An Internet of Things-based spatio-temporal information database management system and method

Abstract

The present invention discloses an Internet of Things-based spatio-temporal information database management system and method, which relates to the technical field of electrical digital data processing. First, the spatio-temporal information of each data collection point and the operation and maintenance information of each edge computing node are obtained, which can more accurately allocate network resources and computing resources, optimize the data transmission path, improve the data transmission efficiency of the database management system, and then perform preprocessing configuration on the target receiving data collection point clusters of each edge computing node to improve the overall data processing speed of the database. By synchronously retrieving and analyzing the operation data of each storage port, possible storage performance problems can be discovered in a timely manner for storage optimization. Finally, the cluster spatio-temporal information is managed by distributed storage, which helps to balance the data access load and improve the response speed and throughput of the spatio-temporal information database management system.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical digital data processing, and specifically to an Internet of Things-based spatio-temporal information database management system and method. Background Art

[0002] With the rapid popularization of intelligent devices and the continuous development of network communication technologies, it is crucial to efficiently and accurately manage and analyze large amounts of spatio-temporal data. Spatio-temporal information data usually includes the status information of objects at specific time and space positions, and is widely used in multiple fields such as smart cities, intelligent transportation, and environmental monitoring. The Internet of Things-based spatio-temporal information database management system can realize the real-time collection, storage, and analysis of spatio-temporal data by combining sensor data with geographic information system technology, improving the application ability of the Internet of Things in intelligent management.

[0003] The prior art, such as the invention patent with the publication number: CN106528793B, discloses a spatio-temporal sharding storage method for a distributed spatial database. The method includes a data storage step and a data migration step. In the data storage of the distributed spatial database, the spatio-temporal characteristics of spatial data are fully considered, and the spatio-temporal information of spatial data is integrated into the data sharding of the distributed database. While multiple database servers perform parallel queries, the server performance is fully utilized, and at the same time, the change of data interest conditions is considered through the data migration step.

[0004] The prior art, such as the invention patent with the publication number: CN113609126B, discloses an integrated storage management method and system for crowdsourced spatio-temporal data. The method includes: analyzing and integrating the data structure of the initial spatio-temporal data to obtain standard spatio-temporal data; constructing a crowdsourced heterogeneous spatio-temporal database according to the standard spatio-temporal data after corresponding level processing; storing the data processing logic lines respectively corresponding to the standard spatio-temporal data and the initial spatio-temporal data into the crowdsourced heterogeneous spatio-temporal database; retrieving the first newly added spatio-temporal data to obtain the first data processing logic line; performing data processing on the first newly added spatio-temporal data according to the first data processing logic line to obtain standard newly added spatio-temporal data; and storing the standard newly added spatio-temporal data into the crowdsourced heterogeneous spatio-temporal database.

[0005] Combining the above solutions, it is found that in the current technical field of spatio-temporal information database management, usually only the data query process is monitored and analyzed. However, there will be data transmission problems during the collection process of spatio-temporal information data, resulting in the ineffective synchronization and update of spatio-temporal information, which not only affects the timeliness and accuracy of data, and cannot receive key spatio-temporal data changes in a timely manner, but may also lead to the lag of operation and maintenance information, with the risks of data loss and processing delay, affecting the overall efficiency and reliability of the system. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a spatio-temporal information database management system based on the Internet of Things, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: A spatio-temporal information database management system based on the Internet of Things, including a data acquisition module, which is used to count each data acquisition point and each edge computing node connected to the Internet of Things, and obtain the spatio-temporal information of each data acquisition point and the operation and maintenance information of each edge computing node.

[0008] A target received data analysis module, which is used to process based on the spatio-temporal information of each data acquisition point and the operation and maintenance information of each edge computing node to obtain a target received data acquisition point cluster of each edge computing node.

[0009] A spatio-temporal information processing and transmission module, which is used to preprocess and configure the spatio-temporal information sent by the target received data acquisition point cluster of each edge computing node, and after the preprocessing and configuration, count the cluster spatio-temporal information of each edge computing node and upload it to the spatio-temporal information database storage port to be stored.

[0010] A distributed storage management module, which is used to synchronously retrieve and analyze the operation data of each storage port through the spatio-temporal information database storage port to be stored, and perform distributed storage management on the cluster spatio-temporal information.

