Attribute synchronization method based on environment perception

Abstract

The invention belongs to the field of attribute synchronization methods of novel identification networks, and discloses an attribute synchronization method based on environmental perception, which comprises the following steps: collecting data by adopting a multi-dimensional identification synchronization layered architecture, outputting a synchronization rule according to environmental perception, and issuing the synchronization rule to terminal equipment; the edge server formulates a synchronization rule based on environmental perception, and issues the generated synchronization rule to the terminal device; after the terminal device receives the synchronization rule, fuzzifying the data of the terminal device, and sensing a real-time environment state to carry out rule matching; and the edge server continuously monitors the synchronization execution condition of the terminal equipment and feeds back data to the central server, and the central server optimizes a synchronization strategy. According to the method, the network load can be optimized, the synchronization overhead can be reduced, and the adaptability and the overall system performance of the multi-dimensional identification network in a complex network environment can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of attribute synchronization methods for a new type of identity network, and specifically relates to an attribute synchronization method based on environmental perception. Background Art

[0002] The current TCP / IP-based identity system has significant limitations and is difficult to effectively support the diverse requirements of future networks. Specifically, the TCP / IP-based identity system uses a single, static mapping method, taking the IP address as the only communication identifier, and showing inherent defects in aspects such as mobility support and heterogeneous device access. In addition, with the increase in the complexity of network management and the need for cross-layer resource integration, the IP address only carries location information and lacks the ability to express semantic information such as device characteristics, service types, and computing resources, and it is no longer able to meet the requirements of the new network environment.

[0003] Therefore, the multi-dimensional identity network introduces object identifiers, connection identifiers, application identifiers, and the corresponding attribute information of the identifiers, supports flexible addressing driven by attributes, and can assist in routing decisions through the attribute information carried by the identifiers. The attribute information of the multi-dimensional identifiers can be used to describe the key states of devices such as cloud servers, routing and forwarding devices, and terminals, such as battery power, geographical location, CPU usage rate, memory occupancy, current wireless network signal strength, available network bandwidth, etc., which is crucial for device management, status monitoring, resource optimization, and system stability.

[0004] In the multi-dimensional identity network, these identifiers and attribute data need to be collected in real time and stored in distributed servers and databases. However, due to the dynamic changes in the topology of the new heterogeneous network, limited computing and communication resources, and the uncertainty of node and link states, maintaining the consistency and efficient synchronization of identifier and attribute data in a distributed environment faces severe challenges. If only relying on periodic updates at a fixed frequency or simple event-triggered updates, problems such as overly frequent synchronization, synchronization lag, data delay, packet loss, or connection interruption may occur, increasing the system burden and network overhead, resulting in decisions based on expired information, and reducing the quality of network services. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides an attribute synchronization method based on environmental perception. This attribute synchronization method can not only optimize network load, reduce synchronization overhead, but also improve the adaptability and overall system performance of the multi-dimensional identity network in a complex network environment.

[0006] To achieve the above object, the present invention is realized through the following technical solutions:

[0007] The present invention is an attribute synchronization method based on environmental perception. The attribute synchronization method specifically includes the following steps:

[0008] Step 1: Collect data using a multi-dimensional identification synchronization hierarchical architecture and output synchronization rules according to environmental perception, and send them to the terminal device; the multi-dimensional identification synchronization hierarchical architecture includes a central server (including a database), an edge server (including a database), a forwarding node, and a terminal device. The edge server periodically collects data of the terminal device to judge the environmental state and determine the data synchronization rules;

[0009] Step 2: The edge server formulates synchronization rules based on environmental perception, namely device status information, network status information, service priority, and fuzzy logic, and sends the generated synchronization rules to the terminal device;

[0010] Step 3: After receiving the synchronization rules, the terminal device performs fuzzy processing on the data of the terminal device, and performs rule matching according to the real-time status, namely device status, network status, and service priority, to match the optimal synchronization rule;

[0011] Step 4: The edge server continuously monitors the synchronization execution situation of the terminal device and feeds back the data to the central server, and the central server optimizes the synchronization strategy.

[0012] A further improvement of the present invention lies in that: the edge server in the multi-dimensional identification synchronization hierarchical architecture is connected to the terminal device through a forwarding node, responsible for data preprocessing, rule distribution, and local decision-making. The edge server collects data of the terminal device, and the central server is responsible for global management, data storage, and advanced optimization. The terminal device includes sensors, intelligent terminals, and drones. The edge server periodically collects data of the terminal device, including: device status information, namely battery power B and CPU load C, network status information, namely signal strength S, link stability L, available bandwidth W, service attribute, namely service priority P. The terminal devices are distributed in various regions and interact with the edge server for data. The edge server and the central server work together to achieve hierarchical data synchronization management, ensuring efficient, low-latency, and energy-saving operation of the system.

