Environment monitoring safety synchronization method and platform for cloud computing

Abstract

The invention provides a cloud computing environment monitoring safety synchronization method and platform, and relates to the technical field of data transmission, and the method comprises the steps: building a data transmission topology, determining a dynamic limiting principle, training a transmission command and control unit to jointly calculate a command and control module, forming an integrated command and control module, configuring the integrated command and control module on a cloud computing platform, and collecting an environment monitoring task. The integrated intelligent control module is combined to carry out relatively independent analysis of the transmission dimension and the calculation dimension, and a target strategy is determined; and executing dynamic self-adaptive transmission regulation and control based on the target transmission strategy, and source end preprocessing and cloud end task calculation based on the target calculation strategy. Through the method and the device, the problems of inaccurate environment monitoring data and low real-time performance caused by insufficient data processing capability, low data transmission efficiency and lack of a dynamic regulation and control mechanism in the prior art can be solved, and the real-time performance of the environment monitoring data is improved by executing dynamic self-adaptive transmission regulation and control and based on source end preprocessing and cloud end task calculation. And efficient, safe and accurate transmission and processing of environment monitoring data can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of data transmission, and in particular to a cloud computing environment monitoring security synchronization method and platform. Background Art

[0002] With the continuous advancement of cloud computing technology, it has been widely used in various fields, including environmental monitoring. Cloud computing can provide efficient, secure and reliable data storage and processing capabilities, providing strong support for environmental monitoring. As environmental problems become increasingly prominent, the demand for environmental monitoring is also growing. Traditional environmental monitoring methods have problems such as low efficiency and inaccurate data, while environmental monitoring methods based on cloud computing can improve the efficiency and accuracy of monitoring.

[0003] At present, the existing environmental monitoring systems still face many challenges in data transmission, processing and regulation. Traditional environmental monitoring sites usually adopt a centralized data processing method, which often has problems of slow processing speed and low efficiency when processing large amounts of data. At the same time, due to the unreasonable data transmission path, it is also easy to cause data transmission delays and losses, further affecting the accuracy and real-time performance of environmental monitoring. During the data transmission process, the existing systems often lack an effective dynamic control mechanism and cannot flexibly adjust the transmission strategy according to changes in network conditions and data volume, which may lead to data transmission congestion and waste of resources. In addition, the existing environmental monitoring system also lacks intelligent data preprocessing functions and cannot effectively screen and process data at the data source end, thereby increasing the burden of data transmission and processing.

[0004] In summary, the existing technology has insufficient data processing capabilities, low data transmission efficiency and lack of dynamic control mechanisms, which leads to inaccurate environmental monitoring data and low real-time performance, further affecting the overall performance and reliability of the environmental monitoring system. Summary of the invention

[0005] The purpose of this application is to provide a cloud computing environmental monitoring security synchronization method and platform to solve the problems of insufficient data processing capabilities, low data transmission efficiency and lack of dynamic control mechanism in the existing technology, which lead to inaccurate environmental monitoring data and low real-time performance.

[0006] In view of the above problems, the present application provides a cloud computing environment monitoring security synchronization method and platform.

[0007] In the first aspect, the present application provides a method for secure synchronization of environmental monitoring in cloud computing, which is implemented through a secure synchronization platform for environmental monitoring in cloud computing, wherein the method includes: establishing a data transmission topology by determining distributed monitoring sites in the environmental monitoring area, wherein the data transmission topology is based on a hierarchical data transmission network from the source to the cloud; determining a dynamic limiting principle, wherein the dynamic limiting principle includes a timestamp management principle and a channel crossing management principle based on transit retention and priority avoidance, and the timestamp management principle is based on the network time protocol as the underlying basis; combining the dynamic limiting principle with the data transmission topology, training a transmission intelligent control unit, and combining a computing intelligent control module to form an integrated intelligent control module, wherein the integrated intelligent control module is configured on a cloud computing platform and has an independent computing power field; collecting environmental monitoring tasks, and performing relatively independent analysis of transmission dimensions and computing dimensions in combination with the integrated intelligent control module to determine the target strategy; identifying the target strategy, executing dynamic adaptive transmission control based on the target transmission strategy, and source preprocessing and cloud task calculation based on the target computing strategy.

