CN113811007B - Device scheduling and resource allocation method, device, electronic device and storage medium - Google Patents

Abstract

The invention provides a device scheduling and resource allocation method, a device, an electronic device and a storage medium, wherein the method comprises the following steps: acquiring equipment information, transmission information and computing resources of a wireless monitoring system; determining an optimization problem of equipment scheduling and resource allocation of the wireless monitoring system based on the equipment information, the transmission information and the computing resources; and solving the determined optimization problem of the equipment scheduling and the resource allocation to determine the equipment scheduling and the resource allocation information of the wireless monitoring system. The method provided by the invention minimizes the total information age of the wireless monitoring system in one monitoring period by jointly optimizing the allocation of the equipment information, the transmission information and the computing resources, and improves the timeliness of the equipment state information in the wireless monitoring system.

Description

Device scheduling and resource allocation method, device, electronic device and storage medium

Technical Field

The present invention relates to the field of communication technology, and in particular to a device scheduling and resource allocation method, device, electronic device and storage medium.

Background Art

With the continuous application of the Internet of Things in industrial communication systems, the Industrial Internet of Things (IIoT) provides a customized architecture and standardized interface for the collection and transmission of industrial system data. By continuously acquiring and processing industrial equipment status information in the production practice of industrial systems, industrial applications can achieve equipment status monitoring, production process optimization, and platform security management. Taking industrial equipment monitoring applications as an example, in order to monitor the equipment status in real time, each industrial equipment will generate 20-100MB of data per second. However, such a huge amount of data poses a huge challenge to the wireless monitoring system of industrial Internet equipment. Due to the limited wireless communication resources and computing resources, the simultaneous transmission of a large amount of equipment data is bound to cause network congestion and a sharp increase in information transmission delay; at the same time, if a large amount of equipment data is not processed in time, it will cause an increase in data queuing delay, resulting in poor timeliness of the acquired industrial system equipment status information.

Therefore, how to improve the timeliness of device status information in the device wireless monitoring system is an important issue that needs to be solved urgently in the industry.

Summary of the invention

The present invention provides a device scheduling and resource allocation method, device, electronic equipment and storage medium, which are used to solve the problem of poor timeliness of device status information in a wireless monitoring system.

The present invention provides a device scheduling and resource allocation method, comprising:

Acquire the device information, transmission information and computing resources of the wireless monitoring system; wherein the device information includes: the distance between the device and the network side network element, the maximum transmission power of the device, the transmission information volume of the device and the unilateral power spectrum density; the transmission information includes: time slot length, channel bandwidth of the subchannel, carrier frequency and channel signal-to-interference-noise ratio threshold; the computing resources include the computing resources available to the information processing server;

Based on the device information, transmission information and computing resources, determine the optimization problem of device scheduling and resource allocation of the wireless monitoring system; wherein the optimization problem of device scheduling and resource allocation is the problem of minimizing the total information age of the wireless monitoring system in a monitoring cycle;

The optimization problem of the device scheduling and resource allocation is solved to determine the device scheduling and resource allocation information of the wireless monitoring system.

According to a device scheduling and resource allocation method provided by the present invention, the optimization problem of device scheduling and resource allocation is expressed as:

P0:

stC1:

C2:

C3:

C4:

C5:

C6:

C7:

Among them, P0 is the objective function of the optimization problem of device scheduling and resource allocation, C1 to C7 are the constraints of the objective function P0; C3 is the access restriction of the communication system, and the access amount of the device in the communication system at any time does not exceed the total number of sub-channels included in the communication system; C4 is the transmission power limit of the device; C5 represents the channel interference limit to ensure the success rate of information transmission; C6 represents that the information transmitted to the network side network element by any device in any time slot can be processed by the information processing server at the next moment; C7 is the system computing resource constraint; the wireless monitoring system includes K devices, at least one network side network element, and an information processing server; M is the total number of orthogonal sub-channels included in the communication system; n is the time slot number, N is the number of time slots included in a monitoring cycle; π _k (n) = {α _kn , β _k,n }; α _k,n represents the information collection status of device k in time slot n, α _k,n ={0,1,2}, α _k,n =0 means that the cache of device k is empty and device k does not collect information in time slot n, α _k,n =1 means that device k collects information and stores it in the cache of device k in time slot n, α _k,n =2 means that the cache of device k is not empty and device k does not collect information in time slot n; β _k,n represents the scheduling of device k in time slot n, β _k,n ={0,1}, β _k,n =0 means that device k does not transmit information in time slot n, β _k,n =1 means that device k transmits information in time slot n; L _k is the amount of information that device k needs to transmit; R _k,n is the amount of information transmitted by device k in time slot n, p _k,n is the transmit power of device k in time slot n; P is the maximum transmit power of the device; h _k is the channel gain of the subchannel occupied by device k; N ₀ is the unilateral power spectrum density; B is the channel bandwidth of the subchannel; γ _th is the channel signal-to-interference-noise ratio threshold; d _k is the distance between device k and the base station, c is the speed of light, f _c is the carrier frequency; T is the time slot length; s _k is the number of CPU cycles required for the information processing server to process 1 bit of data; f _k,n+1 is the computing resources allocated by the information processing server to device k in time slot n+1; f is the total amount of computing resources available to the information processing server; A _s,k (n) is the information age of device k at the beginning of time slot n, Q _k (n) is the amount of cached data of device k at the beginning of time slot n, A _d,k (n) is the information age of device k in the information processing server at the end of time slot n,

According to a device scheduling and resource allocation method provided by the present invention, solving the optimization problem of the device scheduling and resource allocation to determine the device scheduling and resource allocation information of the wireless monitoring system includes:

The optimization problem of equipment scheduling and resource allocation is converted into a convex optimization problem:

Px:

stC8:

C9:

C10:

C11:

C12:

C13:

C14:

C15:

Wherein, Px is the objective function of the convex optimization problem, and C8 to C15 are the constraints of Px: represents the set of devices that the network side network element needs to schedule in time slot n; f _n+1 is the computing resources available to the information processing server in time slot n+1; ε is an infinitesimal value greater than 0; χ is a penalty factor, χ>>1; j is the number of iterations, is any value of β _k,n that meets the constraints C8 and C9; M _n is the number of channels available to the network element at the network side in time slot n;

The convex optimization problem is solved to determine the equipment scheduling and resource allocation information of the wireless monitoring system.

