Industrial internet scene-oriented data cooperative transmission method and related equipment

Abstract

The invention provides an industrial internet scene-oriented data cooperative transmission method and related equipment, and the method comprises the steps: building a first nonlinear information age model corresponding to a target working time slot in a target working period based on the generation time and a growth factor when a target sensor is a service sensor, and obtaining a second nonlinear information age model corresponding to a target working time slot in a target working period based on the generation time and the growth factor; when a target sensor is constructed as a request sensor, a second nonlinear information age model corresponding to a target working time slot in a target working period is constructed, an average nonlinear information age model is constructed based on the second nonlinear information age model, and the average nonlinear information age model is processed through a two-way selection mechanism based on a near-end strategy optimization algorithm; according to the method, timeliness of sensing information of the unscheduled sensor can be taken into consideration, so that bidirectional selection of the scheduled sensor and the unscheduled sensor can be controlled to complete cooperative transmission according to the obtained data cooperative transmission strategy, and timeliness of sensing information is further improved.

Description

Technical Field

[0001] The present 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 Art

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

[0003] However, in actual scenarios, limited channel resources mean that only a limited number of sensors can send perception information to the data processing node in each scheduling cycle, which results in the perception information of unscheduled sensors not being received normally, resulting in the deterioration of the timeliness of the perception 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 purpose, the first aspect of the present application provides a data collaborative transmission method for industrial Internet scenarios, which is applied to a data collaborative transmission system. The system includes an edge node device, a data processing device, and multiple sensors. The method is performed by the edge node device, including:

[0006] Acquire the working time of the system, wherein the working time includes a plurality of working cycles, and each working cycle includes a plurality of working time slots;

[0007] Using each sensor of the plurality of sensors as a target sensor, using each working cycle of the plurality of working cycles as a target working cycle, and using each working time slot of the plurality of working time slots as a target working time slot;

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

[0009] constructing, based on the generation time and the growth factor, 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;

[0010] constructing, based on the generation time and the growth factor, 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;

[0011] Constructing an average nonlinear information age model according to the first nonlinear information age model and the second nonlinear information age model corresponding to all target sensors;

[0012] The average nonlinear information age model is processed by a bidirectional selection mechanism based on a proximal strategy optimization algorithm to obtain a data collaborative transmission strategy, and the multiple sensors are controlled according to the data collaborative transmission strategy to collaboratively transmit the collected perception information to the data processing device in each working time slot in each working cycle.

[0013] Based on the same application concept, the second aspect of the present application provides a data collaborative transmission device for industrial Internet scenarios, the device is arranged in an edge node device and applied to a data collaborative transmission system, the system includes an edge node device, a data processing device and a plurality of sensors, the device includes:

[0014] An acquisition module is configured to acquire the working time of the system, wherein the working time includes a plurality of working cycles, and each working cycle includes a plurality of working time slots;

[0015] a target determination module 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] a data determination module, configured to determine a growth factor of the target sensor in the target working cycle, and obtain a generation time of the received sensing information of the target sensor;

[0017] A first model building module is configured to build, based on the generation time and the growth factor, 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;

[0018] a second model building module configured to build, based on the generation time and the growth factor, 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;

[0019] A third model building module is configured to build an average nonlinear information age model according to 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 proximal strategy optimization algorithm to obtain a data collaborative transmission strategy, and control the multiple sensors to collaboratively transmit the collected perception information to the data processing device in each working time slot in each working cycle according to the data collaborative transmission strategy.

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

[0022] Based on the same application concept, the fourth aspect of the present application provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the method described in the first aspect above.

[0023] From the above, it can be seen that the data collaborative transmission method and related equipment for industrial Internet scenarios provided by the present 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 the growth factor, and 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, and then 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. The average nonlinear information age model is processed by a two-way selection mechanism based on a proximal strategy optimization algorithm, which can take into account the timeliness of the perception information of the unscheduled sensors, so that the two-way selection of the scheduled sensors and the unscheduled sensors can be controlled according to the obtained data collaborative transmission strategy to complete the collaborative transmission, thereby improving the timeliness of the perception information. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 This is a flow chart of a data collaborative transmission method for an industrial Internet scenario according to an embodiment of the present application;

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

[0027] Figure 2B A schematic diagram of the sensor working process of an embodiment of the present 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 the present application;

[0029] Figure 4 A schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0031] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like 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 understandable that before using the technical solutions of each embodiment of the present application, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0033] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to the electronic device, application, server, storage medium or other software or hardware that performs the operation of the technical solution of the present application according to the prompt message.

[0034] As an optional but non-limiting implementation, in response to receiving the user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0035] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation method of this application. Other methods that meet relevant laws and regulations may also be applied to the implementation method of this application.

[0036] In industrial sensor networks, in order to improve the timeliness of perceived information, an effective sensor scheduling method must be designed. However, the traditional scheduling method only considers the single-hop transmission method, that is, the scheduled sensor sends data, and the unscheduled sensor waits on standby. Such a transmission method will lead to a decrease in the timeliness of perceived information. In addition, even in the transmission scenario of multi-node cooperation, the cooperation mechanism is only designed from the perspective of achieving global goals, without considering the interests of the requester and the service provider. Therefore, the node scheduling and cooperative transmission mechanism under the industrial sensor network needs to be explained in detail and a solution is proposed. The problem is explained as follows:

[0037] 1) The problem of poor timeliness of perception information caused by the scheduling method in single-hop transmission mode:

[0038] In the intelligent manufacturing scenario, the industrial sensor network generates a large amount of data every moment, but the actual available channel resources are limited. It is impossible for all sensors to send data to the data processing center at the same time. The traditional data transmission method is that the control center schedules the sensors in the network in each working cycle, and the scheduled sensors directly send data to the data processing center, while the sensors that are not scheduled wait until the scheduling opportunity comes. Such a data transmission method is not good for sensors that are not scheduled, because their perception information is not received in the data processing, and the timeliness of the perception information has been declining. In addition, even if the channel resources are sufficient, some sensor data transmission time may be long and timeliness is low due to channel quality problems, which cannot meet the needs of actual production scenarios. Therefore, the present application improves the traditional data transmission method and proposes a collaborative transmission method, that is, the scheduling sensor directly sends data to the data processing center, and the unscheduled sensors (including sensors that cannot meet their own timeliness requirements) use the scheduling sensor as a relay, first send the data to the scheduling sensor, and then the scheduling sensor sends the data to the data processing center. Such a transmission method can take into account the timeliness of the entire network.

[0039] 2) The traditional multi-node cooperation mechanism ignores the local interests of the two parties. Under the existing cooperation mechanism, the cooperation mechanism is often designed with the goal of achieving global optimization, while ignoring the local interests of the two parties. Considering the above problem 1), the scheduling sensor is a service provider, and it will inevitably sacrifice part of its own interests when providing services, such as additional energy consumption and data perception performance degradation caused by continuous work. Therefore, when a request arrives, the scheduling sensor is not willing to actively provide services. In order to stimulate the enthusiasm of scheduling sensors to participate in collaborative transmission, this application proposes a two-way selection mechanism, that is, the completion of collaborative transmission depends on the two-way selection between the requester (sensor that is not scheduled) and the server (scheduling sensor). In the selection process, the requester and the server will take into account the global goals of the network and their own interests at the same time.

[0040] Among them, Industrial Sensor Network (ISN) refers to an industrial sensor network, which is a communication system installed in an industrial production environment. It has the characteristics of high flexibility, low cost, and low power consumption. Through its built-in wireless communication and perception modules, it can realize real-time monitoring of production status and predictive maintenance, thereby improving the efficiency and safety of industrial production and avoiding unnecessary production accidents.

