Data transmission method, device and equipment of industrial wireless network, storage medium and product

Abstract

The application discloses a data transmission method, device and equipment of an industrial wireless network, a storage medium and a product, relates to the technical field of industrial wireless networks, and the data transmission method of the industrial wireless network comprises the following steps: constructing an optimization objective function according to the information freshness and the overdue rate of each sensor node, determining a Bellman equation based on the optimization objective function and system state information; solving the Bellman equation to obtain the relative value of each system state; determining a scheduling strategy for the next time slot according to the relative value, and scheduling sensors based on the scheduling strategy. Since the relative value of each system state is determined, the scheduling strategy for the next time slot is determined according to the relative value, and then the sensor scheduling is performed. Compared with the existing data transmission mode according to the data collection sequence, the above mode can guarantee the freshness and timeliness of the data delivered by the industrial wireless network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial wireless network, and particularly relates to a data transmission method and device of industrial wireless network, equipment, storage medium and product. BACKGROUND

[0002] In industrial wireless network applications, different types of data may have different requirements for transmission timeliness. Some latency-sensitive deadline data may need to be delivered in time before a specific deadline. For example, in an industrial wireless network system performing real-time monitoring tasks, a monitoring center needs to maintain the freshness of received data and grasp the monitored environment state in time. When an emergency event (such as equipment failure or fire) occurs, the data sensed by a sensor will exceed the corresponding alarm threshold and needs to be transmitted to the monitoring center within a specific latency threshold for emergency decision-making. However, latency deadline data and ordinary data coexist in the network, and have differentiated requirements for transmission latency and information freshness, which are difficult to jointly optimize. In addition, complex time-varying industrial wireless channel conditions also lead to uncertainty in data transmission. Therefore, how to invent a scheduling method capable of jointly optimizing the information freshness and latency of an industrial wireless network system under time-varying channel conditions has become an important challenge. SUMMARY

[0003] The main purpose of the present application is to provide a data transmission method, device, equipment, storage medium and product of industrial wireless network, aiming at solving the technical problems of high information freshness and latency of latency-sensitive deadline data in the prior art.

[0004] To achieve the above-mentioned purpose, the present application provides a data transmission method of industrial wireless network, which comprises the following steps:

[0005] An optimization objective function is constructed according to the information freshness and overdue rate of each sensor node, and a Bellman equation is determined based on the optimization objective function and system state information;

[0006] The Bellman equation is solved to obtain the relative value of each system state;

[0007] A scheduling strategy of the next time slot is determined according to the relative value, and sensor scheduling is performed based on the scheduling strategy.

[0008] Optionally, the step of constructing the optimization objective function according to the information freshness and overdue rate of each sensor node comprises:

[0009] The node information freshness at the sensor node is determined;

[0010] determine destination information freshness according to the node information freshness, and determine information freshness of each sensor node based on the destination information freshness;

[0011] construct an optimization objective function according to the information freshness, the overdue rate and the weight information of each sensor node.

[0012] Optionally, before the step of constructing the optimization objective function according to the information freshness, the overdue rate and the weight information of each sensor node, the method further comprises:

[0013] obtaining a total number of sampling time limit data sampled by the sensor node;

[0014] determining a total number of transmission time limit data successfully transmitted within a time limit deadline;

[0015] determining an overdue rate according to the total number of sampling time limit data and the total number of transmission time limit data.

[0016] Optionally, the step of determining the Bellman equation based on the optimization objective function and system state information comprises:

[0017] obtaining an expected probability of successfully transmitting data and system state information;

[0018] determining a cost representation of performing an action under different system states;

[0019] determining the Bellman equation according to the cost representation, the expected probability, the system state information and the optimization objective function.

[0020] Optionally, the step of solving the Bellman equation to obtain a relative value of each system state, determining a scheduling strategy of a next time slot according to the relative value, and performing sensor scheduling based on the scheduling strategy comprises:

[0021] solving the Bellman equation by using a relative value iteration algorithm to obtain a relative value of each system state;

[0022] selecting a scheduling strategy of a next time slot with a maximum value according to the relative value;

[0023] performing sensor scheduling based on the scheduling strategy.

[0024] Optionally, the step of solving the Bellman equation by using a relative value iteration algorithm to obtain a relative value of each system state comprises:

[0025] selecting a reference state from a system state space, and determining a reference value corresponding to the reference state;

[0026] The relative value iterative algorithm is used to traverse the system state space and the action space in iterative training, and the Bellman equation is used to solve the target value under all state-actions.

[0027] The state relative value is obtained by comparing the reference value and the target value.