According to a device scheduling and resource allocation method provided by the present invention, solving the convex optimization problem to determine the device scheduling and resource allocation information of the wireless monitoring system includes:

For all time slots in the monitoring period, repeat the following steps for each time slot in chronological order:

The convex optimization problem is solved by iterative calculation based on the continuous convex approximation algorithm SCA until the total average information age benefit of the wireless monitoring system in a time slot converges, and a feasible solution of β _k,n is obtained. Feasible solution of r _k,n and A feasible solution Where E _n is the total average information age benefit of the wireless monitoring system in a time slot; based on Calculate the optimal solution of β _k,n based on and Calculate the optimal solution of p _k,n based on Calculate the optimal solution of α _k,n based on and Calculate the optimal solution of f _k,n+1 based on and Calculate _As,k (n) and _Ad,k (n); based on and Calculate f _n+1 based on Calculate _Mn .

According to a device scheduling and resource allocation method provided by the present invention, the Calculate the optimal solution of β _k,n based on and Calculate the optimal solution of p _k,n based on Calculate the optimal solution of α _k,n based on and Calculate the optimal solution of f _k,n+1 include:

based on The optimal solution of β _k,n is calculated using formula C16

C16:

based on and The optimal solution of p _k,n is calculated using formula C17-C20

C17:

C18:

C19:

C20:

based on and Formula C21 is used to calculate the optimal solution for f _k,n+1

C21:

based on The optimal solution of α _k,n is calculated using formula C22

C22:

According to a device scheduling and resource allocation method provided by the present invention, the device scheduling and resource allocation information includes at least one of the following:

The wireless monitoring system includes the information collection status, scheduling status and transmission power of each device in each time slot, and the computing resources allocated by the information processing server to each device in each time slot.

The present invention also provides a device scheduling and resource allocation apparatus, comprising:

An acquisition module is used to acquire device information, transmission information and computing resources of the wireless monitoring system; wherein the device information includes: the distance between the device and the network side network element, the maximum transmission power of the device, the transmission information volume of the device and the unilateral power spectrum density; the transmission information includes: time slot length, channel bandwidth of the subchannel, carrier frequency and channel signal-to-interference-noise ratio threshold; the computing resources include computing resources available to the information processing server;

A determination module, configured to determine the optimization problem of device scheduling and resource allocation of the wireless monitoring system based on the device information, transmission information and computing resources; wherein the optimization problem of device scheduling and resource allocation is the problem of minimizing the total information age of the wireless monitoring system in a monitoring cycle;

The solution module is used to solve the optimization problem of device scheduling and resource allocation, determine the information collection state, scheduling state and transmission power of each device in each time slot in the wireless monitoring system, and the computing resources allocated by the information processing server to each device in each time slot.

The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the device scheduling and resource allocation method as described in any one of the above items are implemented.

The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the device scheduling and resource allocation method as described in any one of the above items.

The device scheduling and resource allocation method, device, electronic device and storage medium provided by the present invention minimize the total information age of the wireless monitoring system within a monitoring cycle by jointly optimizing device information collection, scheduling, transmission power and allocation of communication and computing resources, thereby improving the timeliness of device status information in the wireless monitoring system.

BRIEF DESCRIPTION OF THE DRAWINGS

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

FIG1 is a schematic diagram of an application scenario of the device scheduling and resource allocation method provided by the present invention;

FIG2 is a schematic diagram of a flow chart of a device scheduling and resource allocation method provided by the present invention;

FIG3 is a schematic diagram of an information update cycle of a device provided by the present invention;

FIG4 is a schematic diagram of the structure of the device scheduling and resource allocation apparatus provided by the present invention;

FIG. 5 is a schematic diagram of the structure of an electronic device provided by the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

The present invention provides a device scheduling and resource allocation method, comprising: obtaining device information, transmission information and computing resources of a wireless monitoring system; wherein the device information includes: the distance between the device and the network side network element, the maximum transmission power of the device, the transmission information amount of the device and the unilateral power spectrum density; the transmission information includes: time slot length, channel bandwidth of the subchannel, carrier frequency and channel signal to interference noise ratio threshold; the computing resources include computing resources available to the information processing server; based on the device information, transmission information and computing resources, determining the optimization problem of device scheduling and resource allocation of the wireless monitoring system; wherein the optimization problem of device scheduling and resource allocation is the problem of minimizing the total information age (Age of Information, AoI) of the wireless monitoring system in a monitoring cycle; solving the optimization problem of device scheduling and resource allocation, and determining the device scheduling and resource allocation information of the wireless monitoring system. The device scheduling and resource allocation method provided by the present invention minimizes the total information age of the wireless monitoring system in a monitoring cycle by jointly optimizing device information collection, scheduling, transmission power and allocation of communication and computing resources, thereby improving the timeliness of device status information in the wireless monitoring system.

The device scheduling and resource allocation method provided by the present invention can be applied to the wireless monitoring system of the industrial Internet. Figure 1 is a schematic diagram of the application scenario of the device scheduling and resource allocation method provided by the present invention. As shown in Figure 1, the wireless monitoring system includes: device 101, network side network element 102 and information processing server 103; wherein: device 101 may include any type of industrial equipment that can normally access the industrial Internet; network side network element 102 may include a base station or core network device in a communication system; information processing server 103 may include a desktop computer, etc.

It should be noted that the specific type and number of the device 101 are not limited in the present invention. The device 101 accesses the network side network element 102 in the manner of Orthogonal Frequency Division Multiple Access (OFDMA), and the device 101 sends its own device status information to the network side network element 102 through a wireless transmission channel; wherein the device status information may include information such as the operating status, degree of wear or execution frequency of the device. The information processing server 103 is connected to the network side network element 102, and the network side network element 102 sends the device status information reported by each device to the information processing server 103, and the information processing server 103 processes the device status information to obtain the device status information.

It should be noted that the present invention mainly focuses on the information collection, transmission and processing of multiple devices in a wireless monitoring system within a monitoring cycle. At the same time, in order to ensure the timeliness of the status information of the device, multiple devices will continuously repeat the data collection, transmission and processing within a monitoring cycle.

Next, the device scheduling and resource allocation method provided by the present invention is introduced.

FIG2 is a flow chart of a device scheduling and resource allocation method provided by the present invention. As shown in FIG2 , the method includes:

Step 110, obtaining device information, transmission information and computing resources of the wireless monitoring system; wherein the device information includes: the distance between the device and the network side network element, the maximum transmission power of the device, the transmission information volume of the device and the unilateral power spectrum density; the transmission information includes: time slot length, channel bandwidth of the subchannel, carrier frequency and channel signal-to-interference-noise ratio threshold; the computing resources include computing resources available to the information processing server.

Step 120, based on the device information, transmission information and computing resources, determine the optimization problem of device scheduling and resource allocation of the wireless monitoring system; wherein the optimization problem of device scheduling and resource allocation is the problem of minimizing the total information age of the wireless monitoring system within a monitoring cycle.

