Data collaborative transmission methods and related equipment for industrial internet scenarios

Abstract

This application provides a data collaborative transmission method and related equipment for industrial internet scenarios. Based on generation time and growth factor, a first nonlinear information age model is constructed corresponding to the target working time slot in the target working cycle when the target sensor is a service sensor. Based on generation time and growth factor, a second nonlinear information age model is constructed corresponding to the target working time slot in the target working cycle when the target sensor is a requesting sensor. Based on this, an average nonlinear information age model is constructed. The average nonlinear information age model is processed by a bidirectional selection mechanism based on a near-end strategy optimization algorithm. This can take into account the timeliness of the sensing information of unscheduled sensors. According to the obtained data collaborative transmission strategy, the bidirectional selection between scheduled sensors and unscheduled sensors can be controlled to complete the collaborative transmission, thereby improving the timeliness of sensing information.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a data collaborative transmission method and related equipment for industrial internet scenarios. Background Technology

[0002] The normal operation of a smart factory is inseparable from the monitoring of various production status information by sensors. In the industrial internet scenario, the scheduling of sensors is crucial.

[0003] However, in real-world scenarios, limited channel resources mean that only a limited number of sensors can send sensing information to the data processing node in each scheduling cycle. As a result, the sensing information from unscheduled sensors cannot be received normally, leading to a decrease in the timeliness of the sensing information. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a data collaborative transmission method for industrial internet scenarios to solve the above-mentioned technical problems.

[0005] Based on the above objectives, the first aspect of this application provides a data collaborative transmission method for industrial internet scenarios, applied to a data collaborative transmission system, the system including edge node devices, data processing devices, and multiple sensors, the method being executed by the edge node devices, including:

[0006] The system's working time is obtained, which includes multiple working cycles, and each working cycle includes multiple working time slots.

[0007] Each of the plurality of sensors is designated as a target sensor, each of the plurality of working cycles is designated as a target working cycle, and each of the plurality of working time slots is designated as a target working time slot.

[0008] Determine the growth factor of the target sensor in the target working cycle, and obtain the generation time of the received sensing information of the target sensor;

[0009] Based on the generation time and the growth factor, a first nonlinear information age model is constructed that corresponds to the target working time slot in the target working cycle when the target sensor is a service sensor.

[0010] Based on the generation time and the growth factor, a second nonlinear information age model is constructed that corresponds to the target working time slot in the target working cycle when the target sensor is a requesting sensor.

[0011] An average nonlinear information age model is constructed based on the first and second nonlinear information age models corresponding to all target sensors.

[0012] The average nonlinear information age model is processed by a bidirectional selection mechanism based on a near-end strategy optimization algorithm to obtain a data collaborative transmission strategy. The data collaborative transmission strategy is then used to control the multiple sensors to collaboratively transmit the collected sensing information to the data processing device in each working time slot of each working cycle.

[0013] Based on the same concept, a second aspect of this application provides a data collaborative transmission device for an industrial internet scenario. The device is installed on an edge node device and applied to a data collaborative transmission system. The system includes the edge node device, a data processing device, and multiple sensors. The device includes:

[0014] The acquisition module is configured to acquire the working time of the system, the working time including multiple working cycles, and each working cycle including multiple working time slots;

[0015] The target determination module is configured to use each of the plurality of sensors as a target sensor, each of the plurality of working cycles as a target working cycle, and each of the plurality of working time slots as a target working time slot.

[0016] The data determination module is configured to determine the growth factor of the target sensor in the target working cycle and to acquire the generation time of the received sensing information of the target sensor.

[0017] The first model building module is configured to build a first nonlinear information age model corresponding to the target working time slot in the target working cycle when the target sensor is a service sensor, based on the generation time and the growth factor.

[0018] The second model building module is configured to build a second nonlinear information age model corresponding to the target working time slot in the target working cycle when the target sensor is a requesting sensor, based on the generation time and the growth factor.

[0019] The third model building module is configured to build an average nonlinear information age model based on the first nonlinear information age model and the second nonlinear information age model corresponding to all target sensors.

[0020] The collaborative transmission module is configured to process the average nonlinear information age model through a bidirectional selection mechanism based on a near-end strategy optimization algorithm to obtain a data collaborative transmission strategy, and control the multiple sensors to collaboratively transmit the collected sensing information to the data processing device in each working time slot of each working cycle according to the data collaborative transmission strategy.

[0021] Based on the same concept, a third aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor, when executing the computer program, implements the method described in the first aspect above.

[0022] Based on the same concept, a fourth aspect of this application provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method described in the first aspect above.

[0023] As can be seen from the above, the data collaborative transmission method and related equipment for industrial internet scenarios provided in this application construct a first nonlinear information age model corresponding to the target working time slot in the target working cycle when the target sensor is a service sensor, based on the generation time and growth factor. Then, based on the generation time and growth factor, a second nonlinear information age model corresponding to the target working time slot in the target working cycle is constructed when the target sensor is a requesting sensor. Finally, an average nonlinear information age model is constructed based on the first and second nonlinear information age models corresponding to all target sensors. This average nonlinear information age model is then processed through a bidirectional selection mechanism based on a near-end strategy optimization algorithm. This approach can take into account the timeliness of the sensing information from unscheduled sensors, enabling the bidirectional selection between scheduled and unscheduled sensors to complete collaborative transmission according to the obtained data collaborative transmission strategy, thereby improving the timeliness of the sensing information. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of a data collaborative transmission method for industrial internet scenarios according to an embodiment of this application;

[0026] Figure 2A This is a schematic diagram of a data collaborative transmission system according to an embodiment of this application;

[0027] Figure 2B This is a schematic diagram of the sensor's working process according to an embodiment of this application;

[0028] Figure 3 This is a structural block diagram of a data collaborative transmission device for industrial internet scenarios according to an embodiment of this application;

[0029] Figure 4 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0031] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0032] It is understood that before using the technical solutions of the various embodiments in this application, users will be informed of the type, scope of use, and usage scenarios of the personal information involved in an appropriate manner, and user authorization will be obtained.

[0033] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose, based on the prompt message, whether to provide personal information to the software or hardware such as electronic devices, applications, servers, or storage media performing the operations described in this application.

[0034] As an optional but not limited implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0035] It is understood that the above notification and user authorization process is merely illustrative and does not limit the implementation of this application. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this application.

[0036] In industrial sensor networks, effective sensor scheduling methods are essential for improving the timeliness of sensed information. However, traditional scheduling methods only consider single-hop transmission, where scheduled sensors send data while unscheduled sensors wait in standby mode. This leads to a decline in the timeliness of sensed information. Furthermore, even in multi-node cooperative transmission scenarios, the cooperation mechanism is designed solely from the perspective of achieving global goals, without considering the interests of the requester and service provider. Therefore, a detailed explanation and solution for node scheduling and cooperative transmission mechanisms in industrial sensor networks are needed. The detailed problem is as follows:

[0037] 1) The scheduling method in single-hop transmission mode suffers from poor timeliness of perceived information:

[0038] In smart manufacturing scenarios, industrial sensor networks generate massive amounts of data every moment, but the actual available channel resources are limited. It's impossible for all sensors to simultaneously send data to the data processing center. Traditional data transmission involves the control center scheduling sensors in the network each work cycle. Scheduled sensors directly send their data to the data processing center, while unscheduled sensors wait for their turn. This method is disadvantageous for unscheduled sensors because their sensing information isn't received by the data processing center, leading to a continuous decline in the timeliness of their information. Furthermore, even with sufficient channel resources, channel quality issues can cause some sensors to experience long data transmission times and low timeliness, failing to meet the demands of actual production scenarios. Therefore, this application improves upon the traditional data transmission method by proposing a collaborative transmission approach. Scheduled sensors directly send their data to the data processing center, while unscheduled sensors (including those unable to meet their own timeliness requirements) use the scheduled sensors as relays, first sending their data to the scheduled sensors, which then relay the data to the data processing center. This transmission method balances the timeliness of the entire network.

[0039] 2) The problem of neglecting the local interests of both parties in traditional multi-node cooperation mechanisms. Under existing cooperation mechanisms, the goal is often to achieve global optimization, neglecting the local interests of both parties. Considering the aforementioned problem 1), the scheduling sensor is a service provider, and it will inevitably sacrifice some of its own interests when providing services, such as incurring additional energy consumption and a decrease in data sensing performance due to continuous operation. Therefore, when a request arrives, the scheduling sensor is unwilling to actively provide services. To incentivize the scheduling sensor to participate in cooperative transmission, this application proposes a two-way selection mechanism. The completion of cooperative transmission depends on a two-way selection between the requester (the unscheduled sensor) and the service provider (the scheduling sensor). During the selection process, both the requester and the service provider will simultaneously consider the global goals of the network and their own interests.

