CN113727391A - Method, device and equipment for controlling system operation and storage medium - Google Patents

Method, device and equipment for controlling system operation and storage medium Download PDF

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CN113727391A
CN113727391A CN202111042454.1A CN202111042454A CN113727391A CN 113727391 A CN113727391 A CN 113727391A CN 202111042454 A CN202111042454 A CN 202111042454A CN 113727391 A CN113727391 A CN 113727391A
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base station
value
task
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CN113727391B (en
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毕宿志
贾素敏
林晓辉
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Shenzhen University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/0215—Traffic management, e.g. flow control or congestion control based on user or device properties, e.g. MTC-capable devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/0247—Traffic management, e.g. flow control or congestion control based on conditions of the access network or the infrastructure network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/086—Load balancing or load distribution among access entities
    • H04W28/0861—Load balancing or load distribution among access entities between base stations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/09—Management thereof
    • H04W28/0925—Management thereof using policies
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The present invention relates to the field of wireless communications, and in particular, to a method, an apparatus, a device, and a storage medium for controlling system operation. The invention realizes the improvement of the stability control capability under the condition of unknown random factors of a future system by calculating the task throughput and the transmission time value of single equipment, the calculation frequency value of the individual equipment and the position of the base station when receiving the task transferred by the single equipment. The transmission time value is calculated by deterministic factors such as the transmission rate, the transmission power and the task backlog of a single device, so that the base station position calculated according to the transmission time value is also deterministic, the processing task of the whole system is controlled by the deterministic base station position and the transmission time value as well as the throughput corresponding to the single device and the calculation frequency value during the processing task, and the efficiency of the whole system for processing the task can be improved.

Description

Method, device and equipment for controlling system operation and storage medium
Technical Field
The present invention relates to the field of wireless communications, and in particular, to a method, an apparatus, a device, and a storage medium for controlling system operation.
Background
As shown in fig. 2, Mobile Edge Computing (MEC) enables internet of things devices (single devices) to offload Computing tasks to neighboring Edge servers (e.g., base stations) and perform the Computing tasks in a faster and more energy-efficient manner, thereby significantly reducing Computing latency.
However, deployment of ground infrastructure such as ground MEC servers in large-scale emergency sensor networks for environmental monitoring, field exploration and the like is difficult, costly and flexible. In recent years, base station technology is rapidly developed, and base station auxiliary ground equipment is used for data processing, so that network data processing performance can be flexibly and inexpensively improved, and the service coverage range is expanded.
In the conventional method, the ground equipment is assumed to be powered by a stable power supply, so that a moving track (track formed by the position of the base station) of the base station for receiving the internet of things equipment and preparation work required by the internet of things equipment before the base station arrives can be designed in advance. The reason that the traditional method can draw up the moving track of the base station in advance is that the task quantity of each piece of internet-of-things equipment is assumed to be fixed and known, and the actual situation is that the task quantities of the pieces of internet-of-things equipment in different time slots are different, namely the task quantity corresponding to the pieces of internet-of-things equipment is random, the randomness of the task quantity corresponding to the pieces of internet-of-things equipment causes the randomness of the moving track of the base station, and the superposition of the two randomness causes the low efficiency of a system formed by the base station and the pieces of internet-of-things equipment in processing the task quantities.
In summary, the prior art is less efficient in handling the task load.
Thus, there is a need for improvements and enhancements in the art.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method, a device, equipment and a storage medium for controlling system operation, which solve the problem of low efficiency in processing task load in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method of controlling operation of a system, comprising:
acquiring the individual task backlog, individual transmission rate and individual transmission power corresponding to a single device;
obtaining the individual task throughput corresponding to the single device according to the individual task backlog;
obtaining an individual device calculation frequency value corresponding to the single device and a transmission time value for the single device to transmit the corresponding individual task amount to a base station according to the individual transmission rate, the individual transmission power and the individual task backlog, wherein the base station is used for processing the transmitted individual task amount;
obtaining the position of the base station when the base station receives the individual task volume according to the individual task backlog volume, the individual transmission rate, the transmission time value and the attribute information corresponding to the base station;
and controlling the operation of a system at a set time slot according to the individual task throughput, the individual equipment calculation frequency value, the transmission time value and the base station position, wherein the system consists of the single equipment and the base station, and the set time slot is matched with the transmission time value.
In an implementation manner, the obtaining, according to the individual task backlog, an individual task throughput corresponding to the single device includes:
constructing a first objective function related to an individual task throughput parameter according to the individual task backlog parameter corresponding to the individual task backlog quantity;
constructing a first constraint condition consisting of the value ranges corresponding to the individual task throughput parameters;
obtaining a minimum value of the first objective function under the first constraint condition according to the first constraint condition;
obtaining a value corresponding to the individual task throughput parameter when the first objective function takes the minimum value according to the minimum value of the first objective function;
and obtaining the individual task throughput according to the value corresponding to the individual task throughput parameter.
In one implementation manner, the obtaining, according to the individual transmission rate, the individual transmission power, and the individual task backlog, an individual device calculation frequency value corresponding to the single device, and a transmission time value used by the single device to transmit the corresponding individual task to the base station includes:
constructing a second objective function related to the frequency parameter and the transmission time parameter of the individual equipment according to the individual transmission rate, the individual transmission power and the individual task backlog;
constructing a second constraint condition consisting of a value range corresponding to the individual device frequency parameter and a value range corresponding to the transmission time parameter;
obtaining a minimum value of the second objective function under the second constraint condition according to the second constraint condition;
taking the minimum value according to the second objective function to obtain the value corresponding to the individual equipment frequency parameter and the value corresponding to the transmission time parameter when the second objective function takes the minimum value;
and obtaining the individual equipment calculation frequency value corresponding to the single equipment and the transmission time value used by the single equipment for transmitting the corresponding individual task amount to the base station according to the value corresponding to the individual equipment frequency parameter and the value corresponding to the transmission time parameter.