Optionally, AoI refers to the period of time from when the device information is generated to when it is received by the information processing server. AoI measures the timeliness of device information from the perspective of the information processing server, and is affected by the time spent in multiple information transmission processes such as device information sampling time, transmission time, queuing time, and processing time. The larger the AoI, the longer it takes for the device information to be transmitted from the device to the information processing server, and the worse the timeliness of the device information; the smaller the AoI, the shorter the time it takes for the device information to be transmitted from the device to the information processing server, and the better the timeliness of the device information; the smaller the AoI of the device, the better the system performance.

Step 130, solving the optimization problem of device scheduling and resource allocation, and determining device scheduling and resource allocation information of the wireless monitoring system.

Optionally, the device scheduling and resource allocation information includes at least one of the following: information collection status, scheduling status and transmission power of each device in each time slot in the wireless monitoring system, and computing resources allocated by the information processing server to each device in each time slot.

The method provided by the present invention minimizes the total information age of the wireless monitoring system within a monitoring cycle and improves the timeliness of the device status information in the wireless monitoring system by jointly optimizing device information collection, scheduling, transmission power, and allocation of communication and computing resources.

FIG3 is a schematic diagram of the information update cycle of the device provided by the present invention. As shown in FIG3, an information update cycle of the device includes the steps of waiting for collection, waiting for scheduling, data transmission and data processing. A data update of the device starts after the last data transmission is completed. As shown in FIG3, the device waits for information collection from time _t1 to time _t2 , and collects information at time _t2 . It is assumed that the device immediately completes information collection at the time of collecting information, that is, the time of collecting information at time _t2 is 0. The device stores the collected information in the buffer of the device. The buffer of the device only stores the most recently collected information, that is, the device information stored in the buffer of the device is the latest device status information of the device. The device waits for the scheduling of the network side network element from time _t2 to time _t3 , and the network side network element schedules the device at time _t3 . The network side network element immediately completes the device scheduling at the scheduling time, that is, the time of device scheduling is 0. At the same time, the information collected by the device is transmitted to the network side network element and the information processing server through the wireless transmission channel, and the information collected by the device is further processed by the information processing server. In order to ensure the timeliness of information, the information collected by the device will be processed synchronously, that is, the information transmitted in the current time slot will be processed in the next time slot.

Assume that the location coordinates of the network side network element and the information processing server in the wireless monitoring system shown in Figure 1 are (0,0,H) and (0,0,0) respectively, H is the height of the network side network element, and the location coordinates of the industrial equipment are D _k = (x _k ,y _k ,0). The amount of information that device k needs to transmit is represented by L _k . The transmission power of device k in time slot n is p _k,n , the maximum transmission power of the device is P, n is the time slot number, The total number of orthogonal sub-channels included in the communication system is M, expressed as The channel width of each subchannel is B, and the length of a time slot is T. In any time slot, each device is only allowed to occupy one subchannel to transmit data, and each subchannel can only accommodate one device. The present invention adopts the free space path loss model, and the channel gain of the subchannel occupied by device k is Where c is the speed of light, f _c is the carrier frequency. d _k is the distance between device k and the network element on the network side. Assuming that the interference between different sub-channels is negligible, the amount of information transmitted by device k in time slot n is: Where N ₀ is the unilateral power spectrum density. During the information transmission process, the transmission power of device k remains consistent each time, and the information transmission will not be interrupted. In addition, in order to prevent data transmission failure caused by poor channel quality and affect the timeliness of information, information transmission needs to ensure: Among them, _γth is the channel signal to interference and noise ratio threshold to ensure successful information transmission.

The number of CPU cycles required by the information processing server to process 1 bit of data from device k is s _k . The computing resources allocated by the information processing server to device k in time slot n are f _k,n . In order to meet the information processing requirements, the information processing server must complete the processing of the information transmitted by device k in time slot n within time slot n+1. Therefore, At the same time, the total computing resources allocated by the information processing server to each device in any time slot should be less than the total amount of computing resources available to the information processing server, that is, f _k,n represents the computing resources allocated by the information processing server to device k in time slot n, and f is the total amount of computing resources available to the information processing server.

In each time slot, the network-side network element needs to decide which device will be scheduled to collect or transmit information. For device k, the information collection state of device k in time slot n is represented by α _k,n , α _k,n = {0,1,2}, α _k,n = 0 means that the buffer area of device k in time slot n is empty and device k does not collect information, α _k,n = 1 means that device k collects information in time slot n and stores the information in the buffer area of device k, α _k,n = 2 means that the buffer area of device k in time slot n is not empty and device k does not collect information. The scheduling of device k in time slot n is represented by β _k,n , β _k,n = {0,1}, β _k,n = 0 means that device k does not transmit information in time slot n, β _k,n = 1 means that device k transmits information in time slot n. Since device k uses OFDMA access to communicate with network-side network elements,

The scheduling strategy of device k in time slot n is expressed as π _k (n) = {α _kn , β _k,n }. At any time, the scheduling strategies available to device k are (0,0), (0,1), (1,0), (1,1), (2,0), (2,1). For strategy π _k (n) = (0,1), since the amount of information in the buffer of device k in time slot n is 0, information cannot be transmitted, so π _k (n) = (0,1) is an invalid strategy. π _k (n) = (1,0) means that the information collected by device k in time slot n is not transmitted immediately. This situation does not help to reduce AoI and can be considered an invalid strategy. In addition, data transmission cannot be interrupted, so π _k (n) ≠ (2,0). In summary, the optional scheduling strategies for device k in time slot n are π _k (n) = {(0,0), (1,1), (2,1)}.

The present invention uses AoI as a key indicator to measure the timeliness of the device status information of the system. Let _As,k (n) be the AoI of device k at the beginning of time slot n; _Ad,k (n) be the AoI of device k in the information processing server at the end of time slot n; _Qk (n) represents the amount of cached data in the buffer of device k at the beginning of time slot n. According to the scheduling strategy _πk (n), the amount of cached data of device k in different time slots can be expressed as:

C23:

According to the definition of AoI, if device k collects information in time slot n, the AoI of device k will decrease to 1; otherwise, the AoI of device k will increase by 1. Therefore, the AoI of device k can be expressed as:

C24:

If device k completes the transmission of the information in the buffer at the end of time slot n, the AoI of device k at the information processing server will drop to the AoI of device k at time slot n plus 2; one time slot is used for information transmission, and the information processing server will complete the processing of the information transmitted by device k to the network side network element in the next time slot after the data transmission ends. Otherwise, the AoI of device k at the information processing server will increase by 1.

