Task unloading method of multi-user edge computing system and related equipment

Abstract

The invention discloses a task unloading method of a multi-user edge computing system and related equipment. The method comprises the following steps: constructing a user priority computing model according to different dimension information of users; constructing a multi-user edge computing system model with priority according to the user priority model; constructing a task unloading strategy by taking minimization of energy consumption and weighted waiting time of all users as targets in combination with user priorities and constraint conditions of task unloading; and solving the task unloading strategy to obtain an unloading decision completion decision of each task of each user, and issuing the unloading decision completion decision to each user. The multi-user multi-task unloading method based on the user priority is achieved, and low-power-consumption edge calculation is optimized under the constraint condition. The method can be widely applied to the technical field of wireless communication.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular to a task offloading method and related equipment for a multi-user edge computing system. Background Art

[0002] With the rapid development of the Internet of Things (IoT) and 5G communication technologies, edge computing has become an important means of addressing data processing needs. By deploying computing resources at the edge of the network, close to the data source, edge computing effectively reduces data transmission latency and improves service response speed. Therefore, building efficient edge computing system models, especially in multi-user environments, has become a key research topic.

[0003] In edge computing environments, multiple user devices share limited computing and communication resources. Because different user tasks have varying resource requirements and urgency, properly allocating these resources is crucial. Therefore, building a prioritized multi-user edge computing system model is a critical first step in achieving optimal resource allocation. This model considers the priorities of user tasks and, through various priority management mechanisms, ensures that high-priority tasks are processed promptly while minimizing the waiting time for low-priority tasks.

[0004] In edge computing environments, task offloading decisions directly impact overall system performance and user experience. To optimize the task offloading process, an objective function and constraints based on energy consumption and priority wait time are necessary. Energy consumption optimization aims to extend the battery life of user devices, while priority wait time ensures that high-priority tasks are processed promptly. By constructing this objective function, we can minimize the system's overall energy consumption while ensuring that high-priority tasks are prioritized.

[0005] To solve the aforementioned task offloading problem, a custom algorithm needs to be designed and implemented. This algorithm dynamically adjusts the task offloading strategy based on each user's task characteristics, priority, and energy requirements. Specifically, the algorithm must comprehensively consider the real-time status of the user's device, the computing power of the edge server, and network transmission conditions to make the optimal task offloading decision. This process requires not only efficient computing power but also intelligent optimization strategies to cope with complex and changing network environments and user needs. Currently, a corresponding technical solution is lacking. Summary of the Invention

[0006] In order to solve at least one of the technical problems existing in the prior art to a certain extent, the purpose of the present invention is to provide a task offloading method and related equipment for a priority-based multi-user edge computing system.

[0007] The first technical solution adopted by the present invention is:

[0008] A task offloading method for a multi-user edge computing system comprises the following steps:

[0009] Build a user priority calculation model based on different dimensions of user information;

[0010] Construct a multi-user edge computing system model with priority based on the user priority model;

[0011] Combining user priorities and task offloading constraints, a task offloading strategy is constructed with the goal of minimizing the energy consumption and weighted waiting time of all users.

[0012] Solve the task offloading strategy, obtain the offloading decision of each task for each user, complete the decision, and send it to each user.

[0013] Furthermore, the different dimensional information includes user level, historical performance, task urgency, and resource consumption;

[0014] User priority is calculated as follows:

[0015] p i =w1×user level+w2×historical performance+w3×task urgency+w4×resource consumption

[0016] Where w1, w2, w3, and w4 are weights.

[0017] Furthermore, constructing a multi-user edge computing system model with priority according to the user priority model includes:

[0018] Consider a core network server, a wireless base station BS, an edge computing server placed near the base station, N user equipment UE, the user set is defined as U = {1, 2…, N}, and the user priorities are ρ = {ρ1, ρ2,…, ρ N}, where priority is a positive integer, the smaller the value, the higher the priority; it is defined that each user has a set of computing tasks to be completed Task = {1, 2…, K}; the user equipment UE can access the edge computing server resources through the wireless channel and offload its computing tasks; it is assumed that a user has only one task in an offloading cycle, and only one user can upload a task in each time slot.