In one implementation, the constructing a second constraint condition composed of a value range corresponding to the individual device frequency parameter and a value range corresponding to the transmission time parameter includes:
obtaining the maximum calculation frequency of the CPU corresponding to the single device according to the single device;
obtaining the value range corresponding to the individual equipment frequency parameter between zero and the maximum calculation frequency of the CPU according to the maximum calculation frequency of the CPU;
obtaining a transmission time parameter corresponding to the system according to the transmission time parameter corresponding to the individual device;
according to the system, obtaining the time slot length corresponding to the system;
obtaining that the value of the transmission time parameter corresponding to the single device is greater than or equal to zero and the value of the transmission time parameter corresponding to the system is less than or equal to the time slot length according to the transmission time parameter corresponding to the system and the time slot length;
dividing the value corresponding to the individual device frequency parameter by the attribute value corresponding to the base station and adding the product of the value corresponding to the individual transmission rate parameter and the value corresponding to the transmission time parameter to obtain a calculation result;
obtaining the backlog quantity of the individual tasks of which the calculation result is less than or equal to the calculation result according to the calculation result;
and obtaining the second constraint condition according to the fact that the value range corresponding to the individual device frequency parameter is located between zero and the maximum calculation frequency of the CPU, the value of the transmission time parameter corresponding to the single device is greater than or equal to zero, the value of the transmission time parameter corresponding to the system is less than or equal to the time slot length, and the calculation result is less than or equal to the individual task backlog.
In an implementation manner, the obtaining, according to the individual task backlog, the individual transmission rate, the transmission time value, and the attribute information corresponding to the base station, a position of the base station when the base station receives the individual task volume includes:
constructing a third objective function related to the position parameter of the base station according to the individual task backlog, the individual transmission rate, the transmission time value and the attribute information corresponding to the base station;
according to the attribute information corresponding to the base station, obtaining a starting point position, an end point position and a flight speed corresponding to the base station in the attribute information;
constructing a third constraint condition according to the starting point position, the end point position and the flying speed;
obtaining a minimum value of the third objective function under the third constraint condition according to the third constraint condition;
obtaining a value corresponding to the position parameter of the base station when the third objective function takes the minimum value according to the minimum value of the third objective function;
and obtaining the position of the base station when the base station receives the individual task quantity according to the value corresponding to the position parameter of the base station.
In one implementation, the constructing a third constraint according to the starting position, the ending position and the flying speed includes:
obtaining weight information corresponding to the base station in the attribute information according to the attribute information corresponding to the base station;
acquiring environment information of the base station, an elevation angle corresponding to the base station and the height of the base station from the individual equipment;
and constructing a third constraint condition according to the weight information, the height, the starting point position, the end point position and the flying speed.
In one implementation, the method further comprises:
obtaining system task throughput corresponding to the system according to the individual task throughput;
and obtaining the task processing rate corresponding to the system according to the system task throughput and the set time slot.
In a second aspect, an embodiment of the present invention further provides an apparatus for controlling a method for operating a system, where the apparatus includes the following components:
the data acquisition module is used for acquiring the individual task backlog, the individual transmission rate and the individual transmission power corresponding to the single device;
the throughput calculation module is used for obtaining the individual task throughput corresponding to the single device according to the individual task backlog;
a time calculation module, configured to obtain, according to the individual transmission rate, the individual transmission power, and the individual task backlog, an individual device calculation frequency value corresponding to the single device, and a transmission time value used by the single device to transmit the corresponding individual task amount to a base station, where the base station is configured to process the transmitted individual task amount;
a position calculation module, configured to obtain, according to the individual task backlog, the individual transmission rate, the transmission time value, and attribute information corresponding to the base station, a position of the base station at which the base station receives the individual task;
and the control module is used for controlling the operation of the system in a set time slot according to the individual task throughput, the individual equipment calculation frequency value, the transmission time value and the base station position, wherein the system consists of the single equipment and the base station, and the set time slot is matched with the transmission time value.
In a third aspect, an embodiment of the present invention further provides a terminal device, where the terminal device includes a memory, a processor, and a program that is stored in the memory and is executable on the processor and is used for controlling system operation, and when the processor executes the program that is executable on the processor, the steps of the method for controlling system operation described above are implemented.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where a program for controlling system operation is stored on the computer-readable storage medium, and when the program for controlling system operation is executed by a processor, the steps of the method for controlling system operation described above are implemented.
Has the advantages that: the invention calculates the task throughput, the transmission time value, the calculation frequency value of the individual device and the position of the base station when receiving the task transferred by the individual device, wherein the transmission time value is calculated by deterministic factors such as the transmission rate, the transmission power and the task backlog of the individual device, thereby ensuring that the position of the base station calculated according to the transmission time value is also deterministic, controlling the whole system processing task by the deterministic base station position and the transmission time value as well as the throughput corresponding to the individual device and the frequency value when processing the task, and improving the efficiency of the whole system processing task.
In addition, the invention determines the task throughput of the single device according to the individual task backlog, namely the task volume left after the task of the previous time slot is processed, and determines the task volume (task throughput) required by the current time slot according to the task processing condition of the previous time slot. The backlog of the task amount of the individual device can be prevented, and the stability of the data queue formed by the task amount is improved.
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is a MEC system model of the present invention.
Detailed Description
The technical scheme of the invention is clearly and completely described below by combining the embodiment and the attached drawings of the specification. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It has been found through research that Mobile Edge Computing (MEC) enables internet of things devices (single devices) to offload Computing tasks to neighboring Edge servers (e.g., base stations) as shown in fig. 2, so as to perform the Computing tasks in a faster and more energy-saving manner, thereby significantly reducing the Computing delay.
However, deployment of ground infrastructure such as ground MEC servers in large-scale emergency sensor networks for environmental monitoring, field exploration and the like is difficult, costly and flexible. In recent years, base station technology is rapidly developed, and base station auxiliary ground equipment is used for data processing, so that network data processing performance can be flexibly and inexpensively improved, and the service coverage range is expanded. In the conventional method, the ground equipment is assumed to be powered by a stable power supply, so that a moving track (track formed by the position of the base station) of the base station for receiving the internet of things equipment and preparation work required by the internet of things equipment before the base station arrives can be designed in advance. The reason that the traditional method can draw up the moving track of the base station in advance is that the task quantity of each piece of internet-of-things equipment is assumed to be fixed and known, and the actual situation is that the task quantities of the pieces of internet-of-things equipment in different time slots are different, namely the task quantity corresponding to the pieces of internet-of-things equipment is random, the randomness of the task quantity corresponding to the pieces of internet-of-things equipment causes the randomness of the moving track of the base station, and the superposition of the two randomness causes the low efficiency of a system formed by the base station and the pieces of internet-of-things equipment in processing the task quantities.