Therefore, the AoI of device k at the information processing server at the end of time slot n+1 is expressed as A _d,k (n+1), where

C25:

The present invention uses AoI to measure the timeliness of the equipment information of the wireless monitoring system. AoI is affected by the time consumed in multiple information transmission processes such as the sampling time, transmission time, queuing time and processing time of the equipment information.

The present invention minimizes the AoI of the wireless monitoring system for industrial equipment within a monitoring cycle by jointly optimizing the information collection and transmission power of industrial equipment and the allocation of communication and computing resources. Accordingly, the optimization problem of determining the equipment scheduling and resource allocation of the wireless monitoring system based on equipment information, transmission information and computing resources in FIG2 can be expressed as follows:

P0:

stC1:

C2:

C3:

C4:

C5:

C6:

C7:

Among them, P0 is the objective function of the optimization problem of device scheduling and resource allocation, C1 to C7 are the constraints of the objective function P0; C3 is the access restriction of the communication system, and the access amount of the device in the communication system at any time does not exceed the total number of sub-channels included in the communication system; C4 is the transmission power limit of the device; C5 represents the channel interference limit to ensure the success rate of information transmission; C6 represents that the information transmitted to the network side network element by any device in any time slot can be processed by the information processing server at the next moment; C7 is the system computing resource constraint; the wireless monitoring system includes K devices, at least one network side network element, and an information processing server; M is the total number of orthogonal sub-channels included in the communication system; n is the time slot number, N is the number of time slots included in a monitoring cycle; π _k (n) = {α _kn , β _k,n }; α _k,n represents the information collection status of device k in time slot n, α _k,n ={0,1,2}, α _k,n =0 means that the buffer area of device k is empty and device k does not collect information in time slot n, α _k,n =1 means that device k collects information and stores the information in the buffer area of device k in time slot n, α _k,n =2 means that the buffer area of device k is not empty and device k does not collect information in time slot n; β _k,n represents the scheduling of device k in time slot n, β _k,n ={0,1}, β _k,n =0 means that device k does not transmit information in time slot n, β _k,n =1 means that device k transmits information in time slot n; L _k is the amount of information that device k needs to transmit; R _k,n is the amount of information transmitted by device k in time slot n, p _k,n is the transmit power of device k in time slot n; P is the maximum transmit power of device k; h _k is the channel gain of the subchannel occupied by device k; N ₀ is the unilateral power spectrum density; B is the channel bandwidth of the subchannel; γ _th is the channel signal-to-interference-noise ratio threshold; d _k is the distance between device k and the network side network element, c is the speed of light, f _c is the carrier frequency; T is the time slot length; s _k is the number of CPU cycles required for the information processing server to process 1 bit of data; f _k,n+1 is the computing resources allocated by the information processing server to device k in time slot n+1; f is the total amount of computing resources available to the information processing server; A _s,k (n) is the information age of device k at the beginning of time slot n, Q _k (n) is the amount of cached data of device k at the beginning of time slot n, A _d,k (n) is the information age of device k in the information processing server at the end of time slot n,

For the objective function P0, α _k,n , β _k,n are integer variables, p _k,n , f _k,n are continuous variables, and variables β _k,n and p _k,n are coupled to each other in the constraints C5 and C6 of the objective function P0, and the same variables are also coupled to each other between different time slots. Therefore, the problem P0 is a nonlinear nonconvex optimization problem of mixed integers. At the same time, A _d,k (n) is an implicit expression jointly determined by the variables α _k,n , β _k,n , p _k,n and f _k,n , and the traditional optimization method cannot obtain a closed-form solution to the problem P0. In view of this, the present invention converts the nonlinear nonconvex optimization problem P0 into a convex optimization problem and then solves it.

Specifically, the optimization problem P0 of device scheduling and resource allocation is converted into a convex optimization problem, and then the convex optimization problem is solved to determine the device scheduling and resource allocation information of the wireless monitoring system; wherein: the convex optimization problem can be expressed as:

Px:

stC8:

C9:

C10:

C11:

C12:

C13:

C14:

C15:

Wherein, Px is the objective function of the convex optimization problem, and C8 to C15 are the constraints of Px: represents the set of devices that the network side network element needs to schedule in time slot n; f _n+1 is the computing resources available to the information processing server in time slot n+1; ε is an infinitesimal value greater than 0; χ is a penalty factor, χ>>1; j is the number of iterations, is any value of β _k,n that meets the constraints C8 and C9; M _n is the number of channels available to the network element at the network side in time slot n;

Here we explain the process of converting the optimization problem P0 of equipment scheduling and resource allocation into the convex optimization problem Px:

First, in order to convert the optimization problem P0 of device scheduling and resource allocation into a convex optimization problem Px for description, the present invention analyzes the AoI changes of the equipment and introduces a parameter: the average AoI benefit; based on the average AoI benefit, the objective function of the problem P0 is equivalently replaced to achieve re-planning of the problem P0.

Specifically, the average AoI benefit of device k in time slot n can be expressed as:

C26:

in, It indicates the AoI drop value of device k after the scheduling of time slot n is completed. Since the transmission power of the device remains unchanged during a scheduling process, Indicates the transmission time of the information.

When the monitoring cycle time length is fixed, maximizing the total AoI reduction value is equivalent to maximizing the AoI reduction value of each time slot, that is, the average AoI benefit. By optimizing device scheduling and resource allocation, the maximum total average AoI benefit is obtained, and the total AoI of the system is minimized. Therefore, problem P0 can be transformed into problem P1:

p1:

Among them, the constraints of problem P1 are constraints C1 to C7 of objective function P0.

In view of the mutual coupling characteristics between different time slots in problem P1, the present invention decouples it into multiple continuous short-term optimal problems, that is, decoupling P1 into device scheduling and resource allocation optimization sub-problems in N different time slots, and solving the sub-problems of each time slot in chronological order, the optimal solution to problem P1 is approximately obtained.

Without loss of generality, we need to consider the device scheduling and resource allocation optimization problem of time slot n. At this time, problem P1 can be expressed as:

P2:

stC27:

C28:

C29:

C30:

C31:

C32:

C33:

in, It represents the set of devices that the network side network element needs to schedule in time slot n, _Mn is the number of channels available to the network side network element in time slot n, and fn ₊₁ is the computing resources available to the information processing server in time slot n+1.