[0041] Collaborative transmission: The normal operation of smart factories is inseparable from the monitoring of various production status information by sensors. However, the limited channel resources in actual scenarios mean that only a limited number of sensors can send perception information to the data processing node in each scheduling cycle, which results in the inability to receive the perception information of unscheduled sensors normally, and the timeliness drops sharply. Therefore, collaborative transmission can be introduced into the sensor data transmission network. Collaborative transmission means that in each working cycle, the scheduled sensor directly sends the perception data to the data processing node, and the unscheduled sensor uses the scheduling sensor as a relay to first send the perception data to the scheduling sensor, and then the scheduling sensor sends the data to the data processing center.

[0042] The embodiment of the present application provides a data collaborative transmission method for industrial Internet scenarios. Based on the generation time and the growth factor, a first nonlinear information age model corresponding to the target working time slot in the target working cycle is constructed when the target sensor is a service sensor. Based on the generation time and the 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. Then, an average nonlinear information age model is constructed according to the first nonlinear information age model and the second nonlinear information age model corresponding to all target sensors. The average nonlinear information age model is processed by a two-way selection mechanism based on a proximal strategy optimization algorithm. The timeliness of the perception information of the unscheduled sensors can be taken into account, so that the two-way selection of the scheduled sensors and the unscheduled sensors can be controlled according to the obtained data collaborative transmission strategy to complete the collaborative transmission, thereby improving the timeliness of the perception information.

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

[0044] Step 101, obtaining the working time of the system, wherein the working time includes a plurality of working cycles, and each working cycle includes a plurality of working time slots.

[0045] In this step, if Figure 2A As shown, the present application considers a real-time monitoring system in a factory, which includes a sensor network, edge nodes, and a data processing center. Among them, the sensor network is responsible for monitoring various types of information in the industrial production process, and the edge node is 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 working time of the sensor is divided into several time slots, and T consecutive time slots are divided into a working cycle. n∈{1, 2, ..., N} represents the sensor index, i∈{1, 2, ..., L} represents the working cycle index, and k∈{1, 2, ..., T} represents the time slot index in each working cycle. At the beginning of each working cycle, the sensor first samples the monitoring data, and then the edge node schedules the sensor. The scheduled sensor sends the data to the edge node, and the unscheduled sensor selects the scheduled sensor to send the data to the edge node. represents the transmission time of the nth sensor in the ith cycle. This application assumes that sensors are connected via wired transmission and sensors and edge nodes are connected via wireless transmission. Considering the complex transmission environment inside the factory, the channel between the sensor and the edge node must be suboptimal. n represents the probability of successful transmission from sensor n, p n Time does not change. Use v n，j ∈{0, 1} represents the transmission status of sensor n in the i-th cycle, v n，j =1 indicates successful transmission, otherwise it indicates failed transmission. In the event of a transmission interruption, if the sensor remains in standby mode until the next working cycle begins, it will not only cause the timeliness of the information to deteriorate, but also cause unnecessary energy consumption. Therefore, the present application considers a collaborative transmission method based on sleep. Specifically, sensors are divided into two categories: service sensors (SIS) and request sensors (RIS). Service sensors are sensors that are scheduled in each cycle. They directly transmit data to edge nodes, while request sensors are unscheduled sensors. They need to first transmit data to the service sensor, and then the service sensor transmits the data to the edge node. The service sensor enters sleep mode after completing the transmission of its own data and the request sensor data, and the request sensor enters sleep mode after sending the data to the service sensor.

[0047] The working process of the sensor can be simplified as Figure 2B As shown, the described sensor is a serving sensor SIS (ie, scheduled) in period 1, and a requesting sensor RIS (not scheduled) in periods 2 and 3.

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

[0049] In this step, the above content describes a process of iterating or traversing in a system with multiple sensors, multiple working cycles, and multiple working time slots. In order 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. The present application prefers sensors that collect perception information data.

[0051] Targeting each sensor individually means selecting each of these sensors individually as the target for the current operation or analysis during processing. In other words, the system performs a series of operations on the first sensor, and once completed, the same operations are performed on the second sensor, and so on, until all sensors have been processed. This is usually done to ensure that every sensor in the system is processed or analyzed equally so that comprehensive data is collected.

[0052] The duty cycle refers to the period of time during which the system collects, processes, or communicates data. For example, data collection may be performed every hour, every day, or every week. These periods are different duty cycles.

[0053] Each duty cycle is treated as a target duty cycle, and similar to how sensors are processed, the system selects each of these duty cycles as the current time period of interest. This means that for each sensor, the system considers its performance or data in all duty cycles. This helps analyze the behavior changes of sensors in different time periods, such as the temperature difference between day and night, the activity level on weekdays and weekends, etc.

[0054] A working time slot usually 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 for data collection or processing. Each working time slot is used as a target working time slot. After determining the target sensor and target working cycle, the system will further refine it and use each time slot within each working cycle as an independent analysis unit. This allows the system to have a more detailed understanding of the state or data changes of the sensor in each specific time period.

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

[0056] In summary, the above describes a complex iterative process to achieve comprehensive analysis and data collection of the system by processing each sensor, each working cycle, and each working time 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 perception information of the target sensor.

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

[0059] The process of determining the growth factor typically involves comparing sensor data at the beginning and end of the target duty 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] The sensor's perception information (i.e., the data measured by the sensor) is usually accompanied by a time stamp, indicating when the data was collected or generated. This time information is crucial for analyzing the real-time nature, timing, and correlation of the data with other events or data.

[0061] The generation time of sensor information refers to the exact time when the 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] Obtaining this time information is usually achieved by reading specific fields in the sensor data packet or through the protocol for communicating with the sensor. Once this time information is obtained, it can be compared and analyzed with the time of other data or events to reveal potential relationships and patterns between the data.

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

[0064] Step 104 : constructing 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.

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

[0066] First nonlinear information age model: Based on the above-mentioned generation time, growth factor, and specific conditions of target working cycle and target working time slot, an information age model is constructed. The "first nonlinearity" here means that the model takes into account 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 multiple 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 actual systems. Such a model is crucial for evaluating and optimizing the performance of real-time systems.

[0067] Step 105 : 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.

[0068] In this step, the age of information (AoI) is a measure of the freshness of information, which represents the total time from the generation or update of information to the current time point. In information transmission and real-time systems, it is crucial to keep the age of information low because it ensures the timeliness and accuracy of data.

[0069] The nonlinear model means that the law of information age changing over time is not a simple linear relationship, but may be affected by multiple factors, such as network delay, data processing time, transmission error rate, etc. These factors make the growth of information age show nonlinear characteristics.

[0070] The second nonlinear information age model is a further refinement or extension of the existing model, which is used to more accurately describe the information age change under specific conditions (such as target working cycle and target working time slot). This model takes into account more practical conditions and constraints. This model 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 according to the first nonlinear information age models and the second nonlinear information age models corresponding to all target sensors.

[0072] In this step, in sensor networks and information processing systems, the age of information (AoI) is an important performance indicator used to measure the freshness or timeliness of information. It reflects the time from the generation of information to the receipt of the recipient. In application scenarios where information is frequently updated or real-time requirements are high, such as autonomous driving, remote medical monitoring, real-time communication, etc., reducing the age of information is crucial to improving system performance.

[0073] However, traditional information age models are often based on the linear assumption that information age grows linearly over time. However, in practical applications, due to the influence of various factors such as network congestion, sensor failure, and data processing delay, the growth of information age may show nonlinear characteristics. Therefore, the introduction of a nonlinear information age model can more accurately describe the dynamic changes of 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 delay, the change of sensor sampling frequency, the complexity of data processing, etc., to describe the growth of information age in a nonlinear way.