In order to solve the technical problems, the invention provides a method, a device, equipment and a storage medium for controlling system operation, which solve the problem of low efficiency in processing task load in the prior art. In specific implementation, the invention can improve the task processing efficiency of the whole system by calculating the task throughput of the single device, the transmission time value, the calculation frequency value of the individual device and the position of the base station when receiving the task transferred by the single device, wherein the transmission time value is calculated by deterministic factors such as the transmission rate, the transmission power and the task backlog of the single device.
For example, the system includes device a1, device a2, and base station B, and the tasks to be processed by the system are derived from the external tasks received by device a1 and device a 2. Device a1 may process tasks received by device a1 in conjunction with base station B, and device a2 may process tasks received by device a2 in conjunction with base station B. When processing the task of the device a1, the present embodiment first determines how much the amount of the task backlogged before the device a1 is, then determines how many tasks the device a1 and the base station B need to process for the device a1 together, and determines the time for transferring according to the transmission rate and the transmission power corresponding to the device a1 transferring the task amount to the base station B for processing, and also determines how often the device a1 needs to process the task corresponding to the current timeslot according to the task (the amount of the backlogged task) left in the previous timeslot of the device a 1. And then the position of the base station receiving device A1 when the task is transferred is controlled through the transfer time. The corresponding device a2 performs the same above-described operation, and the base station B can simultaneously process the task transferred by the device a1 and the task transferred by the device a 2. Through the analysis, the individual task throughput, the individual device calculation frequency value, the transmission time value and the base station position obtained in the embodiment are matched with one another, so that the task corresponding to the current time slot can be completed, and the task processing efficiency of the system is improved.
Exemplary method
The method for controlling the operation of the system can be applied to the terminal equipment. In this embodiment, as shown in fig. 1, the method for controlling the operation of the system specifically includes the following steps:
s100, acquiring the individual task backlog, the individual transmission rate and the individual transmission power corresponding to the single device.
The single devices in this embodiment are the devices UE1, UE2, UE3, and UE4 in the drone MEC system model in fig. 1. By collections
Figure BDA0003249821440000071
A total of K user devices are represented and each user device is equipped with an EH component for collecting renewable energy in the environment to power the user device. Each user equipment can unload part of the computing resources to an unmanned aerial vehicle (base station) for computing, and the unmanned aerial vehicle feeds back the computing results to the user equipment after computing. Assuming that the unmanned aerial vehicle provides MEC service for users on the ground within a time period T, and the total time is N time slots, the command is to
Figure BDA0003249821440000081
Is the slot length of the drone and Δ is small enough so that the position of the drone in each slot approximates the initial position of this slot, with the nth slot being denoted by n. And if the unmanned aerial vehicle and the user equipment are controlled to process tasks in the nth time slot, the task backlog quantity of the individual tasks corresponds to the task quantity of the user equipment in the (n-1) th time slot. The individual transmission rate is a rate at which the user equipment (individual device) uploads the task to the drone, and the individual transmission power is a power corresponding to the user equipment when uploading. The specific calculation process is as follows:
suppose that the drone flies at a fixed height h for a time period T and that the maximum flying speed of the drone is vmThe time-varying horizontal coordinate of the drone at time slot n may be denoted as pu[n]＝(xu[n],yu[n]). The initial position of the unmanned plane is pu[1]＝pILast time slot reaches the predetermined destination pu[N+1]＝pF. In order to avoidAnd interference between transmissions, namely, the user equipment transmits information to the unmanned aerial vehicle by using the same bandwidth W in a Time Division Multiple Access (TDMA) transmission mode, and simultaneously, a common Line-of-Sight (LoS) channel probability model is adopted to determine large-scale attenuation of links between the unmanned aerial vehicle and the user equipment.
The geometric LoS probability of the drone and the user device depends on the environmental parameters related to the drone and the drone elevation.
Figure BDA0003249821440000082
Is the LoS probability of the kth ue in the nth slot, which can be approximated as a modified sigmoid function, expressed as:
Figure BDA0003249821440000083
where a and b are parameters relating to the environment, thetak[n]Is the elevation angle, the calculation formula is:
Figure BDA0003249821440000084
similarly, the Non-Line-of-Sight (NLoS) channel probability is equal to
Figure BDA0003249821440000091
Thus, the expected values of the channel power gain are:
Figure BDA0003249821440000092
wherein
Figure BDA0003249821440000093
Is a regularized LoS probability taking into account the fading effects of the NLoS channel, and κ<1，
Figure BDA0003249821440000094
Is the path loss exponent, g0Represents a reference distance d0Channel gain at 1m, dk[n]Is the distance between the nth slot device k and the drone, and
Figure BDA0003249821440000095
within a time slot, the elevation angle θ is very small because the horizontal movement distance of the drone is very small relative to the vertical heightk[n]The change in (c) is negligible. The upload rate (individual transmission rate) R of the device k at the nth time slot is thus determinedk[n]Comprises the following steps:
Figure BDA0003249821440000096
wherein
Figure BDA0003249821440000097
Figure BDA0003249821440000098
PkIs a fixed transmission power, N0Is the noise power.
And S200, obtaining the individual task throughput corresponding to the single device according to the individual task backlog.
In this embodiment, the individual task amount (i.e., individual task throughput) that needs to be processed in the current timeslot is calculated according to the individual task backlog amount of the user equipment (single device) in the previous timeslot (i.e., the task amount that has not been completed by the user equipment in the previous timeslot), which includes two parts: the task amount processed by the user equipment and the task amount processed by the unmanned aerial vehicle (base station) are given. Step S200 includes steps S201, S202, S203, S204, S205:
s201, according to the individual task backlog parameters corresponding to the individual task backlogs, a first objective function related to the individual task throughput parameters is constructed.