[0040] Industrial Sensor Network (ISN) is a communication system installed in industrial production environments. It features high flexibility, low cost, and low power consumption. Through its built-in wireless communication and sensing modules, it can achieve real-time monitoring of production status, enabling predictive maintenance, thereby improving the efficiency and safety of industrial production and avoiding unnecessary production accidents.

[0041] Collaborative Transmission: The normal operation of a smart factory relies on sensors monitoring various production status information. However, in real-world scenarios, limited channel resources mean that only a limited number of sensors can send their sensing information to the data processing node in each scheduling cycle. This results in unscheduled sensors failing to receive their sensing information, drastically reducing timeliness. Therefore, collaborative transmission can be introduced into the sensor data transmission network. Collaborative transmission involves scheduled sensors directly sending their sensing data to the data processing node in each work cycle. Unscheduled sensors, acting as relays, first send their sensing data to the scheduled sensor, which then forwards it to the data processing center.

[0042] This application provides a data collaborative transmission method for industrial internet scenarios. Based on generation time and growth factor, a first nonlinear information age model is constructed for the target working cycle corresponding to the target working time slot when the target sensor is a service sensor. Based on generation time and growth factor, a second nonlinear information age model is constructed for the target working cycle corresponding to the target working time slot when the target sensor is a requesting sensor. Then, an average nonlinear information age model is constructed based on the first and second nonlinear information age models corresponding to all target sensors. The average nonlinear information age model is processed by a bidirectional selection mechanism based on a near-end strategy optimization algorithm. This method can take into account the timeliness of the sensing information of unscheduled sensors, and enable the bidirectional selection between scheduled and unscheduled sensors to complete collaborative transmission according to the obtained data collaborative transmission strategy, thereby improving the timeliness of sensing information.

[0043] like Figure 1 As shown, the method of this embodiment is applied to a data collaborative transmission system, which includes an edge node device, a data processing device, and multiple sensors. The method is executed by the edge node device and includes:

[0044] Step 101: Obtain the working time of the system. The working time includes multiple working cycles, and each working cycle includes multiple working time slots.

[0045] In this step, such as Figure 2A As shown, this application considers a real-time monitoring system in a factory, which includes a sensor network, edge nodes, and a data processing center. The sensor network is responsible for monitoring various types of information during the industrial production process, while the edge nodes are responsible for scheduling and controlling the sensor network and sending the sensed data to the data processing center for further processing and analysis.

[0046] In the system under consideration, the sensor's operating time is divided into several time slots, and the continuous... Each time slot is divided into one work cycle. Representing sensor index, using Represents a work cycle index, using This represents the time slot index within each work cycle. At the beginning of each work cycle, the sensors first sample the monitoring data. Subsequently, the edge nodes schedule the sensors; scheduled sensors send data to the edge nodes, while unscheduled sensors select scheduled sensors to send their data to the edge nodes. Indicates the first The sensor at the first The transmission time is per cycle. This application assumes that sensors are connected via wired transmission and that sensors are connected to edge nodes via wireless transmission. Considering the complex transmission environment within a factory, the channel between the sensors and edge nodes is inevitably non-ideal. Indicates sensor The probability of successful transmission Time remains unchanged. (Use) Indicates sensor In the The transmission status of each cycle, A single line indicates successful transmission, while a single line indicates transmission failure. In the event of a transmission interruption, if the sensor remains in standby mode until the next work cycle begins, it will not only degrade the timeliness of the information but also cause unnecessary energy consumption. Therefore, this application considers a sleep-based cooperative transmission method. Specifically, sensors are divided into two main categories: serving sensors (SIS) and requesting sensors (RIS). Serving sensors are scheduled sensors in each cycle; they directly transmit data to the edge nodes. Requesting sensors are unscheduled sensors; they need to transmit their data to the serving sensors first, and then the serving sensors transmit the data to the edge nodes. Serving sensors enter sleep mode after completing their own data transmission and that of requesting sensors, while requesting sensors enter sleep mode after sending data to the serving sensors.

[0047] The working process of the sensor can be simplified to: Figure 2B As shown, the sensor described is the serving sensor SIS (i.e., scheduled) in cycle 1, and the requesting sensor RIS (not scheduled) in cycles 2 and 3.

[0048] Step 102: Each of the plurality of sensors is designated as a target sensor, each of the plurality of working cycles is designated as a target working cycle, and each of the plurality of working time slots is designated as a target working time slot.

[0049] This step describes an iterative or traversal process in a system containing multiple sensors, multiple duty cycles, and multiple time slots. To explain this process more clearly, it can be broken down into several key steps:

[0050] Each sensor in the system is responsible for collecting or monitoring a certain type of data or environmental parameters. This application prefers sensors to collect sensing information data.

[0051] Treating each sensor as a target sensor means selecting each of these sensors one by one as the target of the current operation or analysis during the processing. In other words, the system performs a series of operations on the first sensor, then performs the same operations on the second sensor, and so on, until all sensors have been processed. This is usually done to ensure that each sensor in the system receives equal processing or analysis, thereby collecting comprehensive data.

[0052] A work cycle refers to the time period during which a system collects, processes, or communicates data. For example, data collection may occur hourly, daily, or weekly; these time periods constitute different work cycles.

[0053] By designating each work cycle as a target work cycle, similar to how sensors are processed, the system selects each of these work cycles as the current period of focus. This means that for each sensor, the system considers its performance or data across all work cycles. This helps in analyzing changes in sensor behavior over different time periods, such as temperature differences between day and night, or activity levels on weekdays and weekends.

[0054] A working time slot typically refers to a more subdivided time period within a working cycle. For example, within an hour, there may be multiple 5-minute, 10-minute, or half-hour time slots used for data acquisition or processing. Each working time slot is designated as a target working time slot. After determining the target sensor and target working cycle, the system further refines this by treating each time slot within each working cycle as an independent unit of analysis. This allows the system to gain a more granular understanding of the sensor's status or data changes within each specific time period.

[0055] This fine-grained analysis is particularly important for applications that require precise control or monitoring, such as industrial automation and environmental monitoring.

[0056] In summary, the above describes a complex iterative process that achieves comprehensive system analysis and data collection by processing each sensor, each work cycle, and each work slot in the system one by one.

[0057] Step 103: Determine the growth factor of the target sensor in the target working cycle, and obtain the generation time of the received sensing information of the target sensor.

[0058] In this step, the growth factor is an indicator used to describe the proportion or rate of change of a quantity (such as the sensor's output value, measured value, etc.) relative to its initial value or a reference value within a specific time period (here referring to the "target working cycle"). In sensor data processing, the growth factor can help understand the trend of sensor data over time, such as the rate of increase or decrease.

[0059] The process of determining the growth factor typically involves comparing sensor data at the beginning and end of a target work cycle and calculating the amount or rate of change between them. The growth factor can be positive (indicating an increase) or negative (indicating a decrease), depending on the trend of the data.

[0060] Sensor-sensed information (i.e., data measured by sensors) is usually accompanied by a timestamp indicating when the data was collected or generated. This timing information is crucial for analyzing the real-time nature, temporal sequence, and correlation with other events or data.

[0061] The generation time of perceived information refers to the exact time when sensor data is collected or measured. This time information is usually recorded in a certain time format (such as year-month-day hour:minute:second) for subsequent data processing and analysis.

[0062] This time information is typically obtained by reading specific fields from sensor data packets or through protocols used to communicate with the sensor. Once this time information is acquired, it can be compared and analyzed with the times of other data or events to reveal potential relationships and patterns between the data.

[0063] It provides key information about data trends and time characteristics, which helps to understand and analyze sensor data more accurately.

[0064] Step 104: Based on the generation time and the growth factor, construct a first nonlinear information age model corresponding to the target working time slot in the target working cycle when the target sensor is a service sensor.

[0065] In this step, information age is an indicator of information freshness and is often used to assess the validity and timeliness of data in real-time systems.

[0066] The first nonlinear information age model: Based on the specific conditions of generation time, growth factor, target working cycle, and target working time slot mentioned above, an information age model is constructed. Here, "first nonlinear" means that the model considers the nonlinear characteristics of information age changing over time; that is, the growth rate of information age may not be constant but is affected by various factors (such as data generation frequency, transmission delay, processing time, etc.). This nonlinear model can more accurately reflect the dynamic changes of information age in real-world systems. Such a model is crucial for evaluating and optimizing the performance of real-time systems.

[0067] Step 105: Based on the generation time and the growth factor, construct a second nonlinear information age model corresponding to the target working time slot in the target working cycle when the target sensor is a requesting sensor.

[0068] In this step, Information Age (AoI) is a metric for measuring the freshness of information; it represents the total time from when the information was generated or updated to the current point in time. Maintaining a low information age is crucial in information transmission and real-time systems because it ensures the timeliness and accuracy of the data.