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

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

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

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

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

[0080] In summary, 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 through a bidirectional selection mechanism based on a proximal strategy optimization algorithm to obtain a data collaborative transmission strategy, and the multiple sensors are controlled according to the data collaborative transmission strategy to collaboratively transmit the collected perception information to the data processing device in each working time slot in 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 the freshness and timeliness of information, 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, Age of Information (AoI) is a measure of the freshness of information, which represents the time since the last update of the information. However, in some cases, the value of information may not decrease linearly over time, so the concept of nonlinear information age is introduced. The average nonlinear information age model is an average value determined based on the nonlinear information age, which is used to evaluate the freshness of information in the entire system or a specific time period.

[0084] Proximal Policy Optimization (PPO) algorithm: is a reinforcement learning algorithm that is particularly suitable for solving continuous action space and complex policy optimization problems. It maximizes the cumulative reward (or minimizes the cumulative cost) by iteratively updating the policy while keeping the change between the new and old policies small enough to ensure the stability of learning. The PPO algorithm combines the idea of ​​Trust Region Policy Optimization (TRPO), but is simpler to implement and easier to adjust.

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

[0086] Through the above scheme, based on the generation time and the growth factor, a first nonlinear information age model corresponding to the target working time slot in the target working cycle is constructed when the target sensor is a service sensor, and based on the generation time and the 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. Then, an average nonlinear information age model is constructed according to the first nonlinear information age model and the second nonlinear information age model corresponding to all target sensors. The average nonlinear information age model is processed by a bidirectional selection mechanism based on a proximal strategy optimization algorithm, which can take into account the timeliness of the perception information of the unscheduled sensors, so that the bidirectional selection of the scheduled sensors and the unscheduled sensors can be controlled according to the obtained data collaborative transmission strategy to complete the collaborative transmission, thereby improving the timeliness of the perception information.

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

[0088] Step A1, obtaining the scheduling decision w of the nth target sensor in the previous working cycle i-1 of the target working cycle i n，i-1 , the cycle length T of the target working cycle i, the transmission time of the nth target sensor in the previous working cycle i-1 of the target working cycle i The coordinated transmission decision of the mth target sensor to the nth target sensor in the previous working cycle i-1 of the target working cycle i The transmission time of the mth target sensor in the previous working cycle i-1 of the target working cycle i The cooperative transmission decision of the nth target sensor in the previous working cycle i-1 of the target working cycle i

[0089] Step A2, based on the w n，i-1 、T、 Said Said and stated The sleep time of the nth target sensor in the target working cycle i is determined by the following formula:

[0090]

[0091] Step A3, based on the and the T is determined by the following formula to determine the growth factor of the target duty cycle

[0092]

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

[0094] (1) Linear information age: Age of information (AoI) is an important indicator to measure the freshness of information from the perspective of the receiver. It is of great significance in real-time monitoring systems and status update systems. Traditionally, AoI is defined as the time difference from the generation of information to the current moment. Assuming that the system is currently at time t, the generation time of the latest data received by the receiver is u(t), then AoI can be expressed as:

[0095] Δ(t)=tu(t).

[0096] (2) Nonlinear information age

[0097] Traditional AoI changes linearly over time. However, in actual application scenarios, especially time-sensitive scenarios such as factory equipment operation status monitoring, on the one hand, before the information is successfully received, the information becomes increasingly obsolete over time. On the other hand, as the working time increases, the sensor performance decreases, and the failure rate (such as thermal failure, sensitivity failure, etc.) increases. Sensor failure will cause serious lag in perceived information. Combining the above two aspects, the freshness of information in industrial scenarios becomes lower and lower over time, and the linear AoI with a constant growth rate cannot reflect the change of information freshness over time. Therefore, this application uses nonlinear AoI as a measure of information timeliness.

[0098] The adopted nonlinear AoI is defined as:

[0099] Δ(k)=e a(k-δ(k))

[0100] Among them, k represents the current time, δ(k) represents the time when the latest data received by the receiver is generated, and a represents the growth factor of the nonlinear AoI, which depends on the working time of the sensor. Therefore, under the system considered in this application, the nonlinear information age of the nth sensor in the kth time slot of the i-th cycle is defined as:

[0101]

[0102] Among them, g n (k) represents the generation time of the data of the nth sensor received by the receiving end, represents the growth factor of the nth sensor in the i-th cycle and is defined as:

[0103]

[0104] in, represents the sleep time of the nth sensor in the i-th cycle, which can be expressed as:

[0105]

[0106] Where T represents the cycle length, w n，i-1 represents the scheduling decision of the nth sensor in the i-1th period, represents the transmission time of the nth sensor in the i-1th cycle, represents the cooperative transmission decision of the nth sensor in the i-1th cycle, Represents the coordinated transmission decision of the m-th sensor to sensor n in the i-1-th cycle.

[0107] In some embodiments, step 104 includes:

[0108] Step B1, based on the generation time g n (k) and the growth factor At the target working time slot k, the initial nonlinear information age model is determined by the following formula

[0109]

[0110] Step B2, in response to the target sensor being a service sensor, based on the The first nonlinear information age model is determined by the following formula

[0111]

[0112] In the above scheme, the information age model:

[0113] (1) Linear information age: Age of information (AoI) is an important indicator to measure the freshness of information from the perspective of the receiver. It is of great significance in real-time monitoring systems and status update systems. Traditionally, AoI is defined as the time difference from the generation of information to the current moment. Assuming that the system is currently at time t, the generation time of the latest data received by the receiver is u(t), then AoI can be expressed as:

[0114] Δ(t)=tu(t).

[0115] (2) Nonlinear information age

[0116] Traditional AoI changes linearly over time. However, in actual application scenarios, especially time-sensitive scenarios such as factory equipment operation status monitoring, on the one hand, before the information is successfully received, the information becomes increasingly obsolete over time. On the other hand, as the working time increases, the sensor performance decreases, and the failure rate (such as thermal failure, sensitivity failure, etc.) increases. Sensor failure will cause serious lag in perceived information. Combining the above two aspects, the freshness of information in industrial scenarios becomes lower and lower over time, and the linear AoI with a constant growth rate cannot reflect the change of information freshness over time. Therefore, this application uses nonlinear AoI as a measure of information timeliness.

[0117] The adopted nonlinear AoI is defined as:

[0118] Δ(k)=e a(k-2(k))

[0119] Among them, k represents the current time, δ(k) represents the time when the latest data received by the receiver is generated, and a represents the growth factor of the nonlinear AoI, which depends on the working time of the sensor. Therefore, under the system considered in this application, the nonlinear information age of the nth sensor in the kth time slot of the i-th cycle is defined as:

[0120]

[0121] Among them, g n (k) represents the generation time of the data of the nth sensor received by the receiving end, represents the growth factor of the nth sensor in the i-th cycle and is defined as:

[0122]

[0123] in, represents the sleep time of the nth sensor in the i-th cycle, which can be expressed as:

[0124]

[0125] Where T represents the cycle length, w n，i-1 represents the scheduling decision of the nth sensor in the i-1th period, represents the transmission time of the nth sensor in the i-1th cycle, represents the cooperative transmission decision of the nth sensor in the i-1th cycle, represents the coordinated transmission decision of the mth sensor to sensor n in the i-1th cycle. and It can be seen from , that the growth factor of the nonlinear AoI of sensor n in the i-th cycle depends on its sleep time in the i-1th cycle, and the sleep time depends on the scheduling and cooperative transmission decision of the sensor in the i-1th cycle. Specifically, when sensor n is scheduled in the i-1th cycle, that is, w n，i-1 =1, its sleep time is equal to the cycle length minus the data transmission time. If the sensor also assists the mth sensor, that is, Its sleep time is equal to the cycle length minus its own transmission time minus the transmission time of the mth sensor; if the nth sensor is not scheduled in the i-1th cycle, it must select a scheduled sensor m to assist it in transmitting data, that is, there must be Therefore, its sleep time is equal to the cycle length minus the transmission time of sensor m. The nonlinear AoI of SIS in period i (i.e., the first nonlinear information age model) can be further expressed as:

[0126]

[0127] In some embodiments, step 105 includes:

[0128] Step C1, based on the generation time g n (k) and the growth factor At the target working time slot k, the initial nonlinear information age model is determined by the following formula

[0129]

[0130] Step C2, in response to the target sensor being a request sensor, based on the The second nonlinear information age model is determined by the following formula

[0131]

[0132] In the above scheme, the information age model:

[0133] (1) Linear information age: Age of information (AoI) is an important indicator to measure the freshness of information from the perspective of the receiver. It is of great significance in real-time monitoring systems and status update systems. Traditionally, AoI is defined as the time difference from the generation of information to the current moment. Assuming that the system is currently at time t, the generation time of the latest data received by the receiver is u(t), then AoI can be expressed as:

[0134] Δ(t)=tu(t).

