A method for adaptively adjusting message update frequency based on device environment perception

Abstract

The present invention discloses a method for adaptively adjusting message update frequency based on device environment perception, including: S1, calculating the information freshness of port safety monitoring according to the perception information of the port monitoring module in the port resource pool; S2, calculating the average distance of modules under different numbers of port monitoring modules; S3, calculating the perceived occupancy frequency of the port monitoring module according to the average distance of different modules; S4, calculating the global channel occupancy rate of all devices within the communication range of port safety monitoring according to the perceived occupancy frequency; S5, constructing an information freshness optimization model according to the perceived occupancy frequency; S6, solving the information freshness optimization model, determining the optimal update frequency, and adaptively adjusting the message update frequency according to the optimal update frequency. The message update frequency adaptive adjustment method provided by the present invention can complete the determination of the optimal transmission frequency according to the module and the communication environment, reduce the system delay and improve the reliability in the port autonomous communication scenario.

Description

Technical Field

[0001] The present invention belongs to the technical field of message transmission, and in particular relates to a method for adaptively adjusting message update frequency based on device environment perception. Background Art

[0002] The investment and construction scale of ports is huge. It is an extremely important task to grasp the port status information in a timely manner, manage and maintain related facilities, and ensure their safe operation. Port monitoring information usually contains status information, such as location, altitude, humidity, temperature, and surrounding placement conditions. This information should be continuously updated according to delay-sensitive requirements. The increase in port monitoring equipment has led to channel congestion under limited spectrum resources. Multiple devices select the same sub-channel resources for transmission, resulting in conflicts in port monitoring information. This conflict causes transmission failure and affects timely transmission, so it is necessary to optimize the transmission frequency based on the port's perception capabilities. However, the transmission frequency optimization scheme and congestion control algorithm for port monitoring have not yet been clarified, and research is needed to ensure the timeliness of port monitoring information.

[0003] In recent years, with the increase in the scale of the ocean transportation industry and the continuous growth of global trade, ports have become important economic nodes. As a gathering point and conversion hub for water and land transportation, ports carry the tasks of ship docking, cargo loading and unloading, transportation and distribution. However, ports face many security threats, such as damage, theft, natural disasters, cyber attacks, etc. Therefore, a system is needed to monitor and manage security threats to ports and their surrounding environment in real time.

[0004] There is a digital twin system of a terminal safety intelligent monitoring platform, which is used for real-time perception of the port environment and rapid analysis and prediction of port safety risks, so as to effectively prevent and respond to the occurrence of port safety incidents and improve the safety and operation efficiency of the port. The invention constructs a basic physical information structure framework through its digital twin system, collects real-time information through various sensors, classifies, analyzes, summarizes and stores the information, stores the collected information in a database, and then uses the updated old data model to identify the data collected inside the database, and performs matching to represent the replacement of information for the data inside the database. Then, the operator uses the real-time data update model to compare and analyze the actual data fluctuation range and the preset threshold in the information comparison model, thereby comparing it with the change state of the external environment change model. Through the reminder of the monitoring and early warning unit, the operator can quickly obtain information about the high value of the terminal safety hazard, which is convenient for the operator to respond quickly.

[0005] The above-mentioned existing technology uses the information obtained by the sensor to be classified, analyzed, summarized, and stored, and then all transmitted to the database for the next step of operation. This has a long delay and has high requirements for the reliability of transmission. As the scale of port equipment and sensors gradually expands, due to limited spectrum resources, the probability of collision of monitoring safety messages transmitted at a fixed frequency increases significantly, thereby deteriorating the reliability of message communication, affecting the timeliness and reliability of port monitoring. The transmission frequency of monitoring safety information has a significant impact on channel occupancy and communication performance, and it needs to be studied to meet the strict application requirements in the port monitoring environment.