[0069] Nonlinear models refer to the fact that the age of information changes over time not as a simple linear relationship, but may be affected by a variety of factors, such as network latency, data processing time, and transmission error rate. These factors make the growth of information age exhibit nonlinear characteristics.

[0070] The second nonlinear information age model is a further refinement or extension of existing models, used to more accurately describe the changes in information age under specific conditions (such as target working cycle and target working time slot). This model considers more practical situations and constraints, and is of great significance for optimizing data transmission and improving system real-time performance and accuracy.

[0071] Step 106: Construct an average nonlinear information age model based on the first nonlinear information age model and the second nonlinear information age model corresponding to all target sensors.

[0072] In this step, within sensor networks and information processing systems, Information Age (AoI) is a crucial performance metric used to measure the freshness or timeliness of information. It reflects the time elapsed from information generation to reception by the receiver. In applications with frequent information updates or high real-time requirements, such as autonomous driving, remote medical monitoring, and real-time communication, reducing information age is essential for improving system performance.

[0073] However, traditional information age models are often based on the linear assumption that information age grows linearly over time. But in practical applications, due to factors such as network congestion, sensor failure, and data processing delays, the growth of information age may exhibit non-linear characteristics. Therefore, introducing a non-linear information age model can more accurately describe the dynamic changes in information age.

[0074] The "First Nonlinear Information Age Model" and the "Second Nonlinear Information Age Model" represent nonlinear information age models constructed for different conditions or different sensor types. These models may consider different influencing factors, such as the distribution of network latency, changes in sensor sampling frequency, and the complexity of data processing, thereby describing the growth of information age in a nonlinear manner.

[0075] "Constructing an average nonlinear information age model" is based on two (or more) nonlinear information age models corresponding to all target sensors. Through a weighted average or synthesis method, an average model that can reflect the information age characteristics of the overall system is obtained. This average model is not simply the average of the information ages of each model, but rather it considers the importance, weight, or degree of influence of each model in the system and performs weighted or synthesis processing.

[0076] The purpose of constructing an average nonlinear information age model is:

[0077] A more comprehensive assessment of system performance: By comprehensively considering the information age characteristics of multiple sensors, the information freshness of the entire system can be assessed more accurately.

[0078] Optimize system resource allocation: Based on the average nonlinear information age model, sensors or links with rapid information age growth can be identified, thereby optimizing resource allocation in a targeted manner, such as increasing bandwidth, adjusting sampling frequency, and optimizing data processing algorithms, to reduce the overall information age of the system.

[0079] Improving system real-time performance: In application scenarios with high real-time requirements, reducing the average information age can improve the system's response speed and decision-making accuracy.

[0080] In conclusion, constructing an average nonlinear information age model is one of the important means to improve the performance of sensor networks and information processing systems.

[0081] Step 107: The average nonlinear information age model is processed by a bidirectional selection mechanism based on a near-end strategy optimization algorithm to obtain a data collaborative transmission strategy. The data collaborative transmission strategy is then used to control the multiple sensors to collaboratively transmit the collected sensing information to the data processing device in each working time slot of each working cycle.

[0082] In this step, the process of optimizing the data collaborative transmission strategy using the Proximal Policy Optimization (PPO) algorithm aims to minimize the "average nonlinear information age" of the data, which is an indicator of information freshness and timeliness, and is especially suitable for scenarios that require real-time or near-real-time data processing.

[0083] Average Nonlinear Information Age Model: In information theory and communication systems, information age (AoI) is a metric for measuring the freshness of information, representing the time elapsed since the information was last updated. However, in some cases, the value of information may not decrease linearly over time, thus introducing the concept of nonlinear information age. The average nonlinear information age model is an average value determined based on nonlinear information age, used to assess the freshness of information across the entire system or within a specific time period.

[0084] Proximal Policy Optimization (PPO) is a reinforcement learning algorithm particularly suitable for solving problems involving continuous action spaces and complex policy optimization. It iteratively updates the policy to maximize cumulative reward (or minimize cumulative cost) while keeping the change between the old and new policies sufficiently small to ensure learning stability. PPO incorporates ideas from Trust Region Policy Optimization (TRPO), but is simpler to implement and easier to tune.

[0085] In practical applications, the sensor will make transmission decisions in each working cycle and time slot based on a strategy optimized by the PPO algorithm. These decisions aim to minimize the value of the average nonlinear information age model, thereby ensuring that the data processing equipment receives the latest and most valuable information. In this way, the system can more effectively utilize limited communication resources, improving the efficiency and accuracy of information processing.

[0086] Based on the above scheme, a first nonlinear information age model is constructed for the target working cycle corresponding to the target working time slot when the target sensor is a service sensor, based on the generation time and growth factor. Then, a second nonlinear information age model is constructed for the target working cycle corresponding to the target working time slot when the target sensor is a requesting sensor, also based on the generation time and growth factor. Finally, an average nonlinear information age model is constructed based on the first and second nonlinear information age models corresponding to all target sensors. This average nonlinear information age model is then processed by a bidirectional selection mechanism based on a near-end strategy optimization algorithm. This approach can take into account the timeliness of information perceived by unscheduled sensors, enabling bidirectional selection between scheduled and unscheduled sensors to complete collaborative transmission according to the obtained data collaborative transmission strategy, thereby improving the timeliness of perceived information.

[0087] In some embodiments, step 103, determining the growth factor of the target sensor in the target operating cycle, includes:

[0088] Step A1, obtain the first The target sensor during the target's working cycle Previous work cycle scheduling decision Target work cycle period length , No. The target sensor during the target's working cycle Previous work cycle Transmission time , No. Each target sensor during the target's working cycle Previous work cycle For the Coordinated transmission decision of individual target sensors , No. Each target sensor during the target's working cycle Previous work cycle Transmission time , No. Each target sensor during the target's working cycle Previous work cycle Cooperative transmission decision .

[0089] Step A2, based on the above The above The above The above The above and stated The first is determined by the following formula. Each target sensor during the target's working cycle hibernation time :

[0090] .

[0091] Step A3, based on the above and stated The growth factor for the target work cycle is determined using the following formula. :

[0092] .

[0093] In the above scheme, the information age model is:

[0094] (1) Linear Information Age: Information age (AoI) is an important indicator for measuring the freshness of information from the receiver's perspective, and it is of great significance in real-time monitoring systems and state update systems. Traditionally, AoI is defined as the time difference between the information's generation and the current moment. Assuming the system is currently at moment [time value missing]... The latest data received by the recipient was generated at the time specified in the original text. Then AoI can be represented as:

[0095] .

[0096] (2) Nonlinear information age

[0097] Traditional AoI (Aspect-Oriented Information) changes linearly over time. However, in practical applications, especially time-sensitive scenarios like monitoring the operational status of factory equipment, information becomes increasingly outdated before it is successfully received. Furthermore, sensor performance degrades over time, and failure rates (such as thermal failures and sensitivity malfunctions) increase with longer operating hours, leading to significant delays in perceived information. In summary, in industrial settings, the freshness of information decreases over time, and a linear AoI with a constant growth rate fails to reflect this information.

[0098] Freshness changes over time, therefore this application uses nonlinear AoI as a measure of information timeliness.

[0099] The nonlinear AoI used is defined as:

[0100]

[0101] in, Indicates the current moment. Indicates the time when the latest data received by the receiver was generated. The growth factor representing the nonlinear AoI depends on the sensor's operating time. Therefore, in the system considered in this application, the first... The sensor at the first The first cycle The nonlinear information age of each time slot is defined as:

[0102]

[0103] in, Represents the latest number received by the receiving end The time when data is generated by each sensor. Representing the The sensor at the first The growth factor for each cycle is defined as:

[0104]

[0105] in, Representing the The sensor at the first The sleep time for each cycle can be expressed as:

[0106]

[0107] in, Represents the period length. Indicates the first The sensor at the first Scheduling decisions for each cycle, Representing the The sensor at the first Transmission time per cycle, Representing the The sensor at the first Coordinated transmission decision-making for each cycle, Representing the The sensor at the first Each cycle for the sensor Coordinated transmission decisions.

[0108] In some embodiments, step 104 includes:

[0109] Step B1, based on the generation time and the growth factors In the target working time slot The initial nonlinear information age model is determined using the following formula. ;

[0110] .

[0111] Step B2, in response to the target sensor being a serving sensor, based on the The first nonlinear information age model is determined by the following formula. :

[0112] .

[0113] In the above scheme, the information age model is:

[0114] (1) Linear Information Age: Information age (AoI) is an important indicator for measuring the freshness of information from the receiver's perspective, and it is of great significance in real-time monitoring systems and state update systems. Traditionally, AoI is defined as the time difference between the information's generation and the current moment. Assuming the system is currently at moment [time value missing]... The latest data received by the recipient was generated at the time specified in the original text. Then AoI can be represented as:

[0115] .