[0011] Further, the spatio-temporal information of each data acquisition point includes the data acquisition frequency, data volume, and data transmission rate of each data acquisition point.

[0012] The operation and maintenance information of each edge computing node includes the memory utilization rate, bandwidth utilization rate, packet processing rate, and CPU utilization rate of each edge computing node.

[0013] Further, the process of processing based on the spatio-temporal information of each data acquisition point and the operation and maintenance information of each edge computing node is as follows: Based on the spatio-temporal information of each data acquisition point, the basic characteristic values of each data acquisition point are processed, and the basic characteristic values of each data acquisition point are used to comprehensively quantify the data acquisition efficiency of each data acquisition point.

[0014] Classify each data acquisition point according to the sensor type to obtain each data acquisition point corresponding to each sensor type, take the average value of the basic characteristic values of each data acquisition point corresponding to each sensor type to obtain the basic characteristic average value of the data acquisition point corresponding to each sensor type, and record it as the comprehensive characteristic value of the data acquisition point corresponding to each sensor type.

[0015] Comprehensively analyze the operation and maintenance information of each edge computing node to obtain the data processing energy efficiency characterization value of each edge computing node, and the data processing energy efficiency characterization value of each edge computing node is used to comprehensively quantify the data processing ability of each edge computing node.

[0016] Further, the process of obtaining the target receiving data collection point cluster of each edge computing node is as follows: Match the data processing energy efficiency characterization value of each edge computing node with the range of the target receiving comprehensive characteristic value of the data collection point corresponding to each data processing energy efficiency characterization value interval stored in the information management database, and count the range of the target receiving comprehensive characteristic value of the data collection point corresponding to the data processing energy efficiency characterization value interval of each edge computing node, which is recorded as the range of the target receiving comprehensive characteristic value of the data collection point of each edge computing node.

[0017] Match the comprehensive characteristic value of the data collection point corresponding to each sensor type with the range of the target receiving comprehensive characteristic value of the data collection point of each edge computing node. If the comprehensive characteristic value of the data collection point corresponding to a certain sensor type is within the range of the target receiving comprehensive characteristic value of the data collection point of a certain edge computing node, then define this sensor type as the target receiving sensor type of this edge computing node, and sequentially traverse and count all the target receiving sensor types of each edge computing node and all the data collection points corresponding to all the target receiving sensor types.

[0018] Unify the data collection points corresponding to all the target receiving sensor types of each edge computing node and record them as the target receiving data collection points corresponding to each edge computing node, thereby integrating the target receiving data collection point cluster of each edge computing node.

[0019] Further, the process of preprocessing and configuring the spatio-temporal information sent by the target receiving data collection point cluster of each edge computing node is as follows: Take the average value of the basic characteristic values of the target receiving data collection points corresponding to each edge computing node to obtain the data transmission evaluation value of each edge computing node.

[0020] Compare the data transmission evaluation value of each edge computing node with the set data transmission evaluation threshold. If the data transmission evaluation value of this edge computing node is lower than the set data transmission evaluation threshold, then continue to preprocess and configure the data of this edge computing node with the current data preprocessing parameters. If the data transmission evaluation value of this edge computing node is higher than or equal to the set data transmission evaluation threshold, then preprocess and configure the data of this edge computing node with the preset data preprocessing parameters in the information management database.

[0021] Further, after the preprocessing configuration, the cluster spatio-temporal information of each edge computing node is counted and uploaded to the storage port of the spatio-temporal information database. The specific process is as follows: After the preprocessing configuration, a data upload feasible signal is output. The spatio-temporal information database receives the data upload feasible signal and transmits the cluster spatio-temporal information of each edge computing node to the storage port of the spatio-temporal information database respectively.

[0022] Further, the storage port of the spatio-temporal information database synchronously retrieves the operation data of each storage port for analysis. The specific process is as follows: The operation data of each storage port includes the memory utilization rate, transmission delay duration, data read / write speed, and data transmission rate of each storage port.

[0023] Based on the operation data of each storage port, the storage performance benchmark value of each storage port is processed. The storage performance benchmark value of each storage port is used to comprehensively quantify the utilization degree of the storage capacity of each storage port.