[0006] In the port monitoring scenario, the relevant standard specifications for the update frequency of port monitoring security messages do not take into account that sending port monitoring information at a fixed transmission frequency will cause collision of the information, which will affect reliability. Summary of the invention

[0007] In view of the above-mentioned deficiencies in the prior art, the method for adaptively adjusting the message update frequency based on device environment perception provided by the present invention solves the problem that as the scale of port monitoring equipment gradually expands, due to limited spectrum resources, the probability of collision of port monitoring information transmitted at a fixed frequency increases significantly, thereby deteriorating the communication reliability between monitoring devices and endangering port safety.

[0008] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a method for adaptively adjusting message update frequency based on device environment perception, comprising the following steps:

[0009] S1. Calculate the information freshness of port security monitoring based on the perception information of the port monitoring module in the port resource pool;

[0010] S2, calculating the average distance of modules under different numbers of port monitoring modules;

[0011] S3, calculating the perceived occupancy frequency of the port monitoring module according to the average distance of different modules;

[0012] S4. Calculate the global channel occupancy rate of all devices within the port security monitoring communication range according to the sensed occupancy frequency;

[0013] S5, constructing an information freshness optimization model based on the perceived occupancy frequency;

[0014] S6. Solve the information freshness optimization model to determine the optimal update frequency, and adaptively adjust the message update frequency based on it.

[0015] Furthermore, in step S1, the information freshness AoI is expressed as:

[0016]

[0017] In the formula, Δ i Represents the long-term average difference, for cyclical business For sudden business μ i represents the important factor of the allocation of the i-th port monitoring module, λ i represents the average update rate of the update task volume of the i-th port monitoring module, ρ i represents the update frequency of the i-th port monitoring module, and N represents the total number of port monitoring modules.

[0018] Furthermore, in step S2, for all port monitoring modules, the average module distance between its receiving module i=0 and the sending module i within its communication range is expressed as:

[0019]

[0020] In the formula, represents the average module spacing, l represents the number of channels, i=0 represents the current port monitoring module, which serves as a receiving module, and N represents the number of port monitoring modules.

[0021] Furthermore, in step S3, the occupancy frequency is sensed for:

[0022]

[0023] In the formula, represents the probability density function of the received signal power, erf(·) represents the error function, P th represents the receiving power sensing threshold pre-configured according to the port standard, P t represents the signal transmission power, express The path loss is represents the average distance between modules, P i r represents the signal receiving power, and σ represents the standard deviation of shadow fading.

[0024] Furthermore, in step S4, the global channel occupancy rate R occ :

[0025]

[0026] In the formula, ρ i represents the update frequency of the i-th port monitoring module, τ represents the time it takes to send a single message, n sc Indicates the number of sub-channels occupied by transmitting a data packet, T estrepresents the channel occupancy estimation period, N sc Indicates the number of sub-channels divided in the frequency domain, Indicates the perceived occupancy frequency.

[0027] Furthermore, in step S5, the information freshness optimization model constructed is:

[0028]

[0029] st i ≥0,i=1,2,...,N

[0030]

[0031] In the formula, Represents the global transmission success probability.

[0032] Furthermore, in step S6, when the update frequencies of the port monitoring modules are inconsistent, the Lagrangian function is introduced to solve the information freshness optimization model, and the optimal update frequency is obtained as follows:

[0033]

[0034] In the formula, μ j represents the important factor of the j-th port monitoring module allocation, λ j represents the important factor assigned to the i-th port monitoring module, and j represents the index of the port monitoring module different from i.

[0035] Furthermore, in step S6, when the update frequencies of the port monitoring modules are consistent, the optimal update frequency is:

[0036]

[0037] The beneficial effects of the present invention are:

[0038] (1) The message update frequency adaptive adjustment method provided by the present invention can determine the optimal transmission frequency according to the module and the communication environment, thereby reducing the system delay and improving the reliability in the port autonomous communication scenario.

[0039] (2) The method provided by the present invention can comprehensively consider factors such as the number of modules, module spacing, channel fading and transmission success rate to adaptively adjust the update frequency, so as to reduce the possibility of information collision, improve reliability and reduce the difference between module-perceived information, thereby improving effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of the method for adaptively adjusting message update frequency based on device environment perception provided by the present invention.