Based on the analysis of data collection, transmission and processing, problem P2 has the following properties: 1) When problem P2 obtains the optimal solution, constraint C32 is a tight constraint; 2) and are interrelated. is the optimal solution of α _kn , is the optimal solution for β _k,n . Therefore, constraints C32 and C33 can be reformulated as:

Introducing variables Transform problem P2 into problem P3:

P3:

stC35:

C36:

C37:

C38:

C39:

Among them, A _s,k (n-1) is a known quantity. Problem P3 is a mixed integer non-convex optimization problem. The challenges in solving P3 mainly come from two aspects: 1. The non-convexity of the objective function and constraints caused by the coupling of discrete variables β _k,n and continuous variables r _k,n ; 2. The non-convexity caused by the integer function in the objective function.

Considering the value of β _k,n , the objective function of P3 can be reformulated as:

Among them, ε is an infinitesimal value greater than 0.

To solve the coupling _between constraints C38 and C39 and the objective function of problem P3, we introduce the _variable Afterwards, the planning is re-performed by applying relevant mathematical methods and introducing constraints C41-C44 to solve the coupling problem.

C41:

C42:

C43:

C44:

Make the integer variable β _k,n continuous and rewrite Formula C35 as Formula C45-C46:

C45:

C46:

Among them, formula C46 is an inverse convex restriction, that is, it is a non-convex set with non-convexity. To solve the non-convexity of formula C46, χ(β _k,n -(β _k,n ) ² ),χ>>1 is introduced into the objective function as a penalty term. Therefore, problem P3 can be equivalently transformed into P4:

P4:

Among them, the constraints of the objective function of P4 are formulas C8-C15. χ>>1 is the penalty factor. To ensure that the value of β _k,n approaches 0 or 1, and to deal with the rounding function in the optimization objective of P4, Alternative In addition, the objective function of P4 is the difference of concave functions, so problem P4 is still non-convex. The continuous convex approximation algorithm (SCA) is used to relax β _k,n -(β _k,n ) ² to deal with the non-convexity of the objective function. Specifically, for a given feasible point Perform a first-order Taylor expansion on β _k,n -(β _k,n ) ² at this point and approximate β _k,n -(β _k,n ) ² as a linear function Among them, the linear function It is expressed as:

C47:

Since the first-order Taylor expansion of a concave function at any point is the global upper bound of the function, β _k,n -(β _k,n ) ² is approximated as a linear function This approximation does not expand the scope of problem P4, so problem P4 can be transformed into problem Px. Obviously, problem Px is a convex optimization problem.

Optionally, solving the convex optimization problem Px to determine an implementation method of device scheduling and resource allocation information of the wireless monitoring system may include:

For all time slots in the monitoring period, repeat the following steps for each time slot in chronological order:

The convex optimization problem is solved by iterative calculation based on the continuous convex approximation algorithm (SCA) until the total average information age benefit of the wireless monitoring system in a time slot converges, and a feasible solution of β _k,n is obtained. A feasible solution and A feasible solution Where E _n is the total average information age benefit of the wireless monitoring system in a time slot; based on Calculate the optimal solution of β _k,n based on and Calculate the optimal solution of p _k,n based on Calculate the optimal solution of α _k,n based on and Calculate the optimal solution of f _k,n+1 based on and Calculate _As,k (n) and _Ad,k (n); based on and Calculate f _n+1 based on Calculate _Mn ; where _En represents the total average information age benefit of the wireless monitoring system in a time slot.

Specifically, the specific solution algorithm for solving the convex optimization problem Px based on SCA using iterative calculation method includes Algorithm 1 and Algorithm 2, which are introduced as follows:

Algorithm 1 is used to achieve single-slot device scheduling and resource allocation optimization.

The input parameters involved in Algorithm 1 include: _Mn , _fn , _As,k (n-1), P, B, T, _N0 , _dk , _Lk , _γth , ε, χ; output parameters include:

Solving the problem Px by iterative calculation based on Algorithm 1 may include steps a) to c):

Step a), problem Px is obtained by formula C40-C47, threshold ψ and maximum number of iterations j _max are set, j=1, and any variable is selected in the feasible domain of problem Px, which is recorded as Among them, the feasible region refers to the linear function in the objective function of the problem Px The maximum range of possible values.

Step b), for a given value Calculated according to formula C47 Substitute it into problem Px, and then use CVX to solve problem Px to obtain a set of suboptimal solutions and target value Let j+1=j, let

Step c), repeat step b) until the target value Satisfying the condition |E _n ^j-1 -E _n ^j |≤ψ or j＝j _max , a set of suboptimal solutions is obtained and target value The feasible solutions of β _k,n are Feasible solutions for r _k,n and A feasible solution

Considering Alternative The impact on transmit power and resource allocation is given by the formula and Adjusting variables The optimal value of is the number of time slots required for user k to transmit data when user k is scheduled in time slot n.

because The constraints of problem Px can still be satisfied, so this adjustment will not change the scheduling strategy. The number of time slots actually occupied by device data transmission and the device's AoI will not change compared to before the adjustment. The advantage of this adjustment is that the device transmission power and optimal allocation of computing resources in the current time slot will be reduced after the adjustment, which is conducive to the system obtaining greater AoI benefits in the next time slot. The optimal solution of β _k,n is calculated using formula C16

C16:

based on and The optimal solution of p _k,n is calculated using formula C17-C20

C17:

C18:

C19:

C20:

based on and Formula C21 is used to calculate the optimal solution for f _k,n+1

C21:

based on The optimal solution of α _k,n is calculated using formula C22

C22:

Algorithm 2 is used to achieve slot-by-slot device scheduling and resource allocation optimization.

Algorithm 2 combines Algorithm 1 to achieve slot-by-slot device scheduling and resource allocation optimization for problem Px. The input parameters of Algorithm 2 include: K,N,M,P,f,B,T,N0,dk,Lk,sk,γ _th ,ε,χ; output parameters include: _{As, k} (n), _{Ad, k} (n)

Algorithm 2 may include the following steps 1) to 8):

Step 1):

Initialization: Time slot n = 1, the initial AoI of different devices is _As,k (0) and _Ad,k (0), and the set of schedulable users is The number of available channels M ₁ =M, and the available channel resources f ₁ =f.

Step 2):

For time slot n, solve problem Px based on Algorithm 1 and obtain a feasible solution for β _k,n Feasible solutions for r _k,n and A feasible solution According to the formula and calculate and Calculated according to formula C16 Calculate the optimal solution of p _k,n according to formula C17-C20 Calculate the optimal solution of f _k,n+1 according to formula C21

Step 3):

if and So:

C48:

C49:

C50:

C51:

Where n' is a time slot after time slot n,

Step 4):

based on and Calculate _As,k (n) and _Ad,k (n), and use formulas C23-C25 to update the _As,k (n) and _Ad,k (n) of the device at time slot n'.

Step 5):

Let n=n+1.

Step 6):

like Then the set of devices that the network side network element needs to schedule in time slot n is is the set after removing device k from the current set of K devices.