[0024] Count the cluster spatio-temporal information data of each edge computing node. The cluster spatio-temporal information data of each edge computing node includes the data processing energy efficiency characterization value of each edge computing node and the number of target receiving data acquisition points.

[0025] Comprehensively analyze the cluster spatio-temporal information data of each edge computing node to obtain the cluster spatio-temporal information evaluation value of each edge computing node. The cluster spatio-temporal information evaluation value of each edge computing node is used to comprehensively quantify the comprehensive performance of each edge computing node.

[0026] Match the storage performance verification value of each edge computing node according to the cluster spatio-temporal information evaluation value of each edge computing node.

[0027] Compare the storage performance benchmark value of each storage port with the storage performance verification value of each edge computing node. If the storage performance benchmark value of a certain storage port is higher than and closest to the storage performance verification value of a certain edge computing node, then mark this storage port as the target storage port of this edge computing node. Traverse in turn to obtain the target storage port of each edge computing node, and transmit the cluster spatio-temporal information of each edge computing node to the corresponding target storage port.

[0028] Further, for the storage performance benchmark value of each storage port, the specific analysis conditions are as follows:

[0029] ;

[0030] In the formula, represents the storage performance benchmark value of the i-th storage port, represents the memory utilization rate of the i-th storage port, represents the reference memory utilization rate of the i-th storage port set, Represents the transmission delay duration of the i-th storage port, Represents the correction factor corresponding to the set transmission delay duration, Represents the data read / write speed of the i-th storage port, Represents the correction factor corresponding to the set data read / write speed, Represents the data transmission rate of the i-th storage port, Represents the correction factor corresponding to the set data transmission rate, where i represents the number of each storage port, , n represents the total number of storage ports, and e represents the natural constant.

[0031] An Internet of Things-based spatio-temporal information database management system further includes: a load balancing module for counting each data storage node in the spatio-temporal information database management system and obtaining the load parameters of each data storage node for load balancing configuration. The specific process is: based on the load parameters of each data storage node, processing to obtain the load evaluation value of each data storage node, and performing load balancing configuration on each data storage node according to the load evaluation value of each data storage node.

[0032] Further, a second aspect of the present invention provides an Internet of Things-based spatio-temporal information database management method, including: counting each data collection point and each edge computing node connected to the Internet of Things, and obtaining the spatio-temporal information of each data collection point and the operation and maintenance information of each edge computing node.

[0033] Processing based on the spatio-temporal information of each data collection point and the operation and maintenance information of each edge computing node to obtain the target receiving data collection point cluster of each edge computing node.

[0034] Performing preprocessing configuration on the spatio-temporal information sent by the target receiving data collection point cluster of each edge computing node, and counting the cluster spatio-temporal information of each edge computing node after preprocessing configuration and uploading it to the spatio-temporal information database storage port to be stored.

[0035] Synchronously retrieving the operation data of each storage port through the spatio-temporal information database storage port to be stored for analysis, and performing distributed storage management on the cluster spatio-temporal information.

[0036] The present invention has the following beneficial effects:

[0037] (1) The present invention provides an Internet of Things-based spatio-temporal information database management system and method. First, the target receiving data collection point clusters of each edge computing node are processed to improve the efficiency and orderliness of data stream transmission. Furthermore, preprocessing configuration is performed on the sent spatio-temporal information, which helps to improve the data transmission rate. Then, the spatio-temporal information of the clusters is statistically uploaded to the storage port of the spatio-temporal information database, enabling centralized data management. Finally, the spatio-temporal information of the clusters is distributed for storage management, improving the reliability of data storage.

[0038] (2) By processing to obtain the target receiving data collection point clusters of each edge computing node and allocating data according to the actual processing capabilities of the edge computing nodes, the present invention can accurately allocate suitable data collection points to the edge computing nodes with the most processing capabilities, maximizing the utilization of the processing capabilities of each edge computing node, improving the data processing efficiency of the edge computing nodes, reducing the situations of overloading and inefficient resource usage, and optimizing the utilization of the computing resources of the edge computing nodes.