[0116] (2) Nonlinear information age

[0117] Traditional AoI (Aspect-Oriented Information) changes linearly over time. However, in practical applications, especially time-sensitive scenarios like monitoring the operational status of factory equipment, information becomes increasingly outdated before it is successfully received. Furthermore, sensor performance degrades over time, and failure rates (such as thermal failures and sensitivity failures) increase with longer operating hours, leading to significant delays in perceived information. In summary, the freshness of information decreases over time in industrial settings, and a linear AoI with a constant growth rate fails to reflect this freshness.

[0118] Since the timeliness of information changes, this application uses nonlinear AoI as a measure of information timeliness.

[0119] The nonlinear AoI used is defined as:

[0120]

[0121] in, Indicates the current moment. Indicates the time when the latest data received by the receiver was generated. The growth factor representing the nonlinear AoI depends on the sensor's operating time. Therefore, in the system considered in this application, the first... The sensor at the first The first cycle The nonlinear information age of each time slot is defined as:

[0122]

[0123] in, Represents the latest number received by the receiving end The time when data is generated by each sensor. Representing the The sensor at the first The growth factor for each cycle is defined as:

[0124]

[0125] in, Representing the The sensor at the first The sleep time for each cycle can be expressed as:

[0126]

[0127] in, Represents the period length. Indicates the first The sensor at the first Scheduling decisions for each cycle, Representing the The sensor at the first Transmission time per cycle, Representing the The sensor at the first Coordinated transmission decision-making for each cycle, Representing the The sensor at the first Each cycle for the sensor Coordinated transmission decisions. From and As can be seen, the sensor In the The growth factor of the nonlinear AoI in the 1st cycle depends on its 1st cycle. The sleep time is determined by the sensor's sleep cycle, and the sleep time depends on the number of cycles. Scheduling and coordinated transmission decisions for each cycle. Specifically, when the sensor In the Each cycle is scheduled, that is At that time, its sleep time is equal to the period length minus the data transmission time. If the sensor also assists the first One sensor, namely Then its sleep time is equal to the period length minus its own transmission time minus the first transmission time. The transmission time of the first sensor; if the first sensor's transmission time is... The sensor at the first If a sensor is not scheduled for a given period, it will necessarily select one of the scheduled sensors. Assisting in the transmission of data must exist. Therefore, its sleep time is equal to the period length minus the sensor's sleep time. Transmission time. Based on the above analysis, combined with... SIS in cycle The nonlinear AoI (i.e., the first nonlinear information age model) can be further expressed as:

[0128] .

[0129] In some embodiments, step 105 includes:

[0130] Step C1, based on the generation time and the growth factors In the target working time slot The initial nonlinear information age model is determined using the following formula. ;

[0131] .

[0132] Step C2, in response to the target sensor being a requesting sensor, based on the... The second nonlinear information age model is determined by the following formula. :

[0133] .

[0134] In the above scheme, the information age model is:

[0135] (1) Linear Information Age: Information age (AoI) is an important indicator for measuring the freshness of information from the receiver's perspective, and it is of great significance in real-time monitoring systems and state update systems. Traditionally, AoI is defined as the time difference between the information's generation and the current moment. Assuming the system is currently at moment [time value missing]... The latest data received by the recipient was generated at the time specified in the original text. Then AoI can be represented as:

[0136] .

[0137] (2) Nonlinear information age

[0138] Traditional AoI (Aspect-Oriented Information) changes linearly over time. However, in practical applications, especially time-sensitive scenarios like monitoring the operational status of factory equipment, information becomes increasingly outdated before it is successfully received. Furthermore, sensor performance degrades over time, and failure rates (such as thermal failures and sensitivity malfunctions) increase with longer operating hours, leading to significant delays in perceived information. In summary, in industrial settings, the freshness of information decreases over time, and a linear AoI with a constant growth rate fails to reflect this information's novelty.

[0139] Freshness changes over time, therefore this application uses nonlinear AoI as a measure of information timeliness.

[0140] The nonlinear AoI used is defined as:

[0141]

[0142] in, Indicates the current moment. Indicates the time when the latest data received by the receiver was generated. The growth factor representing the nonlinear AoI depends on the sensor's operating time. Therefore, in the system considered in this application, the first... The sensor at the first The first cycle The nonlinear information age of each time slot is defined as:

[0143]

[0144] in, Represents the latest number received by the receiving end The time when data is generated by each sensor. Representing the The sensor at the first The growth factor for each cycle is defined as:

[0145]

[0146] in, Representing the The sensor at the first The sleep time for each cycle can be expressed as:

[0147]

[0148] in, Represents the period length. Indicates the first The sensor at the first Scheduling decisions for each cycle, Representing the The sensor at the first Transmission time per cycle, Representing the The sensor at the first Coordinated transmission decision-making for each cycle, Representing the The sensor at the first Each cycle for the sensor Coordinated transmission decisions. From and As can be seen, the sensor In the The growth factor of the nonlinear AoI in the 1st cycle depends on its 1st cycle. The sleep time is determined by the sensor's sleep cycle, and the sleep time depends on the number of cycles. Scheduling and coordinated transmission decisions for each cycle. Specifically, when the sensor In the Each cycle is scheduled, that is At that time, its sleep time is equal to the period length minus the data transmission time. If the sensor also assists the first One sensor, namely Then its sleep time is equal to the period length minus its own transmission time minus the first transmission time. The transmission time of the first sensor; if the first sensor's transmission time is... The sensor at the first If a sensor is not scheduled for a given period, it will necessarily select one of the scheduled sensors. Assisting in the transmission of data must exist. Therefore, its sleep time is equal to the period length minus the sensor's sleep time. of

[0149] Transmission time. Based on the above analysis, combined with... RIS in cycle The nonlinear AoI (i.e., the second nonlinear information age model) can be further expressed as:

[0150]

[0151] In some embodiments, step 106 includes:

[0152] Step D1: Obtain the total number of the multiple sensors. The total number of the multiple work cycles The total number of the multiple working time slots .

[0153] Step D2, based on the The above The above The first nonlinear information age model and the second nonlinear information age model are used to determine the average nonlinear information age model using the following formula:

[0154]

[0155] in, This represents either the first nonlinear information age model or the second nonlinear information age model.

[0156] In the above scheme, the information age model is:

[0157] (1) Linear Information Age: Information age (AoI) is an important indicator for measuring the freshness of information from the receiver's perspective, and it is of great significance in real-time monitoring systems and state update systems. Traditionally, AoI is defined as the time difference between the information's generation and the current moment. Assuming the system is currently at moment [time value missing]... The latest data received by the recipient was generated at the time specified in the original text. Then AoI can be represented as:

[0158] .

[0159] (2) Nonlinear information age

[0160] Traditional AoI (Aspect of Information) changes linearly over time. However, in practical applications, especially in time-sensitive scenarios like monitoring the operational status of factory equipment, information becomes increasingly outdated before it is successfully received. Furthermore, sensor performance degrades over time, and failure rates (such as thermal failures and sensitivity failures) increase with longer operating hours, leading to significant delays in perceived information. Considering these two factors, the freshness of information decreases over time in industrial settings, and a linear AoI with a constant growth rate fails to reflect this change in information freshness. Therefore, this application adopts a non-linear AoI as a measure of information timeliness.

[0161] The nonlinear AoI used is defined as:

[0162]

[0163] in, Indicates the current moment. Indicates the time when the latest data received by the receiver was generated. The growth factor representing the nonlinear AoI depends on the sensor's operating time. Therefore, in the system considered in this application, the first... The sensor at the first The first cycle The nonlinear information age of each time slot is defined as:

[0164]

[0165] in, Represents the latest number received by the receiving end The time when data is generated by each sensor. Representing the The sensor at the first The growth factor for each cycle is defined as:

[0166]

[0167] in, Representing the The sensor at the first The sleep time for each cycle can be expressed as:

[0168]

[0169] in, Represents the period length. Indicates the first The sensor at the first Scheduling decisions for each cycle, Representing the The sensor at the first Transmission time per cycle, Representing the The sensor at the first Coordinated transmission decision-making for each cycle, Representing the The sensor at the first Each cycle for the sensor Coordinated transmission decisions. From and As can be seen, the sensor In the The growth factor of the nonlinear AoI in the 1st cycle depends on its 1st cycle. The sleep time is determined by the sensor's sleep cycle, and the sleep time depends on the number of cycles. Scheduling and coordinated transmission decisions for each cycle. Specifically, when the sensor In the Each cycle is scheduled, that is At that time, its sleep time is equal to the period length minus the data transmission time. If the sensor also assists the first One sensor, namely Then its sleep time is equal to the period length minus its own transmission time minus the first transmission time. The transmission time of the first sensor; if the first sensor's transmission time is... The sensor at the first If a sensor is not scheduled for a given period, it will necessarily select one of the scheduled sensors. Assisting in the transmission of data must exist. Therefore, its sleep time is equal to the period length minus the sensor's sleep time. Transmission time. Based on the above analysis, combined with... SIS in cycle The nonlinear AoI can be further expressed as:

[0170] ;

[0171] RIS in cycle The nonlinear AoI can be further expressed as:

[0172]

[0173] Furthermore, since the sensors in the network are responsible for monitoring different types of information, each sensor has its own information timeliness requirements. For example, sensors responsible for monitoring environmental information have a higher tolerance for information timeliness; outdated information has little impact on the production process. However, sensors responsible for monitoring equipment operating status information have a lower tolerance for information timeliness; even slightly outdated information may lead to production accidents. To meet the above conditions, this application proposes that the peak information age of the information monitored by each sensor in the network should not exceed its maximum tolerable value, i.e.:

[0174] .