[0013] A further improvement of the present invention lies in that: Step 2 specifically includes the following steps:

[0014] Step 2.1: Assume that the data synchronization interval is T Sync , which is expressed by the formula:

[0015]

[0016] where B represents the battery power, C is the CPU load, S is the signal strength, L is the link stability, W is the available bandwidth, P is the task priority, T0 is the default synchronization interval, B max, C max , S max , L max , W max , P min For normalization, the exponential parameters α, β, γ, δ, ∈, ζ adjust the weights of different factors. α > 1 means: emphasizing reducing the synchronization frequency at low battery levels; β > 0 means: reducing the synchronization frequency when the CPU load is high; γ > 1 means: the weaker the signal, the more obvious the elongation of the synchronization interval; δ < 0 means: when the link is unstable, reducing the synchronization interval increases the synchronization frequency; ∈ < 0 means: reducing the synchronization interval when the available bandwidth is large; ζ < 0 means: when the task priority is urgent, reducing the synchronization interval increases the synchronization frequency;

[0017] Step 2.2: The edge server regularly collects device status information (battery level, CPU load), network status information (signal strength, link stability, available bandwidth), and service attributes (task priority), and combines historical data with real-time environment parameters to calculate and form a synchronization interval set:

[0018] T set ={T i |T min <T i <T max}

[0019] where T set is the synchronization interval set, T min is the minimum value, T max is the maximum value, T i is the value of T Sync calculated at the current time (when t = i);

[0020] Step 2.3: The edge server defines a synchronization rule based on fuzzy logic and sends the synchronization rule to the terminal device to adapt to the synchronization requirements of different environments.

[0021] A further improvement of the present invention is that in Step 2.3, the synchronization rule based on fuzzy logic includes:

[0022] Rule 1: When the task priority is normal and the device status and network environment are good, that is, B > 50% and C < 50% and S > -70dBm and L < 1% and W > 50% and P = 1, the periodic update method is adopted. P is set artificially and is divided into ordinary 1, critical 2, and urgent 3 to dynamically calculate the update frequency:

[0023]

[0024] where the set values of the exponential parameters are sent by the edge server together with the synchronization rule.

[0025] Rule 2: When the task priority is normal and the device status or network status is poor, i.e., B < 50% or C > 50% or S < -70dBm or L > 1% or W < 50% and P = 1, use low-frequency periodic reporting to reduce communication overhead:

[0026] T Sync = T max

[0027] Rule 3: When the task priority is urgent and the network status is normal, i.e., S > -80dBm and L < 5% and W > 30% and P = 3, use high-frequency periodic reporting to ensure data timeliness:

[0028] T Sync = T min

[0029] Rule 4: When the task priority is urgent but the network status is poor, i.e., S < -80dBm or L > 5% or W < 30% and P = 3, use threshold-triggered reporting to avoid unnecessary frequent synchronization:

[0030] T Sync = T event

[0031] Among them, T event is the synchronization interval at the time of event triggering;

[0032] Rule 5: When the task priority is urgent, but both the device status and network status are poor, i.e., B < 20% or C > 70% or S < -80dBm or L > 5% or W < 30% and P = 3, use an update method combining periodicity and event triggering to balance data accuracy and energy consumption:

[0033]

[0034] Rule 6: When the task priority is critical and the device status and network status are normal, i.e., B > 20% and C < 70% and S > -80dBm and L < 5% and W > 30% and P = 2, use the periodic update method:

[0035]

[0036] Rule 7: When the task priority is critical, but the device status or network status is poor, i.e., B < 20% or C > 70% or S < -80dBm or L > 5% or W < 30% and P = 2, use low-frequency periodic reporting to reduce communication overhead:

[0037] T Sync = T max

[0038] Rule 8: In other cases, the default synchronization interval is adopted, i.e.:

[0039] T Sync = T0

[0040] The edge server dynamically adjusts Rules 1 - 8 according to environmental changes and sends the rules to the terminal device, enabling the terminal device to dynamically adjust the synchronization method according to the environment.