[0039] (3) By performing preprocessing configuration on the spatio-temporal information sent by the target receiving data collection point clusters of each edge computing node, the present invention can reduce the redundancy of data transmission, lighten the network burden, thereby accelerating the data transmission speed. After the spatio-temporal information of the data collection points is effectively processed, it can better reflect the actual state of the device and environmental changes, reducing errors and losses during data transmission and enhancing the reliability of data transmission.

[0040] (4) By synchronously retrieving and analyzing the operation data of each storage port through the storage port of the spatio-temporal information database and performing distributed storage management on the spatio-temporal information of the clusters, the present invention can optimize the storage and allocation of data, reduce the waste of storage resources, optimize the data flow and storage scheduling process, enhance the overall data transmission efficiency of the system, and the accurate spatio-temporal information and analysis of the data collection point clusters can arrange storage resources more reasonably.

[0041] Of course, it is not necessary for any product implementing the present invention to simultaneously achieve all the above-mentioned advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the connection of the system modules of the present invention.

[0043] Figure 2 It is a schematic diagram of the method flow of the present invention.

[0044] Figure 3 It is an example diagram of the change of the data volume of the data collection point over time. DETAILED DESCRIPTION OF THE INVENTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] Please refer to Figure 1 As shown, the embodiments of the present invention provide a technical solution: a spatio-temporal information database management system based on the Internet of Things, including a data acquisition module, which is used to count each data acquisition point and each edge computing node connected to the Internet of Things, and obtain the spatio-temporal information of each data acquisition point and the operation and maintenance information of each edge computing node.

[0047] A target received data analysis module, which is used to process based on the spatio-temporal information of each data acquisition point and the operation and maintenance information of each edge computing node to obtain a target received data acquisition point cluster of each edge computing node.

[0048] A spatio-temporal information processing and transmission module, which is used to perform preprocessing configuration on the spatio-temporal information sent by the target received data acquisition point cluster of each edge computing node, and after the preprocessing configuration, count the cluster spatio-temporal information of each edge computing node and upload it to the waiting storage port of the spatio-temporal information database.

[0049] A distributed storage management module, which is used to synchronously retrieve and analyze the operation data of each storage port through the waiting storage port of the spatio-temporal information database, and perform distributed storage management on the cluster spatio-temporal information.

[0050] Specifically, the spatio-temporal information of each data acquisition point includes the data acquisition frequency, data volume, and data transmission rate of each data acquisition point.

[0051] It should be noted that Figure 3 is an example diagram of the change of the data volume of the data acquisition point over time. As Figure 3 shown, where the horizontal axis represents the data acquisition time, the vertical axis represents the data volume of the data acquisition point, and the corresponding curve label is a.

[0052] It should be noted that the spatio-temporal information of each data acquisition point also includes time information (such as time stamps, acquisition periods), space information (such as geographical coordinates, altitude), and environmental condition information (such as temperature, humidity).

[0053] It should be noted that the data acquisition frequency of each data acquisition point and the data volume of each data acquisition point can be measured through the system log of the data acquisition system, and the data transmission rate of each data acquisition point can be measured through a network traffic monitoring tool.

[0054] The operation and maintenance information of each edge computing node includes the memory utilization rate, bandwidth utilization rate, data packet processing rate, and CPU utilization rate of each edge computing node.

[0055] It should be noted that the memory utilization rate of each edge computing node reflects the consumption of memory resources when the edge computing node processes data, while the bandwidth utilization rate of each edge computing node reflects the data transmission efficiency of the edge computing node. The data packet processing rate of each edge computing node shows the speed at which the edge computing node processes data, and the CPU utilization rate of each edge computing node is a direct measure of the computing power of the edge computing node. The memory utilization rate, bandwidth utilization rate, data packet processing rate, and CPU utilization rate of each edge computing node can be measured through resource monitoring tools provided by the operating system. For example, the free command in the Linux operating system can measure the memory utilization rate of each edge computing node, and the iptraf command in the Linux operating system can measure the bandwidth utilization rate and data packet processing rate of each edge computing node.

[0056] Specifically, based on the spatio-temporal information of each data collection point and the operation and maintenance information of each edge computing node for processing, the specific process is as follows: based on the spatio-temporal information of each data collection point, the basic characteristic value of each data collection point is processed, and the basic characteristic value of each data collection point is used to comprehensively quantify the data collection efficiency of each data collection point.

