A method and system for realizing information processing

Abstract

The present invention relates to the field of information processing technology, and specifically to a method and system for implementing information processing. The present invention triggers an edge update mechanism through changes in the status data of edge nodes. The edge terminal device sends new real-time operating status data to the edge node, and the edge node compares the new and old data to calculate the incremental data and transmits it to the edge processing end for storage and version number update. When the edge processing end receives incremental data from multiple edge nodes at the same time, conflict detection is performed by comparing the maintenance version number and the new incremental data version number and the data content, and the data is updated according to a predefined conflict resolution strategy. The present invention also formulates an adaptive synchronization adjustment strategy, regularly collects operating status data and change information from the edge node, and adjusts the synchronization frequency according to the change frequency and network load. The present invention can maintain data consistency between the edge processing end and the edge node, and improve the real-time and stability of edge information processing.

Description

Technical Field

[0001] The present invention relates to the technical field of information processing, and in particular to a method and system for implementing information processing. Background Art

[0002] In the computer field, information processing refers to the process of organizing, analyzing and converting raw data to extract useful information. Information processing covers a wide range of areas, including data mining, database management, and real-time data analysis. With the development of information technology and the surge in data volume, edge information processing has become a widely used method.

[0003] Edge information processing places data processing tasks close to the data source, such as near the edge of the network or close to the user terminal device. This approach not only reduces latency and bandwidth requirements, but also optimizes network load and reduces data transmission pressure on the central cloud, thereby improving real-time data analysis and rapid response capabilities. In addition, edge information processing can also improve the reliability of data processing and transmission and reduce operating costs, so it is widely used in the Internet of Things, smart cities, autonomous driving, AR / VR, entertainment media and other fields.

[0004] In existing research, edge information processing usually focuses on improving the efficiency and accuracy of data analysis. Although a certain degree of optimization has been achieved, there are still some shortcomings. In the current situation of data explosion, data conflicts between edge terminals and edge information processing nodes are also key issues that need to be solved urgently. Existing technologies do not consider the data consistency problem in edge processing systems enough, and it is difficult to adapt to data changes in dynamic environments in real time.

[0005] In order to improve the data consistency and dynamic adaptability during information processing and avoid data conflicts affecting scheduling strategies, the present invention proposes a method and system for implementing information processing. Summary of the invention

[0006] The object of the present invention is to provide a method and system for implementing information processing, triggering an edge update mechanism through changes in the status data of edge nodes. The edge terminal device sends new real-time operating status data to the edge node, and the edge node compares the new and old data to calculate the incremental data and transmits it to the edge processing end for storage and version number update. When the edge processing end receives incremental data from multiple edge nodes at the same time, conflict detection is performed by comparing the maintenance version number and the new incremental data version number and the data content, and the data is updated according to a predefined conflict resolution strategy. The present invention also formulates an adaptive synchronization adjustment strategy to regularly collect operating status data and change information from edge nodes, and adjust the synchronization frequency according to the change frequency and network load.

[0007] To achieve the above object, the present invention provides a method and system for implementing information processing, including:

[0008] Collecting the running status data of the edge terminal device and sending it to the edge node, triggering the edge update mechanism when the running status data received by the edge node reaches a preset standard;

[0009] Comparing the running status data with the stored old data on the edge node, calculating the incremental data to be transmitted and processing it into an incremental data packet; compressing the incremental data packet and transmitting it to the edge processing end; the incremental data packet also includes a version number and a timestamp;

[0010] At the edge processing end, receiving the incremental data packet, creating a new version number according to the version number and the timestamp, and generating metadata for each incremental data; the metadata includes the data source and update time; storing the metadata, the incremental data packet and the new version number in a database of the edge processing end;

[0011] When the edge processing end receives the incremental data packet of the edge node again, it compares the version number of the edge node in the edge processing end database with the version number of the incremental data packet; when the version numbers are inconsistent, it compares the operating status data of the edge processing end with that in the incremental data packet; if the operating status data are inconsistent, it updates the data of the edge processing end according to a predefined policy.