[0028] In addition, to achieve the above object, the application further provides an industrial wireless network data transmission device, which comprises:

[0029] The construction module is configured to construct an optimization objective function according to the information freshness and the overdue rate of each sensor node, and determine a Bellman equation based on the optimization objective function and system state information;

[0030] The solving module is configured to solve the Bellman equation to obtain the relative value of each system state.

[0031] The scheduling module is configured to determine a scheduling strategy of a next time slot according to the relative value, and perform sensor scheduling based on the scheduling strategy.

[0032] In addition, to achieve the above object, the application further provides an industrial wireless network data transmission device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the industrial wireless network data transmission method as described above.

[0033] In addition, to achieve the above object, the application further provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the industrial wireless network data transmission method as described above.

[0034] In addition, to achieve the above object, the application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the steps of the industrial wireless network data transmission method as described above.

[0035] The application constructs an optimization objective function according to the information freshness and overdue rate of each sensor node, determines a Bellman equation based on the optimization objective function and system state information, solves the Bellman equation to obtain the relative value of each system state, determines a scheduling strategy for the next time slot according to the relative value, and performs sensor scheduling based on the scheduling strategy. Since the application determines the relative value of each system state, determines the scheduling strategy for the next time slot according to the relative value, and then performs sensor scheduling, the above method of the application can guarantee the freshness and timeliness of data delivery of the industrial wireless network, compared with the existing data transmission method according to the data collection sequence. BRIEF DESCRIPTION OF DRAWINGS

[0036] The accompanying drawings, which are incorporated herein and form part of the specification, illustrate embodiments consistent with the application and, together with the description, further serve to explain the principles of the application.

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, for those of ordinary skill in the art, the other drawings can also be obtained based on these drawings without any creative work.

[0038] Figure 1 A flowchart is provided for the data transmission method of the industrial wireless network according to the first embodiment of the application;

[0039] Figure 2 A system diagram is provided for the data transmission method of the industrial wireless network according to the first embodiment of the application;

[0040] Figure 3 A flowchart is provided for the data transmission method of the industrial wireless network according to the second embodiment of the application;

[0041] Figure 4 A Bellman equation solving flowchart is provided for the data transmission method of the industrial wireless network according to the second embodiment of the application;

[0042] Figure 5 A module structure diagram of the data transmission device of the industrial wireless network according to the embodiment of the application is provided;

[0043] Figure 6 A device structure diagram of the hardware running environment involved in the data transmission method of the industrial wireless network according to the embodiment of the application is provided.

[0044] The object implementation, functional features and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0045] It should be understood that the specific embodiments described herein are merely intended to explain the technical solutions of the present application, and are not intended to limit the present application.

[0046] In order to better understand the technical solutions of the present application, the following will be described in detail in combination with the drawings of the specification and specific embodiments.

[0047] The main solution of the embodiment of the present application is: constructing an optimization objective function according to the information freshness and overdue rate of each sensor node, determining a Bellman equation based on the optimization objective function and system state information; solving the Bellman equation to obtain the relative value of each system state; determining a scheduling strategy for the next time slot according to the relative value, and performing sensor scheduling based on the scheduling strategy. Since the present application determines the relative value of each system state, the scheduling strategy for the next time slot is determined according to the relative value, and then the sensor scheduling is performed. Compared with the existing data transmission method according to the data collection order, the above method of the present application can guarantee the freshness and timeliness of the data delivered by the industrial wireless network. It should be noted that the execution subject of the present embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, an industrial wireless network system, etc. capable of realizing the above functions. The following takes the industrial wireless network system as an example to describe the present embodiment and each of the following embodiments.

[0048] Based on this, the present embodiment of the present application provides a data transmission method of an industrial wireless network, referring to Figure 1 , Figure 1 The flowchart of the first embodiment of the data transmission method of the industrial wireless network of the present application is shown.

[0049] In the present embodiment, the data transmission method of the industrial wireless network comprises steps S10-S40:

[0050] Step S10, constructing an optimization objective function according to the information freshness and overdue rate of each sensor node, and determining a Bellman equation based on the optimization objective function and system state information;

[0051] It should be noted that the Bellman equation can be determined by referring to Figure 2 , Figure 2 The system diagram provided by the first embodiment of the data transmission method of the industrial wireless network of the present application is shown. The industrial wireless network system of the present embodiment is composed of N sensor nodes and a common destination for real-time monitoring. The system is based on time slots, and the sensor node i transmits data to the common destination at a fixed interval G iThe monitored physical state is sampled and the sampled data is stored in a buffer, where i∈{1,2,...,N}, old data in the buffer is replaced by new sampled data; it is assumed that at most one sensor node can deliver a sampled data at the beginning of each time slot, and an acknowledgement / non-acknowledgement is returned at the end of the time slot.