In the embodiment, the individual task throughput is taken as an unknown parameter, an objective function of the parameter is constructed, and the optimal value of the individual task throughput, namely the value capable of improving the task processing efficiency of the whole system, is obtained through the objective function.
S202, constructing a first constraint condition consisting of the value ranges corresponding to the individual task throughput parameters.
S203, obtaining the minimum value of the first objective function under the first constraint condition according to the first constraint condition.
S204, according to the minimum value of the first objective function, obtaining a value corresponding to the individual task throughput parameter when the first objective function takes the minimum value.
S205, obtaining the individual task throughput according to the value corresponding to the individual task throughput parameter.
The overall process of step S200 is as follows:
Figure BDA0003249821440000101
then akThe optimal solution of (d) can be expressed as:
Figure BDA0003249821440000102
i.e. if Qk[n]Below a threshold value w2V, user k will receive all the generated data tasks AkOtherwise, no new data task will be received, Qk[n]And backlogging the individual tasks of the kth user equipment in the nth time slot.
Wherein the content of the first and second substances,
Figure BDA0003249821440000103
is an individual task throughput parameter akOptimum value of (2), QkV is a Lyapunov control parameter, representing a weight of power consumption, for an individual task backlog.
Equation (5) is a first objective function and equation (6) is a first constraint.
S300, obtaining an individual device calculation frequency value corresponding to the single device and a transmission time value used by the single device for transmitting the corresponding individual task amount to a base station according to the individual transmission rate, the individual transmission power and the individual task backlog, wherein the base station is used for processing the transmitted individual task amount.
The individual task throughput of the current slot includes two parts: one part is the task amount processed by the user equipment, and the other part is the task amount transferred to the unmanned aerial vehicle (base station). And the user equipment takes time to transfer the task amount to the drone (transfer time). No matter the user equipment processes the task amount by itself or the unmanned aerial vehicle processes the task amount by the energy consumption, the two energy consumption parts are respectively described as follows:
local computation model (amount of tasks the user equipment itself handles):
fk[n]CPU frequency, C, for user equipment k in time slot nkThe task size calculated locally at time slot n for the CPU cycle required to calculate 1bit input data
Figure BDA0003249821440000111
And consume energy
Figure BDA0003249821440000112
Respectively as follows:
Figure BDA0003249821440000113
wherein gamma iscIs dependent on the effective capacitance coefficient of the CPU chip architecture.
Edge server computation model (amount of tasks transferred to drone processing):
each time slot n may be divided into K sub-time slots
Figure BDA0003249821440000114
And satisfy
Figure BDA0003249821440000115
δk[n]Offloading computing tasks to edge server drones for nth sub-slot user device kThe transfer time. Task size of offload
Figure BDA0003249821440000116
And the energy consumed
Figure BDA0003249821440000117
Respectively as follows:
Figure BDA0003249821440000118
combining (7) and (8) to obtain a computation task l that can be executed in time slot nk[n]And the energy E required to be consumedk[n]Respectively as follows:
Figure BDA0003249821440000119
because the unmanned aerial vehicle has very strong computing power, the length of output result is relatively less, and the edge computing time and the feedback download time are ignored. Therefore, there is no data queue backlog on the drone.
In step S300, calculating the calculation frequency value and the transmission time value of the individual device is equivalent to calculating resource allocation, that is, how much frequency the individual device needs to set for the CPU to meet the calculation requirement when processing the currently backlogged task, and how much time the individual needs to complete the task to be transferred to the unmanned aerial vehicle. The step S300 includes the following steps S301, S302, S303, S304, S305:
s301, constructing a second objective function related to the frequency parameter and the transmission time parameter of the individual device according to the individual transmission rate, the individual transmission power and the individual task backlog.
Figure BDA0003249821440000121
fkIndividual device frequency parameter, δkAs a transmission time parameter, RkIs an individual transmission rate, PkFor individual transmission power, QkBacklogs individual tasks.
Wherein, B &kRepresenting the battery disturbance sequence of the user equipment k, namely the electric quantity of the battery, when calculating the disturbance sequence of the user equipment k at the time slot n
Figure BDA0003249821440000122
When the temperature of the water is higher than the set temperature,
Figure BDA0003249821440000123
assume that consider an energy-aware battery management strategy: if B is presentk[n]Specific battery charge threshold
Figure BDA0003249821440000124
Low, the user equipment k will stop processing data and only collect energy for the battery recharge. In any time slot n, the consumed energy can not exceed the existing battery power Ek[n]：
Figure BDA0003249821440000125
Wherein 1 is{·}Is an indicator function.
The energy collection process is a continuous process of energy packet arrival, and the size of the energy packet available to the user equipment k in the time slot n is
Figure BDA0003249821440000126
And are independently co-distributed. For all n, k, the upper bound of energy packets is satisfied
Figure BDA0003249821440000127
Assuming each energy queue has a finite capacity θkThen energy queue Bk[n]The change process of (2) is as follows:
Figure BDA0003249821440000128
wherein
Figure BDA0003249821440000129
S302, constructing a second constraint condition consisting of the value range corresponding to the individual device frequency parameter and the value range corresponding to the transmission time parameter. The specific process comprises the following steps: obtaining the maximum calculation frequency of the CPU corresponding to the single device according to the single device; obtaining the value range corresponding to the individual equipment frequency parameter between zero and the maximum calculation frequency of the CPU according to the maximum calculation frequency of the CPU; obtaining a transmission time parameter corresponding to the system according to the transmission time parameter corresponding to the individual device; according to the system, obtaining the time slot length corresponding to the system; obtaining that the value of the transmission time parameter corresponding to the single device is greater than or equal to zero and the value of the transmission time parameter corresponding to the system is less than or equal to the time slot length according to the transmission time parameter corresponding to the system and the time slot length; dividing the value corresponding to the individual device frequency parameter by the attribute value corresponding to the base station and adding the product of the value corresponding to the individual transmission rate parameter and the value corresponding to the transmission time parameter to obtain a calculation result; obtaining the backlog quantity of the individual tasks of which the calculation result is less than or equal to the calculation result according to the calculation result; and obtaining the second constraint condition according to the fact that the value range corresponding to the individual device frequency parameter is located between zero and the maximum calculation frequency of the CPU, the value of the transmission time parameter corresponding to the single device is greater than or equal to zero, the value of the transmission time parameter corresponding to the system is less than or equal to the time slot length, and the calculation result is less than or equal to the individual task backlog.