[0008] In the second aspect, the present application also provides a cloud computing environmental monitoring security synchronization platform for executing a cloud computing environmental monitoring security synchronization method as described in the first aspect, wherein the platform includes: a data transmission topology establishment unit, the data transmission topology establishment unit is used to determine the distributed monitoring sites in the environmental monitoring area and establish a data transmission topology, wherein the data transmission topology is based on a hierarchical data transmission network from the source to the cloud; a principle acquisition unit, the principle acquisition unit is used to determine the dynamic limit principle, the dynamic limit principle includes a timestamp management principle based on transit retention and priority avoidance and a channel crossing management principle, and the timestamp management principle is based on the network time protocol as the underlying basis a computing platform configuration unit, the computing platform configuration unit is used to combine the dynamic limiting principle with the data transmission topology, train the transmission intelligent control unit, and jointly calculate the intelligent control module to form an integrated intelligent control module, the integrated intelligent control module is configured on the cloud computing platform, and has an independent computing power field; an independent analysis unit, the independent analysis unit is used to collect environmental monitoring tasks, and combine the integrated intelligent control module to perform relatively independent analysis of the transmission dimension and the computing dimension to determine the target strategy; an adaptive transmission control unit, the adaptive transmission control unit is used to identify the target strategy, execute dynamic adaptive transmission control based on the target transmission strategy, and source-end preprocessing and cloud-based task calculation based on the target computing strategy.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By determining the distributed monitoring sites in the environmental monitoring area, a data transmission topology is established, wherein the data transmission topology is based on a hierarchical data transmission network from the source to the cloud; a dynamic limiting principle is determined, and the dynamic limiting principle includes a timestamp management principle based on transit retention and priority avoidance and a channel crossing management principle, and the timestamp management principle is based on the network time protocol as the underlying basis; in combination with the dynamic limiting principle and the data transmission topology, a transmission intelligent control unit is trained, and a computing intelligent control module is combined to form an integrated intelligent control module, and the integrated intelligent control module is configured on a cloud computing platform and has an independent computing power field; environmental monitoring tasks are collected, and relatively independent analysis of transmission dimensions and computing dimensions is performed in combination with the integrated intelligent control module to determine the target strategy; the target strategy is identified, and dynamic adaptive transmission control based on the target transmission strategy is executed, and source preprocessing and cloud task calculation based on the target calculation strategy are executed, which effectively solves the problems of insufficient data processing capabilities, low data transmission efficiency and lack of dynamic control mechanism in the prior art, resulting in inaccurate environmental monitoring data and low real-time performance. By executing dynamic adaptive transmission control, and based on source preprocessing and cloud task calculation, efficient, secure and accurate transmission and processing of environmental monitoring data can be achieved.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0013] Figure 1 A flowchart of a cloud computing environment monitoring security synchronization method for this application;

[0014] Figure 2 This is a schematic diagram of the structure of a cloud computing environment monitoring security synchronization platform for this application.

[0015] Description of reference numerals:

[0016] A data transmission topology establishing unit 11, a principle acquiring unit 12, a computing platform configuring unit 13, an independent analyzing unit 14, and an adaptive transmission regulating unit 15. DETAILED DESCRIPTION

[0017] The present application provides a cloud computing environmental monitoring security synchronization method and platform to solve the problems of insufficient data processing capabilities, low data transmission efficiency and lack of dynamic control mechanism in the prior art, which lead to inaccurate environmental monitoring data and low real-time performance. By performing dynamic adaptive transmission control and source-side preprocessing and cloud-based task calculation, efficient, secure and accurate transmission and processing of environmental monitoring data can be achieved.

[0018] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0019] Embodiment 1

[0020] Please see attached Figure 1 The present application provides a cloud computing environment monitoring security synchronization method, wherein the method is applied to a cloud computing environment monitoring security synchronization platform, and the method specifically includes the following steps:

[0021] S1: Determine distributed monitoring sites in the environmental monitoring area and establish a data transmission topology, wherein the data transmission topology is based on a hierarchical data transmission network from the source to the cloud;

[0022] Specifically, distributed monitoring sites can be used as monitoring sites for cities, rural areas, industrial areas, major traffic routes and other areas to ensure that the environmental conditions of different types of areas can be effectively monitored. The site should reflect the environmental characteristics of the area to which it belongs, taking into account factors such as geography and climate. According to the purpose of monitoring, such as air pollutant monitoring, choose to set up sites in locations far away from pollution sources and with good air flow. Build a data transmission network from the source end, monitoring site to the cloud to ensure that data can be transmitted efficiently and accurately. Use a highly connected network topology to improve the efficiency and reliability of data transmission. Use programmable switches to build network topology. When selecting a data transmission path, try to avoid interference factors such as traffic noise and building obstructions to ensure accurate data transmission.

[0023] S2: Determine a dynamic limit principle, which includes a timestamp management principle based on transit retention and priority avoidance and a channel crossing management principle, and the timestamp management principle is based on the network time protocol;