[0041] A further improvement of the present invention lies in that: Step 3 specifically includes the following steps:

[0042] Step 3.1: Filter the invalid data of the terminal device, perform fuzzy processing on the extracted key features, convert the data into fuzzy values, and define fuzzy sets:

[0043] Battery level B: {high, medium, low};

[0044] CPU load C: {high, medium, low};

[0045] Signal strength S: {strong, medium, weak};

[0046] Link stability L: {stable, fluctuating, unstable};

[0047] Available bandwidth W: {high, medium, low};

[0048] Task priority P: {normal, critical, urgent};

[0049] Step 3.2: Calculate the fuzzy values through the membership function, compare the magnitudes of the fuzzy values, and output the results in the fuzzy set. For the battery level of the device, there is:

[0050]

[0051] where B low , B mid , B high respectively represent the central points of low, medium, and high battery levels, and σ B controls the smoothness of the curve;

[0052] For the CPU load, there is:

[0053]

[0054] where C low , C mid , C high respectively represent the central points of low, medium, and high loads, and σ C controls the smoothness of the curve;

[0055] For the signal strength, there is:

[0056]

[0057] Among them, S week , S mid , S strong respectively represent the center points of weak, medium, and strong signals, and σ S controls the smoothness of the curve;

[0058] Regarding the link stability:

[0059]

[0060] Among them, L unstable , L mid , L stable respectively represent the center points of unstable, fluctuating, and stable links, and σ L controls the smoothness of the curve;

[0061] Regarding the available bandwidth:

[0062]

[0063] Among them, W low , W mid , W high respectively represent the center points of low, medium, and high available bandwidths, and σ W controls the smoothness of the curve;

[0064] Regarding the task priority:

[0065]

[0066] Step 3.3: Based on the synchronization rules 1 - 8 sent by the edge server in step 2.3 and the fuzzy rule base calculated in step 3.2, match the optimal attribute synchronization strategy to generate the final synchronization update mechanism:

[0067] If the current device power B is 55% and the available bandwidth W is 20%, and the task priority is normal, it is determined through the fuzzy rule base that B = medium and W = high and P = 1, then rule 1 is used to dynamically calculate the update frequency; if the current device CPU load is 65% and the link stability is 0.8%, but the task priority is urgent, it is determined through the fuzzy rule base that C = high and L = stable and P = 3, and rule 3 is selected to adopt the update interval T Sync = T min .

[0068] A further improvement of the present invention is that: the specific steps of step 4 are as follows:

[0069] Step 4.1: The edge server continuously monitors the synchronization execution status of the terminal, records the synchronization effect, analyzes the execution efficiency of each rule using the data synchronization success rate, the bandwidth and power consumed during the synchronization process, and counts the number of executions of each synchronization rule.

[0070] Step 4.2: When the synchronization success rate becomes low (the packet loss rate of the synchronization data packet exceeds 5% within 10 minutes) or the power consumption of the device during synchronization is too large (the power consumption exceeds 5% within 10 minutes), this synchronization rule is determined to be an inefficient synchronization rule; when the triggering probability of this synchronization rule is less than 5% within the past 10 hours, it is determined to be an outdated rule. For inefficient or outdated rules, the edge server performs optimization and adjustment, adjusts the parameters of the synchronization interval calculation formula, and deletes the outdated rules to improve the adaptability of the synchronization strategy.

[0071] Step 4.3: Enhance the adaptive ability, use machine learning to analyze long-term data trends, such as the change trends of device power and CPU load, and the fluctuations of network status over a period of time, and optimize future synchronization strategies. This feedback optimization mechanism enables the attribute synchronization algorithm to have the ability of continuous evolution, and continuously improves its accuracy, stability, and energy efficiency in different application scenarios.

[0072] The beneficial effects of the present invention are as follows:

[0073] (1) The present invention is applicable to the hierarchical architecture design of the central server - edge server - terminal device. The edge server is responsible for local computing, rule distribution, and regularly collects key information such as device status, network status, and service status, providing an accurate decision-making basis for the data synchronization strategy. The central server is used to optimize the data synchronization strategy and improve the adaptability and stability of the overall system.

[0074] (2) The environment-aware attribute synchronization method of the present invention combines environment-aware technology to real-time sense basic device status, network status, service status, etc., and adaptively calculates the optimal synchronization interval, reducing unnecessary data transmission while ensuring data consistency. Especially in the case of a poor network environment, the algorithm can dynamically adjust the synchronization frequency, avoid bandwidth waste, improve data transmission efficiency, and ensure the stable operation of the system.