[0175] In some embodiments, step 107, processing the average nonlinear information age model through a bidirectional selection mechanism based on a proximal policy optimization algorithm to obtain a data collaborative transmission strategy, includes:

[0176] Step E1: Construct the constraints corresponding to the average nonlinear information age model.

[0177] Step E2: Obtain the transmission time of the target sensor in each target working cycle, and use the transmission time of the target sensor in the target working cycle as the state space, wherein the state space is specifically:

[0178]

[0179] in, This represents the time step in the learning process of the proximal policy optimization algorithm. , Indicates the first The transmission time of each target sensor in each target working cycle.

[0180] Step E3: Obtain the scheduling decision for each target work cycle, and use the scheduling decision for each target work cycle as the action space, wherein the action space is specifically:

[0181]

[0182] in, , Indicates the first The first time step Scheduling decisions for each target work cycle.

[0183] Step E4: Perform inverse processing on the average nonlinear information age model to obtain the reward function corresponding to the average nonlinear information age model. The reward function Specifically:

[0184] ;

[0185] Step E5: Determine the state of the current time step based on the state space, determine the action to be executed based on the state of the current time step and the action space, and determine the current scheduling decision based on the action to be executed.

[0186] Step E6: Determine whether the current scheduling decision satisfies the constraints.

[0187] Step E7: In response to no, the reward function is used to process the current scheduling decision until the current scheduling decision satisfies the constraint condition. Alternatively,

[0188] Step E8, in response, then based on the constraints of the current scheduling decision, the initial set of request sensors corresponding to each service sensor is determined.

[0189] Step E9: Based on the initial request sensor set, determine the set of request sensors that meet the expectations of each service sensor using the following pre-built willingness indicators, wherein the willingness indicators are specifically:

[0190]

[0191] in, Indicators of willingness This represents the preset normalization coefficient. This represents the preset first index coefficient. This represents the preset second index coefficient. This represents the preset third index coefficient. Indicates up to the Up to the [number]th work cycle The number of times a request sensor acts as a service sensor. This is expressed as the cycle length of the work cycle. Represented as the first The service sensor in the first Transmission time per work cycle Represented as the first The request sensor is in the first Transmission time per work cycle Represented as the first The service sensor assists the first The age of the nonlinear information brought by the sensor is requested. This indicates the order of work slots.

[0192] Step E10: Based on the set of request sensors that meet the expectations of each service sensor, determine the final service sensor corresponding to each request sensor using the selection index obtained in advance by the following formula, and use the final service sensor corresponding to each request sensor as the data collaborative transmission strategy.

[0193]

[0194] in, Ԑ Indicates the selection of indicators. Indicates the order of work slots. This indicates the total number of work slots. Indicates the order in which the service sensors are located. Indicates the order of work cycles.

[0195] In the above scheme, the optimization problem arises: the nonlinear AoI directly reflects the freshness of the sensed data. Therefore, this application uses the average nonlinear AoI to represent the timeliness of sensed data in the industrial production process. To improve the freshness of the sensed data, this application minimizes the average nonlinear AoI of all sensors by optimizing scheduling and collaborative transmission decisions. and Let the scheduling and cooperative transmission decisions for each cycle be represented respectively. Then, the optimization problem constructed in this application can be expressed as:

[0196]

[0197] Among them, constraints It is a channel constraint on the scheduling sensor, constraint This is a constraint on the collaborative variables, ensuring that the RIS can only select target sensors from the scheduled sensors. Ensure that the number of sensors scheduled in each work cycle does not exceed One, constraint Ensure that the scheduled sensors can meet their own timeliness requirements, and constrain... The remaining transmission time of the SIS must meet the transmission requirements of the RIS, constraining... Ensure that each SIS can help at most one RIS, a constraint. Ensure that each RIS can select an SIS, and constrain... Ensure that the timeliness requirements of each sensor are met, and constrain... This ensures that the total energy consumption of all sensors does not exceed the maximum allowable energy consumption.

[0198] A Bidirectional Selection Mechanism Based on the Proximal Policy Optimization Algorithm: Proximal Policy Optimization (PPO) is a novel policy gradient algorithm that solves the problem of determining the training step size in traditional policy gradient algorithms. It is widely used in solving various continuous and discrete control problems. As can be seen from the optimization problem described above, the optimization problem constructed in this application is an integer nonlinear programming problem. Integer nonlinear programming is often NP-hard and difficult to solve using traditional optimization methods. To overcome this problem, this application designs a bidirectional selection mechanism based on the PPO algorithm, decoupling the scheduling variable from the cooperative transfer variable, while leveraging the superiority of the PPO algorithm in solving integer nonlinear programming problems.

[0199] The successful implementation of the PPO algorithm relies heavily on the design of the state space, action space, and reward function. This application sets the data transmission time of each sensor in each cycle as the state space of the PPO, i.e.:

[0200]

[0201] in, Indicates the time step of PPO learning. , , indicating the first Data transmission time for each sensor per cycle.

[0202] The scheduling decision for each cycle is set as the action space of the PPO, that is:

[0203]

[0204] in, , , indicating the first The first time step Scheduling decisions for each cycle.

[0205] Reward is the only feedback an agent can obtain from the environment, and it directly affects whether the agent can learn towards the desired goal. To minimize the average nonlinear AoI during sensor operation, this application sets the reward function as the reciprocal of the average nonlinear AoI, i.e.:

[0206]

[0207] It is important to note that since nonlinear AoI is related to both scheduling and cooperative transmission decisions, the reward function is not solely dependent on the scheduling decision derived from the PPO algorithm, but also on the cooperative transmission decision obtained through bidirectional selection. The core idea of ​​cooperative transmission is to balance the interests of both the service sensor and the requesting sensor. For the requesting sensor, it aims to find a service sensor that maximizes the timeliness of its perceived data, while the service sensor is more concerned with maximizing the timeliness of its perceived data within a cycle and is unwilling to contribute extra time resources. This selfishness of the service sensor leads to the waste of extra time resources, which is detrimental to the overall network timeliness optimization. Furthermore, considering that the roles of service and requesting sensors are dynamically changing, a service sensor in the current cycle may become a requesting sensor in the next cycle, and selfishness in the current cycle will also lead to helplessness in the next cycle. Therefore, this application designs a bidirectional selection mechanism to determine the cooperative transmission decision. To improve the overall network timeliness, the service sensor considers the requesting sensor's past activity when selecting a requesting sensor. Only when the requesting sensor's past activity meets the service sensor's expectations will the service sensor be willing to contribute its time resources. Simultaneously, contributing time resources increases one's motivation, making it more likely to receive assistance when subsequently requesting sensors. The specific steps of the PPO-based bidirectional selection algorithm are as follows:

[0208] (1): PPO is based on the environmental state at the current time step. Execute action To obtain scheduling decisions ;

[0209] (2): Based on the above optimization problem, judge If the scheduling constraints are met, continue execution; otherwise, return to (1) and give the current action a penalty.

[0210] (3): By scheduling decision First, based on the constraints of the optimization problem described above... and Determine the set of requesting sensors that each service sensor can assist with. Then The set of requesting sensors that meets the expectations of each service sensor is determined based on the following willingness indicators. ;

[0211]

[0212] in, Indicates up to the Request sensor up to the last cycle The first indicator is the number of times the requesting sensor has acted as a service sensor, primarily representing the requesting sensor's past initiative. The second indicator is the idle time rate, which mainly measures how the requesting sensor's transmission time occupies the remaining transmission time of the service sensor. The third indicator is the timeliness, which measures the service sensor's... Assistance request sensor This results in a significant increase in the magnitude of the nonlinear AoI. It can be seen that the more proactive the requesting sensor has been in the past, the lower its time occupancy with the serving sensor (i.e., the higher the serving sensor's idle time rate), the smaller the average nonlinear AoI, and the larger its willingness index, the more likely the serving sensor is to assist the requesting sensor. Furthermore, , , Represents the coefficients of each indicator and , This represents the normalization coefficient.