[0024] Specifically, during the data transmission process, data may be stranded in the transfer node. In order to avoid data congestion and ensure the timeliness of data, timestamps are introduced to manage the retention time of data. Each data packet is timestamped when it is sent to record its sending time. When the transfer node receives the data packet, it will judge its retention time based on the current time and the timestamp of the data packet. If the retention time is too long, take corresponding measures, such as priority transmission or rerouting, to ensure the timely delivery of data. During the peak period of data transmission or network congestion, in order to ensure the timely transmission of critical data, the priority avoidance principle is adopted. That is, different priorities are assigned to data packets according to their importance or urgency. Data packets with high priority will pass through the transfer node first, while data packets with low priority may need to wait or avoid. In the data transmission network between the distributed monitoring site and the cloud, there are multiple channels. In order to avoid interference and conflict between channels, the channel cross management principle is adopted. Channel resources are dynamically allocated according to the data transmission requirements and the real-time status of the channel. For tasks that require a large amount of data transmission, more channel resources are allocated to them; for tasks with a small amount of data, only one or a few channels may be allocated. At the same time, the channel allocation and scheduling strategy will be dynamically adjusted according to the channel load. During the data transmission process, the channel occupancy is monitored in real time to avoid multiple data packets being transmitted simultaneously in the same channel, thereby preventing data conflicts and losses. If a channel conflict is found, the system will immediately adjust and reallocate channel resources. The Network Time Protocol (NTP) is used as the underlying basis for the timestamp management principle. NTP can provide high-precision time correction services to ensure that the time between each monitoring site and the cloud server is synchronized. In this way, the timestamp used in the data transmission process has a unified benchmark, which provides a strong guarantee for the timeliness and accuracy of the data.

[0025] S3: combining the dynamic limit principle with the data transmission topology, training the transmission intelligent control unit, and jointly computing the intelligent control module to form an integrated intelligent control module, wherein the integrated intelligent control module is configured on the cloud computing platform and has an independent computing power domain;

[0026] Specifically, using these principles, the intelligent control unit can dynamically manage the flow of data during data transmission to avoid congestion and conflicts. Based on the established data transmission topology, the intelligent control unit will learn how to most effectively use network resources to ensure that data is efficiently and securely transmitted from the source to the cloud. Use historical data transmission records and environmental monitoring data to train the intelligent control unit so that it can make intelligent decisions based on different network conditions and data characteristics. The computing intelligent control module is responsible for efficient data processing and analysis in the cloud. Combine the transmission intelligent control unit with the computing intelligent control module to form an integrated intelligent control system to achieve intelligent and automated data transmission and processing. Deploy the integrated intelligent control module on the cloud computing platform to utilize its powerful computing power and storage resources. Through the elastic expansion capability of the cloud computing platform, ensure that the integrated intelligent control module can process a large amount of environmental monitoring data. The integrated intelligent control module has an independent computing power domain on the cloud computing platform to ensure that its processing speed and efficiency are not affected by other tasks. Through training and learning, the integrated intelligent control module can make intelligent transmission and processing decisions based on different environments and data characteristics. Combining the dynamic limit principle and data transmission topology, the integrated intelligent control module can efficiently manage the data transmission and processing process, reducing congestion and delays.

[0027] S4: Collect environmental monitoring tasks, combine the integrated intelligent control module to perform relatively independent analysis of the transmission dimension and the calculation dimension, and determine the target strategy;

[0028] Specifically, collect environmental monitoring tasks, such as monitoring air quality, water quality or soil pollution. Combined with the integrated intelligent control module, relatively independent analysis can be performed from the transmission dimension and the computing dimension to determine the optimal target strategy. Transmission dimension analysis evaluates the current network status, including indicators such as bandwidth, latency and packet loss rate. According to the dynamic limit principle, adjust the priority and path of data transmission to ensure the timely transmission of key data. Use encryption technology to ensure the security of data during transmission. Monitor the data transmission process in real time and detect and handle any abnormalities in a timely manner. According to the data transmission topology, optimize resource allocation to avoid waste of network resources. Dynamically adjust the transmission strategy to adapt to changes in network load in different time periods. Computational dimension analysis evaluates the computing resources of the cloud computing platform, including CPU, memory and storage. According to the data volume and processing complexity of the environmental monitoring task, reasonably allocate computing resources. Select appropriate data processing and analysis algorithms to ensure the accuracy of environmental monitoring data. Verify and calibrate the analysis results to improve data quality. Optimize the data processing process to reduce unnecessary computing overhead. Use parallel computing and distributed storage technology to improve computing speed and efficiency. Based on the above analysis of transmission and computing dimensions, the following target strategies can be determined to optimize transmission strategies. According to network conditions and dynamic limit principles, the priority, path and encryption method of data transmission are adjusted to ensure timely, secure and efficient data transmission. Reasonable allocation of computing resources According to the needs of environmental monitoring tasks and the resource status of the cloud computing platform, reasonable allocation of computing resources is carried out to ensure the accuracy and efficiency of data processing.

[0029] S5: Identify the target strategy, perform dynamic adaptive transmission control based on the target transmission strategy, and perform source-end preprocessing and cloud-based task calculation based on the target computing strategy.