Step 7):

based on Calculate the number of channels _Mn available to the network element at the network side in time slot n:

C52:

based on and Calculate the number of computing resources f _n+1 available to the information processing server in time slot n+1:

in, is the indicator function, when When established, The value of is 1; when When it is not established, The value of is 0.

Step 8): Repeat steps 2) to 7) until n=N.

It can be seen from Algorithm 2 that the algorithm proposed in the present invention uses the optimal strategy of each time slot to approximately replace the optimal strategy of the entire monitoring cycle, and the algorithm only requires the state of the system in the current time slot, and does not require the long-term state information of the system. Therefore, the algorithm can effectively adapt to time-varying industrial systems and is suitable for monitoring the equipment status in time-varying systems for long-term goals. In addition, since the algorithm adopts a time slot-by-time slot solution method, it greatly reduces the complexity of the algorithm. This process is more applicable in the equipment status monitoring system of the time-varying industrial Internet.

The device scheduling and resource allocation method provided by the present invention obtains the device information, transmission information and computing resources of the wireless monitoring system, determines the optimization problem of the device scheduling and resource allocation of the wireless monitoring system according to the acquired device information, transmission information and computing resources, solves the determined optimization problem of the device scheduling and resource allocation, and determines the device scheduling and resource allocation information of the wireless monitoring system. The present invention can not only optimize the device scheduling and resource allocation of the wireless monitoring system in a single time slot, but also optimize the device scheduling and resource allocation of multiple time slots by jointly optimizing the device information, transmission information and computing resources, thereby realizing the reasonable allocation of device scheduling and resources, minimizing the total information age of the wireless detection system in a monitoring cycle, and improving the timeliness of the device status information in the wireless monitoring system.

The device scheduling and resource allocation apparatus provided by the present invention is described below. The device scheduling and resource allocation apparatus described below and the device scheduling and resource allocation method described above can be referred to each other.

FIG4 is a schematic diagram of the structure of the device scheduling and resource allocation apparatus provided by the present invention. As shown in FIG4 , the apparatus comprises: an acquisition module 401, a determination module 402 and a solution module 403; wherein,

The acquisition module 401 is used to acquire the device information, transmission information and computing resources of the wireless monitoring system; wherein the device information includes: the distance between the device and the network side network element, the maximum transmission power of the device, the transmission information amount of the device and the unilateral power spectrum density; the transmission information includes: the time slot length, the channel bandwidth of the subchannel, the carrier frequency and the channel signal to interference and noise ratio threshold; the computing resources include the computing resources available to the information processing server;

A determination module 402 is used to determine the optimization problem of device scheduling and resource allocation of the wireless monitoring system based on the device information, transmission information and computing resources; wherein the optimization problem of device scheduling and resource allocation is the problem of minimizing the total information age of the wireless monitoring system in a monitoring cycle;

The solution module 403 is used to solve the optimization problem of device scheduling and resource allocation, determine the information collection status, scheduling status and transmission power of each device in each time slot in the wireless monitoring system, and the computing resources allocated by the information processing server to each device in each time slot.

The device provided by the present invention minimizes the total information age of the wireless monitoring system within a monitoring cycle and improves the timeliness of the device status information in the wireless monitoring system by jointly optimizing device information collection, scheduling, transmission power, and allocation of communication and computing resources.

Optionally, the optimization problem of device scheduling and resource allocation is expressed as:

P0:

stC1:

C2:

C3:

C4:

C5:

C6:

C7:

Among them, P0 is the objective function of the optimization problem of device scheduling and resource allocation, C1 to C7 are the constraints of the objective function P0; C3 is the access restriction of the communication system, and the access amount of the device in the communication system at any time does not exceed the total number of sub-channels included in the communication system; C4 is the transmission power limit of the device; C5 represents the channel interference limit to ensure the success rate of information transmission; C6 represents that the information transmitted to the network side network element by any device in any time slot can be processed by the information processing server at the next moment; C7 is the system computing resource constraint; the wireless monitoring system includes K devices, at least one network side network element, and an information processing server; M is the total number of orthogonal sub-channels included in the communication system; n is the time slot number, N is the number of time slots included in a monitoring cycle; π _k (n) = {α _kn , β _k,n }; α _k,n represents the information collection status of device k in time slot n, α _k,n ={0,1,2}, α _k,n =0 means that the cache of device k is empty and device k does not collect information in time slot n, α _k,n =1 means that device k collects information and stores the information in the cache of device k in time slot n, α _k,n =2 means that the cache of device k is not empty and device k does not collect information in time slot n; β _k,n represents the scheduling of device k in time slot n, β _k,n ={0,1}, β _k,n =0 means that device k does not transmit information in time slot n, β _k,n =1 means that device k transmits information in time slot n; L _k is the amount of information that device k needs to transmit; R _k,n is the amount of information transmitted by device k in time slot n, p _k,n is the transmit power of device k in time slot n; P is the maximum transmit power of the device; h _k is the channel gain of the subchannel occupied by device k; N ₀ is the unilateral power spectrum density; B is the channel bandwidth of the subchannel; γ _th is the channel signal-to-interference-noise ratio threshold; d _k is the distance between device k and the base station, c is the speed of light, f _c is the carrier frequency; T is the time slot length; s _k is the number of CPU cycles required for the information processing server to process 1 bit of data; f _k,n+1 is the computing resources allocated by the information processing server to device k in time slot n+1; f is the total amount of computing resources available to the information processing server; A _s,k (n) is the information age of device k at the beginning of time slot n, Q _k (n) is the amount of cached data of device k at the beginning of time slot n, A _d,k (n) is the information age of device k in the information processing server at the end of time slot n,

Optionally, the solution module 403 is specifically used for:

The optimization problem of equipment scheduling and resource allocation is converted into a convex optimization problem:

Px:

stC8:

C9:

C10:

C11:

C12:

C13:

C14:

C15:

Wherein, Px is the objective function of the convex optimization problem, and C8 to C15 are the constraints of Px: represents the set of devices that the network side network element needs to schedule in time slot n; f _n+1 is the computing resources available to the information processing server in time slot n+1; ε is an infinitesimal value greater than 0; χ is a penalty factor, χ>>1; j is the number of iterations, is any value of β _k,n that meets the constraints C8 and C9; M _n is the number of channels available to the network element at the network side in time slot n;

The convex optimization problem is solved to determine the equipment scheduling and resource allocation information of the wireless monitoring system.