[0213] (4): According to (3), each requesting sensor knows the set of service sensors that can assist it. ,exist The final selected service sensor is determined according to the following formula to obtain the cooperative transmission decision. This means requesting that sensors be selected solely based on the timeliness of information:

[0214] .

[0215] (5): Based on the selected and The quality of the current action is evaluated using a reward function:

[0216] .

[0217] (6): Repeat the above process until the algorithm converges.

[0218] During algorithm execution, the agent selects different behaviors from the action space to interact with the environment, and continuously adjusts its actions based on the rewards after the interaction, thereby learning the best scheduling and cooperative transmission strategy.

[0219] In some embodiments, step E1 includes:

[0220] Step F1, obtain the first The target sensor in the first Scheduling decisions for each target work cycle and the The target sensor in the first Channel conditions for each target duty cycle ;

[0221] Step F2, based on the and stated The first constraint condition is determined using the following formula:

[0222] ;

[0223] Step F3, obtain the first The target sensor in the first The first target work cycle with the help of the first Cooperative transmission variables transmitted by individual target sensors ;

[0224] Step F4, based on the and stated The second constraint condition is determined using the following formula:

[0225] ;

[0226] Step F5: Obtain the preset threshold for the number of sensors to be scheduled in each target work cycle. ;

[0227] Step F6, based on the and stated The third constraint condition is determined using the following formula:

[0228] ,

[0229] in, Indicates the total number of sensors;

[0230] Step F7, obtain the first Transmission time of each target sensor and nonlinear information age ;

[0231] Step F8, based on the The above The above and preset nonlinear information age threshold The fourth constraint condition is determined using the following formula;

[0232] ;

[0233] Step F9: Obtain the total number of work slots. and the Transmission time of each target sensor ;

[0234] Step F10, based on the The above and stated The fifth constraint condition is determined using the following formula:

[0235] ;

[0236] Step F11, based on the The sixth constraint condition is determined using the following formula:

[0237] , , n∈N ;

[0238] Step F12, based on the The seventh constraint condition is determined using the following formula:

[0239] , , m∈N ;

[0240] Step F13: Obtain the peak information age of each target sensor. ;

[0241] Step F14, based on the and preset peak information age threshold The eighth constraint condition is determined using the following formula:

[0242] ;

[0243] Step F15, obtain the first Transmission time of each target sensor , No. Transmission time of each sensor Data transmission power ;

[0244] Step F16, based on the The above The above and stated The number is determined by the following formula. The target sensor in the first Transmission energy consumption per target working cycle :

[0245] ;

[0246] Step F17, obtain sleep power and the The target sensor in the first The first target work cycle with the help of the first Cooperative transmission variables transmitted by individual target sensors Based on the above The above The above The above and stated The number is determined by the following formula. When the target sensor is a service sensor, at the 1st First sleep energy consumption of the target working cycle :

[0247]

[0248] Step F18, based on the The above The above and stated The number is determined by the following formula. When the target sensor is the requesting sensor, in the first... Second sleep energy consumption of each target working cycle :

[0249] ;

[0250] Step F19, obtain standby power Based on the above The above and stated The number is determined by the following formula. The target sensor in the first Standby power consumption per target duty cycle :

[0251] ;

[0252] Step F20, based on the The above The above and stated The following formula is used to determine the first... The target sensor in the first Total energy consumption for each target work cycle :

[0253]

[0254] Step F21, based on the and preset total energy consumption threshold The ninth constraint condition is determined using the following formula:

[0255] ;

[0256] Step F22: The first constraint, the second constraint, the third constraint, the fourth constraint, the fifth constraint, the sixth constraint, the seventh constraint, the eighth constraint, and the ninth constraint are used as constraints corresponding to the average nonlinear information age model.

[0257] In the above scheme, such as Figure 2A As shown, this application considers a real-time monitoring system in a factory, which includes a sensor network, edge nodes, and a data processing center. The sensor network is responsible for monitoring various types of information during the industrial production process, while the edge nodes are responsible for scheduling and controlling the sensor network and sending the sensed data to the data processing center for further processing and analysis.

[0258] In the system under consideration, the sensor's operating time is divided into several time slots, and the continuous... Each time slot is divided into one work cycle. Representing sensor index, using Represents a work cycle index, using This represents the time slot index within each work cycle. At the beginning of each work cycle, the sensors first sample the monitoring data. Subsequently, the edge nodes schedule the sensors; scheduled sensors send data to the edge nodes, while unscheduled sensors select scheduled sensors to send their data to the edge nodes. Indicates the first The sensor at the first The transmission time is per cycle. This application assumes that sensors are connected via wired transmission and that sensors are connected to edge nodes via wireless transmission. Considering the complex transmission environment within a factory, the channel between the sensors and edge nodes is inevitably non-ideal. Indicates sensor The probability of successful transmission Time remains unchanged. (Use) Indicates sensor In the The transmission status of each cycle, A single line indicates successful transmission, while a single line indicates transmission failure. In the event of a transmission interruption, if the sensor remains in standby mode until the next work cycle begins, it will not only degrade the timeliness of the information but also cause unnecessary energy consumption. Therefore, this application considers a sleep-based cooperative transmission method. Specifically, sensors are divided into two main categories: serving sensors (SIS) and requesting sensors (RIS). Serving sensors are scheduled sensors in each cycle; they directly transmit data to the edge nodes. Requesting sensors are unscheduled sensors; they need to transmit their data to the serving sensors first, and then the serving sensors transmit the data to the edge nodes. Serving sensors enter sleep mode after completing their own data transmission and that of requesting sensors, while requesting sensors enter sleep mode after sending data to the serving sensors.

[0259] The working process of the sensor can be simplified to: Figure 2B As shown, the sensor described is the serving sensor SIS (i.e., scheduled) in cycle 1, and the requesting sensor RIS (not scheduled) in cycles 2 and 3.

[0260] Cooperative transmission model:

[0261] Considering the limited channel resources, if all sensors simultaneously send their sensing data to the edge nodes, congestion will inevitably occur, leading to a decrease in timeliness. Therefore, this application assumes that the edge nodes can only schedule a limited number of sensors per cycle. Indicate. (Used) Indicates the first Scheduling decisions for each cycle, Indicates the first The sensor at the first Each cycle is scheduled, and the sensor accordingly becomes an SIS. This indicates that the sensor was not scheduled, and accordingly, it becomes a RIS (Redirected Injection) sensor. To ensure the effectiveness of coordinated transmission, each cycle can only schedule from sensors with better channel conditions, which requires that:

[0262] .

[0263] use Represents a cooperatively transferred variable. Indicates the first One sensor in The first work cycle utilizes the first The sensor transmits data to the edge node; in this case, the first sensor transmits data to the edge node. Each sensor must be scheduled, therefore the following conditions must be met:

[0264] .

[0265] To ensure the integrity of transmitted data, the remaining transmission time of the serving sensor must meet the transmission requirements of the requesting sensor, that is:

[0266] .

[0267] To ensure that the sensing data from each sensor can be successfully received, each requesting sensor must select a serving sensor to send the sensing data to the edge node in each work cycle, which means that the following must be met:

[0268] , m∈N .

[0269] As for service sensors, they can only assist one requesting sensor in transmitting data per cycle, that is:

[0270] , n∈N .

[0271] Information age model:

[0272] (1) Linear Information Age: Information age (AoI) is an important indicator for measuring the freshness of information from the receiver's perspective, and it is of great significance in real-time monitoring systems and state update systems. Traditionally, AoI is defined as the time difference between the information's generation and the current moment. Assuming the system is currently at moment [time value missing]... The latest data received by the recipient was generated at the time specified in the original text. Then AoI can be represented as:

[0273] .

[0274] (2) Nonlinear information age

[0275] Traditional AoI (Aspect of Information) changes linearly over time. However, in practical applications, especially time-sensitive scenarios like monitoring the operational status of factory equipment, information becomes increasingly outdated before it is successfully received. Furthermore, as operating time increases, sensor performance degrades, and the failure rate (e.g., thermal failures, sensitivity failures) increases, leading to significant delays in perceived information. In summary, in industrial settings, the freshness of information decreases over time, and a linear AoI with a constant growth rate fails to reflect this change in information freshness.

[0276] Therefore, this application uses nonlinear AoI as a measure of information timeliness.