[0012] Furthermore, the specific steps of triggering the edge processing end to update include: collecting the operating status data of the edge terminal device and transmitting it to the edge node, the operating status data including captured data value, device status, performance index and location information; triggering an update mechanism when the operating status data exceeds the preset standard, generating the real-time change information, including the triggering reason and related data;

[0013] Furthermore, the step of compressing and transmitting the incremental data packet to the edge processing end includes: on the edge node, recording the new data collected in real time as a new data set and merging and calculating a first hash value, and recording the latest stored data in the edge node database as an old data set and merging and calculating a second hash value; when the first hash value is equal to the second hash value, the incremental data set is empty; when the first hash value is not equal to the second hash value, calculating the incremental data by comparison;

[0014] According to the comparison results, all the incremental data items x in the new data set are integrated to generate an incremental data set, and the first version number and the first timestamp of the incremental data set are recorded; the incremental data set and the corresponding first version number and the first timestamp are packaged into the incremental data packet, compressed and transmitted to the edge processing end.

[0015] Furthermore, the comparative calculation process of the incremental data is as follows: a hash value is calculated for each data item in the new data set and the old data set, which are respectively recorded as the new data hash set and the old data hash set; if the H in the new data hash set is new (x) is not in the old data hash set, then H new The data item x corresponding to (x) is recorded as the incremental data item.

[0016] Further, the step of receiving and processing the incremental data packet at the edge processing end includes: receiving the compressed incremental data packet at the edge processing end, parsing and extracting the incremental data set, and the first version number and the first timestamp of the incremental data set; processing the incremental data set V old (X), based on the first version number V old (X) Generate the second version number V old (X)+1;

[0017] Generate corresponding metadata for the incremental data set, including the data source of the incremental data and the update time when it is received; store the incremental data, the corresponding second version number and the metadata in the database of the edge processing end.

[0018] Furthermore, the process of updating data according to the predefined strategy includes: when the edge processing end receives the incremental data packet P of the edge node again incremental When P incremental Version number V node and the version number V of the edge node in the edge processing terminal database center Make comparisons;

[0019] When V node >V center When comparing V node The corresponding incremental data D node With V center The corresponding metadata D center If D node With D center If the content is consistent, only the version number of the edge processing end is updated to V node +1; if D node With D centerIf the content is inconsistent, it will be handled according to the predefined conflict resolution strategy.

[0020] Furthermore, the process of processing according to the predefined conflict resolution strategy is: selecting the latest write priority strategy, using the incremental data D node Update the D of the edge processing end center ; The edge processing end is the incremental data D node Incrementally update the version number and update the timestamp of the edge processing end to the incremental data D node The timestamp of the

[0021] Furthermore, it also includes setting a synchronization strategy for adaptive adjustment: defining a collection frequency, collecting the operating status data and change information from each edge node; creating a change record list for each edge node, recording the change information;

[0022] Perform statistical analysis on the data change list to calculate the data change frequency; and dynamically set the synchronization strategy based on the data change frequency of the edge node and the network load evaluation result.

[0023] Furthermore, the process of setting the synchronization strategy for adaptive adjustment is: calculating the degree of synchronization requirement according to the current data change frequency and network load of the edge node, and setting the synchronization strategy for adaptive adjustment according to the degree of synchronization requirement; the calculation formula of the degree of synchronization requirement R is:

[0024] R=ω F ×F+ω L ×L;

[0025] Among them, ω F and ω L are the weight coefficients of change frequency F and network load L respectively.

[0026] A system for implementing information processing is also proposed, comprising: a data update monitoring module: collecting operating status data of edge terminal devices and sending them to edge nodes, and triggering an edge update mechanism when the operating status data received by the edge node reaches a preset standard;

[0027] Incremental data transmission module: compare the running status data with the stored old data on the edge node, calculate the incremental data to be transmitted and process it into an incremental data packet; compress the incremental data packet and transmit it to the edge processing end; the incremental data packet also includes a version number and a timestamp;

[0028] Version management module: at the edge processing end, receiving the incremental data packet, creating a new version number according to the version number and the timestamp, generating metadata for each incremental data; the metadata includes the data source and update time; storing the metadata, the incremental data packet and the new version number in the database of the edge processing end;