[0030] Specifically, based on the dynamic adaptive transmission control of the target transmission strategy, the integrated intelligent control module will continuously monitor the network status, including key indicators such as bandwidth, delay, and packet loss rate. These data will serve as the basis for dynamic adaptive transmission control. According to the real-time monitoring results of the network status, the intelligent control module will dynamically adjust the parameters of data transmission, such as the sending rate and data packet size. When the network is congested, the sending rate or the data packet size may be reduced to reduce the network load. According to the priority avoidance strategy in the dynamic limit principle, different transmission priorities are set for different environmental monitoring data. Critical or urgent data will be given a higher priority to ensure that it is transmitted first in the network. Using the data transmission topology, the intelligent control module will dynamically select the best transmission path according to the network status. If the current path is detected to be congested or the quality is reduced, it will switch to the backup path in time. Source-end preprocessing and cloud-end task computing based on the target computing strategy Before the data is sent to the cloud, the necessary data cleaning, compression, and encryption preprocessing operations are performed at the source end. This helps to reduce the amount of data transmitted, improve data transmission efficiency, and ensure data security. After receiving the preprocessed data, the cloud allocates tasks to different computing nodes according to the allocation of computing resources. Utilize the parallel processing capabilities of the cloud computing platform to efficiently complete the analysis, processing and storage tasks of environmental monitoring data.

[0031] Furthermore, step S1 further includes:

[0032] Traversing the distributed monitoring sites to determine a layer of data transmission network between downstream sensor monitoring equipment and corresponding monitoring sites;

[0033] Determine a layer 2 data transmission network between the distributed monitoring site and the cloud computing platform, wherein the layer 2 data transmission network includes multiple sub-network layers;

[0034] The first-layer data transmission network and the second-layer data transmission network are connected to each other via ports to determine the data transmission topology.

[0035] Specifically, all distributed monitoring sites are traversed to ensure the normal operation of each site, and information such as the location, monitoring equipment, and data transmission capacity of each site are recorded. Inside each monitoring site, a layer of data transmission network is established from the sensor monitoring equipment to the site data concentrator. This layer of network is mainly responsible for transmitting environmental data collected by the sensor equipment, such as temperature, humidity, gas concentration, etc., to the site data concentrator. The first layer of network can use wired or wireless methods for data transmission, depending on the layout and conditions inside the site. Including Zigbee, LoRa, etc., they can provide low-power, long-distance data transmission services. The site data concentrator is responsible for receiving and processing data from each sensor device, and then performing operations such as data compression and encryption to prepare to send the data to the second layer of data transmission network. The second layer of data transmission network is mainly responsible for connecting the data concentrators of each monitoring site to the cloud computing platform. This network includes multiple sub-network layers, each of which is responsible for transmitting data from one node to the next node and finally reaching the cloud computing platform. The second layer of network usually uses higher-level transmission protocols, such as TCP / IP, to ensure reliable data transmission. At the same time, this layer also needs to implement strict security measures such as data encryption and identity authentication. After completing the construction of the first and second layer networks, the ports are connected accordingly. The output port of each monitoring site data concentrator is mapped to the input port of the second layer data transmission network.

[0036] Furthermore, the present application also includes:

[0037] Reading the monitoring synchronization records of a predetermined period to mine transmission collision features, wherein the transmission collision features identify a collision source and a collision position;

[0038] Based on the collision position, the second-layer data transmission network is divided into sub-layers to determine a multi-level transit layer, wherein the multi-level transit layer is used for transmission cross regulation;

[0039] The multi-level transit layers are traversed, and the layer-2 data transmission network is interactively configured layer by layer to determine the multiple sub-network layers, wherein the interaction standard includes the interaction of the transmission channel and the interaction with the lower-level transmission channel.

[0040] Specifically, the monitoring synchronization records within a predetermined period are read. These records should contain all information about the data transmission process, including data packets, timestamps, etc. that were successfully and failed to be transmitted. By analyzing these monitoring records, the characteristics of transmission collisions can be mined. Transmission collisions are usually manifested as packet loss, increased latency, or decreased data transmission rate. It is necessary to identify the collision source, that is, the device or node where the collision occurred and the collision location, in the specific location of the second-layer data transmission network. Based on the mined transmission collision characteristics, it is possible to determine which areas or nodes are prone to collisions. The second-layer data transmission network can be divided into sub-layers according to the collision location. These sub-layers can be used as multi-level transit layers for transmission cross-regulation. The design of the transit layer should be able to relieve data transmission pressure and reduce the possibility of collisions. It is necessary to traverse all multi-level transit layers to ensure that each transit layer works as expected. At each transit layer, a layer-by-layer interaction configuration is performed. This includes determining how each transit layer interacts with the transmission channel between the upper and lower levels. For example, a specific communication protocol, data transmission rate, packet format, etc. can be set. Through the interaction configuration, multiple sub-network layers can be determined. Each sub-network layer has relatively independent data transmission capabilities and can interact effectively with other sub-network layers. Interaction standards include but are not limited to transmission channel interaction, such as communication protocols, data transmission rates, etc., and interaction with lower-level transmission channels, such as data packet formats, error detection and correction mechanisms, etc. These standards should ensure that data can be transmitted smoothly and accurately between all levels.