[0057] It should be added that in the process of analyzing the basic characteristic value of each data collection point, the data collection frequency, data volume, and data transmission rate of each data collection point are all de-unified.

[0058] In this embodiment, the basic characteristic value of each data collection point can be obtained through the following analysis method, and the specific analysis conditions are as follows:

[0059] ;

[0060] In the formula, represents the basic characteristic value of the jth data collection point, represents the data collection frequency of the jth data collection point, represents the weight factor corresponding to the set data collection frequency, represents the data volume of the jth data collection point, represents the weight factor corresponding to the set data volume, represents the data transmission rate of the jth data collection point, represents the weight factor corresponding to the set data transmission rate, j represents the number of each data collection point, , represents the total number of data collection points.

[0061] It should be noted that the higher the data acquisition frequency of each data acquisition point, the more data is acquired per unit time, and higher data processing speed and greater processing capacity are required to process the data in real time, and the basic eigenvalue is also larger. The larger the data volume of each data acquisition point, the more information needs to be stored and processed, the demand for data processing capacity increases, and the basic eigenvalue is also larger. The higher the data transmission rate of each data acquisition point, the faster the data transmission speed, and more bandwidth and data processing capacity are required to receive and process the data, and the basic eigenvalue is also larger.

[0062] It should be added that in this embodiment, the weight factors corresponding to the preset data acquisition frequency, data volume, and data transmission rate are obtained from the information management database.

[0063] It should be explained that the weight factors corresponding to the data acquisition frequency, data volume, and data transmission rate are respectively used to adjust the importance of the data acquisition frequency, data volume, and data transmission rate of each data acquisition point in the process of analyzing the basic eigenvalue. There is a pre-set mapping relationship between the spatio-temporal information of each real-time data acquisition point and the corresponding weight factor in the information management database. Through the pre-set mapping relationship, the weight factor corresponding to the spatio-temporal information of each real-time data acquisition point can be matched. For example, the data acquisition frequency, data volume, and data transmission rate of each data acquisition point are respectively matched with the pre-set mapping relationship to obtain the weight factors corresponding to the data acquisition frequency, data volume, and data transmission rate of each data acquisition point.

[0064] In this implementation plan, there is a correlation between the data acquisition frequency, data volume, and data transmission rate of each data acquisition point, and they do not exist independently. For example, the level of the data acquisition frequency directly affects the amount of data generated per unit time, and the increase in data volume has a higher demand for the data transmission rate. Data acquisition points with high data acquisition frequencies will generate a large amount of data, and a higher data transmission rate is required to ensure real-time data transmission. Lower data transmission rates may lead to data backlogs and delays. By comprehensively analyzing the basic eigenvalues of each data acquisition point, the accuracy of analyzing the actual workload and performance bottlenecks of the data acquisition points can be improved, thereby providing data support for optimizing data acquisition methods.

[0065] Classify each data acquisition point according to the sensor type to obtain the data acquisition points corresponding to each sensor type. Take the average value of the basic eigenvalues of the data acquisition points corresponding to each sensor type to obtain the basic feature average value of the data acquisition points corresponding to each sensor type, which is recorded as the comprehensive feature value of the data acquisition points corresponding to each sensor type.

[0066] It should be added that obtaining the comprehensive characteristic values of the data acquisition points corresponding to each sensor type can reflect the average level of each sensor type in terms of data acquisition efficiency.

[0067] It should be noted that the sensor types include, but are not limited to, satellite positioning sensors, environmental sensors (such as temperature and humidity sensors, gas sensors, pressure sensors, etc.), vision sensors (such as cameras, lidar, etc.), and acoustic sensors (such as microphones, acoustic wave sensors, etc.).

[0068] Performing comprehensive analysis on the operation and maintenance information of each edge computing node to obtain the data processing energy efficiency characterization value of each edge computing node, and the data processing energy efficiency characterization value of each edge computing node is used to comprehensively quantify the data processing ability of each edge computing node.