[0075] (3) The present invention integrates the fuzzy control theory and the adaptive adjustment strategy, dynamically optimizes the data synchronization rules for different environments, has wide applicability, and through the feedback optimization mechanism, the system can continuously learn and adjust synchronization parameters, further improving the synchronization efficiency and environmental adaptability. It is applicable to scenarios such as smart cities, industrial Internet of Things, vehicle Internet of Things, and edge computing, and can stably operate in complex network environments to meet the data synchronization requirements of large-scale distributed devices. Description of the Drawings

[0076] Figure 1It is a schematic diagram of the multi-dimensional identification synchronization hierarchical architecture of the present invention.

[0077] Figure 2 It is a flowchart of the attribute synchronization method of the present invention.

[0078] Figure 3 It is a flowchart of the edge server environment perception of the present invention.

[0079] Figure 4 It is a flowchart of the terminal device synchronization of the present invention. Detailed implementation manners

[0080] The following will disclose the implementation manners of the present invention in diagrams. For the sake of clarity, many practical details will be described together in the following narrative. However, it should be understood that these practical details are not used to limit the present invention. That is to say, in some implementation manners of the present invention, these practical details are not necessary.

[0081] As Figure 1 shown in the multi-dimensional identification synchronization hierarchical architecture diagram, it includes a central server (including a database), an edge server (including a database), a forwarding node, and a terminal device. This hierarchical architecture ensures that the multi-dimensional identification network can synchronize and store information such as identification, device status, network status, and service attributes through environment perception technology, and supports the addressing and routing of multi-dimensional identifications.

[0082] Among them, the terminal devices include sensors, smart terminals, drones, etc. distributed in various regions, and report identification and attribute data through synchronization policies, including service identification, device identification, connection identification, power, geographical location, CPU usage rate, memory occupancy, current wireless network signal strength, available network bandwidth, and other information. The edge server connects to the terminal devices through the forwarding node, collects the data of the terminal devices, and perceives the surrounding environmental status to determine the data synchronization policy. The information that can be used to judge the environmental status includes: device status information such as power, CPU load, network status information such as signal strength, link stability, available bandwidth, and service attribute, that is, service priority, etc. The central server is responsible for global management, data storage, and advanced optimization. The edge server and the central server work together to achieve hierarchical data synchronization management, ensuring the efficient, low-latency, and energy-saving operation of the system.

[0083] As Figure 2 shown, the present invention provides an attribute synchronization method based on environment perception, which specifically includes the following steps:

[0084] Step 1: Collect data using a multi-dimensional identification synchronous hierarchical architecture and output synchronous rules based on environmental perception, and send them to terminal devices; the multi-dimensional identification synchronous hierarchical architecture includes a central server (including a database), an edge server (including a database), a forwarding node, and a terminal device. The edge server periodically collects data of the terminal device to judge the environmental state and determine the data synchronization rules; this multi-dimensional identification synchronous hierarchical architecture ensures that the multi-dimensional identification network can synchronize and store information such as identification, device status, network status, and service attributes through environmental perception technology, supporting the addressing and routing of multi-dimensional identification.

[0085] In the multi-dimensional identification synchronous hierarchical architecture, the edge server is connected to the terminal device through a forwarding node, responsible for data preprocessing, rule distribution, and local decision-making. The edge server collects data of the terminal device, and the central server is responsible for global management, data storage, and advanced optimization. The terminal device includes sensors, intelligent terminals, and drones. The edge server periodically collects data of the terminal device, including: device status information, i.e., battery level B and CPU load C; network status information, i.e., signal strength S, link stability L, available bandwidth W; service attribute, i.e., service priority P. The terminal devices are distributed in various regions, interact with the edge server, and report identification and attribute data through a synchronization strategy, including service identification, device identification, connection identification, battery level, geographical location, CPU usage rate, memory occupancy, current wireless network signal strength, available network bandwidth, etc. The edge server and the central server work together to achieve hierarchical data synchronization management, ensuring the efficient, low-latency, and energy-saving operation of the system.

[0086] Step 2: The edge server formulates synchronization rules based on environmental perception, i.e., device status information, network status information, service priority, and fuzzy logic, and sends the generated synchronization rules to the terminal devices. As Figure 3 shown, it specifically includes the following steps:

[0087] Step 2.1: Assume that the data synchronization interval is T Sync , which is expressed by the formula:

[0088]