[0052] It should be noted that the objective function can be a function constructed by the information freshness calculation formula and the overdue rate calculation formula of each sensor node in the industrial wireless network system. The Bellman equation is also known as the dynamic programming equation, which is a necessary condition for the mathematical optimization method of dynamic programming, and is used to describe the relationship between the value of the decision problem at a specific time point and the reward from the initial selection and the value of the decision problem derived from the initial selection. The Bellman equation can be a formula for calculating the corresponding value of the system in each state. The Bellman equation determined based on the optimization objective function and system state information can be an equation constructed based on the history channel state and system state in the optimization objective function and system state information.

[0053] Further, the step of constructing an optimization objective function according to the information freshness and the overdue rate of each sensor node can include: determining the node information freshness at the sensor node;

[0054] The destination information freshness is determined according to the node information freshness, and the information freshness of each sensor node is determined based on the destination information freshness;

[0055] The optimization objective function is constructed according to the information freshness, the overdue rate and the weight information of each sensor node.

[0056] It should be noted that the above-mentioned information freshness and overdue rate can be calculated according to the method described in the following embodiments. Figure 2 The embodiment is directed to an industrial wireless network system with time delay deadline constraint and ordinary data, and the weighted sum function of information freshness and time delay overdue rate is minimized by scheduling sensor nodes at each time slot. Under the condition of state time-varying wireless channel, the probability of successful data delivery under the current time slot channel state is calculated, and then the optimal scheduling method based on the relative value iteration algorithm is obtained by using the Markov decision process and the sequential decision characteristics of the system, so as to realize the joint optimization of average information freshness and time delay.

[0057] It is assumed that the channel state is time-varying, and the channel is modeled as a finite Markov channel, then Θ={θ1,θ2,...,θ M} is the state space of each channel, and M is the number of possible channel states; let β i (t) represent the channel state of sensor node i at time slot t, then the channel state transition probability expression is as follows formula (1):

[0058]

[0059] In addition, the probability of data being successfully transmitted to the destination is i (t) under the channel state β

[0060] Considering that the timeliness requirement of data delivery of the same physical state sampled by the sensor node at different time instants can be different, only when the value of the physical state sampled by the sensor exceeds a predetermined threshold, it is necessary to be successfully transmitted within a given latency deadline. The data that needs to be transmitted within the latency deadline is referred to as latency-critical data. Assuming that the change of the physical state is random, the probability that the sampled data is latency-critical data when the sensor node i samples is k i , and the probability that the sampled data is normal data is 1-k i .

[0061] The information freshness can be calculated according to the sampling characteristics of the sensor node i, let be the information freshness at the sensor node (i.e., the node information freshness); α i (t) ∈ {0, 1} indicates whether the sensor node i samples the physical state, if sampling at the beginning of the time slot t, then α i (t) = 1, otherwise α i (t) = 0; based on the above definition, iteration is shown in the following formula (2):

[0062]

[0063] When sampling a new state at the beginning of the time slot t, the at the time slot t will be updated to 0, otherwise it will be updated to

[0064] Let denote the destination information freshness related to the sensor node i (i.e., the destination information freshness); if the data of the sensor node i is successfully transmitted to the destination at the time slot t, the destination information freshness can be calculated as otherwise increase by one at the next time slot; assuming that u i (t) ∈ {0, 1} indicates whether the sensor node i is scheduled, if scheduled at the time slot t, then u i (t) = 1, otherwise u i (t) = 0; when the sensor node i is scheduled, let v i (t) ∈ {0, 1} indicate whether the data scheduled at the time slot t is successfully transmitted to the destination, if successfully transmitted, then v i (t) = 1, otherwise v i (t) = 0; based on the above definition, The iterative process is shown in equation (3) as follows:

[0065]

[0066] The information freshness of each sensor node based on the destination information freshness can be calculated by averaging the destination information freshness of each sensor to obtain an average destination information freshness That is, the information freshness of each sensor node.

[0067] Further, before the step of constructing an optimization objective function according to the information freshness of each sensor node, the overdue rate, and the weight information, the method further comprises:

[0068] Obtaining a total number of sampling time limit data sampled by the sensor node;

[0069] Determining a total number of transmission time limit data successfully transmitted within the time limit period;

[0070] Determining an overdue rate according to the total number of sampling time limit data and the total number of transmission time limit data.