[0277] The nonlinear AoI used is defined as:

[0278]

[0279] in, Indicates the current moment. Indicates the time when the latest data received by the receiver was generated. The growth factor representing the nonlinear AoI depends on the sensor's operating time. Therefore, in the system considered in this application, the first... The sensor at the first The first cycle The nonlinear information age of each time slot is defined as:

[0280]

[0281] in, Represents the latest number received by the receiving end The time when data is generated by each sensor. Representing the The sensor at the first The growth factor for each cycle is defined as:

[0282]

[0283] in, Representing the The sensor at the first The sleep time for each cycle can be expressed as:

[0284]

[0285] in, Represents the period length. Indicates the first The sensor at the first Scheduling decisions for each cycle, Representing the The sensor at the first Transmission time per cycle, Representing the The sensor at the first Coordinated transmission decision-making for each cycle, Representing the The sensor at the first Each cycle for the sensor Coordinated transmission decisions. From and As can be seen, the sensor In the The growth factor of the nonlinear AoI in the 1st cycle depends on its 1st cycle. The sleep time is determined by the sensor's sleep cycle, and the sleep time depends on the number of cycles. Scheduling and coordinated transmission decisions for each cycle. Specifically, when the sensor In the Each cycle is scheduled, that is At that time, its sleep time is equal to the period length minus the data transmission time. If the sensor also assists the first One sensor, namely Then its sleep time is equal to the period length minus its own transmission time minus the first transmission time. The transmission time of the first sensor; if the first sensor's transmission time is... The sensor at the first If a sensor is not scheduled for a given period, it will necessarily select one of the scheduled sensors. Assisting in the transmission of data must exist. Therefore, its sleep time is equal to the period length minus the sensor's sleep time. Transmission time. Based on the above analysis, combined with... SIS in cycle The nonlinear AoI can be further expressed as:

[0286] ;

[0287] RIS in cycle The nonlinear AoI can be further expressed as:

[0288]

[0289] Furthermore, since the sensors in the network are responsible for monitoring different types of information, each sensor has its own information timeliness requirements. For example, sensors responsible for monitoring environmental information have a higher tolerance for information timeliness; outdated information has little impact on the production process. However, sensors responsible for monitoring equipment operating status information have a lower tolerance for information timeliness; even slightly outdated information may lead to production accidents. To meet the above conditions, this application proposes that the peak information age of the information monitored by each sensor in the network should not exceed its maximum tolerable value, i.e.:

[0290] .

[0291] Energy consumption model:

[0292] The energy consumption of a sensor mainly consists of three parts: data transmission energy consumption, sleep energy consumption, and standby energy consumption.

[0293] The specific energy consumption for each part is shown below:

[0294] use Indicating data transmission power, then the first... The sensor at the first The transmission energy consumption per working cycle can be expressed as:

[0295]

[0296] in, Representing the Data transmission time of each sensor, Representing the The data transmission time of the sensor, if the sensor in the first... In addition to transmitting its own data, each cycle also assists the first... When a sensor transmits data, its transmission energy consumption includes the energy consumed by its own data transmission and the energy consumption of the sensors it assists.

[0297] use Let represent the sleep power. Then, the sleep power consumption of the serving sensor (SIS) and the requesting sensor (RIS) can be expressed as follows:

[0298]

[0299]

[0300] in, and These represent the sleep times of SIS and RIS, respectively, which can be determined according to... The conclusion is as follows.

[0301] The RIS is in standby mode while waiting for SIS transmission; therefore, standby power consumption primarily refers to the RIS itself. Indicates standby power, then the sensor In the The standby power consumption per cycle can be expressed as:

[0302]

[0303] In summary, sensors In the The total energy consumption for each cycle can be expressed as:

[0304] .

[0305] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0306] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0307] Based on the same concept, and corresponding to any of the above embodiments, this application also provides a data collaborative transmission device for industrial internet scenarios.

[0308] refer to Figure 3 The aforementioned data collaborative transmission device for industrial internet scenarios is installed on edge node devices and applied to a data collaborative transmission system. The system includes edge node devices, data processing devices, and multiple sensors. The device includes:

[0309] The acquisition module 301 is configured to acquire the working time of the system, the working time including multiple working cycles, and each working cycle including multiple working time slots;

[0310] The target determination module 302 is configured to use each of the plurality of sensors as a target sensor, each of the plurality of working cycles as a target working cycle, and each of the plurality of working time slots as a target working time slot.

[0311] The data determination module 303 is configured to determine the growth factor of the target sensor in the target working cycle and to obtain the generation time of the received sensing information of the target sensor.

[0312] The first model construction module 304 is configured to construct a first nonlinear information age model corresponding to the target working time slot in the target working cycle when the target sensor is a service sensor, based on the generation time and the growth factor.

[0313] The second model construction module 305 is configured to construct a second nonlinear information age model corresponding to the target working time slot in the target working cycle when the target sensor is a requesting sensor, based on the generation time and the growth factor.

[0314] The third model construction module 306 is configured to construct an average nonlinear information age model based on the first nonlinear information age model and the second nonlinear information age model corresponding to all target sensors.

[0315] The collaborative transmission module 307 is configured to process the average nonlinear information age model through a bidirectional selection mechanism based on a near-end strategy optimization algorithm to obtain a data collaborative transmission strategy, and control the multiple sensors to collaboratively transmit the collected sensing information to the data processing device in each working time slot of each working cycle according to the data collaborative transmission strategy.

[0316] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0317] The apparatus described above is used to implement the corresponding data collaborative transmission method for industrial internet scenarios in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0318] Based on the same concept, and corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the data collaborative transmission method for industrial internet scenarios described in any of the above embodiments.

[0319] Figure 4 This illustration shows a more specific hardware structure diagram of an electronic device provided in this embodiment. The device may include: a processor 401, a memory 402, an input / output interface 403, a communication interface 404, and a bus 405. The processor 401, memory 402, input / output interface 403, and communication interface 404 are interconnected internally via the bus 405.

[0320] The processor 401 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0321] The memory 402 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 402 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 402 and is called and executed by the processor 401.

[0322] Input / output interface 403 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0323] Communication interface 404 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0324] Bus 405 includes a pathway for transmitting information between various components of the device, such as processor 401, memory 402, input / output interface 403, and communication interface 404.

[0325] It should be noted that although the above-described device only shows the processor 401, memory 402, input / output interface 403, communication interface 404, and bus 405, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0326] The electronic devices described above are used to implement the corresponding data collaborative transmission method for industrial internet scenarios in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0327] Based on the same application concept, and corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to execute the data collaborative transmission method for industrial internet scenarios as described in any of the above embodiments.

[0328] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0329] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the data collaborative transmission method for industrial Internet scenarios as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0330] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application is limited to these examples; under the concept of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in detail for the sake of brevity.

[0331] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0332] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0333] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A data collaborative transmission method for industrial internet scenarios, characterized in that, An application is made in a data collaborative transmission system, the system including edge node devices, data processing devices, and multiple sensors, wherein the method is executed by the edge node devices and includes: The system's working time is obtained, which includes multiple working cycles, and each working cycle includes multiple working time slots. Each of the plurality of sensors is designated as a target sensor, each of the plurality of working cycles is designated as a target working cycle, and each of the plurality of working time slots is designated as a target working time slot. Determine the growth factor of the target sensor in the target working cycle, and obtain the generation time of the received sensing information of the target sensor; Based on the generation time and the growth factor, a first nonlinear information age model is constructed for the target working cycle corresponding to the target working time slot when the target sensor is a serving sensor, wherein the serving sensor is the sensor scheduled in each cycle; Based on the generation time and the growth factor, a second nonlinear information age model is constructed for the target working cycle corresponding to the target working time slot when the target sensor is a requesting sensor, wherein the requesting sensor is an unscheduled sensor; An average nonlinear information age model is constructed based on the first and second nonlinear information age models corresponding to all target sensors. The average nonlinear information age model is processed by a bidirectional selection mechanism based on a near-end strategy optimization algorithm to obtain a data collaborative transmission strategy. The data collaborative transmission strategy is then used to control the multiple sensors to collaboratively transmit the collected sensing information to the data processing device in each working time slot of each working cycle. Determining the growth factor of the target sensor during the target operating cycle includes: Get the The target sensor during the target's working cycle Previous work cycle Scheduling decisions Target work cycle period length , No. The target sensor during the target's working cycle Previous work cycle Transmission time , No. Each target sensor during the target's working cycle Previous work cycle For the first Coordinated transmission decision of individual target sensors , No. Each target sensor during the target's working cycle Previous work cycle Transmission time , No. Each target sensor during the target's working cycle Previous work cycle Cooperative transmission decision ; Based on the above The above The above The above The above and stated The first is determined by the following formula. Each target sensor during the target's working cycle hibernation time : ； Based on the above and stated The growth factor for the target work cycle is determined using the following formula. : ； The construction of a first nonlinear information age model corresponding to the target working time slot in the target working cycle, based on the generation time and the growth factor, when the target sensor is a service sensor, includes: Based on the generation time and the growth factors In the target working time slot The initial nonlinear information age model is determined using the following formula. ; ； In response to the target sensor being a serving sensor, based on the The first nonlinear information age model is determined by the following formula. : ； The step of constructing a second nonlinear information age model corresponding to the target working time slot in the target working cycle when the target sensor is a requesting sensor, based on the generation time and the growth factor, includes: Based on the generation time and the growth factors In the target working time slot The initial nonlinear information age model is determined using the following formula. ; ； In response to the target sensor being a requesting sensor, based on the The second nonlinear information age model is determined by the following formula. : 。 2. The method according to claim 1, characterized in that, The step of constructing an average nonlinear information age model based on the first and second nonlinear information age models corresponding to all target sensors includes: Obtain the total number of the multiple sensors The total number of the multiple work cycles The total number of the multiple working time slots ; Based on the above The above The above The first nonlinear information age model and the second nonlinear information age model are used to determine the average nonlinear information age model using the following formula: in, This represents either the first nonlinear information age model or the second nonlinear information age model.