[0041] Furthermore, step S5 of the present application also includes:

[0042] Constructing a preprocessing model, wherein the preprocessing model includes a plurality of parallel preprocessing branches, each of which is obtained by training based on a configuration algorithm for distinguishing data defect types;

[0043] For the distributed monitoring site, configuring the preprocessing model at the data port;

[0044] Receive the source end environmental data returned by the sensor monitoring data, perform data defect correction and sensitivity processing in combination with the preprocessing model, and determine the effective source end data.

[0045] Specifically, analyze historical data or sample data to identify common data defect types, such as missing values, outliers, and noisy data. For each data defect type, build a preprocessing branch. Use a specific configuration algorithm to handle the corresponding data defect. Use a sample data set containing various data defects to train each preprocessing branch. The goal of training is to enable the branch to accurately identify and correct the corresponding data defects. Integrate all trained preprocessing branches into a unified preprocessing model. This model can handle multiple data defects in parallel. Configure the preprocessing model at the data port of the distributed monitoring site. This ensures that all data returned from the sensor monitoring device will first be processed by the preprocessing model. Deploy the trained preprocessing model to the selected configuration point. Ensure the compatibility of the model with the data port for seamless integration. When the sensor monitoring device returns the data to the distributed monitoring site, the data will first be received by the preprocessing model. The preprocessing model will make corresponding corrections to the data according to the various preprocessing branches within it. For example, fill in missing values, remove or replace outliers, reduce noise, etc. For some sensitive data, such as information involving personal privacy or security, the preprocessing model also needs to perform corresponding desensitization processing to ensure the security and compliance of the data. The data processed by the preprocessing model will be regarded as valid source data. This data will be sent to the next stage for further analysis or storage.

[0046] Furthermore, the present application also includes:

[0047] Identify the target computing strategy and determine data computing requirements;

[0048] In combination with the data computing requirements, the source end environment data is subjected to defect identification, and the preprocessing branch of the mapping is activated to perform data calibration;

[0049] Select sensitive data based on the source environment data, perform data differential privacy processing after data calibration, and determine the valid source data;

[0050] Among them, differential privacy processing methods include:

[0051] In combination with the data computing requirements, determining a differential relaxation based on data utilization and data, wherein the differential relaxation corresponds to a data sensitivity type one by one;

[0052] Based on the differential relaxation, data differential privacy processing is performed.

[0053] Specifically, according to the business scenario and target computing strategy, list specific data computing requirements, such as data aggregation, statistical analysis, trend prediction, etc. Perform quality checks on the received source environment data to identify existing data defects, such as missing values ​​and outliers. According to the identified data defect type, activate the preprocessing branch of the mapping to calibrate the data. Calibration methods include filling missing values, removing or replacing outliers, etc. According to business needs, determine which data is sensitive data. Specially mark and process these sensitive data. Further check the calibrated data to ensure the accuracy and completeness of the data. Combined with data computing requirements, analyze the data utilization and the relaxation requirements for differential privacy. Differential relaxation corresponds to the data sensitivity type one by one, and different types of data may require different degrees of privacy protection. Based on the determined differential relaxation, perform differential privacy processing on the data. Differential privacy processing methods include adding random noise, data aggregation, etc. to protect individual privacy while retaining data availability. Data after differential privacy processing will be regarded as valid source data and can be used for subsequent data analysis and calculation.

[0054] Furthermore, the present application also includes:

[0055] Performing timestamp marking on the valid source data, wherein the timestamp marking includes a transmission timestamp and a synchronization timestamp, and the synchronization timestamp is a synchronization time of the cloud computing platform based on the environmental monitoring task;

[0056] The target transmission strategy is used as the initial strategy and executed, and the timestamp interval and the network time protocol are combined, and dynamic adaptive transmission control is performed based on the transmission intelligent control unit by performing spatiotemporal analysis.

[0057] Specifically, when valid source data is generated or received, a transmission timestamp is immediately added to it. This timestamp records the specific time when the data is sent at the monitoring site or device. In addition to the transmission timestamp, a synchronization timestamp is added. This timestamp is based on the synchronization time of the cloud computing platform for the environmental monitoring task. In order to ensure the accuracy of time, the monitoring site and the cloud computing platform need to maintain time synchronization. This is achieved through the network time protocol NTP. According to business needs and network conditions, an initial target transmission strategy is determined. This strategy includes data transmission frequency, packet size, transmission priority, etc. Using the marked transmission timestamp and synchronization timestamp, the delay and jitter of the data during transmission can be calculated. The network time protocol NTP is used to maintain time synchronization between the site and the cloud computing platform to ensure the accuracy of the timestamp. The transmission intelligent control unit is responsible for dynamically adjusting the transmission strategy based on the current network conditions and historical transmission performance data. Combined with the timestamp data and network status information, spatiotemporal analysis is performed. This includes analyzing the time pattern and spatial distribution of data transmission to identify potential bottlenecks and optimization points. Based on the results of the spatiotemporal analysis, the transmission intelligent control unit dynamically adjusts the transmission strategy. For example, the frequency of sending data packets may be increased or decreased, the size of data packets may be changed, or the data transmission path may be adjusted. These adjustments are intended to optimize data transmission efficiency, reduce delays and packet loss, and ensure the timeliness and integrity of data.