[0029] Conflict detection and resolution module: when the edge processing end receives the incremental data packet of the edge node again, compare the version number of the edge node in the edge processing end database with the version number of the incremental data packet; when the version numbers are inconsistent, compare the running status data of the edge processing end with that in the incremental data packet; if the running status data are inconsistent, update the data of the edge processing end according to the predefined strategy;

[0030] Adaptive synchronization adjustment module: collects the operating status data and change information of the specified time from the edge nodes; records the number of data changes of each edge node to evaluate the network load status and change trend, and sets the synchronization strategy for adaptive adjustment.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. The present invention improves the data transmission efficiency in edge information processing through an incremental data transmission module. At the edge node, the new and old data are compared, and only the changed data is transmitted, which reduces the amount of data that needs to be transmitted and reduces bandwidth consumption; the incremental data is compressed before transmission, which further reduces the volume of the incremental data packet and speeds up the transmission speed, which is suitable for edge network environments with limited bandwidth; a lightweight communication protocol is used to reduce communication overhead and improve the reliability and real-time performance of data transmission. This method can cope with the bandwidth pressure and transmission delay problems caused by explosive data growth, improve the speed of edge information transmission, and provide transmission guarantee for subsequent real-time data consistency between edge nodes and edge processing terminals.

[0033] 2. The present invention realizes data consistency management between the edge terminal and the edge processing end through the version management module and the conflict detection and resolution module. By generating a unique version number and timestamp for each incremental data, the historical changes of the data can be accurately recorded to ensure that each update has a clear version record for easy backtracking; when the edge node updates the data again, the data version number of the edge node and the edge processing end is compared to identify inconsistent data; the data of the edge processing end is updated according to the predefined conflict resolution strategy to ensure that the edge processing end always keeps the latest data version. This method ensures the data reliability and consistency of the edge node and the edge processing end, and helps to reduce data conflicts in the edge information system.

[0034] 3. The present invention uses an adaptive synchronization adjustment module to dynamically adjust the synchronization strategy according to the data change frequency and network load status of the edge node, thereby achieving efficient utilization and flexible adaptation of the edge system computing resources. By regularly recording and analyzing the data changes of the edge computing nodes to analyze the change trends, real-time monitoring of the network bandwidth and network load, and formulating and adjusting the synchronization strategy in advance, network congestion and waste of resources can be avoided. When the data change frequency is high and the network load is low, increase the synchronization frequency to ensure real-time performance; when the data change frequency is low and the network load is high, reduce the synchronization frequency to save resources. This method improves the overall adaptability of edge information processing and resource management efficiency by optimizing the synchronization frequency, enabling the edge system to flexibly respond to data changes in a dynamic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flowchart of a method for implementing information processing provided by the present invention;

[0036] Figure 2 A schematic diagram of the structure of a system for implementing information processing provided by the present invention;

[0037] Figure 3 A structural schematic diagram of an adaptive synchronization adjustment strategy provided by the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] The present invention provides a method for implementing information processing, which is applied to a system for implementing information processing. For specific method flow charts and system schematic diagrams, refer to Figure 1 and Figure 2 .

[0040] Embodiment 1

[0041] The traffic flow in modern cities brings great pressure to traffic management. By deploying an intelligent traffic management system, traffic conditions can be monitored in real time, scheduling efficiency can be improved, and traffic congestion can be reduced. This embodiment describes a method and system for realizing information processing proposed by the present invention through an edge computing system for traffic scheduling.

[0042] As an embodiment of the present invention, refer to Figure 1S10 in the embodiment is used for a data update monitoring module of a system for realizing information processing. The operation status data of the edge node is collected, and when the preset standard is reached, the edge processing end update mechanism is triggered. Specifically:

[0043] Edge nodes are computing devices deployed at the edge of the network, responsible for collecting and initially processing operating status data from terminal devices. Configure the collection frequency of edge nodes to regularly collect operating status data through the connected terminal device interface, including captured data, device status, performance indicators, and location information.