[0041] Figure 2 The present invention provides a port resource pool structure.

[0042] Figure 3 The AoI under the influence of the number of modules at different transmission success probabilities provided by the present invention.

[0043] Figure 4 The present invention provides the optimal update frequency under the influence of different numbers of modules and receiving power perception thresholds. DETAILED DESCRIPTION

[0044] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0045] Embodiment 1:

[0046] The embodiment of the present invention provides a method for adaptively adjusting message update frequency based on device environment perception, such as Figure 1 As shown, the following steps are included:

[0047] S1. Calculate the information freshness of port security monitoring based on the perception information of the port monitoring module in the port resource pool;

[0048] S2, calculating the average distance of modules under different numbers of port monitoring modules;

[0049] S3, calculating the perceived occupancy frequency of the port monitoring module according to the average distance of different modules;

[0050] S4. Calculate the global channel occupancy rate of all devices within the port security monitoring communication range according to the sensed occupancy frequency;

[0051] S5, constructing an information freshness optimization model based on the perceived occupancy frequency;

[0052] S6. Solve the information freshness optimization model to determine the optimal update frequency, and adaptively adjust the message update frequency based on it.

[0053] In an embodiment of the present invention, the end-to-end delay experienced by the port monitoring safety message is consistent with the definition of the age of information (Age of Infromation, AoI), that is, the time consumed from the generation of the message by the end module to the successful reception by the destination module. AoI is a newly proposed metric for representing the freshness of information in the present invention. It not only reflects the transmission delay of the data packet, but also includes the time consumed by the retransmission of the data packet due to the failure to obtain resources in time to send it out and the failure of the target module to correctly receive the message. Therefore, in an embodiment of the present invention, AoI is defined as the difference between the total information obtained by the port monitoring module and the information sent by other surrounding modules per unit time. In an embodiment of the present invention, it is considered to convert the optimization of the performance of the port monitoring system into a system AoI optimization problem. It is important to keep the AoI low because network performance is highly dependent on the timely update and reliable transmission of neighborhood information.

[0054] In the embodiment of the present invention, in the port resource pool, during the port monitoring module perception update process, it is assumed that the update frequency of the i-th module in the system is defined as ρ i For the monitoring modules in the system, the update task of the i-th module is L i (t), and L i The arrival rate of (t) is λ i , where λ i (i=1,2,…) are independent of each other, t i,j It represents the time of the jth perception information update of the i-th module, and the sending interval between the j-1th and jth updates is τ i,j .

[0055] The i-th module updates the task L i (t) The actual perceived total information of the process is:

[0056]

[0057] The average difference between the total information obtained by the actual perception module and the information sent by other surrounding modules is used as a measure of the timeliness of the system. The specific formula is as follows:

[0058]

[0059] If there are m updates in the interval [0,T], then:

[0060]

[0061] In the formula, A j is the interval [t i,j-1 ,t i,j )’s average difference.

[0062] The long-term average difference of the i-th module is:

[0063]

[0064] The perception monitoring module updates the perception information for any jth time, that is, τ i =τ i,j = d, then in [t i,j-1 ,t i,j-1 +d), the update task volume is The expected difference is:

[0065]

[0066] Then we can get:

[0067]

[0068] In step S1 of the embodiment of the present invention, according to the existence of periodic services and burst services in port security monitoring, the information freshness AoI is obtained and expressed as:

[0069]

[0070] In the formula, Δ i Represents the long-term average difference, for cyclical business For sudden business μ i represents the important factor of the allocation of the i-th port monitoring module, λ i represents the average update rate of the update task volume of the i-th port monitoring module, ρ i represents the update frequency of the i-th port monitoring module, and N represents the total number of port monitoring modules.