The second constraint satisfies the following relation:
Figure BDA0003249821440000131
Figure BDA0003249821440000132
Figure BDA0003249821440000133
Figure BDA0003249821440000135
calculating the maximum frequency of the CPU corresponding to a single device;
Figure BDA0003249821440000134
the sum of the transfer time corresponding to the system, namely the sum of the transfer time corresponding to all individual devices in the system; time slot length corresponding to delta system; ckAnd the attribute value corresponding to the base station (unmanned aerial vehicle). Wherein C iskIncluding known aerodynamic parameters related to the weight of the drone.
And S303, obtaining the minimum value of the second objective function under the second constraint condition according to the second constraint condition.
Under the second constraint, the minimum value of equation (10a) is calculated.
S304, taking the minimum value according to the second objective function, and obtaining the value corresponding to the individual equipment frequency parameter and the value corresponding to the transmission time parameter when the second objective function takes the minimum value.
In this embodiment, a lagrangian dual decomposition method is used to obtain a value corresponding to the individual device frequency parameter and a value corresponding to the transmission time parameter when the second objective function takes the minimum value, where the value corresponding to the individual device frequency parameter is the optimal individual device frequency, and the value corresponding to the transmission time parameter is the optimal transmission time value. The specific process is as follows:
inputting: location of user
Figure BDA0003249821440000141
Present drone position puThe current backlog queue
Figure BDA0003249821440000142
And battery power
Figure BDA0003249821440000143
(II) initialization: sigma0←0.01,
Figure BDA0003249821440000144
A sufficiently large value, pu'←pu(ii) a Channel gain gkAnd transmission rate
Figure BDA0003249821440000145
(III)
Figure BDA0003249821440000146
LB←0；
(IV) repeating
Figure BDA0003249821440000147
To obtain
Figure BDA0003249821440000148
If it is not
Figure BDA0003249821440000149
Then LB ═ λ; otherwise UB ═ λ.
(V) until | UB-LB | is less than or equal to delta0Outputting the resource allocation optimum value
Figure BDA00032498214400001410
Wherein L is a Lyapunov function.
S305, obtaining an individual device calculation frequency value corresponding to the single device and a transmission time value used by the single device to transmit the corresponding individual task amount to the base station according to the value corresponding to the individual device frequency parameter and the value corresponding to the transmission time parameter.
Optimum value
Figure BDA00032498214400001411
Calculating frequency values, optimal values for the individual devices corresponding to the single device
Figure BDA00032498214400001412
I.e. the transmission time value used by the single device to transfer the corresponding individual task quantity to the base station.
S400, obtaining the position of the base station when the base station receives the individual task volume according to the individual task backlog volume, the individual transmission rate, the transmission time value and the attribute information corresponding to the base station.
The base station position of this embodiment is the position when unmanned aerial vehicle receives the partial task that user equipment shifted, and user equipment shifts partial task for unmanned aerial vehicle, and unmanned aerial vehicle need be close to user equipment and just can receive this part of task.
Step S400 includes steps S401, S402, S403, S404, S405 as follows:
s401, according to the individual task backlog, the individual transmission rate, the transmission time value and the attribute information corresponding to the base station, a third objective function related to the position parameter of the base station is constructed.
The third objective function is as follows:
Figure BDA0003249821440000151
wherein p isu′As a position parameter, QkBacklog for individual tasks, RkIs a measure of the individual transmission rate,
Figure BDA0003249821440000157
for an optimum value of transmission time, C1、C2、C4The method is characterized in that known dynamics parameters relevant to importance in attribute information corresponding to a base station (unmanned aerial vehicle), v is the flight speed of the unmanned aerial vehicle, and v is the flight speed of the unmanned aerial vehicletipFor unmanned aerial vehicle rotorTip speed of the blade. v. oftipAnd C1、C2、C4Satisfies the following relation:
Figure BDA0003249821440000152
PUAVis unmanned aerial vehicle power. And P isUAVWith unmanned plane propulsion energy consumption EUAVAnd satisfies the following relation:
EUAV[n]＝PUAV[n]Δ
pu[1]＝pI,pu[N+1]＝pF,
Figure BDA0003249821440000153
Figure BDA0003249821440000154
pu[1]＝pI,pu[N+1]＝pFthe starting position and destination of the drone are made clear.
Figure BDA0003249821440000155
Limiting the maximum speed of the drone by limiting the displacement of the drone movement within a time slot.
Figure BDA0003249821440000156
The method is characterized in that when the unmanned aerial vehicle flies at the maximum speed within the flying time N-N, the distance between the destination of the unmanned aerial vehicle and the position of the unmanned aerial vehicle at the moment of N +1 cannot exceed the maximum displacement, and meanwhile, the unmanned aerial vehicle can reach the destination within the designated time.
S402, constructing a third constraint condition according to the attribute information corresponding to the base station, specifically including: according to the attribute information corresponding to the base station, obtaining the starting position, the ending position, the flight speed and the weight information corresponding to the base station in the attribute information; acquiring environment information of the base station, an elevation angle corresponding to the base station and the height of the base station from the individual equipment; and constructing a third constraint condition according to the weight information, the height, the starting point position, the end point position and the flying speed.
The third constraint satisfies the following relation:
Figure BDA0003249821440000161
Figure BDA0003249821440000162
||pu'-pu||≤vmΔ, (13d)
||pF-pu'||≤vm(N-n)Δ. (13e)
pustarting position, pFEnd position, C3Known kinetic parameters, v, relevant to importance in attribute information corresponding to base station (unmanned aerial vehicle)mThe maximum flying speed of the unmanned aerial vehicle.