[0044] When the collected data exceeds its corresponding preset threshold, the update mechanism is triggered; the reason for triggering the update mechanism and the related change data are recorded, the change information is generated, and the version number and timestamp are added to the change information.

[0045] This embodiment uses edge computing devices deployed at traffic intersections to connect various traffic sensors, including video surveillance cameras, vehicle flow detectors, speed detectors, and environmental sensors. The edge nodes collect various operating status data in real time once a minute through the connected traffic sensors, including but not limited to vehicle flow, average speed, traffic signal status, and environmental data.

[0046] The traffic volume V(t), average vehicle speed S(t), traffic signal status C(t) and environmental data E(t) collected by the edge node at time t can be expressed as D(t) = {V(t), S(t), C(t), E(t)}, where D(t) represents all data items collected at time t.

[0047] Set the threshold V of the vehicle flow V(t) th The threshold S of the average vehicle speed S(t) is 30 vehicles / minute. th is 35 km / h, the threshold C of the traffic signal state C(t) th The state of the signal light changes. The triggering conditions for the update mechanism are:

[0048] if(V(t)>V th )∨(S(t)>S th )∨(C(t)=C th );

[0049] Generate the real-time change information ΔD(t)={V(t), S(t), C(t)}, including adding the version number V pkg and timestamp T pkg .

[0050] This embodiment uses edge nodes to periodically collect operating status data and set preset thresholds. When the threshold corresponding to the data type is exceeded, the update mechanism is triggered to avoid data updates when there is no demand, which helps reduce the burden on the edge computing system. Recording the reasons for triggering the update mechanism and the changed data improves the maintainability and scalability of the edge computing system.

[0051] Reference Figure 1 S20 in the example is used for an incremental data transmission module of a system for implementing information processing. At the edge node, new and old data are compared, incremental data to be transmitted is calculated and processed into incremental data packets; the incremental data packets are compressed and transmitted to the edge processing end; the incremental data packets also include a version number and a timestamp. Specifically:

[0052] Collect real-time data and record the new data as the new data set D new , and use H new = hash(D new ) Calculate the first hash value H new , record the latest stored data in the database as the old data set D old , and use H old = hash(D old ) Calculate the second hash value H old ;

[0053] If H new =H old , then the incremental data set is empty; if H new ≠H old , calculating the incremental data by comparison;

[0054] The comparative calculation process of the incremental data is as follows: new and the old data set D old Calculate the hash value for each data item in and record them as the new data hash set H new-items and old data hash set H old-items ;

[0055] For each new data hash set H new-items The hash value H of the data item in new-item-i ,like Then H new-item-i The corresponding D new-i Mark as incremental data item and record its version number V new-i and timestamp T new-i .

[0056] Integrate all incremental data items, corresponding version numbers and timestamps, and package them into incremental data packets P incremental, compress it, and transmit it to the edge data end. In this embodiment, examples of the collected real-time data and the calculated incremental data are shown in Table 1.

[0057] Table 1 Incremental data calculation example

[0058] Data Item Collected Value Stored data Remark Is it an increment? <![CDATA[V(t1)]]> 80 vehicles / minute 45 vehicles / minute <![CDATA[V(t1)>V th ]]> yes <![CDATA[S(t1)]]> 45 km / h 38 km / h <![CDATA[S(t1)>V th ]]> yes <![CDATA[C(t1)]]> Green Light Green Light / no <![CDATA[E(t1)]]> 25℃ 22℃ / yes

[0059] According to Table 1, it can be concluded that the incremental data packet obtained by incremental data calculation is:

[0060] P incremental ={V=80,,S=45,E=25,ver=1.0.2,

[0061] timestamp="2024-11-01T10:05:00Z"};

[0062] This embodiment collects new data in real time and calculates hash values ​​for the new and old data sets, so as to quickly determine whether the incremental data set is empty and avoid the time and space consumption caused by directly comparing the data. After the incremental data is calculated, it is integrated and a version number and timestamp are added, which facilitates subsequent data tracking and version management and ensures data consistency and integrity. In addition, the incremental data packet is compressed and transmitted, which can reduce the bandwidth required for data transmission and improve the overall transmission speed.