[0089] Among them, B represents the battery level, in percentage of unit battery level (%), and the synchronization frequency is reduced when the battery level is low. C is the CPU load, in percentage of unit load (%), and the synchronization frequency is reduced when the load is high. S is the signal strength, in signal strength (unit: dBm), and the transmission of non-critical data is reduced when the signal is weak. L is the link stability, measured by the packet loss rate (%), and the synchronization frequency of non-critical data is reduced when the link is unstable, while the synchronization frequency of critical data is increased. W is the available bandwidth capacity, in percentage of unit capacity (%), and the synchronization frequency is reduced when the available capacity is low. P is the task priority, usually a discrete level, such as 1-3, and the larger the value, the higher the priority, and high-priority tasks are synchronized first. T0 is the default synchronization interval, in s (seconds); B max 、C max 、S max 、L max 、W max 、P min are used for normalization. The exponential parameters α, β, γ, δ, ∈, ζ adjust the weights of different factors. α>1 means: emphasizing reducing the synchronization frequency when the battery level is low; β>0 means: reducing the synchronization frequency when the CPU load is high; γ>1 means: the weaker the signal, the more obvious the elongation of the synchronization interval; δ<0 means: when the link is unstable, reducing the synchronization interval means increasing the synchronization frequency; ∈<0 means: reducing the synchronization interval when the available bandwidth is large; ζ<0 means: when the task priority is high, reducing the synchronization interval means increasing the synchronization frequency;

[0090] Step 2.2: The edge server regularly collects device information, resource status, and network link status, and combines historical data with real-time environmental parameters to calculate and form a synchronization interval set:

[0091] T set ={T i |T min <T i <T max}

[0092] Among them, T set is the synchronization interval set, T min is the minimum value, T max is the maximum value, and T i is the value of T calculated at the current time (when t = i); Sync

[0093] Step 2.3: The edge server defines synchronization rules based on fuzzy logic and distributes the synchronization rules to the terminal devices to meet the synchronization requirements in different environments. Specifically, the synchronization rules based on fuzzy logic include:

[0094] ​Rule 1: When the task priority is normal and the device status and network environment are good, that is, when B > 50%, C < 50%, S > -70dBm, L < 1%, W > 50%, and P = 1 (manually set, divided into normal 1, critical 2, and urgent 3), the periodic update method is adopted to dynamically calculate the update frequency:

[0095]

[0096] Among them, the set value of the exponential parameter is sent by the edge server together with the synchronization rule.

[0097] Rule 2: When the task priority is normal and the device status is poor or the network status is poor, that is, when B < 50% or C > 50% or S < -70dBm or L > 1% or W < 50% and P = 1, the low-frequency periodic reporting is adopted to reduce the communication overhead:

[0098] T Sync = T max

[0099] Rule 3: When the task priority is urgent and the network status is normal, that is, when S > -80dBm, L < 5%, W > 30%, and P = 3, the high-frequency periodic reporting is adopted to ensure data timeliness:

[0100] T Sync = T min

[0101] Rule 4: When the task priority is urgent but the network status is poor, that is, when S < -80dBm or L > 5% or W < 30% and P = 3, the threshold-triggered reporting is adopted to avoid unnecessary frequent synchronization:

[0102] T Sync = T event

[0103] Among them, T event is the synchronization interval at the time of event triggering;

[0104] Rule 5: When the task priority is urgent but both the device status and the network status are poor, that is, when B < 20% or C > 70% or S < -80dBm or L > 5% or W < 30% and P = 3, the update method combining periodicity and event triggering is adopted to balance data accuracy and energy consumption:

[0105]

[0106] Rule 6: When the task priority is critical and the device status and network status are normal, that is, when B > 20%, C < 70%, S > -80dBm, L < 5%, W > 30%, and P = 2, the periodic update method is adopted:

[0107]

[0108] Rule 7: When the task priority is critical, but the device status or network status is poor, that is, B < 20% or C > 70% or S < -80dBm or L > 5% or W < 30% and P = 2, use low-frequency periodic reporting to reduce communication overhead:

[0109] T Sync = T max

[0110] Rule 8: In other cases, use the default synchronization interval, that is:

[0111] T Sync = T0

[0112] The edge server dynamically adjusts Rules 1 - 8 according to environmental changes and sends the rules to the terminal device, enabling the terminal device to dynamically adjust the synchronization method according to the environment.

[0113] Step 3. After receiving the synchronization rules, the terminal device performs fuzzy processing on the data of the terminal device, and performs rule matching according to the real-time environmental status, namely device status, network status, and service priority, to match the optimal synchronization rule; judge the trigger mechanism according to the updated attributes, and dynamically adjust the synchronization frequency in combination with the task priority, network status, and battery level to avoid unnecessary frequent updates. High-priority data is reported immediately to ensure real-time synchronization of critical data. Low-priority data is uploaded in batches to reduce communication overhead and optimize bandwidth utilization. As Figure 4 shown, it specifically includes the following steps:

[0114] Step 3.1. Filter the invalid data of the terminal device, extract key features such as signal strength, service priority, battery level, etc., perform fuzzy processing, and convert the data into fuzzy values, such as "strong signal", "extremely unstable link", "low battery", etc.; define fuzzy sets:

[0115] Battery level B: {high, medium, low};

[0116] CPU load C: {high, medium, low};

[0117] Signal strength S: {strong, medium, weak};

[0118] Link stability L: {stable, fluctuating, unstable};

[0119] Available bandwidth W: {high, medium, low};

[0120] Task priority P: {normal, critical, urgent};

[0121] Step 3.2: Calculate the fuzzy values through the membership function, compare the magnitudes of the fuzzy values, and output the results in the fuzzy set. For the device's battery power, there are:

[0122]

[0123] Among them, B low , B mid , B high respectively represent the central points of low, medium, and high battery power, and can take 20, 50, 80. σ B controls the smoothness of the curve and is usually taken between 5 - 10 to adjust the change speed of the curve.

[0124] For the CPU load, there are:

[0125]

[0126] Among them, C low , C mid , C high respectively represent the central points of low, medium, and high loads, and can take 15, 50, 85. σ C controls the smoothness of the curve and is usually taken between 15 - 20 to adjust the change speed of the curve;

[0127] For the signal strength, there are:

[0128]

[0129] Among them, S week , S mid , S strong respectively represent the central points of weak, medium, and strong signals, and can take -85, -50, -20. σ S controls the smoothness of the curve and is usually taken between 10 - 15 to adjust the change speed of the curve;

[0130] For the link stability, there are:

[0131]

[0132] Among them, L unstable , L mid , L stable respectively represent the central points of unstable, fluctuating, and stable links, and can take 90, 96, 99. σ L controls the smoothness of the curve and is usually taken between 2 - 8 to adjust the change speed of the curve;

[0133] For the available bandwidth, there are:

[0134]

[0135] Among them, W low , Wmid , W high represent the central points of low, medium, and high available bandwidths respectively, and can take 15, 50, and 85. σ W controls the smoothness of the curve, and is usually taken between 15 - 20 to adjust the change speed of the curve;

[0136] Regarding the task priority:

[0137]

[0138] Step 3.3: Based on the synchronization rules 1 - 8 sent by the edge server in Step 2.3 and the fuzzy rule base calculated in Step 3.2, match the optimal attribute synchronization strategy to generate the final synchronization update mechanism:

[0139] If the current device battery level B is 55% and the available bandwidth W is 20%, and the task priority is normal, it is determined through the fuzzy rule base that: B = medium, W = high, and P = 1, then rule 1 is used to dynamically calculate the update frequency; if the current device CPU load is 65% and the link stability is 0.8%, but the task priority is urgent, it is determined through the fuzzy rule base that: C = high, L = stable, and P = 3, select the update interval T adopted by rule 3 Sync = T min .

[0140] This process ensures that the synchronization strategy can be dynamically adjusted according to the real - time environment, improving the accuracy and adaptability of synchronization.

[0141] Step 4: The edge server continuously monitors the synchronization execution status of the terminal device and feeds back the data to the central server, and the central server optimizes the synchronization strategy, which specifically includes the following steps:

[0142] Step 4.1: The edge server continuously monitors the synchronization execution status of the terminal, records the synchronization effect, analyzes the execution efficiency of each rule using the data synchronization success rate, the bandwidth and power consumption during the synchronization process, and counts the execution times of each synchronization rule;

[0143] Step 4.2: When the synchronization success rate becomes low, that is, the packet loss rate of the synchronization data packet exceeds 5% within 10 minutes, or the power consumption of the device during synchronization is too large, that is, the power consumption exceeds 5% within 10 minutes, determine that this synchronization rule is an inefficient synchronization rule; when the triggering probability of this synchronization rule is less than 5% in the past 10 hours, determine that it is an outdated rule. For inefficient or outdated rules, the edge server makes optimization adjustments, adjusts the parameters of the synchronization interval calculation formula, and deletes the outdated rules to improve the adaptability of the synchronization strategy;

[0144] Step 4.3: Enhance the adaptive ability. Analyze long-term data trends using machine learning, such as the change trends of device power and CPU load over a period of time, and the fluctuations of network status, to optimize future synchronization strategies. This feedback optimization mechanism enables the attribute synchronization algorithm to have the ability of continuous evolution, and continuously improve its accuracy, stability and energy efficiency in different application scenarios.