[0019] Furthermore, the task offloading strategy is constructed by combining user priorities and task offloading constraints with the goal of minimizing the energy consumption and weighted waiting time of all users, including:

[0020] In an unloading cycle, let A ij ∈{0,1} represents the offloading decision of user i in time slot j; when a ij = 0, the task will be executed locally; when aij = 1, user i's task will be offloaded to the edge computing server for processing in time slot j; define A = {a 11 ,a 11 ,…,a NM} Compute the set of offloading decisions for all mobile users;

[0021] Computational overhead is considered in terms of energy consumption and waiting time in local computing and edge servers:

[0022] The local computing energy consumption is:

[0023]

[0024] Where, γ i represents the energy consumed per CPU cycle, c i represents the number of CPU cycles required to complete the task of computing user i; M represents the number of time slots contained in an offloading cycle;

[0025] The energy consumption of edge computing user i is:

[0026]

[0027] Among them, p i Where b is the transmission power of user i i Indicates the total data volume of user i's task, in bits, r ij represents the uplink throughput of user i in time slot j;

[0028] The energy consumption of all users is expressed as follows:

[0029]

[0030] Where N is the number of user equipment UE;

[0031] The waiting time is expressed as:

[0032]

[0033] Where τ is the interval of each small slot; j represents the slot number selected for task offloading;

[0034] The total waiting time of all users after considering priority weighting is:

[0035]

[0036] Furthermore, the task offloading problem is formulated as a constrained optimization problem:

[0037]

[0038] Where, Ei To calculate the energy consumption of unloading; Energy consumption that can only be calculated locally;

[0039] The goal of the optimization problem is to minimize the sum of the total energy consumption of mobile user devices and the priority-weighted waiting time by deploying task offloading; constraint C1 is the upper bound of energy consumption; constraint C2 indicates that the decision variable for task offloading is a binary variable; constraint C3 means that a user can only upload a task in one time slot; and constraint C4 means that there can only be one user per time slot.

[0040] Furthermore, solving the task offloading strategy, obtaining the offloading decision of each task for each user, and issuing the decision to each user include:

[0041] User priority sorting: sorting users according to their priority to ensure that users with high priority have priority in selecting time slots;

[0042] Energy Constraint Judgment: Based on the energy consumption constraint, determine whether the task needs to be offloaded. If all time slots cannot meet the constraint, the task is executed locally. Otherwise, the time slots that meet the constraint and the corresponding energy consumption are recorded.

[0043] Time slot selection and sorting: Using the time slots and energy consumption that each user meets the requirements in the previous step, sort each user's time slots individually by energy consumption from smallest to largest. Select the first available time slot based on user priority. If the time slot has been selected by a user with a higher priority, it is postponed to the next available time slot. If no time slots meet the requirements, the task is executed locally.

[0044] Offloading strategy distribution: The final offloading strategy is distributed to each user; so that the user can choose to perform local calculations on the task or offload it to the edge computing server in a certain time slot based on the offloading decision.

[0045] The second technical solution adopted by the present invention is:

[0046] A task offloading system for a multi-user edge computing system, comprising:

[0047] Priority calculation module, used to build a user priority calculation model based on different dimensional information of users;

[0048] A model building module, used to build a multi-user edge computing system model with priority according to the user priority model;

[0049] A policy building module is used to combine user priorities and task offloading constraints to build a task offloading policy with the goal of minimizing the energy consumption and weighted waiting time of all users;

[0050] The strategy solving module is used to solve the task offloading strategy, obtain the offloading decision completion decision of each task for each user, and send it to each user.

[0051] The third technical solution adopted by the present invention is:

[0052] An electronic device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, at least one program, the code set, or the instruction set is loaded and executed by the processor to implement a task offloading method for a multi-user edge computing system as described above.