[0058] Furthermore, the present application also includes:

[0059] Retrieve environmental monitoring records for a predetermined period and determine the stability and fault tolerance of the data transmission and data processing dimensions;

[0060] Performing an over-limit judgment on the stability and the fault tolerance rate, and generating a module learning instruction if the lower limit threshold is not met;

[0061] Based on the module learning instruction, the abnormal record part is screened, and clustering processing, common feature mining within the cluster and individual feature mining between clusters are performed to perform incremental learning on the integrated intelligent control module.

[0062] Specifically, retrieve environmental monitoring records within a predetermined period from the database of the environmental monitoring system. These records should include data related to data transmission and data processing. Analyze indicators such as the success rate, failure rate, and delay time during data transmission. Calculate the stability of data transmission based on these indicators, for example, by evaluating by counting the proportion of successfully transmitted data packets. Calculate the fault tolerance of data processing, for example, by counting the proportion of abnormal situations that the system can correctly handle. Set the lower limit threshold of stability and fault tolerance based on historical data and business requirements. Compare the calculated stability and fault tolerance with the lower limit threshold. If any indicator does not meet the lower limit threshold, it is determined to be over-limit. When it is determined to be over-limit, generate module learning instructions to trigger the subsequent incremental learning process. Based on the module learning instructions, filter out the abnormal record part from the environmental monitoring records. Cluster the filtered abnormal records and classify similar abnormal records into one category. Perform common feature mining on each cluster to find out the common cause of the abnormality. Perform individual feature mining on different clusters to identify the abnormal patterns unique to each cluster. Use the mined common and individual features to perform incremental learning on the integrated intelligent control module. Through learning, the module can better identify and handle similar abnormal situations, improving the stability and fault tolerance of the system.

[0063] In summary, the cloud computing environment monitoring security synchronization method provided by this application has the following technical effects:

[0064] By determining the distributed monitoring sites in the environmental monitoring area, a data transmission topology is established, wherein the data transmission topology is based on a hierarchical data transmission network from the source to the cloud; a dynamic limiting principle is determined, and the dynamic limiting principle includes a timestamp management principle based on transit retention and priority avoidance and a channel crossing management principle, and the timestamp management principle is based on the network time protocol as the underlying basis; in combination with the dynamic limiting principle and the data transmission topology, a transmission intelligent control unit is trained, and a computing intelligent control module is combined to form an integrated intelligent control module, and the integrated intelligent control module is configured on a cloud computing platform and has an independent computing power field; environmental monitoring tasks are collected, and relatively independent analysis of transmission dimensions and computing dimensions is performed in combination with the integrated intelligent control module to determine the target strategy; the target strategy is identified, and dynamic adaptive transmission control based on the target transmission strategy is executed, and source preprocessing and cloud task calculation based on the target calculation strategy are executed, which effectively solves the problems of insufficient data processing capabilities, low data transmission efficiency and lack of dynamic control mechanism in the prior art, resulting in inaccurate environmental monitoring data and low real-time performance. By executing dynamic adaptive transmission control, and based on source preprocessing and cloud task calculation, efficient, secure and accurate transmission and processing of environmental monitoring data can be achieved.

[0065] Embodiment 2

[0066] Based on the same inventive concept as the cloud computing environment monitoring security synchronization method in the aforementioned embodiment, the present application also provides a cloud computing environment monitoring security synchronization platform, please refer to the attached Figure 2 , the platform comprises:

[0067] A data transmission topology establishing unit 11, wherein the data transmission topology establishing unit 11 is used to determine distributed monitoring sites in the environmental monitoring area and establish a data transmission topology, wherein the data transmission topology is based on a hierarchical data transmission network from the source end to the cloud end;

[0068] A principle acquisition unit 12, the principle acquisition unit 12 is used to determine a dynamic limit principle, the dynamic limit principle includes a timestamp management principle based on transit retention and priority avoidance and a channel crossing management principle, the timestamp management principle is based on a network time protocol as an underlying basis;

[0069] A computing platform configuration unit 13, the computing platform configuration unit 13 is used to combine the dynamic limit principle with the data transmission topology, train the transmission intelligent control unit, and combine the computing intelligent control module to form an integrated intelligent control module, the integrated intelligent control module is configured on the cloud computing platform, and has an independent computing power field;

[0070] An independent analysis unit 14, which is used to collect environmental monitoring tasks, perform relatively independent analysis of the transmission dimension and the calculation dimension in combination with the integrated intelligent control module, and determine the target strategy;

[0071] The adaptive transmission control unit 15 is used to identify the target strategy, perform dynamic adaptive transmission control based on the target transmission strategy, and perform source end preprocessing and cloud task calculation based on the target calculation strategy.