[0069] In this embodiment, the data processing energy efficiency characterization value of each edge computing node can be obtained through the following analysis method, and the specific analysis conditions are as follows:

[0070] ;

[0071] In the formula, represents the data processing energy efficiency characterization value of the kth edge computing node, represents the memory utilization rate of the kth edge computing node, represents the reference memory utilization rate of the kth edge computing node set, represents the bandwidth utilization rate of the kth edge computing node, represents the reference bandwidth utilization rate of the kth edge computing node set, represents the data packet processing rate of the kth edge computing node, represents the reference data packet processing rate of the kth edge computing node set, represents the CPU utilization rate of the kth edge computing node, represents the reference CPU utilization rate of the kth edge computing node set, k represents the number of each edge computing node, , represents the total number of edge computing nodes.

[0072] It should be noted that the greater the absolute value of the difference between the operation and maintenance information of each edge computing node and the reference operation and maintenance information corresponding to the operation and maintenance information, the smaller the data processing energy efficiency characterization value of each edge computing node. For example, the greater the absolute value of the difference between the memory utilization rate of the edge computing node and the reference memory utilization rate, the less effectively the memory resources of the edge computing node are utilized. It may be that the memory is excessive, resulting in resource waste, or the memory is insufficient, resulting in limited processing capacity, thereby reducing the data processing energy efficiency characterization value of the edge computing node. The greater the absolute value of the difference between the bandwidth utilization rate of the edge computing node and the reference bandwidth utilization rate, the less effectively the bandwidth of the node may be utilized. It may be that the bandwidth is excessive, resulting in increased costs, or the bandwidth is insufficient, resulting in data transmission delays, which will also lead to a decrease in the data processing energy efficiency characterization value of the edge computing node. The combined effect of these parameters constitutes the data processing energy efficiency characterization value of the edge computing node, which can effectively evaluate the data processing ability of the edge computing node.

[0073] In this implementation plan, there is a correlation among the memory utilization rate, bandwidth utilization rate, packet processing rate, and CPU utilization rate of each edge computing node, and they do not exist independently. For example, due to high-load computing tasks often requiring more memory support, it will increase the CPU load. Therefore, the memory utilization rate and CPU utilization rate usually show a certain positive correlation. A high bandwidth utilization rate indicates that the node needs to process higher-frequency data streams, which may cause bottlenecks in the memory and CPU when processing network data. An excessively high memory utilization rate leads to an increase in the CPU utilization rate, while a low bandwidth utilization rate limits the improvement of the packet processing rate. By comprehensively analyzing the data processing energy efficiency characterization value of each edge computing node, the comprehensive data processing ability of the edge computing node in actual applications can be reflected, providing a data basis for improving the overall efficiency and stability of the system.

[0074] Specifically, to obtain the target receiving data collection point cluster of each edge computing node, the specific process is as follows: Match the data processing energy efficiency characterization value of each edge computing node with the range of the target receiving comprehensive characteristic values of the data collection points corresponding to each data processing energy efficiency characterization value interval stored in the information management database, and count the range of the target receiving comprehensive characteristic values of the data collection points corresponding to the data processing energy efficiency characterization value interval of each edge computing node, which is recorded as the range of the target receiving comprehensive characteristic values of the data collection points of each edge computing node.

[0075] Match the comprehensive eigenvalue of the data acquisition points corresponding to each sensor type with the target receiving comprehensive eigenvalue range of the data acquisition points of each edge computing node. If the comprehensive eigenvalue of the data acquisition points corresponding to a certain sensor type falls within the target receiving comprehensive eigenvalue range of the data acquisition points of a certain edge computing node, then define this sensor type as the target receiving sensor type of this edge computing node, and traverse and count all the target receiving sensor types of each edge computing node and all the data acquisition points corresponding to all the target receiving sensor types in turn.

[0076] Uniformly record the data acquisition points corresponding to all the target receiving sensor types of each edge computing node as the respective target receiving data acquisition points corresponding to each edge computing node, and thus integrate the target receiving data acquisition point clusters of each edge computing node.

[0077] In this implementation plan, based on the basic eigenvalues of each data acquisition point and the data processing energy efficiency characterization values of each edge computing node, comprehensively analyze to obtain the target receiving data acquisition point clusters of each edge computing node, which can improve the resource utilization efficiency of the system, optimize the real-time performance and accuracy of data processing, ensure that the edge computing node can receive data according to the data processing capacity of the node, and thus improve the performance and reliability of the entire system.