[0135] (2) Nonlinear information age

[0136] Traditional AoI changes linearly over time. However, in actual application scenarios, especially time-sensitive scenarios such as factory equipment operation status monitoring, on the one hand, before the information is successfully received, the information becomes increasingly obsolete over time. On the other hand, as the working time increases, the sensor performance decreases, and the failure rate (such as thermal failure, sensitivity failure, etc.) increases. Sensor failure will cause serious lag in perceived information. Combining the above two aspects, the freshness of information in industrial scenarios becomes lower and lower over time, and the linear AoI with a constant growth rate cannot reflect the change of information freshness over time. Therefore, this application uses nonlinear AoI as a measure of information timeliness.

[0137] The adopted nonlinear AoI is defined as:

[0138] Δ(k)=e a(k-δ(k))

[0139] Among them, k represents the current time, δ(k) represents the time when the latest data received by the receiver is generated, and a represents the growth factor of the nonlinear AoI, which depends on the working time of the sensor. Therefore, under the system considered in this application, the nonlinear information age of the nth sensor in the kth time slot of the i-th cycle is defined as:

[0140]

[0141] Among them, g n (k) represents the generation time of the data of the nth sensor received by the receiving end, represents the growth factor of the nth sensor in the i-th cycle and is defined as:

[0142]

[0143] in, represents the sleep time of the nth sensor in the i-th cycle, which can be expressed as:

[0144]

[0145] Where T represents the cycle length, w n，i-1 represents the scheduling decision of the nth sensor in the i-1th period, represents the transmission time of the nth sensor in the i-1th cycle, represents the cooperative transmission decision of the nth sensor in the i-1th cycle, represents the coordinated transmission decision of the mth sensor to sensor n in the i-1th cycle. and It can be seen from , that the growth factor of the nonlinear AoI of sensor n in the i-th cycle depends on its sleep time in the i-1th cycle, and the sleep time depends on the scheduling and cooperative transmission decision of the sensor in the i-1th cycle. Specifically, when sensor n is scheduled in the i-1th cycle, that is, w n，i-1 =1, its sleep time is equal to the cycle length minus the data transmission time. If the sensor also assists the mth sensor, that is, Its sleep time is equal to the cycle length minus its own transmission time minus the transmission time of the mth sensor; if the nth sensor is not scheduled in the i-1th cycle, it must select a scheduled sensor m to assist it in transmitting data, that is, there must be Therefore, its sleep time is equal to the cycle length minus the transmission time of sensor m. The nonlinear AoI of RIS in period i (i.e., the second nonlinear information age model) can be further expressed as:

[0146]

[0147] In some embodiments, step 106 includes:

[0148] Step D1, obtaining the total number N of the multiple sensors, the total number L of the multiple working cycles, and the total number T of the multiple working time slots.

[0149] Step D2, based on the N, the L, the T, the first nonlinear information age model and the second nonlinear information age model, determine the average nonlinear information age model by the following formula:

[0150]

[0151] Among them, Δ n，i (k) represents the first nonlinear information age model or the second nonlinear information age model.

[0152] In the above scheme, the information age model:

[0153] (1) Linear information age: Age of information (AoI) is an important indicator to measure the freshness of information from the perspective of the receiver. It is of great significance in real-time monitoring systems and status update systems. Traditionally, AoI is defined as the time difference from the generation of information to the current moment. Assuming that the system is currently at time t, the generation time of the latest data received by the receiver is u(t), then AoI can be expressed as:

[0154] Δ(t)=tu(t).

[0155] (2) Nonlinear information age

[0156] Traditional AoI changes linearly over time. However, in actual application scenarios, especially time-sensitive scenarios such as factory equipment operation status monitoring, on the one hand, before the information is successfully received, the information becomes increasingly obsolete over time. On the other hand, as the working time increases, the sensor performance decreases, and the failure rate (such as thermal failure, sensitivity failure, etc.) increases. Sensor failure will cause serious lag in perceived information. Combining the above two aspects, the freshness of information in industrial scenarios becomes lower and lower over time, and the linear AoI with a constant growth rate cannot reflect the change of information freshness over time. Therefore, this application uses nonlinear AoI as a measure of information timeliness.

[0157] The adopted nonlinear AoI is defined as:

[0158] Δ(k)=e a(k-δ(k))

[0159] Among them, k represents the current time, δ(k) represents the time when the latest data received by the receiver is generated, and a represents the growth factor of the nonlinear AoI, which depends on the working time of the sensor. Therefore, under the system considered in this application, the nonlinear information age of the nth sensor in the kth time slot of the i-th cycle is defined as:

[0160]

[0161] Among them, g n (k) represents the generation time of the data of the nth sensor received by the receiving end, represents the growth factor of the nth sensor in the i-th cycle and is defined as:

[0162]

[0163] in, represents the sleep time of the nth sensor in the i-th cycle, which can be expressed as:

[0164]

[0165] Where T represents the cycle length, w n，i-1 represents the scheduling decision of the nth sensor in the i-1th period, represents the transmission time of the nth sensor in the i-1th cycle, represents the cooperative transmission decision of the nth sensor in the i-1th cycle, represents the coordinated transmission decision of the mth sensor to sensor n in the i-1th cycle. and It can be seen from , that the growth factor of the nonlinear AoI of sensor n in the i-th cycle depends on its sleep time in the i-1th cycle, and the sleep time depends on the scheduling and cooperative transmission decision of the sensor in the i-1th cycle. Specifically, when sensor n is scheduled in the i-1th cycle, that is, w n，i-1 =1, its sleep time is equal to the cycle length minus the data transmission time. If the sensor also assists the mth sensor, that is, Its sleep time is equal to the cycle length minus its own transmission time minus the transmission time of the mth sensor; if the nth sensor is not scheduled in the i-1th cycle, it must select a scheduled sensor m to assist it in transmitting data, that is, there must be Therefore, its sleep time is equal to the cycle length minus the transmission time of sensor m. The nonlinear AoI of SIS in period i can be further expressed as:

[0166]

[0167] The nonlinear AoI of RIS in period i can be further expressed as:

[0168]

[0169] In addition, 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, and 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, and slightly outdated information may cause production accidents. In order to meet the above conditions, this application believes that the peak information age of the information monitored by each sensor in the network cannot exceed the maximum value it can tolerate, that is:

[0170]

[0171] In some embodiments, in step 107, the processing of the average nonlinear information age model by a bidirectional selection mechanism based on a proximal strategy optimization algorithm to obtain a data collaborative transmission strategy includes:

[0172] Step E1, constructing constraint conditions corresponding to the average nonlinear information age model.

[0173] Step E2, obtaining the transmission time of the target sensor in each target working cycle, and using the transmission time of the target sensor in the target working cycle as a state space, wherein the state space is specifically:

[0174]

[0175] Among them, j represents the time step of the proximal policy optimization algorithm learning, represents the transmission time of each target sensor in the i-th target working cycle.