[0145] The above are only the embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, various changes and modifications can be made to the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. A method for attribute synchronization based on environment perception, characterized in that: The attribute synchronization method specifically comprises the following steps: Step S1: Use a multi-dimensional identification synchronization hierarchical architecture to collect data and output synchronization rules based on environmental perception, and send them to terminal devices; the multi-dimensional identification synchronization hierarchical architecture includes a central server, an edge server forwarding node and terminal devices, and the edge server periodically collects data from the terminal device to determine the environmental status and determine the data synchronization rules; Step S2: The edge server formulates synchronization rules based on environment perception, i.e., device status information, network status information, service priority and fuzzy logic, and sends the generated synchronization rules to the terminal device; Step S3: After receiving the synchronization rule, the terminal device performs fuzzy processing on the data of the terminal device, and senses the real-time environment status, i.e., the device status, network status, and service priority to perform rule matching and match the optimal synchronization rule; Step S4: The edge server continuously monitors the synchronization execution status of the terminal device and feeds the data back to the central server, which optimizes the synchronization strategy.

2. The method for synchronizing attributes based on environment perception according to claim 1, characterized in that: The edge server in the multi-dimensional identification synchronization layered architecture is connected to the terminal device through the forwarding node, and is responsible for data preprocessing, rule issuance and local decision-making. The edge server collects data from the terminal device, and the central server is responsible for global management, data storage and advanced optimization. The terminal devices include sensors, smart terminals, and drones. The edge server periodically collects data from the terminal devices, including: device status information, namely power B and CPU load C, network status information, namely signal strength S, link stability L, available bandwidth W, and service attributes, namely service priority P. The terminal devices are distributed in various areas and interact with the edge server for data. The edge server works in conjunction with the central server to achieve hierarchical data synchronization management.

3. The method for synchronizing attributes based on environment perception according to claim 1, characterized in that: The step S2 specifically includes the following steps: Step 2.1: Assume that the data synchronization interval is T Sync , the formula is: Among them, B represents the power, C is the CPU load, S is the signal strength, L is the link stability, W is the available bandwidth, P is the task priority, T0 is the default synchronization interval, B max , C max , S max , L max , W max , P min Used for normalization, the exponential parameters α, β, γ, δ, ∈, ζ adjust the weights of different factors. α>1 means: reduce the synchronization frequency when emphasizing low power, β>0 means: reduce the synchronization frequency when the CPU load is high, γ>1 means: the weaker the signal, the more obvious the synchronization interval is, δ<0 means: when the link is unstable, reduce the synchronization interval, that is, increase the synchronization frequency, ∈<0 means: reduce the synchronization interval when the available bandwidth is large, ζ<0 means: when the task priority is urgent, reduce the synchronization interval, that is, increase the synchronization frequency; Step 2.2: The edge server regularly collects device status information, i.e., power, CPU load, network status information, i.e., signal strength, link stability, available bandwidth, and service attributes, i.e., task priority, and combines the above historically collected data with real-time environmental parameters to calculate and form a synchronization interval set: T set ={T i |T min <T i <T max } Among them, T set is the synchronization interval set, T min is the minimum value, T max is the maximum value, T i is the update frequency T calculated at the current time, i.e., t=i Sync value; Step 2.3: The edge server defines synchronization rules based on fuzzy logic and sends the synchronization rules to the terminal devices to adapt to the synchronization requirements of different environments.

4. The method for synchronizing attributes based on environment perception according to claim 3, characterized in that: In step 2.3, the synchronization rules based on fuzzy logic include: Rule 1: When the task priority is normal, the device status and network environment are good, that is, the power B>50% and the CPU load C<50% and the signal strength S>-70dBm and the link stability L<1% and the available bandwidth W>50% and the service priority P=1, a periodic update method is adopted, and the service priority is manually set and divided into normal 1, critical 2, and urgent 3. The update frequency is dynamically calculated: Among them, the setting value of the index parameter is sent by the edge server together with the synchronization rule; Rule 2: When the service priority is normal and the device status is poor or the network environment is poor, that is, the power B<50% or the CPU load C>50% or the signal strength S<-70dBm or the link stability L>1% or the available bandwidth W<50% and the service priority P=1, low-frequency periodic reporting is used to reduce communication overhead: T Sync =T max Rule 3: When the task priority is urgent and there is no abnormality in the network environment, that is, the signal strength S>-80dBm and the link stability L<5% and the available bandwidth W>30% and the service priority P=3, high-frequency periodic reporting is adopted to ensure the timeliness of data: T Sync =T min Rule 4: When the task priority is urgent but the network environment is poor, that is, when the signal strength S<-80dBm or the link stability L>5% or the available bandwidth W<30% and the service priority P=3, the threshold is used to trigger the report to avoid unnecessary frequent synchronization: T Sync =T event Among them, T event It is the synchronization interval when the event is triggered; Rule 5: When the task priority is urgent, but the device status and network environment are both poor, that is, when the battery B<20% or the CPU load C>70% or the signal strength S<-80dBm or the link stability L>5% or the available bandwidth W<30% and the service priority P=3, a combination of periodic and event-triggered updates is used to balance data accuracy and energy consumption: Rule 6: When the task priority is critical and there is no abnormality in the device status and network status, that is, the power B>20% and the CPU load C<70% and the signal strength S>-80dBm and the link stability L<5% and the available bandwidth W>30% and the service priority P=2, the periodic update method is adopted: Rule 7: When the task priority is critical, but the device status or network environment is poor, that is, the battery level B < 20% or the CPU load C > 70% or the signal strength S < -80dBm or the link stability L > 5% or the available bandwidth W < 30% and the service priority P = 2, low-frequency periodic reporting is used to reduce communication overhead: T Sync =T max Rule 8: Except for the cases in Rule 1 to Rule 7, the default synchronization interval is used, that is: T Sync =T0 The edge server dynamically adjusts rules 1 to 8 according to environmental changes, and sends the rules to the terminal device, allowing the terminal device to dynamically adjust the synchronization mode according to the environment.