The third objective function of step S401 and the third constraint condition of step S402 are transformed by the following procedures:
obtain the optimum value
Figure BDA0003249821440000163
The unmanned aerial vehicle trajectory optimization problem can be described as:
Figure BDA0003249821440000164
||pF-pu'||≤vm(N-n)Δ. (13e)
the above equation is a non-convex problem, so we solve the non-convex problem using a successive convex approximation. First, we introduce an auxiliary slack variable y, where
Figure BDA0003249821440000165
Then (14) can be written as:
Figure BDA0003249821440000166
(13e) can be written as:
Figure BDA0003249821440000167
introducing an auxiliary variable xik：
Figure BDA0003249821440000171
Proposition 1: suppose that
Figure BDA0003249821440000172
For the coordinates of the drone at the ith iteration, the lower bound for the transmission rate of user k is:
Figure BDA0003249821440000173
wherein
Figure BDA0003249821440000174
Applying a first order taylor expansion to (13e), deriving a global dip lower bound as:
Figure BDA0003249821440000175
wherein
Figure BDA0003249821440000176
After approximating the velocity constraints and objectives using concave lower bounds (19) and (20), the trajectory optimization in (21) yields a third objective function and a third constraint in the l-th iteration.
And S403, obtaining a minimum value of the third objective function under the third constraint condition according to the third constraint condition.
The specific solving process is as follows:
inputting: time of unloading
Figure BDA0003249821440000177
And drone position pu；
(II) initialization: ∈ ← 0.01;
Figure BDA0003249821440000178
(III) repetition
Calculating y from (21)(l)；
Solving a convex optimization problem (14) using cvx; the optimization variables and target values are respectively noted
Figure BDA0003249821440000179
And G(l)；
Updating a local value:
Figure BDA00032498214400001710
updating the lower bound value according to (19) and (20)
Figure BDA00032498214400001711
And Y(l+1){pu',y}；
Updating l to l + 1;
(IV) until | G(l)-G(l-1)| ≦ ε 2, output: unmanned aerial vehicle next moment position pu'
S404, according to the minimum value of the third objective function, obtaining a value corresponding to the position parameter of the base station when the minimum value of the third objective function is obtained;
s405, according to the value corresponding to the position parameter of the base station, obtaining the position of the base station when the base station receives the individual task amount.
P when the minimum value is taken in the formula (13a)u′The corresponding value is the base station position when the base station receives the individual task amount, namely the optimal position.
S500, controlling the operation of a system in a set time slot according to the individual task throughput, the individual equipment calculation frequency value, the transmission time value and the base station position, wherein the system consists of the single equipment and the base station, and the set time slot is matched with the transmission time value.
After four parameter values related to controlling the whole system are calculated, namely the individual task throughput, the individual device calculation frequency value, the transmission time value and the base station position, the whole system can be controlled to process the task capacity of the individual device.
The steps S100-S500 of this embodiment are to change the control of the whole system operation by the unknown time slot into the control of the current time slot system operation according to the last time slot task backlog, and through ak、fk、δkAnd pu′The individual equipment and the unmanned aerial vehicle are controlled to process the task amount, the problems of unmanned aerial vehicle propulsion amount minimization and system throughput maximization are solved through controlling the four parameters, and the specific conversion process is as follows:
it is intended to minimize the long term drone mean propulsion energy and maximize the system throughput by jointly optimizing energy harvesting decisions, drone trajectories and computational resource allocation (including user equipment's CPU frequency and offload duration), where the throughput of the system is n slots
Figure BDA0003249821440000181
The drone propulsion energy minimization and system throughput maximization problem can be expressed as:
Figure BDA0003249821440000182
Figure BDA0003249821440000183
Figure BDA0003249821440000184
Figure BDA0003249821440000191
Figure BDA0003249821440000192
Figure BDA0003249821440000193
Figure BDA0003249821440000194
pu[1]＝pI,pu[N+1]＝pF,
Figure BDA0003249821440000195
Figure BDA0003249821440000196
wherein { f }k[n]},{δk[n]},{ak[n]},pu[n]And the k-dimensional vectors are respectively the CPU frequency, unloading duration, bearable tasks and the position of the unmanned aerial vehicle corresponding to the user equipment in the time slot n. w is a1And w2Weight the unmanned aerial vehicle energy and the importance of the overall undertaking task. (22c) And (22d) is the CPU frequency limit and offload time limit of the user equipment. (22e) Is an information causal constraint, i.e. the data executed in n slots cannot exceed the queuing backlog at time n. (22f) Are capable of assuming tasksAnd (4) restraining.
Figure BDA0003249821440000197
Is the consumption of energy to the user equipment,
Figure BDA0003249821440000198
Figure BDA0003249821440000199
these three relationships are constraints on the speed of the drone.
The design of the computational offload policy for the MEC system serving the energy concentration device becomes more complex compared to conventional mobile cloud computing systems with battery-powered devices. In the case of random arrival of energy and data, i.e., making online decisions at each time horizon without future information, it is difficult to meet the long-term queue stability constraint. Furthermore, the use of data and energy buffers combines current control decisions with future control decisions. The PLOT algorithm applies disturbance Lyapunov to optimize and decouple control decisions of different time slots, and meanwhile long-term stability constraint is guaranteed.
Battery disturbance sequence for user equipment k in time slot n
Figure BDA00032498214400001910
The current total sequence backlog may be represented as a vector
Figure BDA0003249821440000201
Thus, the Lyapunov function L (Q [ n ]]) And a Lyapunov drift DeltaL (Q [ n ] in one time slot]) Comprises the following steps:
Figure BDA0003249821440000202
Figure BDA0003249821440000203
adopting a method of minimizing drift and penalty, wherein the upper bound of the drift and penalty in the time slot n is as follows:
Figure BDA0003249821440000204
where V >0 is a Lyapunov control parameter, representing a weight of power consumption. The original uncertainty problem can be translated into a certainty problem at time slot n:
Figure BDA0003249821440000205
the constraint condition is (22c-22f)
And
Figure BDA0003249821440000206
Figure BDA0003249821440000207
and
Figure BDA0003249821440000208
in contrast, the energy causality constraint in the single slot problem is eliminated
Figure BDA0003249821440000209
For simplicity, the time variable n is removed and p is usedu'In place of pu[n+1]The deterministic optimization problem is rewritten as:
Figure BDA00032498214400002010
Figure BDA00032498214400002011
Figure BDA00032498214400002012
Figure BDA00032498214400002013
Figure BDA00032498214400002014
||pu'-pu||≤vmΔ, (27f)
||pF-pu'||≤vm(N-n)Δ. (27g)
Figure BDA0003249821440000211
only by parameters of the current system state (e.g., data and battery queue state, data arrival, energy packet arrival, current drone location, current time). Therefore, the control action in the current single time slot can be optimized only by the observation of the current information, and the online control under the condition of unknown future information is realized.