[0176] Step E3, obtaining the scheduling decision of each target working cycle, and using the scheduling decision of each target working cycle as the action space, wherein the action space is specifically:

[0177] a j = {W j，1 , W j，2 , ..., W j，L}

[0178] Among them, W j，i ={w j，1，i , w j，2，i ,…,w j，N，i}, i∈{1, 2, ..., L} represents the scheduling decision of the i-th target working cycle at the j-th time step.

[0179] Step E4, performing inverse processing on the average nonlinear information age model to obtain a reward function r corresponding to the average nonlinear information age model, wherein the reward function r is specifically:

[0180]

[0181] Step E5, determining the state of the current time step according to the state space, determining the currently executed action based on the state of the current time step and the action space, and determining the current scheduling decision according to the currently executed action.

[0182] Step E6, determining whether the current scheduling decision satisfies the constraint condition.

[0183] Step E7: If the response is no, the current scheduling decision is processed using the reward function until the current scheduling decision satisfies the constraint condition. Or,

[0184] Step E8, in response to yes, determining an initial request sensor set corresponding to each service sensor based on the constraint condition of the current scheduling decision.

[0185] Step E9, based on the initial request sensor set, determine the request sensor set that meets the expectations of each service sensor through the following pre-constructed willingness index, wherein the willingness index is specifically:

[0186]

[0187] in, represents the willingness index, γ represents the preset normalization coefficient, ω1 represents the preset first index coefficient, ω2 represents the preset second index coefficient, ω3 represents the preset third index coefficient, represents the number of times the mth request sensor acts as a service sensor until the ith working cycle, T represents the cycle length of the working cycle, It is represented as the transmission time of the nth service sensor in the ith working cycle, It is represented as the transmission time of the mth request sensor in the i-th working cycle, It represents the size of the nonlinear information age brought by the nth service sensor assisting the mth request sensor, and k represents the order of the working time slot.

[0188] Step E10, based on the request sensor set that meets the expectation of each service sensor, the final service sensor corresponding to each request sensor is determined by the selection index pre-obtained by the following formula, and the final service sensor corresponding to each request sensor is used as the data collaborative transmission strategy;

[0189]

[0190] Where n represents the selection index, k represents the order of the working time slots, T represents the total number of working time slots, m represents the order of serving sensors, and i represents the order of working cycles.

[0191] In the above scheme, the optimization problem is formed as follows: Nonlinear AoI directly reflects the freshness of the perception data. Therefore, this application uses the average nonlinear AoI to represent the timeliness of the perception data in the industrial production process. In order to improve the freshness of the perception data, this application minimizes the average nonlinear AoI of all sensors by optimizing scheduling and coordinated transmission decisions. i and X i Representing the scheduling and cooperative transmission decisions of each cycle respectively, the optimization problem constructed in this application can be expressed as:

[0192]

[0193] stC1:w n，i v n，i =1,

[0194] C2:

[0195] C3:

[0196] C4:

[0197] C5:

[0198] C6:

[0199] C7:

[0200] C8:

[0201] C9:

[0202] Among them, constraint C1 is the channel constraint for scheduled sensors, constraint C2 is the constraint for cooperative variables, ensuring that RIS can only select target sensors from scheduled sensors, constraint C3 ensures that the number of sensors scheduled in each working cycle does not exceed M, constraint C4 ensures that the scheduled sensors can meet their own timeliness requirements, constraint C5 requires that the remaining transmission time of SIS must meet the transmission requirements of RIS, constraint C6 ensures that each SIS can only help one RIS at most, constraint C7 ensures that each RIS can select one SIS, constraint C8 ensures that the timeliness requirements of each sensor must be met, and constraint C9 ensures that the total energy consumption of all sensors cannot exceed the maximum allowable energy consumption.

[0203] Bidirectional selection mechanism based on proximal policy optimization algorithm: Proximal Policy Optimization (PPO) algorithm is a new type of policy gradient algorithm, which solves the problem of difficult to determine the training step size in traditional policy gradient algorithms, and is widely used in solving various continuous control problems and discrete control problems. From the above optimization problem, it can be seen that the optimization problem constructed by this application is an integer nonlinear programming problem, and integer nonlinear programming is often an NP-hard problem, which is difficult to solve through traditional optimization methods. In order to overcome the above problems, this application designs a bidirectional selection mechanism based on the PPO algorithm, which decouples the scheduling variables from the cooperative transmission variables, and at the same time utilizes the superiority of the PPO algorithm in solving integer nonlinear programming.

[0204] The successful implementation of the PPO algorithm is inseparable from 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 PPO, namely:

[0205]

[0206] Among them, j represents the time step of PPO learning, represents the data transmission time of each sensor in the i-th cycle.

[0207] The scheduling decision of each cycle is set as the action space of PPO, that is:

[0208] a j = {W j，1 , W j，2 , ..., W j，L}

[0209] Among them, W j，i ={w j，1，i , w j，2，i , ..., w j，N，i}, i∈{1, 2, ..., L}, represents the scheduling decision of the i-th period at the j-th time step.

[0210] Reward is the only feedback that the agent can get from the environment, which directly affects whether the agent can learn towards the desired goal. In order to minimize the average nonlinear AoI during the sensor operation, this application sets the reward function to the inverse of the average nonlinear AoI, that is:

[0211]

[0212] It should be noted that since nonlinear AoI is related to both scheduling and collaborative transmission decisions, the reward function is not entirely dependent on the scheduling decision obtained by the PPO algorithm, but also on the collaborative transmission decision obtained by two-way selection. The core idea of ​​collaborative transmission is to take into account the interests of both the service sensor and the requesting sensor. For the requesting sensor, it hopes to find a service sensor that makes its perceived data most timely, while the service sensor is more concerned about maximizing the timeliness of its perceived data in a cycle and is unwilling to contribute excess time resources. Such selfishness of the service sensor will cause excess time resources to be wasted, which is not conducive to the optimal timeliness of the network as a whole. In addition, considering that the identities of the service sensor and the requesting sensor are dynamically changing, the service sensor of the current cycle may become the requesting sensor in the next cycle, and the selfishness in the current cycle will also lead to helplessness in the next cycle. Therefore, this application designs a two-way selection mechanism to determine the collaborative transmission decision. In order to improve the timeliness of the entire network, the service sensor will consider the past enthusiasm of the requesting sensor when selecting the requesting sensor. Only when the past enthusiasm of the requesting sensor meets the expectations of the service sensor will the service sensor be willing to contribute its own time resources. At the same time, contributing time resources once will increase your enthusiasm, making it more likely that you will be assisted when you become a requesting sensor in the future. The specific steps of the PPO-based two-way selection algorithm are:

[0213] (1): PPO is based on the environment state s at the current time step j Execute action a j , get the scheduling decision W;

[0214] (2): According to the above optimization problem, determine whether W satisfies the scheduling constraints. If so, continue to execute downward. If not, return to (1) and give a penalty to the current action.

[0215] (3): Based on the scheduling decision W, we first determine the set R of requesting sensors that each serving sensor can help according to the constraints C5 and C8 of the above optimization problem. n，1 , then in R n，1 The request sensor set R that meets the expectations of each service sensor is determined according to the following willingness index: n，2 ;

[0216]

[0217] in, represents the number of times the requesting sensor m has acted as a service sensor up to the ith cycle, which mainly represents the initiative of the requesting sensor in the past. The second item is the time idle rate indicator, which mainly measures the occupation of the requesting sensor's transmission time to the remaining transmission time of the service sensor. The third item is the timeliness indicator, which measures the size of the nonlinear AoI brought by the service sensor n assisting the requesting sensor m. It can be seen that the more active the requesting sensor was in the past, the lower the time occupation rate of the service sensor (that is, the higher the time idle rate of the service sensor), the smaller the average nonlinear AoI, the greater the willingness index, and the more likely the service sensor is to assist the requesting sensor. In addition, w1, w2, and w3 represent the coefficients of each indicator and w1+w2+w3=1, and γ represents the normalization coefficient.