[0071] It should be noted that, according to the sampling characteristics of the sensor node, the updating process of the time delay of the time limit data is analyzed; let μ i (t)∈{0,1} represent whether the data is time limit data, if it is time limit data, then μ i (t) = 1, otherwise μ i (t) = 0; the time delay of the time limit data is defined as the time from data sampling to its successful transmission to the destination, and the time delay of the time limit data that fails to be successfully transmitted is assumed to be ∞; since the time delay is mainly composed of the waiting time delay in the buffer and the transmission time delay, once the time limit data is successfully transmitted in time slot t, μ i (t) = 1, the transmission time delay can be calculated as At this time, if there is , the time limit data is delivered within the time limit period L i , otherwise the data is overdue. In the long-term data sampling and transmission process, let z i (t) represent the total number of time limit data successfully transmitted within the time limit period from the initial time slot to time slot t. That is, the total number of transmission time limit data, z i (t) is updated as shown in equation (4) as follows:

[0072]

[0073] When the time limit data of sensor node i is successfully transmitted at time slot t, and the transmission time delay is less than the time limit period L iThen in the next time slot, z i (t+1) will be updated to z i (t)+1, otherwise it remains unchanged.

[0074] Let n i (t) be the total number of sampled deadline data at sensor node i at time slot t since the initial time slot. That is, the total number of sampled deadline data, then n i (t) evolves as shown in equation (5):

[0075]

[0076] Further, according to the freshness and latency of each sensor node information obtained, the deadline-related overdue rate is calculated, and the target optimization expression of joint optimization of average information freshness and overdue rate is constructed.

[0077] For each sensor node i, the average destination information freshness is used to measure the data freshness at time slot t, as shown in equation (6):

[0078]

[0079] For the deadline data of sensor node i, the overdue rate δ i is used to measure the reliability of the system, defined as the ratio of the number of deadline data whose latency exceeds the deadline to the total number of long-term sampled deadline data, as shown in equation (7):

[0080]

[0081] The joint optimization target J i (t) of sensor node i is constructed, defined as the weighted sum of average destination information freshness and overdue rate, as shown in equation (8):

[0082]

[0083] Where λ i is the weight of the overdue rate of sensor node i, with a value range of (0, 1), which can be set according to the use scenario.

[0084] According to the joint optimization target, the scheduling strategy is designed to minimize the average long-term J i (t) value of each sensor node, and the optimization objective function is constructed as shown in equation (9):

[0085]

[0086] Where π represents a feasible strategy, and Π represents the set of all feasible strategies.

[0087] Solving the Bellman equation to obtain the relative value of each system state;

[0088] It should be noted that the solving the Bellman equation to obtain the relative value of each system state can be to fully utilize the historical channel state and statistical knowledge, consider the sequential decision characteristics of the system, adopt a Markov decision process model including four elements of system state space, action space, transition probability and cost, solve the Bellman equation by using a relative value iteration algorithm to obtain the relative value of each system state. Specifically, the relative value iteration algorithm first selects a reference state s re f Then, in each round of iterative training, the system state space S is traversed to solve the value function of all state-action pairs Then, based on the Bellman equation, the state-action pair with the minimum state-action pair value is selected as the value of state s, and after a round of iteration, the value of the reference state is subtracted as the relative state value of the current state, and after I max times of iteration, the iteration process is terminated to obtain the optimal scheduling strategy set π(s) based on V(s) and V(s).

[0089] Further, the step S20 can include: selecting a reference state from the system state space, and determining a reference value corresponding to the reference state;

[0090] The relative value iteration algorithm is used to traverse the system state space and the action space in iterative training respectively, and the target value under all state-actions is solved based on the Bellman equation;

[0091] The reference value and the target value are compared to obtain the state relative value.

[0092] It should be noted that the system state space can include the state space and action space corresponding to the industrial wireless network system, and the reference state can be a state information in the numerous states and actions of the industrial wireless network system. The reference value corresponding to the reference state can be solved according to the Bellman equation. The relative value iteration algorithm is used to traverse the system state space and the action space in iterative training respectively, and the target value under all state-actions is solved based on the Bellman equation, which can be to traverse the state space and action space of the industrial wireless network system, and the target value under each state-action combination is calculated by being brought into the Bellman equation, and the target value is subtracted from the reference value to obtain the state relative value.

[0093] ​Step S30, determining a scheduling policy of a next time slot according to the relative values, and performing sensor scheduling based on the scheduling policy.

[0094] It should be noted that the determining of the scheduling policy of the next time slot according to the relative values and the performing of the sensor scheduling based on the scheduling policy can be determining a state-action pair with the maximum relative value according to the relative values, and determining a scheduling policy of the state-action pair.

