm to obtain an updated task allocation plan;

[0122] The task allocation scheme verification module 250 is used to perform update verification on the updated task allocation scheme based on the initial task allocation scheme and a preset threshold to obtain an update verification result; when the update verification result is verification success, the updated task allocation scheme is determined as the optimal task allocation scheme.

[0123] Among them, battlefield node device information includes the upload rate of the terminal device, the download rate of the terminal device, the computing frequency of the terminal device, the computing frequency of the edge server and the computing task copy information;

[0124] The battlefield computing task information includes the task vector of the battlefield computing, the input data volume of the computing task, the output data volume of the computing task, the priority of the computing task and the importance of the task data.

[0125] Optionally, the objective function construction module 220 is further configured to:

[0126] Build a task offloading delay model and task energy consumption model based on battlefield node equipment information and battlefield computing task information;

[0127] With the goal of minimizing the sum of task offloading delay and energy consumption, the objective function and constraint parameters are constructed according to the task offloading delay model and the task energy consumption model.

[0128] Among them, the task offloading delay model includes the battlefield task delay model and the redundant data generation delay model;

[0129] The battlefield mission delay model includes the terminal computing delay model and upload delay model for offloading on the terminal device of the battlefield node device; the container computing delay model for offloading on the edge server of the battlefield node device; and the return delay model after offloading is completed on the edge server of the battlefield node device.

[0130] The redundant data generation delay model includes the erasure code redundant data delay model and the replica redundant data delay model;

[0131] The task energy consumption model includes the terminal device energy consumption model and the edge server energy consumption model.

[0132] The objective function is as follows (1):

[0133] (1);

[0134] Where X is the allocation rule; The total cost of using the X allocation rule for the system;

[0135] The parameter constraints are as follows (2):

[0136] (2);

[0137] Where T is the task offloading delay; E is the task transmission energy consumption; is the delay weight, is the energy consumption weight, and ; N is the number of terminal device tasks; M is the number of edge servers.

[0138] Optionally, the task allocation scheme updating module 240 is further configured to:

[0139] Based on the objective function and the constraint parameters, according to the preliminary task allocation plan, the single task allocation matrix set is updated by a first updating rule;

[0140] Based on the objective function and the constraint parameters, according to the preliminary task allocation plan, the global allocation matrix set is updated by the second updating rule;

[0141] Calculate based on the individual task allocation matrix set and the global allocation matrix set to obtain the allocation adjustment trend;

[0142] According to the allocation adjustment trend, the preliminary task allocation plan is updated to obtain an updated task allocation plan.

[0143] Optionally, the task allocation scheme verification module 250 is further configured to:

[0144] Based on the optimal task allocation scheme and battlefield computing task information, the corresponding computing task priority and the importance of the corresponding task data under the optimal task allocation are obtained.

[0145] Calculate the task importance based on the priority of the corresponding computing task and the importance of the corresponding task data;

[0146] Verify the importance of tasks based on preset importance thresholds;

[0147] When the task importance is greater than or equal to the preset importance threshold, data fault-tolerant recovery is performed on the computing task information corresponding to the optimal task allocation solution based on the computing task replica information;

[0148] When the importance of the task is less than the preset importance threshold, the computing task information corresponding to the optimal task allocation plan is restored through erasure code fault tolerance through the data blocks and check blocks built into the battlefield node device.

[0149] This paper proposes a battlefield distributed edge computing method based on differentiated fault tolerance. By co-optimizing task offloading and fault tolerance mechanisms, the real-time and reliability of battlefield information processing are significantly improved. An objective function is constructed based on data transmission latency and transmission energy consumption; parameter optimization is performed using the objective function minimization as the optimization problem. Data is stored in replicas and an erasure code mechanism is used to recover data from failed nodes by integrating these data blocks and check blocks. This presents a battlefield distributed edge computing method that balances real-time and continuity for battlefield environments.

[0150] Figure 3 This is a schematic diagram of the structure of a battlefield distributed edge computing device provided by an embodiment of the present invention. Figure 3 As shown, battlefield distributed edge computing equipment may include the above Figure 2The battlefield distributed edge computing device based on differentiated fault tolerance is shown. Optionally, the battlefield distributed edge computing device 310 may include a first processor 2001.

[0151] Optionally, the battlefield distributed edge computing device 310 may also include a memory 2002 and a transceiver 2003.

[0152] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0153] The following combination Figure 3 The following describes the components of the battlefield distributed edge computing device 310:

[0154] The first processor 2001 is the control center of the battlefield distributed edge computing device 310 and can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0155] Optionally, the first processor 2001 can perform various functions of the battlefield distributed edge computing device 310 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0156] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 3 CPU0 and CPU1 are shown in FIG.

[0157] In a specific implementation, as an embodiment, the battlefield distributed edge computing device 310 may also include multiple processors, such as Figure 3 1 and 2. The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0158] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled by the first processor 2001 for execution. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0159] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001 or exist independently and accessed through the interface circuit ( Figure 3 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0160] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0161] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 3 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0162] Optionally, the transceiver 2003 may be integrated with the first processor 2001 or may exist independently and communicate with the first processor 2001 through the interface circuit of the battlefield distributed edge computing device 310 ( Figure 3 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0163] It should be noted that Figure 3 The structure of the battlefield distributed edge computing device 310 shown in the figure does not constitute a limitation on the router. The actual knowledge structure identification device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0164] In addition, the technical effects of the battlefield distributed edge computing device 310 can refer to the technical effects of the battlefield distributed edge computing method based on differentiated fault tolerance described in the above method embodiment, and will not be repeated here.

[0165] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), or may 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 gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

[0166] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0167] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0168] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0169] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0170] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0171] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians 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 invention.