[0218] (4): According to (3), each requesting sensor knows the set of service sensors S that can assist it m , in S m The collaborative transmission decision X is obtained by determining the final selected service sensor according to the following formula, that is, the requesting sensor only uses information timeliness as the selection indicator:

[0219]

[0220] (5): The reward function evaluates the quality of the current action based on the selected W and X:

[0221]

[0222] (6): Repeat the above process until the algorithm converges

[0223] During the execution of the algorithm, the agent selects different behaviors from the action space to interact with the environment, and continuously adjusts its actions through the rewards after the interaction, thereby learning the optimal scheduling and collaborative transmission strategy.

[0224] In some embodiments, step E1 comprises:

[0225] Step F1, obtain the scheduling decision w of the nth target sensor in the i-th target working cycle n，i , and the channel condition v of the nth target sensor in the ith target duty cycle n，i ;

[0226] Step F2, based on the w n，i and the v n，i The first constraint is determined by the following formula:

[0227] w n，i v n，i =1;

[0228] Step F3, obtaining the coordinated transmission variable transmitted by the mth target sensor in the ith target working cycle with the help of the nth target sensor

[0229] Step F4, based on the and the w n，i The second constraint is determined by the following formula:

[0230]

[0231] Step F5, obtaining a preset threshold M of the number of scheduled sensors in each target working cycle;

[0232] Step F6, based on the M and the w n，i The third constraint is determined by the following formula:

[0233]

[0234] Where N represents the total number of sensors;

[0235] Step F7, obtaining the transmission time of the nth target sensor and the nonlinear information age Δ n ;

[0236] Step F8, based on the w n，i , The Δ n and the preset nonlinear information age threshold Δ n The fourth constraint is determined by the following formula:

[0237]

[0238] Step F9, obtaining the total number of working time slots T and the transmission time of the mth target sensor

[0239] Step F10, based on the Said and stated The fifth constraint is determined by the following formula:

[0240]

[0241] Step F11, based on the The sixth constraint is determined by the following formula:

[0242]

[0243] Step F12, based on the The seventh constraint is determined by the following formula:

[0244]

[0245] Step F13, obtaining the peak information age of each target sensor

[0246] Step F14, based on the and the preset peak information age threshold Δ n The eighth constraint is determined by the following formula:

[0247]

[0248] Step F15, obtaining the transmission time of the nth target sensor Transmission time of the mth sensor Data transmission power P T ;

[0249] Step F16, based on the P T , Said and stated The transmission energy consumption of the nth target sensor in the i-th target working cycle is determined by the following formula:

[0250]

[0251] Step F17, obtaining the sleep power P S and the cooperative transmission variable transmitted by the nth target sensor in the i-th target working cycle with the help of the mth target sensor Based on the P T 、T、 Said and stated The first dormancy energy consumption in the i-th target working cycle when the n-th target sensor is a service sensor is determined by the following formula:

[0252]

[0253] Step F18, based on the P S 、T、 and stated The second sleep energy consumption in the i-th target working cycle when the n-th target sensor is the request sensor is determined by the following formula:

[0254]

[0255] Step F19, obtaining the standby power P A , based on the The P A and stated The standby energy consumption of the nth target sensor in the i-th target working cycle is determined by the following formula:

[0256]

[0257] Step F20, based on the Said Said and stated The total energy consumption of the nth target sensor in the i-th target working cycle is determined by the following formula:

[0258]

[0259] Step F21, based on the The ninth constraint condition is determined by the following formula:

[0260]

[0261] Step F22, taking 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 as constraints corresponding to the average nonlinear information age model.

[0262] In the above scheme, if Figure 2AAs shown, the present application considers a real-time monitoring system in a factory, which includes a sensor network, edge nodes, and a data processing center. Among them, the sensor network is responsible for monitoring various types of information in the industrial production process, and the edge node is responsible for scheduling and controlling the sensor network and sending the sensed data to the data processing center for further processing and analysis.

[0263] In the system under consideration, the working time of the sensor is divided into several time slots, and T consecutive time slots are divided into a working cycle. n∈{1, 2, ..., N} represents the sensor index, i∈{1, 2, ..., L} represents the working cycle index, and k∈{1, 2, ..., T} represents the time slot index in each working cycle. At the beginning of each working cycle, the sensor first samples the monitoring data, and then the edge node schedules the sensor. The scheduled sensor sends the data to the edge node, and the unscheduled sensor selects the scheduled sensor to send the data to the edge node. represents the transmission time of the nth sensor in the ith cycle. This application assumes that sensors are connected via wired transmission and sensors and edge nodes are connected via wireless transmission. Considering the complex transmission environment inside the factory, the channel between the sensor and the edge node must be suboptimal. n represents the probability of successful transmission from sensor n, p n Time does not change. Use v n，j ∈{0, 1} represents the transmission status of sensor n in the i-th cycle, v n，j =1 indicates successful transmission, otherwise it indicates failed transmission. In the event of a transmission interruption, if the sensor remains in standby mode until the next working cycle begins, it will not only cause the timeliness of the information to deteriorate, but also cause unnecessary energy consumption. Therefore, the present application considers a collaborative transmission method based on sleep. Specifically, sensors are divided into two categories: service sensors (SIS) and request sensors (RIS). Service sensors are sensors that are scheduled in each cycle. They directly transmit data to edge nodes, while request sensors are unscheduled sensors. They need to first transmit data to the service sensor, and then the service sensor transmits the data to the edge node. The service sensor enters sleep mode after completing the transmission of its own data and the request sensor data, and the request sensor enters sleep mode after sending the data to the service sensor.

[0264] The working process of the sensor can be simplified as Figure 2B As shown, the described sensor is a serving sensor SIS (ie, scheduled) in period 1, and a requesting sensor RIS (not scheduled) in periods 2 and 3.

[0265] Co-transmission model:

[0266] Considering the limited channel resources, if all sensors send the sensing data to the edge node at the same time, congestion will inevitably occur, resulting in poor timeliness. Therefore, this application assumes that the edge node can only schedule a limited number of sensors in each cycle, represented by M (M < N). n，i represents the scheduling decision in the ith period, w n，i =1 means that the nth sensor is scheduled in the i-th cycle, and accordingly the sensor becomes a SIS, w n，i =0 means not scheduled, and the sensor becomes RIS accordingly. In order to ensure the effectiveness of cooperative transmission, only sensors with better channel conditions can be scheduled in each cycle, that is, the following conditions must be met:

[0267] w n，i v n，i =1.

[0268] use represents the co-transfer variable, It means that the mth sensor transmits data to the edge node with the help of the nth sensor in the i-th working cycle. In this case, the nth sensor must be scheduled, so it needs to meet the following conditions:

[0269]

[0270] In order to ensure the integrity of the transmitted data, the remaining transmission time of the service sensor must meet the transmission requirements of the requesting sensor, that is:

[0271]

[0272] In order to ensure that the perception data of each sensor can be successfully received, each requesting sensor must select a service sensor in each working cycle to send the perception data to the edge node, that is, it needs to meet the following conditions:

[0273]

[0274] As for the service sensor, it can only help one request sensor to transmit data in one cycle, that is:

[0275]

[0276] Information Age Model:

[0277] (1) Linear information age: Age of information (AoI) is an important indicator to measure the freshness of information from the perspective of the receiver. It is of great significance in real-time monitoring systems and status update systems. Traditionally, AoI is defined as the time difference from the generation of information to the current moment. Assuming that the system is currently at time t, the generation time of the latest data received by the receiver is u(t), then AoI can be expressed as:

[0278] Δ(t)=tu(t).