[0095] The embodiment constructs an optimization objective function according to the information freshness and the expiration rate of each sensor node, determines a Bellman equation based on the optimization objective function and system state information, solves the Bellman equation to obtain the relative values of each system state, determines a scheduling policy of a next time slot according to the relative values, and performs sensor scheduling based on the scheduling policy. Since the embodiment determines the relative values of each system state, and determines a scheduling policy of a next time slot according to the relative values and then performs sensor scheduling, the above-mentioned manner of the embodiment can guarantee the freshness and timeliness of the industrial wireless network in delivering data, as compared with the existing manner of performing data transmission according to the data collection sequence.

[0096] Based on the first embodiment, in the second embodiment, the same or similar contents as the above-mentioned first embodiment can be referred to the above description, and will not be described in detail. On this basis, please refer to Figure 2 , the step S10 further includes steps S101-S103:

[0097] Step S101: obtaining an expected probability of successful data transmission and system state information;

[0098] It should be noted that the expected probability of successful data transmission at time slot t can be calculated according to historical channel state and statistical knowledge The expression is as follows:

[0099]

[0100] wherein is the probability of the sensor node i transferring from the channel state y i (t-1) to the channel state y at time slot t, p y is the data transmission success probability of the channel in the state y;

[0101] The state of the system can be expressed as s∈S, wherein S is a system state space set, and the expression of the state s(t) of the system at time slot t is as follows, fully considering the available history of the channel state of the system and the related statistical knowledge of the information freshness:

[0102] s(t)=(a s (t),a d(t), β(t-1), n(t), z(t), t)

[0103] wherein, is the vector of information freshness of all sensor nodes i, is the vector of information freshness of all sensor nodes i related to the destination, β(t) = (β i (t)) is the vector of channel state of the last time slot known by all sensor nodes i, n(t) = (n i (t)) is the vector of the total number of samples of delay-limited data of all sensor nodes i, z(t) = (z i (t)) is the vector of the total number of delay-limited data successfully transmitted by all sensor nodes i within the deadline. Let u(t) = (u i (t)) ∈ Α represent the scheduling action for all sensor nodes at the beginning of time slot t, wherein Α represents the set of all possible scheduling actions.

[0104] Step S102: determining the cost representation of performing actions in different system states;

[0105] It should be noted that the cost representation of performing actions in different system states can be: let Pr(s'|s, u) be the probability of the system moving to the next time slot state s' = (a s ,a d ,β,n,z,t m ) after performing action u in state s = (a s′ ,a d′ ,β,n,z,t m ), wherein t m is the time slot index; analyzing the sensor node data sampling update and scheduling decision process, the transition probability of each component of the system state follows the following rules, wherein the transition probability of the time slot index can be calculated as follows:

[0106]

[0107] Analyzing the update process of the number of samples of delay-limited data, the state transition probability of the total number of samples of this type of data can be calculated as follows:

[0108]

[0109] When the data of sensor node i is delay-limited data, μ i (t) = 1, the transition probability of the total number of successful transmissions of delay-limited data can be calculated as follows:

[0110]

[0111] When the data packet of sensor node i is ordinary data μi When t) = 0, the total number of successful transmission of deadline data remains unchanged in the next state, and the transition probability is 1, otherwise the transition probability is 0.

[0112] The transition probability of the channel state can be calculated as follows:

[0113]

[0114] The transition probability of the information freshness at the node of the sensor node i can be calculated as follows by analyzing the information freshness sampling update process of the sensor node:

[0115]

[0116] The transition probability of the destination information freshness can be calculated as follows by analyzing the destination information freshness scheduling update process of the sensor node for two types of data:

[0117]

[0118] When the data of the sensor node i is deadline data, the transition probability of the destination information freshness can be calculated as follows:

[0119]

[0120] For different scheduling decisions, and according to the objective function, that is, the optimization objective function, the cost of executing the action u(t) under different system states s(t) is represented as C(s(t),u(t)), that is, the cost of executing the action under different system states, which can be calculated as follows:

[0121]

[0122] Wherein represents the J i expected value of the sensor node i not being scheduled at time slot t, represents the J i expected value of the sensor node i being scheduled at time slot t,

[0123]

[0124] Wherein r i (t)∈{0,1} represents whether sampling will be performed in the next time slot, when the next time slot will perform sampling, and k i the probability of sampling is deadline data, then r i (t) = 1, otherwise r i (t) = 0; e i(t) e {0, 1} indicates whether the time delay of data is less than the deadline, when then e i (t) = 1, otherwise e i (t) = 0.

[0125] Step S103: determining a Bellman equation according to the cost representation, the expected probability, the system state information and the optimization objective function.