[0078] Specifically, perform preprocessing configuration on the spatio-temporal information sent by the target receiving data acquisition point clusters of each edge computing node. The specific process is as follows: Take the average value of the basic eigenvalues of the respective target receiving data acquisition points corresponding to each edge computing node to obtain the data transmission evaluation value of each edge computing node.

[0079] It should be noted that by analyzing the data transmission evaluation values of each edge computing node, the data acquisition volume of each edge computing node can be evaluated, and then the data preprocessing parameters can be configured to improve the data transmission efficiency of each edge computing node.

[0080] Compare the data transmission evaluation value of each edge computing node with the set data transmission evaluation threshold. If the data transmission evaluation value of this edge computing node is lower than the set data transmission evaluation threshold, then continue to perform preprocessing configuration on the data of this edge computing node with the current data preprocessing parameters. If the data transmission evaluation value of this edge computing node is higher than or equal to the set data transmission evaluation threshold, then perform preprocessing configuration on the data of this edge computing node with the preset data preprocessing parameters in the information management database.

[0081] It should be noted that the data preprocessing parameters include the data transmission bandwidth and the data compression ratio. The data transmission evaluation values of each edge computing node are matched with the data preprocessing parameters corresponding to the data transmission evaluation value intervals stored in the information management database, and the data preprocessing parameters corresponding to the intervals where the data transmission evaluation values of each edge computing node are located are counted, which are recorded as the preset data preprocessing parameters.

[0082] In this implementation plan, based on the basic characteristic values of each target receiving data acquisition point corresponding to each edge computing node, the data transmission evaluation values of each edge computing node are analyzed, and then preprocessing configuration is carried out. The system can respond to the real-time requirements of data processing faster, which helps to optimize the network bandwidth and data transmission rate, and reduces the data transmission delay caused by network congestion.

[0083] Specifically, after the preprocessing configuration, the cluster spatio-temporal information of each edge computing node is counted and uploaded to the storage port of the spatio-temporal information database. The specific process is as follows: after the preprocessing configuration, a data upload feasible signal is output, and the spatio-temporal information database receives the data upload feasible signal, and the cluster spatio-temporal information of each edge computing node is respectively transmitted to the storage port of the spatio-temporal information database.

[0084] Specifically, the storage port of the spatio-temporal information database synchronously retrieves the operation data of each storage port for analysis. The specific process is as follows: the operation data of each storage port includes the memory utilization rate, transmission delay duration, data read / write speed, and data transmission rate of each storage port.

[0085] It should be noted that the transmission delay duration of each storage port can be measured using network testing tools (such as ping, iperf), and the data read / write speed of each storage port can be measured through storage performance testing software (such as CrystalDiskMark).

[0086] Based on the operation data of each storage port, the storage performance benchmark value of each storage port is processed. The storage performance benchmark value of each storage port is used to comprehensively quantify the utilization degree of the storage capacity of each storage port.

[0087] It should be added that in the process of analyzing the storage performance benchmark value of each storage port, the transmission delay duration, data read / write speed, and data transmission rate of each storage port are all de-unified.

[0088] Specifically, the storage performance benchmark value of each storage port has the following specific analysis conditions:

[0089] ;

[0090] In the formula, represents the storage performance benchmark value of the i-th storage port, Indicates the memory utilization rate of the i-th storage port, Indicates the reference memory utilization rate of the set i-th storage port, Indicates the transmission delay duration of the i-th storage port, Indicates the correction factor corresponding to the set transmission delay duration, Indicates the data read / write speed of the i-th storage port, Indicates the correction factor corresponding to the set data read / write speed, Indicates the data transmission rate of the i-th storage port, Indicates the correction factor corresponding to the set data transmission rate. i represents the number of each storage port, , n represents the total number of storage ports, and e represents the natural constant.

[0091] It should be noted that the smaller the absolute value of the difference between the memory utilization rate of each storage port and the reference memory utilization rate, the more effectively the memory resources are utilized, the larger the storage performance benchmark value of the storage port, the shorter the transmission delay duration, the shorter the data access time, the faster the response speed of the storage system, and the greater the data storage capacity. The larger the storage performance benchmark value of the storage port, the greater the data read / write speed, and the more data the storage port can read or write per unit time. A higher data read / write speed indicates that the storage port can process a large amount of data faster, thus having a greater data storage capacity, and the storage performance benchmark value of the storage port is also larger. The larger the data transmission rate, the more data the storage port can transmit per unit time. A higher data transmission rate means that the storage port can move data faster, and the data storage capacity of the storage port is usually stronger, and the storage performance benchmark value of the storage port is also larger.