[0279] (2) Nonlinear information age

[0280] Traditional AoI changes linearly over time. However, in actual application scenarios, especially time-sensitive scenarios such as factory equipment operation status monitoring, on the one hand, before the information is successfully received, the information becomes increasingly obsolete over time. On the other hand, as the working time increases, the sensor performance decreases, and the failure rate (such as thermal failure, sensitivity failure, etc.) increases. Sensor failure will cause a serious lag in perceived information. Combining the above two aspects, the freshness of information in industrial scenarios becomes lower and lower over time, and the linear AoI with a constant growth rate cannot reflect the change of information freshness over time.

[0281] Therefore, this application uses nonlinear AoI as a measure of information timeliness.

[0282] The adopted nonlinear AoI is defined as:

[0283] Δ(k)=e a(k-δ(k))

[0284] Among them, k represents the current time, δ(k) represents the time when the latest data received by the receiver is generated, and a represents the growth factor of the nonlinear AoI, which depends on the working time of the sensor. Therefore, under the system considered in this application, the nonlinear information age of the nth sensor in the kth time slot of the i-th cycle is defined as:

[0285]

[0286] Among them, g n (k) represents the generation time of the data of the nth sensor received by the receiving end, represents the growth factor of the nth sensor in the i-th cycle and is defined as:

[0287]

[0288] in, represents the sleep time of the nth sensor in the i-th cycle, which can be expressed as:

[0289]

[0290] Where T represents the cycle length, w n，i-1 represents the scheduling decision of the nth sensor in the i-1th period, represents the transmission time of the nth sensor in the i-1th cycle, represents the cooperative transmission decision of the nth sensor in the i-1th cycle, represents the coordinated transmission decision of the mth sensor to sensor n in the i-1th cycle. and It can be seen from , that the growth factor of the nonlinear AoI of sensor n in the i-th cycle depends on its sleep time in the i-1th cycle, and the sleep time depends on the scheduling and cooperative transmission decision of the sensor in the i-1th cycle. Specifically, when sensor n is scheduled in the i-1th cycle, that is, w n，i-1 =1, its sleep time is equal to the cycle length minus the data transmission time. If the sensor also assists the mth sensor, that is, Its sleep time is equal to the cycle length minus its own transmission time minus the transmission time of the mth sensor; if the nth sensor is not scheduled in the i-1th cycle, it must select a scheduled sensor m to assist it in transmitting data, that is, there must be Therefore, its sleep time is equal to the cycle length minus the transmission time of sensor m. The nonlinear AoI of SIS in period i can be further expressed as:

[0291]

[0292] The nonlinear AoI of RIS in period 1 can be further expressed as:

[0293]

[0294] In addition, 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, and 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, and slightly outdated information may cause production accidents. In order to meet the above conditions, this application believes that the peak information age of the information monitored by each sensor in the network cannot exceed the maximum value it can tolerate, that is:

[0295]

[0296] Energy consumption model:

[0297] The energy consumption of the sensor mainly consists of three parts: data transmission energy consumption, sleep energy consumption and standby energy consumption.

[0298] The specific energy consumption of each part is as follows:

[0299] Use P T represents the data transmission power, then the transmission energy consumption of the nth sensor in the i-th working cycle can be expressed as:

[0300]

[0301] in, represents the data transmission time of the nth sensor, represents the data transmission time of the mth sensor. If the sensor assists the mth sensor in transmitting data in addition to its own data in the ith cycle, its transmission energy consumption includes the transmission energy consumption of its own data and the transmission energy consumption of the assisted sensor.

[0302] Use P S represents the sleep power, then the sleep energy consumption of the service sensor SIS and the request sensor RIS can be expressed as:

[0303]

[0304] in, and Respectively represent the sleep time of SIS and RIS, which can be based on Conclude.

[0305] RIS is in standby mode when waiting for SIS transmission, so the standby energy consumption is mainly for RIS. A represents the standby power, then the standby energy consumption of sensor n in the i-th cycle can be expressed as:

[0306]

[0307] In summary, the total energy consumption of sensor n in the i-th cycle can be expressed as:

[0308]

[0309] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0310] It should be noted that the above describes some embodiments of the present 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 an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0311] Based on the same application concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a data collaborative transmission device for industrial Internet scenarios.

[0312] refer to Figure 3 The data collaborative transmission device for industrial Internet scenarios is arranged in an edge node device and applied to a data collaborative transmission system. The system includes an edge node device, a data processing device and a plurality of sensors. The device includes:

[0313] An acquisition module 301 is configured to acquire the working time of the system, where the working time includes a plurality of working cycles, and each working cycle includes a plurality of working time slots;

[0314] The target determination module 302 is configured to use each sensor of the plurality of sensors as a target sensor, each working cycle of the plurality of working cycles as a target working cycle, and each working time slot of the plurality of working time slots as a target working time slot;

[0315] The data determination module 303 is configured to determine a growth factor of the target sensor in the target working cycle and obtain a generation time of the received sensing information of the target sensor;

[0316] A first model building module 304 is configured to build, based on the generation time and the growth factor, 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;

[0317] A second model building module 305 is configured to build, based on the generation time and the growth factor, 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;

[0318] The third model building module 306 is configured to build an average nonlinear information age model according to the first nonlinear information age model and the second nonlinear information age model corresponding to all target sensors;

[0319] The collaborative transmission module 307 is configured to process the average nonlinear information age model through a bidirectional selection mechanism based on a proximal strategy optimization algorithm to obtain a data collaborative transmission strategy, and control the multiple sensors to collaboratively transmit the collected perception information to the data processing device in each working time slot in each working cycle according to the data collaborative transmission strategy.

[0320] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0321] The device of the above-mentioned embodiment is used to implement the corresponding data collaborative transmission method for industrial Internet scenarios in any of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0322] Based on the same application concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the data collaborative transmission method for the industrial Internet scenario described in any of the above embodiments is implemented.

[0323] Figure 4 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and 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, the memory 402, the input / output interface 403, and the communication interface 404 are connected to each other in communication within the device through the bus 405.

[0324] The processor 401 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an 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.

[0325] 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 an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 402 and called and executed by the processor 401.

[0326] The input / output interface 403 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0327] The communication interface 404 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0328] The bus 405 comprises a pathway for transmitting information between the various components of the device (eg, the processor 401 , the memory 402 , the input / output interface 403 , and the communication interface 404 ).

[0329] It should be noted that, although the above device only shows the processor 401, the memory 402, the input / output interface 403, the communication interface 404 and the bus 405, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0330] The electronic device of the above-mentioned embodiment is used to implement the corresponding data collaborative transmission method for industrial Internet scenarios in any of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0331] Based on the same application concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the data collaborative transmission method for industrial Internet scenarios as described in any of the above embodiments.

[0332] 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, modules of programs, 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 technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0333] The computer instructions stored in the storage medium of the above embodiment are used to enable 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.

[0334] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0335] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0336] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, 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 use the embodiments discussed.

[0337] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the present application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the protection scope of the present application.

Claims

1. A data collaborative transmission method for industrial Internet scenarios, characterized in that: Applied to a data collaborative transmission system, the system includes an edge node device, a data processing device, and a plurality of sensors, and the method is executed by the edge node device, including: Acquire the working time of the system, wherein the working time includes a plurality of working cycles, and each working cycle includes a plurality of working time slots; Using each sensor of the plurality of sensors as a target sensor, using each working cycle of the plurality of working cycles as a target working cycle, and using each working time slot of the plurality of working time slots as a target working time slot; Determine a growth factor of the target sensor in the target working cycle, and obtain a generation time of the received sensing information of the target sensor; constructing, based on the generation time and the growth factor, 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; constructing, based on the generation time and the growth factor, 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; Constructing an average nonlinear information age model according to the first nonlinear information age model and the second nonlinear information age model corresponding to all target sensors; The average nonlinear information age model is processed by a bidirectional selection mechanism based on a proximal strategy optimization algorithm to obtain a data collaborative transmission strategy, and the multiple sensors are controlled according to the data collaborative transmission strategy to collaboratively transmit the collected perception information to the data processing device in each working time slot in each working cycle.