[0072] Furthermore, the data transmission topology establishing unit 11 in the platform is also used for:

[0073] Traversing the distributed monitoring sites to determine a layer of data transmission network between downstream sensor monitoring equipment and corresponding monitoring sites;

[0074] Determine a layer 2 data transmission network between the distributed monitoring site and the cloud computing platform, wherein the layer 2 data transmission network includes multiple sub-network layers;

[0075] The first-layer data transmission network and the second-layer data transmission network are connected to each other via ports to determine the data transmission topology.

[0076] Furthermore, the platform further includes a cross-regulation unit, which is used to:

[0077] Reading the monitoring synchronization records of a predetermined period to mine transmission collision features, wherein the transmission collision features identify a collision source and a collision position;

[0078] Based on the collision position, the second-layer data transmission network is divided into sub-layers to determine a multi-level transit layer, wherein the multi-level transit layer is used for transmission cross regulation;

[0079] The multi-level transit layers are traversed, and the layer-2 data transmission network is interactively configured layer by layer to determine the multiple sub-network layers, wherein the interaction standard includes the interaction of the transmission channel and the interaction with the lower-level transmission channel.

[0080] Furthermore, the adaptive transmission control unit 15 in the platform is also used for:

[0081] Constructing a preprocessing model, wherein the preprocessing model includes a plurality of parallel preprocessing branches, each of which is obtained by training based on a configuration algorithm for distinguishing data defect types;

[0082] For the distributed monitoring site, configuring the preprocessing model at the data port;

[0083] Receive the source end environmental data returned by the sensor monitoring data, perform data defect correction and sensitivity processing in combination with the preprocessing model, and determine the effective source end data.

[0084] Furthermore, the platform further includes a valid source data determination unit, and the valid source data determination unit is used to:

[0085] Identify the target computing strategy and determine data computing requirements;

[0086] In combination with the data computing requirements, the source end environment data is subjected to defect identification, and the preprocessing branch of the mapping is activated to perform data calibration;

[0087] Select sensitive data based on the source environment data, perform data differential privacy processing after data calibration, and determine the valid source data;

[0088] Among them, differential privacy processing methods include:

[0089] In combination with the data computing requirements, determining a differential relaxation based on data utilization and data, wherein the differential relaxation corresponds to a data sensitivity type one by one;

[0090] Based on the differential relaxation, data differential privacy processing is performed.

[0091] Furthermore, the platform further includes an adaptive transmission control unit, and the adaptive transmission control unit is used to:

[0092] Performing timestamp marking on the valid source data, wherein the timestamp marking includes a transmission timestamp and a synchronization timestamp, and the synchronization timestamp is a synchronization time of the cloud computing platform based on the environmental monitoring task;

[0093] The target transmission strategy is used as the initial strategy and executed, and the timestamp interval and the network time protocol are combined, and dynamic adaptive transmission control is performed based on the transmission intelligent control unit by performing spatiotemporal analysis.

[0094] Furthermore, the platform further includes an incremental learning unit, and the incremental learning unit is used to:

[0095] Retrieve environmental monitoring records for a predetermined period and determine the stability and fault tolerance of the data transmission and data processing dimensions;

[0096] Performing an over-limit judgment on the stability and the fault tolerance rate, and generating a module learning instruction if the lower limit threshold is not met;

[0097] Based on the module learning instruction, the abnormal record part is screened, and clustering processing, common feature mining within the cluster and individual feature mining between clusters are performed to perform incremental learning on the integrated intelligent control module.

[0098] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The cloud computing environment monitoring security synchronization method and specific examples in the first embodiment are also applicable to the cloud computing environment monitoring security synchronization platform of the present embodiment. Through the above detailed description of the cloud computing environment monitoring security synchronization method, those skilled in the art can clearly know the cloud computing environment monitoring security synchronization platform of the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the platform disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0099] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0100] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A cloud computing environment monitoring security synchronization method, characterized in that: The method comprises: Determine distributed monitoring sites in the environmental monitoring area and establish a data transmission topology, wherein the data transmission topology is based on a hierarchical data transmission network from the source to the cloud; Determine dynamic limiting principles, including timestamp management principles based on transit retention and priority avoidance and channel crossing management principles, and the timestamp management principles are based on the network time protocol as the underlying basis; Combining the dynamic limit principle with the data transmission topology, training the transmission intelligent control unit, and combining the computing intelligent control module to form an integrated intelligent control module, the integrated intelligent control module is configured on the cloud computing platform and has an independent computing power domain; Collect environmental monitoring tasks, combine the integrated intelligent control module to perform relatively independent analysis of the transmission dimension and the calculation dimension, and determine the target strategy; Identify the target strategy, perform dynamic adaptive transmission control based on the target transmission strategy, and perform source-end preprocessing and cloud-based task calculation based on the target computing strategy.