[0126] It should be noted that the Bellman equation determined according to the cost representation, the expected probability, the system state information and the optimization objective function can be:

[0127]

[0128] where V(s(t)) is the value of the state s(t), and the value of V(s(t)) can be solved by a relative value iteration method, so as to obtain the optimal scheduling decision in each state. Specifically, in the first iteration training, the system state space S is traversed, and the value function of the state-action pair of the current iteration round is calculated as follows:

[0129]

[0130] All function values are calculated, and the state value function of the current state is calculated as follows:

[0131]

[0132] After one iteration, the state relative value V I (s) is calculated as follows:

[0133]

[0134] The optimal scheduling strategy based on the relative value iteration algorithm is obtained as follows: At the beginning of each time slot, the current state s of the system is obtained, the action with the maximum state-action pair value is selected based on the current state, and the corresponding sensor node is scheduled, so as to minimize the objective function, and realize the joint optimization of the long-term average information freshness and the time delay of the system.

[0135] ​​The embodiment is applied to an industrial wireless network system with time-limited data and common data, so as to ensure the transmission of sufficient time-limited data within a threshold while improving the timeliness of common data transmission, thereby ensuring the real-time performance and reliability of the industrial wireless network system. The embodiment fully utilizes historical channel state information to evaluate the current channel state, and then utilizes statistical data of historical AoI and overdue rate as a cost function of different scheduling actions to effectively evaluate the value of scheduling actions, and solves the optimal offline scheduling strategy by a relative value iteration method, effectively reduces the freshness of device information while meeting the overdue rate constraint of time-limited data, and has a lower requirement on the computing performance of the device deploying the scheduling strategy.

[0136] In specific implementation, reference can be made to Figure 4 , Figure 4 The Bellman equation solving flowchart provided for the second embodiment of the data transmission method of the industrial wireless network; the specific steps are as follows:

[0137] V1-V3: initialize the system state space S, the action space A, the iteration index I and the maximum iteration number I max , and select a reference state s ref ∈S.

[0138] V4: start iteration training, and judge whether I≤I max , I max is the preset maximum iteration number, if yes, steps V5-V8 are executed, otherwise, V9 is executed.

[0139] V5-V8: traverse the system state space, traverse the action space, calculate the value function and the state value function of all state-action pairs in the Ith iteration, and calculate the state relative value function V I (s) after one iteration, let I increase by one, and then return to step V4 to judge whether the iteration index meets the condition.

[0140] V3-V5: update the local information freshness of each sensor node at the start of the time slot, that is, the node information freshness and the destination information freshness.

[0141] V9: after a certain number of iteration training, the optimal scheduling strategy π(s) is obtained.

[0142] V10-V11: according to the obtained optimal scheduling strategy π(s), the sensor nodes are scheduled in each time slot according to the strategy π(s) at the start of each time slot, and the information freshness and the overdue rate of the sensor nodes are calculated and updated after the end of each time slot scheduling.

[0143] The embodiment is directed to an industrial wireless network system for jointly sampling delay deadline-sensitive data and common data, and minimizes a weighted sum function of information freshness and delay deadline expiration rate by scheduling sensor nodes in each time slot, calculates the probability of successful data delivery in the current time slot channel state under the state time-varying wireless channel condition, uses a Markov decision process based on the sequential decision characteristics of the system to obtain an optimal scheduling method based on a relative value iteration algorithm, and realizes joint optimization of average information freshness and delay. The freshness and timeliness of the data delivered by the industrial wireless network are ensured.

[0144] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the data transmission method of the industrial wireless network of the present application. More forms of simple transformation based on this technical concept are within the protection scope of the present application.

[0145] The present application also provides an industrial wireless network data transmission device, please refer to Figure 5 The industrial wireless network data transmission device comprises:

[0146] The construction module 10 is configured to construct an optimization objective function according to the information freshness and expiration rate of each sensor node, and determine a Bellman equation based on the optimization objective function and system state information;

[0147] The solving module 20 is configured to solve the Bellman equation to obtain the relative value of each system state;

[0148] The scheduling module 30 is configured to determine a scheduling strategy for the next time slot according to the relative value, and schedule sensors based on the scheduling strategy.

[0149] The embodiment constructs an optimization objective function according to the information freshness and expiration rate of each sensor node, determines a Bellman equation based on the optimization objective function and system state information, solves the Bellman equation to obtain the relative value of each system state, determines a scheduling strategy for the next time slot according to the relative value, and schedules sensors based on the scheduling strategy. Since the embodiment determines the relative value of each system state, determines a scheduling strategy for the next time slot according to the relative value, and then schedules sensors. Compared with the existing data transmission method according to the data collection sequence, the above method of the embodiment can ensure the freshness and timeliness of the data delivered by the industrial wireless network.