Optionally, solving the convex optimization problem to determine the device scheduling and resource allocation information of the wireless monitoring system includes:

For all time slots in the monitoring period, repeat the following steps for each time slot in chronological order:

The convex optimization problem is solved by iterative calculation based on the continuous convex approximation algorithm SCA until the total average information age benefit of the wireless monitoring system in a time slot converges, and a feasible solution of β _k,n is obtained. Feasible solutions for r _k,n and A feasible solution Where E _n is the total average information age benefit of the wireless monitoring system in a time slot; based on Calculate the optimal solution of β _k,n based on and Calculate the optimal solution of p _k,n based on Calculate the optimal solution of α _k,n based on and Calculate the optimal solution of f _k,n+1 based on and Calculate _As,k (n) and _Ad,k (n); based on and Calculate f _n+1 based on Calculate _Mn .

Optionally, the Calculate the optimal solution of β _k,n based on and Calculate the optimal solution of p _k,n based on Calculate the optimal solution of α _k,n based on and Calculate the optimal solution of f _k,n+1 include:

based on The optimal solution of β _k,n is calculated using formula C16

C16:

based on and The optimal solution of p _k,n is calculated using formula C17-C20

C17:

C18:

C19:

C20:

based on and Formula C21 is used to calculate the optimal solution for f _k,n+1

C21:

based on The optimal solution of α _k,n is calculated using formula C22

C22:

Optionally, the device scheduling and resource allocation information includes at least one of the following: information collection status, scheduling status and transmission power of each device in the wireless monitoring system in each time slot, and computing resources allocated by the information processing server to each device in each time slot.

Figure 5 illustrates a schematic diagram of the physical structure of an electronic device. As shown in Figure 5, the electronic device may include: a processor (processor) 510, a communication interface (Communications Interface) 520, a memory (memory) 530 and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call the logic instructions in the memory 530 to execute the device scheduling and resource allocation method, which includes: obtaining device information, transmission information and computing resources of the wireless monitoring system; wherein the device information includes: the distance between the device and the network side network element, the maximum transmission power of the device, the transmission information amount of the device and the unilateral power spectrum density; the transmission information includes: time slot length, channel bandwidth of the subchannel, carrier frequency and channel signal-to-interference-noise ratio threshold; the computing resources include computing resources available to the information processing server; based on the device information, transmission information and computing resources, determine the optimization problem of device scheduling and resource allocation of the wireless monitoring system; wherein the optimization problem of device scheduling and resource allocation is the problem of minimizing the total information age of the wireless monitoring system within a monitoring cycle; solve the optimization problem of device scheduling and resource allocation to determine the device scheduling and resource allocation information of the wireless monitoring system.

In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the device scheduling and resource allocation method provided by the above methods, the method including: obtaining device information, transmission information and computing resources of a wireless monitoring system; wherein the device information includes: the distance between the device and the network side network element, the maximum transmission power of the device, the transmission information volume of the device and the unilateral power spectrum density; the transmission information includes: time slot length, channel bandwidth of the subchannel, carrier frequency and channel signal-to-interference-noise ratio threshold; the computing resources include computing resources available to an information processing server; based on the device information, transmission information and computing resources, determining the optimization problem of device scheduling and resource allocation of the wireless monitoring system; wherein the optimization problem of device scheduling and resource allocation is the problem of minimizing the total information age of the wireless monitoring system within a monitoring cycle; solving the optimization problem of device scheduling and resource allocation, and determining the device scheduling and resource allocation information of the wireless monitoring system.

On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the device scheduling and resource allocation method provided in the above-mentioned embodiments, the method comprising: obtaining device information, transmission information and computing resources of a wireless monitoring system; wherein the device information comprises: the distance between the device and the network side network element, the maximum transmission power of the device, the amount of transmission information of the device and the unilateral power spectrum density; the transmission information comprises: the time slot length, the channel bandwidth of the subchannel, the carrier frequency and the channel signal-to-interference-noise ratio threshold; the computing resources comprise the computing resources available to the information processing server; based on the device information, transmission information and computing resources, determining the optimization problem of device scheduling and resource allocation of the wireless monitoring system; wherein the optimization problem of device scheduling and resource allocation is the problem of minimizing the total information age of the wireless monitoring system within a monitoring cycle; solving the optimization problem of device scheduling and resource allocation, and determining the device scheduling and resource allocation information of the wireless monitoring system.

The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (8)