2. The method according to claim 1, characterized in that The establishing of a data transmission topology comprises: Traversing the distributed monitoring sites to determine a layer of data transmission network between downstream sensor monitoring equipment and corresponding monitoring sites; Determine a layer 2 data transmission network between the distributed monitoring site and the cloud computing platform, wherein the layer 2 data transmission network includes a plurality of sub-network layers; The first-layer data transmission network and the second-layer data transmission network are connected to each other via ports to determine the data transmission topology.

3. The method according to claim 2, characterized in that The layer 2 data transmission network includes multiple sub-network layers, including: Reading the monitoring synchronization records of a predetermined period to mine transmission collision features, wherein the transmission collision features identify a collision source and a collision position; Based on the collision position, the second-layer data transmission network is divided into sub-layers to determine a multi-level transit layer, wherein the multi-level transit layer is used for transmission cross regulation; The multi-level transit layers are traversed, and the layer-2 data transmission network is interactively configured layer by layer to determine the multiple sub-network layers, wherein the interaction standard includes the interaction of the transmission channel and the interaction with the lower-level transmission channel.

4. The method according to claim 1, characterized in that The source-end preprocessing based on the target computing strategy includes: Constructing a preprocessing model, wherein the preprocessing model includes a plurality of parallel preprocessing branches, each of which is obtained by training based on a configuration algorithm for distinguishing data defect types; For the distributed monitoring site, configuring the preprocessing model at the data port; Receive the source end environmental data returned by the sensor monitoring data, perform data defect correction and sensitivity processing in combination with the preprocessing model, and determine the effective source end data.

5. The method according to claim 4, characterized in that The data defect correction and sensitivity processing in combination with the preprocessing model includes: Identify the target computing strategy and determine data computing requirements; In combination with the data computing requirements, the source end environment data is subjected to defect identification, and the preprocessing branch of the mapping is activated to perform data calibration; Select sensitive data based on the source environment data, perform data differential privacy processing after data calibration, and determine the valid source data; Among them, differential privacy processing methods include: In combination with the data computing requirements, determining a differential relaxation based on data utilization and data, wherein the differential relaxation corresponds to a data sensitivity type one by one; Based on the differential relaxation, data differential privacy processing is performed.

6. The method according to claim 5, characterized in that Performing timestamp marking on the valid source data, wherein the timestamp marking includes a transmission timestamp and a synchronization timestamp, and the synchronization timestamp is a synchronization time of the cloud computing platform based on the environmental monitoring task; The target transmission strategy is used as the initial strategy and executed, and the timestamp interval and the network time protocol are combined, and dynamic adaptive transmission control is performed based on the transmission intelligent control unit by performing spatiotemporal analysis.

7. The method according to claim 1, characterized in that The method further comprises: Retrieve environmental monitoring records for a predetermined period and determine the stability and fault tolerance of the data transmission and data processing dimensions; Performing an over-limit judgment on the stability and the fault tolerance rate, and generating a module learning instruction if the lower limit threshold is not met; Based on the module learning instruction, the abnormal record part is screened, and clustering processing, common feature mining within the cluster and individual feature mining between clusters are performed to perform incremental learning on the integrated intelligent control module.

8. A cloud computing environment monitoring security synchronization platform, characterized in that: For implementing the steps of the method according to any one of claims 1 to 7, the platform comprises: A data transmission topology establishing unit, the data transmission topology establishing unit is used to determine distributed monitoring sites in the environmental monitoring area and establish a data transmission topology, wherein the data transmission topology is based on a hierarchical data transmission network from the source end to the cloud end; A principle acquisition unit, the principle acquisition unit is used to determine a dynamic limit principle, the dynamic limit principle includes a timestamp management principle based on transit retention and priority avoidance and a channel crossing management principle, the timestamp management principle is based on the network time protocol as the underlying basis; A computing platform configuration unit, the computing platform configuration unit is used to combine the dynamic limit principle with the data transmission topology, train the transmission intelligent control unit, and combine the computing intelligent control module to form an integrated intelligent control module, the integrated intelligent control module is configured on the cloud computing platform, and has an independent computing power field; An independent analysis unit, which is used to collect environmental monitoring tasks, and to perform relatively independent analysis of the transmission dimension and the calculation dimension in combination with the integrated intelligent control module to determine the target strategy; An adaptive transmission control unit, the adaptive transmission control unit is used to identify the target strategy, perform dynamic adaptive transmission control based on the target transmission strategy, and source end preprocessing and cloud task calculation based on the target calculation strategy.

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