[0172] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0173] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0174] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0175] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0176] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical disks.

[0177] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A battlefield distributed edge computing method based on differentiated fault tolerance, characterized in that: The method comprises: Obtain battlefield node equipment information based on battlefield node equipment basic parameters; collect information through sensors carried by battlefield node equipment to obtain battlefield computing task information; Constructing an objective function and constraint parameters according to the battlefield node equipment information and battlefield computing task information; According to the battlefield node device information and the battlefield computing task information, computing task unloading is performed through initial allocation rules to obtain a preliminary task allocation plan; According to the objective function and the constraint parameters, the preliminary task allocation scheme is optimized and updated by using a dynamic task allocation optimization algorithm to obtain an updated task allocation scheme; Based on the initial task allocation plan and the preset threshold, the updated task allocation plan is updated and verified to obtain an updated verification result; when the updated verification result is verification success, the updated task allocation plan is determined as the optimal task allocation plan.

2. The battlefield distributed edge computing method based on differentiated fault tolerance according to claim 1 is characterized in that: The battlefield node device information includes the upload rate of the terminal device, the download rate of the terminal device, the computing frequency of the terminal device, the computing frequency of the edge server and the computing task copy information; The battlefield computing task information includes the task vector of the battlefield computing, the input data volume of the computing task, the output data volume of the computing task, the priority of the computing task and the importance of the task data.

3. The battlefield distributed edge computing method based on differentiated fault tolerance according to claim 1 is characterized in that: The constructing of the objective function and constraint parameters according to the battlefield node device information and the battlefield computing task information includes: Constructing a task offloading delay model and a task energy consumption model according to the battlefield node equipment information and the battlefield computing task information; Taking minimizing the sum of task offloading delay and energy consumption as the goal, an objective function and constraint parameters are constructed according to the task offloading delay model and the task energy consumption model.

4. The battlefield distributed edge computing method based on differentiated fault tolerance according to claim 3 is characterized in that: The task offloading delay model includes a battlefield task delay model and a redundant data generation delay model; The battlefield task delay model includes a terminal calculation delay model and an upload delay model for executing unloading on a terminal device of a battlefield node device; a container calculation delay model for executing unloading on an edge server of a battlefield node device; and a return delay model after executing unloading on an edge server of a battlefield node device. The redundant data generation delay model includes an erasure code redundant data delay model and a replica redundant data delay model; The task energy consumption model includes a terminal device energy consumption model and an edge server energy consumption model.

5. The battlefield distributed edge computing method based on differentiated fault tolerance according to claim 1 is characterized in that: The objective function is as follows (1): （1）； Among them, X is the allocation rule; The total cost of using X allocation rule for the system; The parameter constraints are as follows: （2）； Where T is the task offloading delay; E is the task transmission energy consumption; is the delay weight, is the energy consumption weight, and ; N is the number of terminal device tasks; M is the number of edge servers.

6. The battlefield distributed edge computing method based on differentiated fault tolerance according to claim 1 is characterized in that: The step of optimizing and updating the preliminary task allocation scheme by using a dynamic task allocation optimization algorithm according to the objective function and the constraint parameters to obtain an updated task allocation scheme includes: Based on the objective function and the constraint parameters, and according to the preliminary task allocation scheme, updating a single task allocation matrix set by a first updating rule; Based on the objective function and the constraint parameters, and according to the preliminary task allocation scheme, updating the global allocation matrix set by a second updating rule; Calculating according to the single task allocation matrix set and the global allocation matrix set to obtain an allocation adjustment trend; According to the allocation adjustment trend, the preliminary task allocation plan is updated to obtain an updated task allocation plan.

7. The battlefield distributed edge computing method based on differentiated fault tolerance according to claim 2 is characterized in that: When the verification result is successful, after determining the updated task allocation scheme as the optimal task allocation scheme, the method further includes: Based on the optimal task allocation scheme, the priority of the corresponding computing task and the importance of the corresponding task data under the optimal task allocation are obtained according to the battlefield computing task information. Calculate the task importance based on the priority of the corresponding computing task and the importance of the corresponding task data; Verifying the importance of the task based on a preset importance threshold; When the task importance is greater than or equal to a preset importance threshold, data fault-tolerant recovery is performed on the computing task information corresponding to the optimal task allocation scheme according to the computing task copy information; When the task importance is less than a preset importance threshold, the computing task information corresponding to the optimal task allocation plan is restored using erasure code fault tolerance through the data blocks and check blocks built into the battlefield node device.

8. A battlefield distributed edge computing device based on differentiated fault tolerance, the battlefield distributed edge computing device based on differentiated fault tolerance is used to implement the battlefield distributed edge computing method based on differentiated fault tolerance as described in any one of claims 1-7, characterized in that: The device comprises: The battlefield computing information acquisition module is used to obtain battlefield node equipment information based on the basic parameters of the battlefield node equipment; collect information through sensors carried by the battlefield node equipment to obtain battlefield computing task information; An objective function construction module, used to construct an objective function and constraint parameters according to the battlefield node equipment information and battlefield computing task information; A preliminary task allocation module, used to perform computing task unloading according to the battlefield node equipment information and the battlefield computing task information through an initial allocation rule to obtain a preliminary task allocation plan; A task allocation scheme updating module is used to optimize and update the preliminary task allocation scheme according to the objective function and constraint parameters by using a dynamic task allocation optimization algorithm to obtain an updated task allocation scheme; The task allocation scheme verification module is used to perform update verification on the updated task allocation scheme based on the initial task allocation scheme and a preset threshold to obtain an update verification result; when the update verification result is verification success, the updated task allocation scheme is determined as the optimal task allocation scheme.

9. A battlefield distributed edge computing device, characterized in that: The battlefield distributed edge computing device includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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