[0150] The industrial wireless network data transmission device provided by the present application adopts the industrial wireless network data transmission method in the above embodiment, and can solve the technical problems of the information freshness and high latency of the latency-limited data sensitive to latency in the prior art. Compared with the prior art, the industrial wireless network data transmission device provided by the present application has the same beneficial effects as the industrial wireless network data transmission method provided by the above embodiment, and other technical features in the industrial wireless network data transmission device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0151] The present application provides an industrial wireless network data transmission device, which comprises at least one processor and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the industrial wireless network data transmission method in the above embodiment one.

[0152] Reference will now be made to the drawings, in which Figure 6 which shows a structural schematic diagram of the industrial wireless network data transmission device suitable for implementing the embodiments of the present application. The industrial wireless network data transmission device in the embodiments of the present application can include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description), PMPs (Portable Media Player), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. Figure 6 The industrial wireless network data transmission device shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present application.

[0153] As Figure 6As shown, the data transmission device of the industrial wireless network can include a processing apparatus 1001 (for example, a central processor, a graphics processor, etc.), which can perform various appropriate actions and processes according to programs stored in a read only memory (ROM: Read Only Memory) 1002 or programs loaded from a storage apparatus 1003 into a random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the data transmission device of the industrial wireless network are also stored. The processing apparatus 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input apparatus 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output apparatus 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; the storage apparatus 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication apparatus 1009. The communication apparatus 1009 can allow the data transmission device of the industrial wireless network to communicate with other devices wirelessly or wiredly to exchange data. Although the data transmission device of the industrial wireless network with various systems is shown in the figure, it should be understood that all the shown systems are not required to be implemented or possessed. More or less systems can be alternatively implemented or possessed.

[0154] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program codes for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication apparatus, or installed from the storage apparatus 1003, or installed from the ROM 1002. When the computer program is executed by the processing apparatus 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are executed.

[0155] The data transmission device of the industrial wireless network provided in the present application adopts the industrial wireless network data transmission method in the above-mentioned embodiments, and can solve the technical problems of high latency and low information freshness of latency-sensitive time limit data in the prior art. Compared with the prior art, the data transmission device of the industrial wireless network provided in the present application has the same beneficial effects as the industrial wireless network data transmission method provided in the above-mentioned embodiments, and other technical features in the data transmission device of the industrial wireless network are the same as the features disclosed in the above-mentioned embodiments, which will not be described here.

[0156] It should be understood that various parts of the present application can be realized with hardware, software, firmware or a combination thereof. In the above description of embodiments, specific features, structures, materials or characteristics can be combined in any appropriate manner in any one or more embodiments or examples.

[0157] The above description is merely illustrative of the application and is not intended to limit the scope of the application. Any variations and modifications that can be made by any person skilled in the art within the spirit and scope of the application are intended to be encompassed by the application. Therefore, the scope of the application should be determined by the appended claims.

[0158] The present application provides a computer readable storage medium having stored thereon computer readable program instructions (i.e., computer programs) for performing the data transmission method of the industrial wireless network in the above-described embodiments.

[0159] The computer readable storage medium provided by the present application may, for example, be a U disk, but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system or device, or any combination thereof. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read only memory (ROM), an erasable programmable read only memory (EPROM or flash memory), an optical fiber, a portable compact disk read only memory (CD-ROM), an optical storage device, a magnetic storage device, or any appropriate combination thereof. In the present embodiment, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer readable storage medium can be transmitted in any appropriate medium, including but not limited to an electrical wire, an optical cable, an RF (Radio Frequency) cable, etc., or any appropriate combination thereof.

[0160] The above computer readable storage medium can be contained in the data transmission device of the industrial wireless network; or can exist separately and not be assembled into the data transmission device of the industrial wireless network.

[0161] The computer readable storage medium described above carries one or more programs, which, when executed by the data transmission device of the industrial wireless network, cause the data transmission device of the industrial wireless network to perform the data transmission method of the industrial wireless network.

[0162] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0163] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a procedure, or a part of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in some cases, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by a dedicated hardware-based system that carries out specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0164] The modules involved in the embodiments of the present application can be implemented in software or in hardware. In some cases, the names of the modules do not constitute a limitation on the modules themselves.