2. The method according to claim 1, characterized in that Determining the growth factor of the target sensor in the target working cycle includes: Get the scheduling decision w of the nth target sensor in the previous working cycle i-1 of the target working cycle i n，i-1 , the cycle length T of the target working cycle i, the transmission time of the nth target sensor in the previous working cycle i-1 of the target working cycle i The coordinated transmission decision of the mth target sensor to the nth target sensor in the previous working cycle i-1 of the target working cycle i The transmission time of the mth target sensor in the previous working cycle i-1 of the target working cycle i The cooperative transmission decision of the nth target sensor in the previous working cycle i-1 of the target working cycle i Based on the w n，i-1 、T、 Said Said and stated The sleep time of the nth target sensor in the target working cycle i is determined by the following formula: Based on the and the T is determined by the following formula to determine the growth factor of the target duty cycle 3. The method according to claim 2, characterized in that The constructing, based on the generation time and the growth factor, of 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 comprises: Based on the generation time g n (k) and the growth factor At the target working time slot k, the initial nonlinear information age model is determined by the following formula In response to the target sensor being a service sensor, based on the The first nonlinear information age model is determined by the following formula 4. The method according to claim 2, characterized in that: The constructing, based on the generation time and the growth factor, 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, comprises: Based on the generation time g n (k) and the growth factor At the target working time slot k, the initial nonlinear information age model is determined by the following formula In response to the target sensor being a request sensor, based on the The second nonlinear information age model is determined by the following formula 5. The method according to claim 1, characterized in that The method of constructing an average nonlinear information age model according to the first nonlinear information age model and the second nonlinear information age model corresponding to all target sensors includes: Obtaining a total number N of the plurality of sensors, a total number L of the plurality of working cycles, and a total number T of the plurality of working time slots; Based on the N, the L, the T, the first nonlinear information age model and the second nonlinear information age model, the average nonlinear information age model is determined by the following formula: Among them, Δ n，i (k) represents the first nonlinear information age model or the second nonlinear information age model.

6. The method according to claim 5, characterized in that The method processes the average nonlinear information age model through a bidirectional selection mechanism based on a proximal strategy optimization algorithm to obtain a data collaborative transmission strategy, including: constructing 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: Among them, j represents the time step of the proximal policy optimization algorithm learning, i∈{1, 2, ..., L} represents the transmission time of each target sensor in the i-th target working cycle; Obtain the scheduling decision of each target working cycle, and use the scheduling decision of each target working cycle as an action space, wherein the action space is specifically: a j ={W j，1 ,W j，2 ,...,W j，L } Among them, W j，i ={w j，1，i , w j，2，i , ..., w j，N，i }, i∈{1, 2, ..., l} represents the scheduling decision of the i-th target working cycle at the j-th time step; The average nonlinear information age model is subjected to inverse processing to obtain a reward function r corresponding to the average nonlinear information age model. The reward function r is specifically: Determine and obtain the state of the current time step according to the state space, determine the currently executed action based on the state of the current time step and the action space, and determine the current scheduling decision according to the currently executed action; Determining whether the current scheduling decision satisfies the constraint condition; In response to no, the current scheduling decision is processed using the reward function until the current scheduling decision satisfies the constraint condition; or, In response to the above, determining an initial request sensor set corresponding to each serving sensor based on the constraint condition of the current scheduling decision; Based on the initial request sensor set, a request sensor set that meets the expectations of each service sensor is determined by the following pre-constructed willingness index, wherein the willingness index is specifically: in, represents the willingness index, γ represents the preset normalization coefficient, ω1 represents the preset first index coefficient, ω2 represents the preset second index coefficient, ω3 represents the preset third index coefficient, represents the number of times the mth request sensor acts as a service sensor until the ith working cycle, T represents the cycle length of the working cycle, It is represented as the transmission time of the nth service sensor in the ith working cycle, It is represented as the transmission time of the mth request sensor in the i-th working cycle, It represents the size of the nonlinear information age brought by the nth service sensor assisting the mth request sensor, and k represents the order of the working time slot; 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 by the following formula, and the final service sensor corresponding to each request sensor is used as the data collaborative transmission strategy; Where n represents the selection index, k represents the order of the working time slots, T represents the total number of working time slots, m represents the order of serving sensors, and i represents the order of working cycles.

7. The method according to claim 6, characterized in that The constructing of constraint conditions corresponding to the average nonlinear information age model includes: Get the scheduling decision w of the nth target sensor in the i-th target working cycle n，i , and the channel condition v of the nth target sensor in the ith target duty cycle n，i ; Based on the w n，i and the v n，i The first constraint is determined by the following formula: w n，i v n，i =1; Get the coordinated transmission variable transmitted by the mth target sensor in the ith target working cycle with the help of the nth target sensor Based on the and the w n，i The second constraint is determined by the following formula: Obtaining a preset threshold M of the number of scheduled sensors in each target working cycle; Based on the M and the w n，i The third constraint is determined by the following formula: Where N represents the total number of sensors; Get the transmission time of the nth target sensor and the nonlinear information age Δ n ; Based on the w n，i , The Δ n and the preset nonlinear information age threshold Δ n The fourth constraint is determined by the following formula: Get the total number of working time slots T and the transmission time of the mth target sensor Based on the Said and stated The fifth constraint is determined by the following formula: Based on the The sixth constraint is determined by the following formula: Based on the The seventh constraint is determined by the following formula: Get the peak information age of each target sensor Based on the and the preset peak information age threshold Δ n The eighth constraint is determined by the following formula: Get the transmission time of the nth target sensor Transmission time of the mth sensor Data transmission power P T ; Based on the P T , Said and stated The transmission energy consumption of the nth target sensor in the i-th target working cycle is determined by the following formula: Get the sleep power P S and the cooperative transmission variable transmitted by the nth target sensor in the i-th target working cycle with the help of the mth target sensor Based on the P T 、T、 Said and stated The first dormancy energy consumption in the i-th target working cycle when the n-th target sensor is a service sensor is determined by the following formula: Based on the P S 、T、 and stated The second sleep energy consumption in the i-th target working cycle when the n-th target sensor is the request sensor is determined by the following formula: Get the standby power P A , based on the The P A and stated The standby energy consumption of the nth target sensor in the i-th target working cycle is determined by the following formula: Based on the Said Said and stated The total energy consumption of the nth target sensor in the i-th target working cycle is determined by the following formula: Based on the The ninth constraint condition is determined by 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.

8. A data collaborative transmission device for industrial Internet scenarios, characterized in that: The device is arranged in an edge node device and applied to a data collaborative transmission system. The system includes an edge node device, a data processing device and a plurality of sensors. The device includes: An acquisition module is configured to acquire the working time of the system, wherein the working time includes a plurality of working cycles, and each working cycle includes a plurality of working time slots; a target determination module 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; a data determination module, configured to determine a growth factor of the target sensor in the target working cycle, and obtain a generation time of the received sensing information of the target sensor; A first model building module is configured to build, based on the generation time and the growth factor, 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; a second model building module configured to build, based on the generation time and the growth factor, 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; A third model building module is configured to build an average nonlinear information age model according to 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 proximal strategy optimization algorithm to obtain a data collaborative transmission strategy, and control the multiple sensors to collaboratively transmit the collected perception information to the data processing device in each working time slot in each working cycle according to the data collaborative transmission strategy.

9. 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, the method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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