[0071] Specifically, in this embodiment, for periodic services, the update interval is fixed and is the best choice. If module i has m in the interval [0, T] i updates, the optimal update time interval should be τ i.j =T / (m i +1). For all updates j, let T→∞. Then this update scheme makes module i update at rate ρ i Uniform update, where Due to d i =1 / ρ i ,get:

[0072]

[0073] Combining the above formula, the long-term average difference of cyclical business is:

[0074]

[0075] The average update arrival rate of the i-th module is λ i , and consider assigning an important factor μ to each module i , then the long-term average difference of the entire system is defined as the information freshness AoI of the system:

[0076]

[0077] In this embodiment, for periodic services, the task volume L is updated. i (t) obeys λ i Poisson distribution, then the update time of module i is related to the rate ρ i The exponential relationship is that the update processes of different modules are independent of each other. Using the exponential distribution, we can get E[A]:

[0078]

[0079] The long-run mean difference with Poisson arrivals is then:

[0080]

[0081] Similarly, the average update arrival rate of the i-th module is λ i , and consider assigning an important factor μ to each module i , then the long-term average difference of the entire system is defined as the information freshness AoI of the system:

[0082]

[0083] In step S2 of the embodiment of the present invention, all port monitoring safety information is broadcast using a direct link in the 5.9GHz frequency band to avoid message conflicts and improve communication efficiency. Consider that there are N monitoring modules randomly distributed in the bidirectional l channel in the communication range D, and the set of module numbers is N={0,1,2,…,N-1}, i∈N, i is used to indicate the module. i=0 represents the current module, which estimates the channel occupancy ratio (COR) from a self-perspective and adjusts the message transmission frequency based on the port monitoring module environment. The module spacing s is defined as the distance between two modules. When the modules are in different channels, consider ignoring the lateral distance, and calculate the module spacing after the modules overlap in the same channel. According to the empirical model verification of the data measured in the port, the module spacing s under different module densities ρ obeys an exponential distribution, and the distribution of the module spacing s can be expressed as:

[0084] f S (s) = ρe -ρs

[0085] Where ρ = N / 2Dl (modules / meter), l is the number of channels. The average module spacing is calculated as follows:

[0086]

[0087] It can be obtained that for all port monitoring modules, the average module distance between its receiving module i=0 and the sending module i within its communication range is expressed as:

[0088]

[0089] In the formula, represents the average module spacing, l represents the number of channels, i=0 represents the current port monitoring module, which serves as a receiving module, and N represents the number of port monitoring modules.

[0090] In step S3 of the embodiment of the present invention, since the port monitoring safety information broadcast by the transmitting device experiences different distances and channel conditions during the propagation process, the port monitoring safety information received signal power perceived by the receiving devices at different locations is different. Therefore, in this embodiment, the perceived occupancy probability is defined as As a channel load contribution factor, it is used to reflect the probability that port monitoring safety information occupies sub-channel resources in a single frame.

[0091] In order to more closely reflect the large-scale fading characteristics of actual wireless channel transmission in road scenes, it is considered that channel fading is mainly the result of the superposition of path loss and shadow fading. i ) is a function of the distance between the transceiver and the receiver, and is modeled based on the Winner+B1 path loss model. Shadow fading ψ s h follows a normal distribution with a mean of 0 and a variance of σ. Therefore, the probability density function of the received signal power is:

[0092]

[0093] Among them, P t is the signal transmission power. It represents the probability that the device detects that the channel is used for port monitoring safety information transmission, expressed as the probability that the received signal power perceived by the receiving device at a given distance for the message broadcast by the i-th sending device is greater than the threshold. It can be calculated as follows:

[0094]

[0095] In the formula, represents the probability density function of the received signal power, erf(·) represents the error function, P th represents the receiving power sensing threshold pre-configured according to the port standard, Pt represents the signal transmission power, express The path loss is represents the average distance between modules, P i r represents the signal receiving power, and σ represents the standard deviation of shadow fading. Depends on the average device distance, for the device itself,

[0096] In order to ensure the timeliness and reliability of port monitoring information, the device should adaptively adjust the transmission frequency according to the busyness of the channel. We define the channel occupancy rate (COR) as an indicator for evaluating channel load, which reflects the occupancy of the channel by the device transmitting port monitoring information. COR is modeled based on the actual transmission frequency of port monitoring information and the structure of the port resource pool. Figure 2 The port resource pool structure is shown, where each white grid represents a subchannel resource (SFR) in a single frame. If the SFRs are used or reserved by a device, they are marked with other colors. Time slots and subchannels are the smallest allocation units of time domain and frequency domain resources, respectively. Port monitoring information is carried by subchannel resources in a single frame, and can occupy one or more continuous subchannels for transmission according to the packet size. sc is the number of sub-channels divided in the frequency domain, n sc It is the number of sub-channels occupied by transmitting a data packet.