[0063] In this embodiment, when the hash values ​​of the new and old data sets are different, the data items that need to be transmitted are calculated by comparing the hash values ​​of the data items in the two data sets; this reduces the time cost of data comparison. Accurate identification of incremental data items helps the edge processing end to centrally process the changed data, reduces data honor, optimizes the data processing flow and cost of the edge processing end, and improves the efficiency of storage and processing.

[0064] Further, refer to Figure 1 S30 in the embodiment is applied to a version management module of a system for implementing information processing. The incremental data packet is received, a new version number is created according to the version number and the timestamp, and metadata is generated for each incremental data; the metadata includes the data source and update time; the metadata, the incremental data packet and the version number are stored in a database of the edge processing end. Specifically:

[0065] The edge processing end receives the incremental data packet from the edge node, parses and extracts the incremental data, the first version number V1 and the first timestamp T1; for the incremental data, the edge processing end generates V2 according to its first version number V1. In this embodiment, the generation rule is set to incremental generation, that is, V2=V1+1, which facilitates tracing the data version.

[0066] Generate metadata for the incremental data, including the data source and the accepted update time, and then store the metadata, incremental data, and the corresponding second version number V2 in the database of the edge processing end.

[0067] In this embodiment, the edge processing end receives the incremental data packet P from the edge node of the traffic intersection. incremental , including incremental data, the first version number and timestamp. Parse and extract the incremental data {V=80,,S=45,E=25}, the first version number V1=1.0.2 and the first timestamp T1="2024-11-01T10:05:00Z"; through the incremental generation rule, the incremental data generates the second version number V2=V1+1=1.0.3;

[0068] The generated metadata is Dsource = "Traffic sensor", T2 = "2024-11-01T10:06:00Z";

[0069] The incremental data, the corresponding second version number, and the metadata are stored in a database of the edge processing end.

[0070] This embodiment receives and parses incremental data packets from the edge processing end, extracts the incremental data and the first version number therein, generates a second version number for the incremental data based on the first version number in an incremental manner, which helps to clarify the data change history; the metadata provides richer contextual information, providing a data and index basis for subsequent conflict detection and resolution.

[0071] Further, refer to Figure 1 S40 in the embodiment is applied to a conflict detection and resolution module of a system for implementing information processing. When the edge processing end receives the incremental data packet of the edge node again, the version number and operation status data of the edge node and the edge processing end are compared; if the data item version number of the edge node is inconsistent with the version number of the edge processing end, the data of the edge node and the edge processing end are updated according to a predefined strategy. Specifically:

[0072] When the edge processing end receives the incremental data packet P of the edge node again incremental When P incremental Version number V node and the version number V of the edge node in the edge processing terminal database center Make comparisons;

[0073] When V node >V center When comparing V node The corresponding incremental data D node With V center The corresponding metadata Dcenter If D node With D center If the content is consistent, only the version number of the edge processing end is updated to V node +1; if D node With D center If the content is inconsistent, it is processed according to the predefined conflict resolution strategy. In this embodiment, the data maintained by the edge node at the edge processing end and the incremental data sent by the edge node to the edge processing end are shown in Table 2.

[0074] Table 2 Data to be detected for conflicts

[0075]

[0076] According to the data in Table 2, the edge processing end will compare the incremental data with the data currently maintained for the A1 node. First, the version number is compared. If the incremental data version number of node A1 is greater than the version number currently maintained at the edge data end, the data content is compared item by item. After comparing the data of node A1, the traffic flow has changed, confirming that the data maintained by the edge processing end of node A1 is inconsistent with the incremental data content. At this time, it is processed according to the predefined conflict resolution strategy.

[0077] This embodiment uses a conflict detection module to preliminarily confirm whether the data has changed by comparing the version numbers of the edge node and the edge processing end. If the version numbers are inconsistent, the data is compared item by item. When the node maintains data and incremental data content at the edge processing end, the predefined conflict resolution strategy is triggered for processing. This avoids errors or losses caused by inconsistent data versions at the edge processing end, and can also efficiently synchronize and update data.