3. The method according to claim 2, characterized in that, The process of processing the average nonlinear information age model through a bidirectional selection mechanism based on a near-end policy optimization algorithm to obtain a data collaborative transmission strategy includes: Construct constraints corresponding to the average nonlinear information age model; The transmission time of the target sensor in each target working cycle is obtained, and the transmission time of the target sensor in the target working cycle is used as the state space, wherein the state space is specifically: in, This represents the time step in the learning process of the proximal policy optimization algorithm. , Indicates the first The transmission time of each target sensor in each target working cycle; Obtain the scheduling decision for each target work cycle, and use the scheduling decision for each target work cycle as the action space, wherein the action space is specifically: in, , Indicates the first The first time step Scheduling decisions for each target work cycle; The average nonlinear information age model is processed by taking its reciprocal to obtain the reward function corresponding to the average nonlinear information age model. The reward function Specifically: ； The state of the current time step is determined based on the state space, the action to be executed is determined based on the state of the current time step and the action space, and the current scheduling decision is determined based on the action to be executed. Determine whether the current scheduling decision satisfies the constraints. If the response is no, then the current scheduling decision is processed using the reward function until the current scheduling decision satisfies the constraint condition; or, In response, the initial set of request sensors corresponding to each service sensor is determined based on the constraints of the current scheduling decision. Based on the initial set of request sensors, the set of request sensors that meets the expectations of each service sensor is determined through the following pre-built willingness indicators, wherein the willingness indicators are specifically: in, Indicators of willingness This represents the preset normalization coefficient. This represents the preset first index coefficient. This represents the preset second index coefficient. This represents the preset third index coefficient. Indicates up to the Up to the [number]th work cycle The number of times a request sensor acts as a service sensor. This is expressed as the cycle length of the work cycle. Represented as the first The service sensor in the first Transmission time per work cycle Represented as the first The request sensor is in the first Transmission time per work cycle Represented as the first The service sensor assists the first The age of the nonlinear information brought by the sensor is requested. This indicates the order of the work slots; Based on the set of request sensors that meet the expectations of each service sensor, the final service sensor corresponding to each request sensor is determined by the selection index obtained in advance through the following formula, and the final service sensor corresponding to each request sensor is used as the data collaborative transmission strategy. in, Indicates the selection of indicators. Indicates the order of work slots. This indicates the total number of work slots. Indicates the order in which the service sensors are located. Indicates the order of work cycles.

4. The method according to claim 3, characterized in that, The constraints for constructing the average nonlinear information age model include: Get the The target sensor in the first Scheduling decisions for each target work cycle and the The target sensor in the first Channel conditions for each target duty cycle ; Based on the above and stated The first constraint condition is determined using the following formula: ； Get the The target sensor in the first The first target work cycle with the help of the first Cooperative transmission variables transmitted by individual target sensors ; Based on the above and stated The second constraint condition is determined using the following formula: ； Obtain the preset threshold for the number of sensors scheduled in each target work cycle. ; Based on the above and stated The third constraint condition is determined using the following formula: ， in, Indicates the total number of sensors; Get the Transmission time of each target sensor and nonlinear information age ; Based on the above The above The above and preset nonlinear information age threshold The fourth constraint condition is determined using the following formula; ； Get the total number of work slots and the Transmission time of each target sensor ; Based on the above The above and stated The fifth constraint condition is determined using the following formula: ； Based on the above The sixth constraint condition is determined using the following formula: ， ， n∈ {1,2,…,N}； Based on the above The seventh constraint condition is determined using the following formula: ， ， m∈ {1,2,…,N}； Acquire peak information age from each target sensor ; Based on the above and preset peak information age threshold The eighth constraint condition is determined using the following formula: ； Get the Transmission time of each target sensor , No. Transmission time of each sensor Data transmission power ; Based on the above The above The above and stated The number is determined by the following formula. The target sensor in the first Transmission energy consumption per target duty cycle : ； Obtain sleep power and the The target sensor in the first The first target work cycle with the help of the first Cooperative transmission variables transmitted by individual target sensors Based on the above The above The above The above and stated The number is determined by the following formula. When the target sensor is a service sensor, at the 1st First sleep energy consumption of the target working cycle : Based on the above The above The above and stated The number is determined by the following formula. When the target sensor is the requesting sensor, in the first... Second sleep energy consumption of each target working cycle : ； Get standby power Based on the above The above and stated The number is determined by the following formula. The target sensor in the first Standby power consumption per target duty cycle : ； Based on the above The above The above and stated The following formula is used to determine the first... The target sensor in the first Total energy consumption for each target work cycle : Based on the above and preset total energy consumption threshold The ninth constraint condition is determined using the following formula: ； The first constraint, the second constraint, the third constraint, the fourth constraint, the fifth constraint, the sixth constraint, the seventh constraint, the eighth constraint, and the ninth constraint are used as constraints corresponding to the average nonlinear information age model.

5. A data collaborative transmission device for industrial internet scenarios, characterized in that, The device is installed on an edge node device and applied to a data collaborative transmission system. The system includes an edge node device, a data processing device, and multiple sensors. The device includes: The acquisition module is configured to acquire the working time of the system, the working time including multiple working cycles, and each working cycle including multiple working time slots; The target determination module is configured to use each of the plurality of sensors as a target sensor, each of the plurality of working cycles as a target working cycle, and each of the plurality of working time slots as a target working time slot. The data determination module is configured to determine the growth factor of the target sensor in the target working cycle and to acquire the generation time of the received sensing information of the target sensor. The first model building module is configured to build a first nonlinear information age model corresponding to the target working time slot in the target working cycle when the target sensor is a service sensor, based on the generation time and the growth factor, wherein the service sensor is a sensor that is scheduled in each cycle; The second model building module is configured to build a second nonlinear information age model corresponding to the target working time slot in the target working cycle when the target sensor is a requesting sensor, based on the generation time and the growth factor, wherein the requesting sensor is an unscheduled sensor; The third model building module is configured to build an average nonlinear information age model based on the first nonlinear information age model and the second nonlinear information age model corresponding to all target sensors. The collaborative transmission module is configured to process the average nonlinear information age model through a bidirectional selection mechanism based on a near-end strategy optimization algorithm to obtain a data collaborative transmission strategy, and control the multiple sensors to collaboratively transmit the collected sensing information to the data processing device in each working time slot of each working cycle according to the data collaborative transmission strategy. The data determination module is specifically configured as follows: Get the The target sensor during the target's working cycle Previous work cycle Scheduling decisions Target work cycle period length , No. The target sensor during the target's working cycle Previous work cycle Transmission time , No. Each target sensor during the target's working cycle Previous work cycle For the first Coordinated transmission decision of individual target sensors , No. Each target sensor during the target's working cycle Previous work cycle Transmission time , No. Each target sensor during the target's working cycle Previous work cycle Cooperative transmission decision ; Based on the above The above The above The above The above and stated The first is determined by the following formula. Each target sensor during the target's working cycle hibernation time : ； Based on the above and stated The growth factor for the target work cycle is determined using the following formula. : ； The first model construction module is specifically configured as follows: Based on the generation time and the growth factors In the target working time slot The initial nonlinear information age model is determined using the following formula. ; ； In response to the target sensor being a serving sensor, based on the The first nonlinear information age model is determined by the following formula. : ； The second model construction module is specifically configured as follows: Based on the generation time and the growth factors In the target working time slot The initial nonlinear information age model is determined using the following formula. ; ； In response to the target sensor being a requesting sensor, based on the The second nonlinear information age model is determined by the following formula. : 。 6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1 to 4.

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