5. The method for synchronizing attributes based on environment perception according to claim 1, characterized in that: Step S3 specifically includes the following steps: Step 3.1: Filter invalid data from terminal devices, extract key features, perform fuzzy processing, convert data into fuzzy values, and define fuzzy sets: Battery level B: {high, medium, low}; CPU load C: {high, medium, low}; Signal strength S: {strong, medium, weak}; Link stability L: {stable, fluctuating, unstable}; Available bandwidth W: {high, medium, low}; Task priority P: {urgent, critical, normal}; Step 3.2, calculate the fuzzy value through the membership function, compare the size of the fuzzy value and output the result in the fuzzy set. For the power of the device, there are: Among them, B low ,B mid ,B high Represent the center points of low, medium and high power respectively, σ B Control the smoothness of the curve; For CPU load: Among them, C low ,C mid ,C high Represent the center points of low, medium and high loads respectively, σ C Control the smoothness of the curve; For signal strength: Among them, S week ,S mid ,S strong Represent the center points of weak, medium and strong signals respectively, σ S Control the smoothness of the curve; For link stability: Among them, L unstable ,L mid ,L stable Respectively represent the center points of unstable, fluctuating, and stable links, σ L Control the smoothness of the curve; The available bandwidths are: Among them, W low ,W mid ,W high Represent the center points of low, medium and high available bandwidth respectively, σ W Control the smoothness of the curve; The task priorities are: Step 3.3: Based on the synchronization rules sent by the edge server in step 2.3 and the fuzzy rule base calculated in step 3.2, match the optimal attribute synchronization strategy and generate the final synchronization update mechanism: If the current device power B is 55% and the available bandwidth W is 20%, the priority of the task is normal, and the fuzzy rule base determines that: B = medium and W = high and P = 1, then rule 1 is used to dynamically calculate the update frequency; if the current device CPU load is 65%, the link stability is 0.8%, but the priority of the task is urgent, and the fuzzy rule base determines that: C = high and L = stable and P = 3, then the update interval T used by rule 3 is selected Sync =T min .

6. The method for synchronizing attributes based on environment perception according to claim 1, characterized in that: The step S4 specifically includes the following steps: Step 4.1, the edge server continuously monitors the synchronization execution of the terminal, records the synchronization effect, uses the data synchronization success rate, the bandwidth and power consumed during the synchronization process to analyze the execution efficiency of each rule, and counts the number of executions of each synchronization rule; Step 4.2: When the synchronization success rate becomes low, that is, the packet loss rate of the synchronization data packet exceeds 5% within 10 minutes, or the power consumption during device synchronization is too large, that is, the power consumption exceeds 5% within 10 minutes, the synchronization rule is determined to be an inefficient synchronization rule; when the probability of triggering this synchronization rule in the past 10 hours is less than 5%, it is determined to be an outdated rule. For inefficient or outdated rules, the edge server optimizes and adjusts the parameters of the synchronization interval calculation formula and deletes outdated rules to improve the adaptability of the synchronization strategy; Step 4.3: Use machine learning to analyze long-term data trends, such as the changing trends of device power and CPU load over a period of time, and the fluctuations in the network environment. Use supervised learning to train the optimal synchronization strategy model based on historical data to optimize future synchronization strategies.

Images