[0078] The predefined conflict resolution strategy is: select the latest write priority strategy, use the incremental data D node Update the D of the edge data end center ; The edge processing end is the incremental data D node Incrementally update the version number, i.e. V node +1; Update the timestamp of the edge processing end to the incremental data D node In this embodiment, Table 3 provides some examples of data update using the latest write priority strategy by multiple edge nodes and edge processing terminals at traffic intersections.

[0079] Table 3. Data update example at edge processing end

[0080]

[0081] This embodiment updates the data of the edge processing end through the latest write priority strategy, ensuring the consistency and real-time performance of the edge node and edge processing end data. When other nodes send data requests to the edge processing end, conflicts caused by inconsistent data can be avoided. Each update is based on the latest incremental data and timestamp, avoiding the transmission and storage of redundant data and improving data processing efficiency.

[0082] Embodiment 2

[0083] This embodiment describes a specific implementation method of an adaptive synchronization adjustment strategy module in a system for implementing information processing proposed by the present invention in conjunction with an edge computing system for traffic scheduling.

[0084] Reference Figure 1 S50 in the embodiment is applied to an adaptive synchronization adjustment strategy module of a system for implementing information processing. The operation status data and change information of a specific time are collected from the edge nodes; the number of data changes of each edge node is recorded to evaluate the network load status and change trend, and the synchronization strategy is set for adaptive adjustment. Specifically:

[0085] Define a collection frequency to collect the operating status data and change information from each edge node; create a change record list for each edge node to record the change information. This embodiment defines the collection frequency as once per minute, with every 10 minutes as a statistical cycle; Table 4 gives an example of a change record list for edge node A-001.

[0086] Table 4 Change record list of edge node A-001

[0087] Timestamp Data Item Range of change Number of changes Data Source Device Status 2024-11-09T08:00:05 Traffic flow +5 vehicles / minute 5 Traffic Sensors normal 2024-11-09T08:10:05 Traffic flow -2 vehicles / minute 3 Traffic Sensors normal 2024-11-09T08:20:05 Average speed +3 km / h 4 Speed ​​Sensor normal 2024-11-09T08:30:05 Ambient temperature +1℃ 1 Environmental Sensors normal 2024-11-09T08:40:05 Average speed -2 km / h 3 Speed ​​Sensor normal 2024-11-09T08:50:05 Traffic flow -3 vehicles / minute 5 Traffic Sensors normal

[0088] The edge node counts the number of changes in each statistical period and calculates the change frequency based on the number of changes; in this embodiment, the change frequency of each node is calculated based on the maximum number of changes of all data items of the edge node.

[0089] The network load is evaluated once in each statistical period. The evaluation steps of the network load include: generating the network load of each edge node by weighting according to the CPU utilization I1, memory utilization I2 and network bandwidth I3 of the edge node; the network load L node The calculation formula is:

[0090] L node =γ1×I1+γ2×I2+γ3×I3;

[0091] Among them, γ1, γ2 and γ3 are weight coefficients of CPU utilization I1, memory utilization I2 and network bandwidth I3 respectively, which are set to 0.4, 0.3 and 0.4 in this embodiment. Perform statistical analysis on the data changes in the data change list to determine the change trend; dynamically set the synchronization strategy based on the evaluation results of the network load and the change trend.

[0092] Reference Figure 3 , Figure 3 It is used to set the synchronization strategy for adaptive adjustment, including: calculating the synchronization requirement degree according to the current data change frequency F of the edge node and the network load L, and setting the synchronization strategy for adaptive adjustment according to the synchronization requirement degree. The calculation formula of the synchronization requirement degree R is:

[0093] R=ω F ×F+ω L ×L;

[0094] In the above formula, ω F and ω L are weight coefficients of the change frequency F and the network load L, respectively. In this embodiment, ω F and ω L Set to 0.6 and 0.4 respectively.

[0095] In this embodiment, the statistical cycle is set to 10 minutes, the number of data changes of the edge node is counted in each statistical cycle, and the change frequency is calculated according to the number of changes; the first threshold is set to 0.5, and the second threshold is set to 45%. The synchronization frequency between the edge node and the edge processing end is adjusted according to the degree of synchronization demand. Table 5 gives some examples of adaptive adjustment of the edge node at the traffic intersection based on the synchronization strategy.