1. A device scheduling and resource allocation method, characterized in that it includes: Acquire the device information, transmission information and computing resources of the wireless monitoring system; wherein the device information includes: the distance between the device and the network side network element, the maximum transmission power of the device, the transmission information volume of the device and the unilateral power spectrum density; the transmission information includes: time slot length, channel bandwidth of the subchannel, carrier frequency and channel signal-to-interference-noise ratio threshold; the computing resources include the computing resources available to the information processing server; Based on the device information, transmission information and computing resources, determine the optimization problem of device scheduling and resource allocation of the wireless monitoring system; wherein the optimization problem of device scheduling and resource allocation is the problem of minimizing the total information age of the wireless monitoring system in a monitoring cycle; Solving the optimization problem of device scheduling and resource allocation to determine device scheduling and resource allocation information of the wireless monitoring system; The optimization problem of equipment scheduling and resource allocation is expressed as: P0: Among them, P0 is the objective function of the optimization problem of device scheduling and resource allocation, C1 to C7 are the constraints of the objective function P0; C3 is the access restriction of the communication system, and the access amount of the device in the communication system at any time does not exceed the total number of sub-channels included in the communication system; C4 is the transmission power limit of the device; C5 represents the channel interference limit to ensure the success rate of information transmission; C6 represents that the information transmitted to the network side network element by any device in any time slot can be processed by the information processing server at the next moment; C7 is the system computing resource constraint; the wireless monitoring system includes K devices, at least one network side network element, and an information processing server; M is the total number of orthogonal sub-channels included in the communication system; n is the time slot number, N is the number of time slots included in a monitoring cycle; π _k (n) = {α _kn , β _k,n }; α _k,n represents the information collection status of device k in time slot n, α _k,n ={0,1,2}, α _k,n =0 means that the cache of device k is empty and device k does not collect information in time slot n, α _k,n =1 means that device k collects information and stores the information in the cache of device k in time slot n, α _k,n =2 means that the cache of device k is not empty and device k does not collect information in time slot n; β _k,n represents the scheduling of device k in time slot n, β _k,n ={0,1}, β _k,n =0 means that device k does not transmit information in time slot n, β _k,n =1 means that device k transmits information in time slot n; L _k is the amount of information that device k needs to transmit; R _k,n is the amount of information transmitted by device k in time slot n, p _k,n is the transmit power of device k in time slot n; P is the maximum transmit power of the device; h _k is the channel gain of the subchannel occupied by device k; N ₀ is the unilateral power spectrum density; B is the channel bandwidth of the subchannel; γ _th is the channel signal-to-interference-noise ratio threshold; d _k is the distance between device k and the base station, c is the speed of light, f _c is the carrier frequency; T is the time slot length; s _k is the number of CPU cycles required for the information processing server to process 1 bit of data; f _k,n+1 is the computing resources allocated by the information processing server to device k in time slot n+1; f is the total amount of computing resources available to the information processing server; A _s,k (n) is the information age of device k at the beginning of time slot n, Q _k (n) is the amount of cached data of device k at the beginning of time slot n, A _d,k (n) is the information age of device k in the information processing server at the end of time slot n, 2. The device scheduling and resource allocation method according to claim 1, characterized in that the step of solving the optimization problem of the device scheduling and resource allocation to determine the device scheduling and resource allocation information of the wireless monitoring system comprises: The optimization problem of equipment scheduling and resource allocation is converted into a convex optimization problem: Px: Wherein, Px is the objective function of the convex optimization problem, and C8 to C15 are the constraints of Px: represents the set of devices that the network side network element needs to schedule in time slot n; f _n+1 is the computing resources available to the information processing server in time slot n+1; ε is an infinitesimal value greater than 0; χ is a penalty factor, χ>>1; j is the number of iterations, is any value of β _k,n that meets the constraints C8 and C9; M _n is the number of channels available to the network element at the network side in time slot n; The convex optimization problem is solved to determine the equipment scheduling and resource allocation information of the wireless monitoring system. 3. The device scheduling and resource allocation method according to claim 2, characterized in that the solving of the convex optimization problem to determine the device scheduling and resource allocation information of the wireless monitoring system comprises: For all time slots in the monitoring period, repeat the following steps for each time slot in chronological order: The convex optimization problem is solved by iterative calculation based on the continuous convex approximation algorithm SCA until the total average information age benefit of the wireless monitoring system in a time slot converges, and a feasible solution of β _k,n is obtained. Feasible solutions for r _k,n and A feasible solution Where E _n is the total average information age benefit of the wireless monitoring system in a time slot; based on Calculate the optimal solution of β _k,n based on and Calculate the optimal solution of p _k,n based on Calculate the optimal solution of α _k,n based on and Calculate the optimal solution of f _k,n+1 based on and Calculate _As,k (n) and _Ad,k (n); based on and Calculate f _n+1 based on Calculate _Mn . 4. The device scheduling and resource allocation method according to claim 3, characterized in that the Calculate the optimal solution of β _k,n based on and Calculate the optimal solution of p _k,n based on Calculate the optimal solution of α _k,n based on and Calculate the optimal solution of f _k,n+1 include: based on The optimal solution of β _k,n is calculated using formula C16 based on and The optimal solution of p _k,n is calculated using formula C17-C20 based on and Formula C21 is used to calculate the optimal solution for f _k,n+1 based on The optimal solution of α _k,n is calculated using formula C22 5. The device scheduling and resource allocation method according to any one of claims 1 to 4, characterized in that the device scheduling and resource allocation information includes at least one of the following: The wireless monitoring system includes the information collection status, scheduling status and transmission power of each device in each time slot, and the computing resources allocated by the information processing server to each device in each time slot. 6. A device scheduling and resource allocation apparatus, characterized in that it comprises: An acquisition module is used to acquire device information, transmission information and computing resources of the wireless monitoring system; wherein the device information includes: the distance between the device and the network side network element, the maximum transmission power of the device, the transmission information volume of the device and the unilateral power spectrum density; the transmission information includes: time slot length, channel bandwidth of the subchannel, carrier frequency and channel signal-to-interference-noise ratio threshold; the computing resources include computing resources available to the information processing server; A determination module, configured to determine the optimization problem of device scheduling and resource allocation of the wireless monitoring system based on the device information, transmission information and computing resources; wherein the optimization problem of device scheduling and resource allocation is the problem of minimizing the total information age of the wireless monitoring system in a monitoring cycle; A solution module, used to solve the optimization problem of device scheduling and resource allocation, determine the information collection state, scheduling state and transmission power of each device in each time slot of the wireless monitoring system, and the computing resources allocated by the information processing server to each device in each time slot; The optimization problem of equipment scheduling and resource allocation is expressed as: P0: Among them, P0 is the objective function of the optimization problem of device scheduling and resource allocation, C1 to C7 are the constraints of the objective function P0; C3 is the access restriction of the communication system, and the access amount of the device in the communication system at any time does not exceed the total number of sub-channels included in the communication system; C4 is the transmission power limit of the device; C5 represents the channel interference limit to ensure the success rate of information transmission; C6 represents that the information transmitted to the network side network element by any device in any time slot can be processed by the information processing server at the next moment; C7 is the system computing resource constraint; the wireless monitoring system includes K devices, at least one network side network element, and an information processing server; M is the total number of orthogonal sub-channels included in the communication system; n is the time slot number, N is the number of time slots included in a monitoring cycle; π _k (n) = {α _kn , β _k,n }; α _k,n represents the information collection status of device k in time slot n, α _k,n ={0,1,2}, α _k,n =0 means that the cache of device k is empty and device k does not collect information in time slot n, α _k,n =1 means that device k collects information and stores the information in the cache of device k in time slot n, α _k,n =2 means that the cache of device k is not empty and device k does not collect information in time slot n; β _k,n represents the scheduling of device k in time slot n, β _k,n ={0,1}, β _k,n =0 means that device k does not transmit information in time slot n, β _k,n =1 means that device k transmits information in time slot n; L _k is the amount of information that device k needs to transmit; R _k,n is the amount of information transmitted by device k in time slot n, p _k,n is the transmit power of device k in time slot n; P is the maximum transmit power of the device; h _k is the channel gain of the subchannel occupied by device k; N ₀ is the unilateral power spectrum density; B is the channel bandwidth of the subchannel; γ _th is the channel signal-to-interference-noise ratio threshold; d _k is the distance between device k and the base station, c is the speed of light, f _c is the carrier frequency; T is the time slot length; s _k is the number of CPU cycles required for the information processing server to process 1 bit of data; f _k,n+1 is the computing resources allocated by the information processing server to device k in time slot n+1; f is the total amount of computing resources available to the information processing server; A _s,k (n) is the information age of device k at the beginning of time slot n, Q _k (n) is the amount of cached data of device k at the beginning of time slot n, A _d,k (n) is the information age of device k in the information processing server at the end of time slot n, 7. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the device scheduling and resource allocation method as described in any one of claims 1 to 5 are implemented. 8. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the device scheduling and resource allocation method according to any one of claims 1 to 5 are implemented.