Inputting an initial value:
Figure BDA0003249821440000212
the algorithm, taking time slot n as an example, executes the steps of:
(ii) observation
Figure BDA0003249821440000213
And pu[n]By solving the problem
Figure BDA00032498214400002113
To obtain
Figure BDA0003249821440000214
And
Figure BDA0003249821440000215
for each user equipment k, the following steps need to be performed:
obtaining energy from the environment;
undertake
Figure BDA0003249821440000216
Calculating tasks of bits and loading the tasks to a data sequence;
according to the step (i)
Figure BDA0003249821440000217
Computing a computing task size locally to a user device
Figure BDA0003249821440000218
According to the step (i)
Figure BDA0003249821440000219
Calculating size of user equipment offload data
Figure BDA00032498214400002110
According to the formula
Figure BDA00032498214400002111
Calculating a data sequence Q of n +1 time slotsk[n+1]；
According to the formula
Figure BDA00032498214400002112
Calculating an energy sequence B of n +1 time slotsk[n+1]。
From the above calculation process, the task backlog Q of the last time slot n is obtained in this embodimentk[n]The amount of tasks and the energy sequence to be processed by the current time slot n +1 can be calculated.
In summary, the present invention calculates the task throughput of a single device, a transmission time value calculated from deterministic factors such as transmission rate, transmission power and task backlog of the single device, a frequency value calculated by the individual device, and a location of the base station at the time of receiving a task transferred by the single device, such that the location of the base station calculated from the transmission time value is also deterministic, by which the location of the base station and the transmission time are deterministicThe value, the throughput corresponding to the single device and the frequency value when the task is processed control the whole system to process the task, and the efficiency of the whole system to process the task can be improved. In addition, the invention determines the task throughput of the single device according to the individual task backlog, namely the task volume left after the task of the previous time slot is processed, and determines the task volume (task throughput) required by the current time slot according to the task processing condition of the previous time slot. The backlog of the task amount of the individual device can be prevented, and the stability of the data queue formed by the task amount is improved. The method solves the problem that the unmanned aerial vehicle senses and controls a in each time slot data respectivelykComputing resource allocation fk,δkAnd unmanned aerial vehicle trajectory control pu'. At the next moment, the control algorithm can be repeatedly used according to new observation data, and the unmanned aerial vehicle control method at the new moment is solved. Theoretical analysis verifies that the method can effectively control the data queue length of the ground internet of things node and improve the network data processing rate under the condition of completely not knowing the random energy acquisition and data load information in the future.
Exemplary devices
This embodiment also provides a certain device, the device includes following component parts:
the data acquisition module is used for acquiring the individual task backlog, the individual transmission rate and the individual transmission power corresponding to the single device;
the throughput calculation module is used for obtaining the individual task throughput corresponding to the single device according to the individual task backlog;
a time calculation module, configured to obtain, according to the individual transmission rate, the individual transmission power, and the individual task backlog, an individual device calculation frequency value corresponding to the single device, and a transmission time value used by the single device to transmit the corresponding individual task amount to a base station, where the base station is configured to process the transmitted individual task amount;
a position calculation module, configured to obtain, according to the individual task backlog, the individual transmission rate, the transmission time value, and attribute information corresponding to the base station, a position of the base station at which the base station receives the individual task;
a control module for controlling the operation of the system at a set time slot according to the individual task throughput, the individual device calculation frequency value, the transmission time value, and the base station position, wherein the system is composed of the individual device and the base station, and the set time slot is matched with the transmission time value
Based on the foregoing embodiment, the present invention further provides a terminal device, where the terminal device includes a memory, a processor, and a program that is stored in the memory and is executable on the processor and controls a system to operate, and when the processor executes the program that is executable on the processor, the steps of the method for controlling the system to operate are implemented.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
In summary, the present invention discloses a method, an apparatus, a device and a storage medium for controlling system operation, wherein the method comprises: acquiring the individual task backlog, individual transmission rate and individual transmission power corresponding to a single device; obtaining the individual task throughput corresponding to the single device according to the individual task backlog; obtaining an individual device calculation frequency value corresponding to the single device and a transmission time value for the single device to transmit the corresponding individual task amount to a base station according to the individual transmission rate, the individual transmission power and the individual task backlog, wherein the base station is used for processing the transmitted individual task amount; obtaining the position of the base station when the base station receives the individual task volume according to the individual task backlog volume, the individual transmission rate, the transmission time value and the attribute information corresponding to the base station; and controlling the operation of a system in a set time slot according to the individual task throughput, the individual equipment calculation frequency value, the transmission time value and the base station position, wherein the system consists of the single equipment and the base station, and the set time slot is matched with the transmission time value.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A method of controlling operation of a system, comprising:
acquiring the individual task backlog, individual transmission rate and individual transmission power corresponding to a single device;
obtaining the individual task throughput corresponding to the single device according to the individual task backlog;
obtaining an individual device calculation frequency value corresponding to the single device and a transmission time value for the single device to transmit the corresponding individual task amount to a base station according to the individual transmission rate, the individual transmission power and the individual task backlog, wherein the base station is used for processing the transmitted individual task amount;
obtaining the position of the base station when the base station receives the individual task volume according to the individual task backlog volume, the individual transmission rate, the transmission time value and the attribute information corresponding to the base station;
and controlling the operation of a system at a set time slot according to the individual task throughput, the individual equipment calculation frequency value, the transmission time value and the base station position, wherein the system consists of the single equipment and the base station, and the set time slot is matched with the transmission time value.