[0097] Furthermore, based on the resource window characteristics of the port and the basic safety message content of the broadcast, we propose a method to calculate the channel occupancy ratio COR estimation. From the perspective of the receiving device (i=0), the channel occupancy ratio COR of the i-th device can be expressed as:

[0098]

[0099] In the formula, f i t is the transmission frequency of the ith device, τ is the time it takes to send a single message (e.g. 1ms), T est Yes Figure 2 Specifically, the numerator of the above formula represents the COR estimation period with f i t After the port monitoring information is transmitted for a certain distance, the receiving device can perceive the number of SFRs occupied by the sending device i. The denominator means the total number of SFRs in the entire estimation period in the past.

[0100] Calculate the channel occupancy of a single device Finally, we sum the CORs of the N devices within the communication range, including the channel occupancy of the current device (i=0) and its neighboring devices Therefore, the global channel occupancy R at each estimation cycle is obtained occ , the calculation formula is as follows:

[0101]

[0102] In order to obtain the optimal update frequency in the subsequent solution process and make the procedure formula concise, let:

[0103]

[0104] After determining the number of divided sub-channels and a reasonable estimation period, that is, N sc and T est When the value is fixed, is a fixed value, then in step S4, the global channel occupancy R occ It is expressed as:

[0105]

[0106] In the formula, ρ i represents the update frequency of the i-th port monitoring module, τ represents the time it takes to send a single message, n sc Indicates the number of sub-channels occupied by transmitting a data packet, T est represents the channel occupancy estimation period, N sc Indicates the number of sub-channels divided in the frequency domain, Indicates the perceived occupancy frequency.

[0107] In step S5 of the embodiment of the present invention, the focus of the present invention is to improve the timeliness of port monitoring information. Low latency requires frequent transmission of port monitoring information, which may cause collision of port monitoring information and thus lead to low communication reliability, and vice versa. Therefore, it is necessary to comprehensively weigh the relationship between the reliability and latency of port monitoring information. The present invention uses information freshness AoI as a performance evaluation indicator. AoI can be used in port monitoring scenarios that require timely status updates. The long-term average difference of the entire system is defined as the information freshness AoI of the system:

[0108]

[0109] The AoI in the formula not only takes into account the delay affected by the message update frequency, but also takes into account the delay caused by the retransmission of the message in order to be successfully transmitted. Minimizing AoI can minimize the transmission delay while avoiding too small transmission intervals, which will increase the probability of collision and affect the delay. Therefore, the AoI minimization problem is defined to comprehensively weigh the delay and reliability to determine the optimal transmission frequency, and the information freshness optimization model is constructed as follows:

[0110]

[0111] st i ≥0,i=1,2,…,N

[0112]

[0113] In the formula, Represents the global transmission success probability.

[0114] Among them, p suc is the current global channel occupancy R occ The transmission success probability under represents the reliability of the perception update message transmission. At the same time, considering the security of the business, it is necessary to set the global transmission success probability is a constant less than 1, such as 0.9. The proportion of channel resources that can be used by security service perception update messages is limited, and the remaining channel resources are reserved for non-security services. i ≥0,i=1,2,...,N ensures that the optimal update frequency of the solution meets the port monitoring standard. Considering the port monitoring scenario, frequent transmission is required to ensure low transmission latency, which may cause collision of perception update messages and lead to low reliability. Therefore, the constraint The reliability of the update process is guaranteed, and the probability of successful transmission meets the requirements.