[0096] Table 5 Some examples of edge node adaptive adjustment

[0097]

[0098] This embodiment collects the running status data and change information of the edge nodes by defining the collection frequency, and can monitor the trend of data traffic data changes in real time; then evaluates the current network load of the node, dynamically adjusts the synchronization strategy between the node and the edge processing end, improves the synchronization efficiency and optimizes the network resource utilization. This method can enhance the adaptive ability of the edge computing system in combination with the actual changes in the traffic data of the edge nodes.

[0099] This embodiment calculates the degree of synchronization demand by combining the data change frequency with the evaluation results of the network load, so that the edge computing system can dynamically and adaptively adjust the synchronization frequency. When the data change frequency of the node is high and the network load is low, the system can appropriately increase the synchronization frequency to ensure that real-time data synchronization is prioritized; for nodes with fewer changes, appropriately reducing the synchronization frequency can save bandwidth and avoid unnecessary waste of resources.

[0100] The embodiment of the present application uses an incremental data transmission module to compare new and old data at the edge node, and only transmits the data that changes in real time to the processing end, thereby improving the data transmission efficiency in edge information processing. The incremental data is compressed before transmission, and a lightweight communication protocol is used to reduce communication overhead and speed up the transmission speed, which has high applicability in edge network environments with limited bandwidth. The version management module and the conflict detection and resolution module realize the data consistency between the edge terminal and the edge processing end. The version number generated by the edge node for each incremental data and the version number updated by the edge processing end for each incremental data can accurately record the historical changes in the data, ensuring that each update has a clear and clear version record; when multiple edge nodes update data at the same time, the data from the edge node and the data maintained by the edge processing end for each node are compared by version number and data content, and inconsistent data is updated to ensure that the edge processing end always maintains the latest data version, improve the real-time consistency of data between the edge node and the edge processing end, and reduce the impact of data conflicts on the stability of the edge information system. The adaptive synchronization adjustment module dynamically adjusts the synchronization strategy according to the data change frequency and network load of the edge node, realizing efficient utilization and flexible adaptation of edge system computing resources.

[0101] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for implementing information processing, characterized in that: include: Collecting the running status data of the edge terminal device and sending it to the edge node, triggering the edge update mechanism when the running status data received by the edge node reaches a preset standard; The edge node compares the running status data with the stored old data, calculates the incremental data to be transmitted and processes it into an incremental data packet; compresses the incremental data packet and transmits it to the edge processing end; the incremental data packet also includes a version number and a timestamp; At the edge processing end, receiving the incremental data packet, creating a new version number according to the version number and the timestamp, and generating metadata for each incremental data; the metadata includes the data source and update time; storing the metadata, the incremental data packet and the new version number in a database of the edge processing end; When the edge processing end receives the incremental data packet of the edge node again, it compares the version number of the edge node in the edge processing end database with the version number of the incremental data packet; when the version numbers are inconsistent, it compares the operating status data of the edge processing end with that in the incremental data packet; if the operating status data are inconsistent, it updates the data of the edge processing end according to a predefined policy.

2. A method for implementing information processing according to claim 1, characterized in that: The specific steps of triggering the edge processing end update include: The operating status data of the edge terminal device is collected and transmitted to the edge node, wherein the operating status data includes captured data values, device status, performance indicators and location information; when the operating status data exceeds the preset standard, an update mechanism is triggered; and the edge node replaces the data stored in the database with the new operating status data.

3. A method for implementing information processing according to claim 1, characterized in that: The step of compressing and transmitting the incremental data packet comprises: On the edge node, the new data collected in real time is recorded as a new data set and merged to calculate a first hash value, and the latest stored data in the edge node database is recorded as an old data set and merged to calculate a second hash value; when the first hash value is equal to the second hash value, the incremental data set is empty; when the first hash value is not equal to the second hash value, the incremental data is calculated by comparison; According to the comparison results, all the incremental data items x in the incremental data set are integrated to generate an incremental data set, and the first version number and the first timestamp of the incremental data set are recorded; the incremental data set and the corresponding first version number and the first timestamp are packaged into the incremental data packet, compressed and transmitted to the edge processing end.