[0053] The fourth technical solution adopted by the present invention is:

[0054] A computer-readable storage medium stores at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement a task offloading method for a multi-user edge computing system as described above.

[0055] The fifth technical solution adopted by the present invention is:

[0056] A computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the above-mentioned task offloading method for a multi-user edge computing system.

[0057] The beneficial effects of the present invention are as follows: the present invention proposes a task offloading method and related equipment for a multi-user edge computing system. Aiming at the practical problem of multiple users sharing limited computing and communication resources, a system model with a priority management mechanism is constructed, which can effectively and reasonably allocate resources according to task priorities, ensure that high-priority tasks are processed in a timely manner, and reduce the waiting time of low-priority tasks. By introducing the optimization goals of energy consumption and priority waiting time, the balance between user device energy consumption and system response speed is taken into account, and the overall system performance and user experience are improved. In addition, the present invention designs an adaptive offloading decision algorithm, which can dynamically adjust the offloading strategy according to user task characteristics, device status, edge server computing power and network conditions, and realizes the coordinated optimization of energy consumption and task response delay. This solution has strong practicality and adaptability in the complex and changeable multi-user edge computing environment, and makes up for the shortcomings of the existing technical solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present invention or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0059] Figure 1 Schematic diagram of a priority-based multi-user mobile edge computing system model in an embodiment of the present invention;

[0060] Figure 2 This is a diagram showing an example of edge computing offloading decision-making in an embodiment of the present invention;

[0061] Figure 3 This is a detailed flow chart of user task offloading and uplink throughput prediction in an embodiment of the present invention;

[0062] Figure 4 This is a step flow chart of a task offloading method for a multi-user edge computing system in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application. For the step numbers in the following embodiments, they are provided only for the convenience of explanation and are not intended to limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0064] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the embodiments of the present application. The singular forms of "a", "said", and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise clearly defined, words such as setting, installing, and connecting should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above words in the present invention in combination with the specific content of the technical solution.

[0065] In the description of this application, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on this application.

[0066] In the description of this application, "several" means one or more, "many" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The terms "first" and "second" are used solely to distinguish technical features and are not to be construed as indicating or implying relative importance, or as implicitly specifying the number or order of the technical features indicated.

[0067] In the description of this application, "and / or" describes the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0068] Example 1

[0069] like Figure 4 As shown, this embodiment provides a priority-based task offloading method for a multi-user edge computing system, including the following steps:

[0070] S1. Build a user priority calculation model based on different dimensional information of users.

[0071] In some embodiments, when designing user priority, the following key dimensions are mainly considered: user level (user's VIP level, membership level, etc.), historical performance (user's past behavior data, such as task success rate, completion time, resource utilization, etc.), task urgency (the urgency of the user's current requested task), and resource consumption (the consumption of system resources by the user's previous tasks). User priority calculation model, user priority can be calculated by a multi-dimensional comprehensive scoring method. The specific formula is as follows:

[0072] p i =w1×user level+w2×historical performance+w3×task urgency+w4×resource consumption, where w1, w2, w3, and w4 are weights that can be adjusted according to actual needs; i∈{1,2…,N} represents the i-th user.

[0073] S2. Construct a multi-user edge computing system model with priority based on the user priority model.

[0074] As an implementation method, Figure 1 As shown in the figure, a multi-user multi-task mobile edge computing system model with priority is constructed. Considering 1 core network server, 1 wireless base station BS, an edge computing server placed near the base station, N user equipment UE, the user set is defined as U = {1,2…,N}, and the user priorities are ρ = {ρ1,ρ2,…,ρ N}, where priority is a positive integer, the smaller the value, the higher the priority. It is defined that each user has a set of computing tasks to be completed Task = {1, 2…, K}. Mobile user devices can access edge computing server resources through wireless channels and offload their computing tasks. The embodiment of the present invention considers quasi-static scenarios, such as Figure 2 As shown, the mobile device set remains unchanged during the computation offloading period (e.g., within a few seconds), which is an offloading period. It is assumed that each user has only one task in an offloading period, and only one user can upload a task in each time slot.