[0165] The readable storage medium provided by the application is a computer readable storage medium, and the computer readable storage medium stores computer readable program instructions (namely, a computer program) for executing the data transmission method of the industrial wireless network, and can solve the technical problems of high information freshness and high time delay of time delay limited data sensitive to time delay in the prior art. Compared with the prior art, the computer readable storage medium provided by the application has the same beneficial effects as the data transmission method of the industrial wireless network provided by the above-mentioned embodiments, and will not be described here.

[0166] The application further provides a computer program product comprising a computer program, which, when executed by a processor, implements the steps of the data transmission method of the industrial wireless network as described above.

[0167] The computer program product provided by the application can solve the technical problems of high information freshness and high time delay of time delay limited data sensitive to time delay in the prior art. Compared with the prior art, the computer program product provided by the application has the same beneficial effects as the data transmission method of the industrial wireless network provided by the above-mentioned embodiments, and will not be described here.

[0168] The above-mentioned is only part of the embodiments of the application, and does not limit the patent scope of the application, and any equivalent structural transformation, direct / indirect application in other related technical fields made by using the content of the specification and drawings of the application within the technical concept of the application are included in the patent protection scope of the application.

Claims

1. A data transmission method of an industrial wireless network, characterized by, The data transmission method of the industrial wireless network comprises the following steps: An optimization objective function is constructed according to the information freshness and expiration rate of each sensor node, and a Bellman equation is determined based on the optimization objective function and system state information; The Bellman equation is solved to obtain the relative value of each system state; A scheduling strategy for the next time slot is determined according to the relative value, and sensor scheduling is performed based on the scheduling strategy; The step of solving the Bellman equation to obtain the relative value of each system state comprises: A reference state is selected from the system state space, and a reference value corresponding to the reference state is determined; A relative value iteration algorithm is used to traverse the system state space and action space respectively in iterative training, and the target value under all state-actions is solved based on the Bellman equation; The relative value of each system state is obtained based on the target value minus the reference value.

2. The data transmission method of the industrial wireless network according to claim 1, wherein, The step of constructing the optimization objective function according to the information freshness and expiration rate of each sensor node comprises: The node information freshness at the sensor node is determined; The destination information freshness is determined according to the node information freshness, and the information freshness of each sensor node is determined based on the destination information freshness; The optimization objective function is constructed according to the information freshness, expiration rate and weight information of each sensor node.

3. The data transmission method of the industrial wireless network according to claim 2, wherein, Before the step of constructing the optimization objective function according to the information freshness, expiration rate and weight information of each sensor node, the following steps are further included: The total number of sampling time limit data sampled by the sensor node is obtained; The total number of transmission time limit data successfully transmitted within the time limit is determined; The expiration rate is determined according to the total number of sampling time limit data and the total number of transmission time limit data.

4. The data transmission method of the industrial wireless network of claim 1, wherein, The step of determining the Bellman equation based on the optimization objective function and system state information comprises: The expected probability of successfully transmitting data and system state information are obtained; The cost representation of performing actions under different system states is determined; The Bellman equation is determined according to the cost representation, the expected probability, the system state information and the optimization objective function.

5. The data transmission method of an industrial wireless network according to any one of claims 1 to 4, characterized in that, The steps of solving the Bellman equation to obtain the relative value of each system state, determining the scheduling strategy for the next time slot according to the relative value, and performing sensor scheduling based on the scheduling strategy comprise: The Bellman equation is solved to obtain the relative value of each system state using a relative value iteration algorithm; The scheduling strategy for the next time slot with the maximum value is selected according to the relative value; Sensor scheduling is performed based on the scheduling strategy.

6. A data transmission apparatus of an industrial wireless network, characterized by comprising: The data transmission device of the industrial wireless network comprises: A construction module for constructing an optimization objective function according to the information freshness and expiration rate of each sensor node, and determining a Bellman equation based on the optimization objective function and system state information; A solving module for solving the Bellman equation to obtain the relative value of each system state; A scheduling module for determining a scheduling strategy for the next time slot according to the relative value, and performing sensor scheduling based on the scheduling strategy; The step of solving the Bellman equation to obtain the relative value of each system state comprises: selecting a reference state from a system state space, and determining a reference value corresponding to the reference state; iteratively traversing the system state space and the action space in an iterative training using a relative value iteration algorithm, and solving the target value under all state-action based on the Bellman equation; obtaining the relative value of each system state based on the target value minus the reference value.

7. A data transmission device of an industrial wireless network, characterized by comprising: The device comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the data transmission method of the industrial wireless network according to any one of claims 1 to 5.

8. A storage medium, characterized by The storage medium is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the data transmission method of the industrial wireless network according to any one of claims 1 to 5.

9. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is executed by the processor to implement the steps of the data transmission method of the industrial wireless network according to any one of claims 1 to 5.

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