[0115] The probability of successful transmission is p suc is a function of CBR and can be calculated as follows

[0116]

[0117] Combining formulas The obtained CBR estimate can be used to evaluate the transmission success rate of the perception update message in the current channel environment. It is the transmission success rate when the modules select the same channel resources for transmission, which may lead to data conflicts. sucIt is related to the remaining available resource set obtained after resource sensing. The more SFRs there are in the remaining available resource set, the lower the probability that the module and the neighboring module reserve the same resources. The corresponding p suc On the other hand, due to R occ It is related to the number of modules, so p suc It is also affected by the number of modules within communication range.

[0118] Since the global channel occupancy R occ Further expressed as According to the constraint condition ρ i ≥0,i=1,2,...,N can be obtained Therefore, the above information freshness optimization model is optimized to obtain:

[0119]

[0120] st i ≥0,i=1,2,…,N

[0121]

[0122] Based on the above model, in step S6 of this embodiment, when solving the information freshness optimization model, when the update frequencies of the port monitoring modules are inconsistent, the information freshness optimization model is solved by introducing the Lagrangian function; wherein the Lagrangian function is:

[0123]

[0124] Among them, α≥0, β≥0. Since the optimization objective is a convex function, using the KKT condition, we get:

[0125]

[0126] For all modules i, the complementary slack condition is:

[0127]

[0128] In the above formula, when When , the optimization objective can be minimized, so α≠0, when ρ i = 0, the optimization target μ i λ i / (2ρ i )→∞, so β=0, and the above formula is converted into μ i λ i / (2ρ i 2 )=α, then:

[0129]

[0130] because Can get The optimal update frequency is:

[0131]

[0132] In the formula, μ j represents the important factor of the j-th port monitoring module allocation, λ j Represents μ i represents the important factor assigned to the i-th port monitoring module, and j represents the index of the port monitoring module different from i.

[0133] In step S6 of the embodiment of the present invention, when the update frequencies of the port monitoring modules are consistent, the optimization target is Its first-order derivative is:

[0134]

[0135] And the second-order derivative of AoI is always greater than 0, so we only need to solve the maximum value of ρ to get the minimum AoI.

[0136] By constraints get Therefore, the optimal update frequency is:

[0137]

[0138] It can be seen from the above formula that the optimal update frequency is related to the number of modules within the communication range and the target transmission success rate.

[0139] Embodiment 2:

[0140] In this embodiment, in order to verify that the method proposed in Example 1 can minimize the freshness of information and improve the delay and reliability of port monitoring information, the parameter configuration in Table 1 is taken as an example to illustrate the effect of the embodiment of the present invention.

[0141] Table 1 Simulation parameters

[0142]

[0143] Under the simulation parameter settings shown in Table 1, the proposed system model and the mutual influence between variables are verified by simulation, and then the simulation results of the information age of the proposed transmission frequency adaptive adjustment mechanism are shown. In addition, the rate control mechanism specified in the existing standard is compared to verify the performance superiority of the method of the present invention, as shown in Table 1. Figure 3 and Figure 4 shown.

[0144] Depend on Figure 3It can be seen that when the demand transmission success rate increases, in order to meet this requirement, the module update frequency in the system decreases, and the information freshness AoI gradually increases. In addition, when the demand transmission success rate is greater (closer to 1), the optimal update frequency in the system decreases faster, which leads to a larger AoI, and the growth rate of AoI is more obvious, indicating that too low an update frequency will lead to the inability to timely interact with perception information between modules, which will endanger the driving safety of the modules.

[0145] Depend on Figure 4 It can be seen that when the number of modules changes from 10 to 100, the modules are configured with different receiving power perception threshold parameters P th The best update frequency and information freshness of the system are obtained under the higher P th This will result in a lower estimation result of the channel occupancy rate, which in turn increases the transmission success rate, and ultimately affects the optimal solution with a higher update frequency and lower statistical system information freshness. th =-90dBm and P th = -100dBm, the optimal update frequency and AoI curve are close to each other, P th The optimal update frequency has little impact on the result, and can be personalized according to the service quality requirements of the business. th The value can be increased with the improvement of service quality requirements. However, the increase in service quality requirements causes P th The increase will make AoI decrease as the number of modules increases, which means that the scheme of this study can comprehensively consider factors such as the number of modules, module spacing, channel fading and transmission success rate to adaptively adjust the update frequency, so as to reduce the possibility of information collision, improve reliability and reduce the difference between module perception information, and improve effectiveness.