4. A method for implementing information processing according to claim 3, characterized in that: The comparative calculation process of the incremental data is as follows: Calculate the hash value for each data item in the new data set and the old data set, and store them in the new data hash set and the old data hash set respectively; if the H in the new data hash set new (x) is not in the old data hash set, then H new The data item x corresponding to (x) is recorded as an incremental data item and stored in the incremental data set.

5. A method for implementing information processing according to claim 1, characterized in that: The edge processing end receives and processes the incremental data packet; The edge processing end receives the incremental data packet, parses and extracts the incremental data set and the first version number and the first timestamp of the incremental data set; For the incremental data set, generating a second version number based on the first version number; Generating corresponding metadata for the incremental data set, including the data source of the incremental data and the update time when it was received; The incremental data, the corresponding second version number, and the metadata are stored in a database of the edge processing end.

6. A method for implementing information processing according to claim 1, characterized in that: The process of updating data according to predefined policies includes: When the edge processing end receives the incremental data packet P of the edge node again incremental When P incremental Version number V node and the version number V of the edge node in the edge processing terminal database center Make comparisons; When V node >V center When comparing V node The corresponding incremental data D node With V center Corresponding metadata D center If D node With D center If the content is consistent, only the version number of the edge processing end is updated to V node +1; if D node With D center If the content is inconsistent, it will be handled according to the predefined conflict resolution strategy.

7. A method for implementing information processing according to claim 6, characterized in that: The process of handling according to the predefined conflict resolution strategy is: Select the latest write priority strategy and use the incremental data D node Update the D of the edge processing end center ; The edge processing end is the incremental data D node Incrementally update the version number and update the timestamp of the edge processing end to the incremental data D node The timestamp of the 8. A method for implementing information processing according to claim 1, characterized in that: It also includes setting the synchronization strategy for adaptive adjustment: Define a collection frequency to collect the operating status data and change information from each edge node; create a change record list for each edge node to record the change information; Perform statistical analysis on the data change list to calculate the data change frequency; and dynamically set the synchronization strategy based on the data change frequency of the edge node and the network load evaluation result.

9. A method for implementing information processing according to claim 8, characterized in that: The process of setting the synchronization strategy for adaptive adjustment is: The synchronization requirement degree is calculated according to the current data change frequency and network load of the edge node, and the synchronization strategy is set according to the synchronization requirement degree for adaptive adjustment; the calculation formula of the synchronization requirement degree R is: R=ω F ×F+ω L ×L; Among them, ω F and ω L are the weight coefficients of change frequency F and network load L respectively.

10. A system for implementing information processing, characterized in that: include: Data update monitoring module: collects the operating status data of the edge terminal device and sends it to the edge node, and triggers the edge update mechanism when the operating status data received by the edge node reaches a preset standard; Incremental data transmission module: compare the running status data with the stored old data on the edge node, calculate the incremental data to be transmitted and process it into an incremental data packet; compress the incremental data packet and transmit it to the edge processing end; the incremental data packet also includes a version number and a timestamp; Version management module: at the edge processing end, receiving the incremental data packet, creating a new version number according to the version number and the timestamp, and generating metadata for each incremental data; the metadata includes the data source and update time; Storing the metadata, the incremental data packet, and the new version number in a database of the edge processing end; Conflict detection and resolution module: when the edge processing end receives the incremental data packet of the edge node again, compares the version number of the edge node in the edge processing end database with the version number of the incremental data packet; when the version numbers are inconsistent, compares the running status data in the edge processing end and the incremental data packet; If the operating status data is inconsistent, updating the data of the edge processing end according to a predefined strategy; Adaptive synchronization adjustment module: collects the operating status data and change information of the specified time from the edge nodes; records the number of data changes of each edge node to evaluate the network load status and change trend, and sets the synchronization strategy for adaptive adjustment.

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