[0075] S3. Combining user priorities and task offloading constraints, a task offloading strategy is constructed with the goal of minimizing the energy consumption and weighted waiting time of all users.

[0076] In some embodiments, after predicting the uplink throughput of the user, it is necessary to calculate the total energy consumption of all users and the corresponding unloading decision. Therefore, it is defined that the unloading decision of user i task in time slot j is a ij ∈{0,1}, represents the unloading decision of user i in time slot j. ij = 0, the task will be executed locally; when a ij = 1, user i's task will be offloaded to the edge computing server for processing in time slot j. Define A = {a 11 ,a 11 ,…,a NM The offloading decision set for all mobile user computing tasks. The offloading decision set for all mobile user computing tasks. There are two task processing methods: 1) local computing; 2) offloading to edge computing servers.

[0077] a) Tasks are processed locally

[0078] When the task is processed locally, that is, a ij =0, local computing energy consumption is:

[0079]

[0080] Where, γ i represents the energy consumed per CPU cycle, c i Indicates the number of CPU cycles required to complete the computation task for user i.

[0081] b) Offload computing to edge computing servers

[0082] Since the amount of data of the calculation result is small, the embodiment of the present invention ignores the result return time of the edge computing server. Define the total data volume of user i as b i , in bits; the uplink throughput of user i in time slot j is r ij , the transmission power of user i is p i , then the energy consumption of edge computing user i is:

[0083]

[0084] The waiting time is:

[0085]

[0086] Where τ is the interval of each mini-slot.

[0087] In summary, considering that tasks can be calculated locally or offloaded to edge computing servers, the energy consumption of all users can be expressed as:

[0088]

[0089] The total waiting time of all users after considering priority weighting is:

[0090]

[0091] According to the overall goal of minimizing the energy consumption and weighted waiting time of mobile user equipment, the task offloading problem can be formulated as a constrained optimization problem:

[0092]

[0093] The goal of the optimization problem is to minimize the sum of the total energy consumption of mobile user devices and the sum of their priority-weighted waiting times by deploying task offloading. Constraint C1 is the upper bound on energy consumption; constraint C2 indicates that the decision variable for task offloading is a binary variable; constraint C3 states that a user can only upload tasks within a time slot; and constraint C4 states that only one user can be present in each time slot.

[0094] S4. Solve the task offloading strategy, obtain the offloading decision of each task for each user, complete the decision, and send it to each user.

[0095] Specifically, the above optimization problem is a binary programming problem, so the following algorithm can be used to solve the offloading decision and send the offloading decision to each user. The flowchart is as follows Figure 3 shown.

[0096] S41. User priority sorting: First, users are sorted according to their priorities to ensure that users with higher priorities have priority in selecting time slots.

[0097] S42. Energy Consumption Constraint Judgment: Based on the above energy consumption constraints, determine whether the task needs to be offloaded. If all time slots cannot meet the constraints, the task is executed locally; otherwise, the time slots that meet the constraints and the corresponding energy consumption are recorded.

[0098] S43. Time Slot Selection and Sorting: Using the time slots and energy consumption requirements for each user from the previous step, sort each user's time slots individually by energy consumption, from lowest to highest. Select the first available time slot according to the user priority order from step S41. If the time slot has already been selected by a user with a higher priority, the task is postponed to the next available time slot. If no time slots meet the requirements, the task is executed locally.

[0099] S44. Offloading strategy distribution: The final offloading strategy is distributed to each user. Based on the offloading decision, the user can choose to perform local computation on the task or offload the task to the edge computing server for computation in a certain time slot.

[0100] In summary, the present invention implements a multi-user multi-task offloading method with user priority and optimizes low-power edge computing under constraints.