[0146] Based on the above analysis, the message update frequency adaptive adjustment method based on module environment perception described in the present invention can determine the optimal transmission frequency according to the module and the communication environment, reduce the system delay and improve the reliability in the port autonomous communication scenario.

[0147] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0148] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for adaptively adjusting message update frequency based on device environment perception, characterized in that: The following steps are involved: S1. Calculate the information freshness of port security monitoring based on the perception information of the port monitoring module in the port resource pool; S2, calculating the average distance of modules under different numbers of port monitoring modules; S3, calculating the perceived occupancy frequency of the port monitoring module according to the average distance of different modules; S4. Calculate the global channel occupancy rate of all devices within the port security monitoring communication range according to the sensed occupancy frequency; S5, constructing an information freshness optimization model based on the perceived occupancy frequency; S6. Solve the information freshness optimization model to determine the optimal update frequency, and adaptively adjust the message update frequency based on it.

2. The method for adaptively adjusting message update frequency based on device environment perception according to claim 1, characterized in that: In step S1, the information freshness AoI is expressed as: In the formula, Δ i Represents the long-term average difference, for cyclical business For sudden business μ i represents the important factor of the allocation of the i-th port monitoring module, λ i represents the average update rate of the update task volume of the i-th port monitoring module, ρ i represents the update frequency of the i-th port monitoring module, and N represents the total number of port monitoring modules.

3. The method for adaptively adjusting message update frequency based on device environment perception according to claim 1, characterized in that: In step S2, for all port monitoring modules, the average module distance between its receiving module i=0 and the sending module i within its communication range is expressed as: In the formula, represents the average module spacing, l represents the number of channels, i=0 represents the current port monitoring module, which serves as a receiving module, and N represents the number of port monitoring modules.

4. The method for adaptively adjusting message update frequency based on device environment perception according to claim 1, characterized in that: In step S3, the occupied frequency is sensed for: In the formula, represents the probability density function of the received signal power, erf(·) represents the error function, P th represents the receiving power sensing threshold pre-configured according to the port standard, P t represents the signal transmission power, express The path loss is represents the average distance between modules, P i r represents the signal receiving power, and σ represents the standard deviation of shadow fading.

5. The method for adaptively adjusting message update frequency based on device environment perception according to claim 2, characterized in that: In step S4, the global channel occupancy rate R occ : In the formula, ρ i represents the update frequency of the i-th port monitoring module, τ represents the time it takes to send a single message, n sc Indicates the number of sub-channels occupied by transmitting a data packet, T est represents the channel occupancy estimation period, N sc Indicates the number of sub-channels divided in the frequency domain, Indicates the perceived occupancy frequency.

6. The method for adaptively adjusting message update frequency based on device environment perception according to claim 5, characterized in that: In step S5, the information freshness optimization model constructed is: s.t.ρ i ≥0,i=1,2,...,N In the formula, Represents the global transmission success probability.

7. The method for adaptively adjusting message update frequency based on device environment perception according to claim 6, characterized in that: In step S6, when the update frequencies of the port monitoring modules are inconsistent, the Lagrangian function is introduced to solve the information freshness optimization model, and the optimal update frequency is obtained as follows: In the formula, μ j represents the important factor of the j-th port monitoring module allocation, λ j represents the average update rate of the update task volume of the j-th port monitoring module, and j represents the port monitoring module index different from i.

8. The method for adaptively adjusting message update frequency based on device environment perception according to claim 6, characterized in that: In step S6, when the update frequencies of the port monitoring modules are consistent, the optimal update frequency is:

Images