2. The method of controlling system operation according to claim 1, wherein said deriving an individual task throughput for the single device based on the individual task backlog comprises:
constructing a first objective function related to an individual task throughput parameter according to the individual task backlog parameter corresponding to the individual task backlog quantity;
constructing a first constraint condition consisting of the value ranges corresponding to the individual task throughput parameters;
obtaining a minimum value of the first objective function under the first constraint condition according to the first constraint condition;
obtaining a value corresponding to the individual task throughput parameter when the first objective function takes the minimum value according to the minimum value of the first objective function;
and obtaining the individual task throughput according to the value corresponding to the individual task throughput parameter.
3. The method for controlling system operation according to claim 1, wherein the obtaining of the calculated frequency value of the individual device corresponding to the single device and the transmission time value for the single device to transmit the corresponding individual task amount to the base station according to the individual transmission rate, the individual transmission power, and the individual task backlog amount comprises:
constructing a second objective function related to the frequency parameter and the transmission time parameter of the individual equipment according to the individual transmission rate, the individual transmission power and the individual task backlog;
constructing a second constraint condition consisting of a value range corresponding to the individual device frequency parameter and a value range corresponding to the transmission time parameter;
obtaining a minimum value of the second objective function under the second constraint condition according to the second constraint condition;
taking the minimum value according to the second objective function to obtain the value corresponding to the individual equipment frequency parameter and the value corresponding to the transmission time parameter when the second objective function takes the minimum value;
and obtaining the individual equipment calculation frequency value corresponding to the single equipment and the transmission time value used by the single equipment for transmitting the corresponding individual task amount to the base station according to the value corresponding to the individual equipment frequency parameter and the value corresponding to the transmission time parameter.
4. The method of claim 3, wherein the constructing a second constraint consisting of a range of values corresponding to the individual device frequency parameter and a range of values corresponding to the transmission time parameter comprises:
obtaining the maximum calculation frequency of the CPU corresponding to the single device according to the single device;
obtaining the value range corresponding to the individual equipment frequency parameter between zero and the maximum calculation frequency of the CPU according to the maximum calculation frequency of the CPU;
obtaining a transmission time parameter corresponding to the system according to the transmission time parameter corresponding to the individual device;
according to the system, obtaining the time slot length corresponding to the system;
obtaining that the value of the transmission time parameter corresponding to the single device is greater than or equal to zero and the value of the transmission time parameter corresponding to the system is less than or equal to the time slot length according to the transmission time parameter corresponding to the system and the time slot length;
dividing the value corresponding to the individual device frequency parameter by the attribute value corresponding to the base station and adding the product of the value corresponding to the individual transmission rate parameter and the value corresponding to the transmission time parameter to obtain a calculation result;
obtaining the backlog quantity of the individual tasks of which the calculation result is less than or equal to the calculation result according to the calculation result;
and obtaining the second constraint condition according to the fact that the value range corresponding to the individual device frequency parameter is located between zero and the maximum calculation frequency of the CPU, the value of the transmission time parameter corresponding to the single device is greater than or equal to zero, the value of the transmission time parameter corresponding to the system is less than or equal to the time slot length, and the calculation result is less than or equal to the individual task backlog.
5. The method of claim 1, wherein obtaining the location of the base station at which the base station receives the individual task volume according to the individual task backlog, the individual transmission rate, the transmission time value, and the attribute information corresponding to the base station comprises:
constructing a third objective function related to the position parameter of the base station according to the individual task backlog, the individual transmission rate, the transmission time value and the attribute information corresponding to the base station;
constructing a third constraint condition according to the attribute information corresponding to the base station;
obtaining a minimum value of the third objective function under the third constraint condition according to the third constraint condition;
obtaining a value corresponding to the position parameter of the base station when the third objective function takes the minimum value according to the minimum value of the third objective function;
and obtaining the position of the base station when the base station receives the individual task quantity according to the value corresponding to the position parameter of the base station.
6. The method of claim 5, wherein the constructing the third constraint according to the attribute information corresponding to the base station comprises:
according to the attribute information corresponding to the base station, obtaining the starting position, the ending position, the flight speed and the weight information corresponding to the base station in the attribute information;
acquiring environment information of the base station, an elevation angle corresponding to the base station and the height of the base station from the individual equipment;
and constructing a third constraint condition according to the weight information, the height, the starting point position, the end point position and the flying speed.
7. The method of controlling operation of a system as set forth in claim 1, further including:
obtaining system task throughput corresponding to the system according to the individual task throughput;
and obtaining the task processing rate corresponding to the system according to the system task throughput and the set time slot.
8. An apparatus for controlling a method of operating a system, the apparatus comprising:
the data acquisition module is used for acquiring the individual task backlog, the individual transmission rate and the individual transmission power corresponding to the single device;
the throughput calculation module is used for obtaining the individual task throughput corresponding to the single device according to the individual task backlog;
a time calculation module, configured to obtain, according to the individual transmission rate, the individual transmission power, and the individual task backlog, an individual device calculation frequency value corresponding to the single device, and a transmission time value used by the single device to transmit the corresponding individual task amount to a base station, where the base station is configured to process the transmitted individual task amount;
a position calculation module, configured to obtain, according to the individual task backlog, the individual transmission rate, the transmission time value, and attribute information corresponding to the base station, a position of the base station at which the base station receives the individual task;
and the control module is used for controlling the operation of the system in a set time slot according to the individual task throughput, the individual equipment calculation frequency value, the transmission time value and the base station position, wherein the system consists of the single equipment and the base station, and the set time slot is matched with the transmission time value.
9. A terminal device, characterized in that the terminal device comprises a memory, a processor and a program stored in the memory and executable on the processor for controlling system operation, and when the processor executes the program for controlling system operation, the steps of the method for controlling system operation according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium, having stored thereon a program for controlling system operations, which when executed by a processor, performs the steps of a method for controlling system operations according to any one of claims 1 to 7.
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