[0101] Example 2

[0102] This embodiment provides a task offloading system for a multi-user edge computing system, including:

[0103] Priority calculation module, used to build a user priority calculation model based on different dimensional information of users;

[0104] A model building module, used to build a multi-user edge computing system model with priority according to the user priority model;

[0105] A policy building module is used to combine user priorities and task offloading constraints to build a task offloading policy with the goal of minimizing the energy consumption and weighted waiting time of all users;

[0106] The strategy solving module is used to solve the task offloading strategy, obtain the offloading decision completion decision of each task for each user, and send it to each user.

[0107] Since the device is a task offloading system of a multi-user edge computing system in an embodiment of the present invention, and the principle of solving the problem by the system is similar to that of the method, the implementation of the system can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0108] Example 3

[0109] An embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the following Figure 4 A task offloading method for a multi-user edge computing system is shown.

[0110] It is understood that the memory may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory may be used to store instructions, programs, codes, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function, instructions for implementing the various method embodiments described above, etc.; the data storage area may store data created based on the use of the server, etc.

[0111] The processor may include one or more processing cores. The processor utilizes various interfaces and circuits to connect various components within the server. It executes various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory, as well as accessing data stored in memory. Optionally, the processor may be implemented using at least one of the following hardware forms: digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor may integrate one or a combination of a central processing unit (CPU) and a modem. The CPU primarily processes the operating system and application programs, while the modem handles wireless communications. It is understood that the modem may not be integrated into the processor and may be implemented separately via a single chip.

[0112] Since the electronic device is an electronic device corresponding to the task offloading method of a multi-user edge computing system in an embodiment of the present invention, and the principle of solving the problem by the electronic device is similar to that of the method, the implementation of the electronic device can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0113] Example 4

[0114] An embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, the at least one program, the code set or instruction set is loaded and executed by a processor to implement the following Figure 4 A task offloading method for a multi-user edge computing system is shown.

[0115] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program. The program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0116] Since the storage medium is the storage medium corresponding to the task offloading method of a multi-user edge computing system in an embodiment of the present invention, and the principle of solving the problem by the storage medium is similar to that of the method, the implementation of the storage medium can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0117] Example 5

[0118] In some possible implementations, various aspects of the method of the embodiment of the present invention may also be implemented in the form of a program product, which includes program code. When the program product is run on a computer device, the program code is used to cause the computer device to perform the steps of the task offloading method for a multi-user edge computing system according to various exemplary embodiments of the present application described above in this specification. Among them, the executable computer program code or "code" for executing each embodiment can be written in a high-level programming language such as C, C++, Python, Smalltalk, Java, JavaScript, Visual Basic, structured query language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0119] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0120] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0121] The above embodiments are intended only to illustrate the technical concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the contents of the present invention and implement them accordingly. They are not intended to limit the scope of protection of the present invention. Any equivalent changes or modifications made based on the essence of the present invention are intended to be covered by the scope of protection of the present invention.

Claims

1. A task offloading method for a multi-user edge computing system, characterized in that: The following steps are involved: Build a user priority calculation model based on different dimensions of user information; Construct a multi-user edge computing system model with priority based on the user priority model; Combining user priorities and task offloading constraints, a task offloading strategy is constructed with the goal of minimizing the energy consumption and weighted waiting time of all users. Solve the task offloading strategy, obtain the offloading decision of each task for each user, complete the decision, and send it to each user.

2. The task offloading method of a multi-user edge computing system according to claim 1, characterized in that: The different dimensional information includes user level, historical performance, task urgency, and resource consumption; User priority is calculated as follows: p i =w1×user level+w2×historical performance+w3×task urgency+w4×resource consumption Where w1, w2, w3, and w4 are weights.

3. The task offloading method of a multi-user edge computing system according to claim 1, characterized in that: The method of constructing a multi-user edge computing system model with priority according to the user priority model includes: Consider a core network server, a wireless base station BS, an edge computing server placed near the base station, N user equipment UE, the user set is defined as U = {1, 2…, N}, and the user priorities are ρ = {ρ1, ρ2,…, ρ N }, where priority is a positive integer, the smaller the value, the higher the priority; it is defined that each user has a set of computing tasks to be completed Task = {1, 2…, K}; the user equipment UE can access the edge computing server resources through the wireless channel and offload its computing tasks; it is assumed that a user has only one task in an offloading cycle, and only one user can upload a task in each time slot.

4. The task offloading method of a multi-user edge computing system according to claim 1, characterized in that: The task offloading strategy is constructed by combining user priorities and task offloading constraints with the goal of minimizing the energy consumption and weighted waiting time of all users, including: In an unloading cycle, let a ij ∈{0,1} represents the offloading decision of user i in time slot j; when a ij = 0, the task will be executed locally; when a ij = 1, user i's task will be offloaded to the edge computing server for processing in time slot j; define A = {a 11 ,a 11 ,…,a NM } Compute the set of offloading decisions for all mobile users; Computational overhead is considered in terms of energy consumption and waiting time in local computing and edge servers: The local computing energy consumption is: Where, γ i represents the energy consumed per CPU cycle, c i represents the number of CPU cycles required to complete the task of computing user i; M represents the number of time slots contained in an offloading cycle; The energy consumption of edge computing user i is: Among them, p i Where b is the transmission power of user i i represents the total data volume of user i’s task, r ij represents the uplink throughput of user i in time slot j; The energy consumption of all users is expressed as follows: Where N is the number of user equipment UE; The waiting time is expressed as: Where τ is the interval of each time slot; j represents the time slot number selected for task offloading; The total waiting time of all users after considering priority weighting is:

5. The task offloading method of a multi-user edge computing system according to claim 4, characterized in that: The task offloading problem is formulated as a constrained optimization problem: Where, E i To calculate the energy consumption of unloading; Energy consumption that can only be calculated locally; The goal of the optimization problem is to minimize the sum of the total energy consumption of mobile user devices and the priority-weighted waiting time by deploying task offloading; constraint C1 is the upper bound of energy consumption; constraint C2 indicates that the decision variable for task offloading is a binary variable; constraint C3 means that a user can only upload a task in one time slot; and constraint C4 means that there can only be one user per time slot.

6. The task offloading method of a multi-user edge computing system according to claim 1, characterized in that: Solving the task offloading strategy, obtaining the offloading decision of each task for each user, and issuing it to each user, includes: User priority sorting: sorting users according to their priority to ensure that users with high priority have priority in selecting time slots; Energy Constraint Judgment: Based on the energy consumption constraint, determine whether the task needs to be offloaded. If all time slots cannot meet the constraint, the task is executed locally. Otherwise, the time slots that meet the constraint and the corresponding energy consumption are recorded. Time slot selection and sorting: Using the time slots and energy consumption that each user meets the requirements in the previous step, sort each user's time slots individually by energy consumption from smallest to largest. Select the first available time slot based on user priority. If the time slot has been selected by a user with a higher priority, it is postponed to the next available time slot. If no time slots meet the requirements, the task is executed locally. Offloading strategy distribution: The final offloading strategy is distributed to each user; so that the user can choose to perform local calculations on the task or offload it to the edge computing server in a certain time slot based on the offloading decision.

7. A task offloading system for a multi-user edge computing system, characterized in that: include: Priority calculation module, used to build a user priority calculation model based on different dimensional information of users; The model building module is used to build a prioritized multi-user edge computing system model based on the user priority model. The policy building module is used to combine user priorities and task offloading constraints to build a task offloading strategy with the goal of minimizing the energy consumption and weighted waiting time of all users. The strategy solving module is used to solve the task offloading strategy, obtain the offloading decision completion decision of each task for each user, and send it to each user.

8. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product comprises computer instructions, which are used to perform the method according to any one of claims 1 to 6 when executed by a processor.

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