[0092] It should be supplemented that in this embodiment, the correction factors corresponding to the preset transmission delay duration, data read / write speed, and data transmission rate are obtained from the information management database.

[0093] It should be explained that the correction factors corresponding to the transmission delay duration, data read / write speed, and data transmission rate are respectively used to adjust the importance of the transmission delay duration, data read / write speed, and data transmission rate of each storage port in the process of analyzing the storage performance benchmark value. There is a pre-set mapping relationship between the real-time operating data of each storage port and the corresponding correction factors in the information management database. Through the pre-set mapping relationship, the correction factors corresponding to the real-time operating data of each storage port can be matched. For example, the transmission delay duration, data read / write speed, and data transmission rate of each storage port are respectively matched with the pre-set mapping relationship to obtain the correction factors corresponding to the transmission delay duration, data read / write speed, and data transmission rate of each storage port.

[0094] In this implementation, there is a correlation among the memory utilization rate, transmission delay duration, data read / write speed, and data transmission rate of each storage port, and they do not exist independently. For example, the memory utilization rate reflects the resource consumption degree of the storage port under a specific load. A high memory utilization rate usually means a heavy load on the storage port, which may lead to an increase in the transmission delay duration and affect the data read / write speed and data transmission rate at the same time. When the memory usage is close to full load, the system requires more time to process and buffer data, resulting in an increase in the transmission delay duration and further affecting the stability of the data read / write speed. By comprehensively analyzing the storage performance benchmark values of each storage port, the true data storage capacity of the storage port can be more accurately reflected, which helps optimize the system design and resource allocation.

[0095] Statistically analyze the cluster spatio-temporal information data of each edge computing node. The cluster spatio-temporal information data of each edge computing node includes the data processing energy efficiency characterization value of each edge computing node and the number of target receiving data collection points.

[0096] Comprehensively analyze the cluster spatio-temporal information data of each edge computing node to obtain the cluster spatio-temporal information evaluation value of each edge computing node. The cluster spatio-temporal information evaluation value of each edge computing node is used to comprehensively quantify the comprehensive performance of each edge computing node.

[0097] In this embodiment, the cluster spatio-temporal information evaluation value of each edge computing node can be obtained through the following analysis method. The specific analysis conditions are as follows:

[0098] ;

[0099] In the formula, represents the cluster spatio-temporal information evaluation value of the kth edge computing node, represents the data processing energy efficiency characterization value of the kth edge computing node, represents the compensation factor corresponding to the set data processing energy efficiency characterization value, represents the number of target receiving data collection points of the kth edge computing node, represents the compensation factor corresponding to the set number of target receiving data collection points. k represents the number of each edge computing node, , represents the total number of edge computing nodes, and e represents the natural constant.

[0100] It should be added that in this embodiment, the compensation factor corresponding to the preset data processing energy efficiency characterization value and the compensation factor corresponding to the number of target receiving data collection points are obtained from the information management database.

[0101] It should be noted that the data processing energy efficiency characterization value and the compensation factor corresponding to the number of target receiving data collection points are respectively used to adjust the importance of the data processing energy efficiency characterization value and the number of target receiving data collection points of each edge computing node in the process of analyzing and obtaining the cluster spatio-temporal information evaluation value. There is a pre-set mapping relationship between the real-time cluster spatio-temporal information data of each edge computing node and the corresponding compensation factor in the information management database. Through the pre-set mapping relationship, the compensation factor corresponding to the real-time cluster spatio-temporal information data of each edge computing node can be matched. For example, the data processing energy efficiency characterization value and the number of target receiving data collection points of each edge computing node are respectively matched with the pre-set mapping relationship to obtain the compensation factor corresponding to the data processing energy efficiency characterization value and the number of target receiving data collection points of each edge computing node.

[0102] It should be added that comprehensively analyzing the data processing energy efficiency characterization value and the number of target receiving data collection points of each edge computing node to obtain the cluster spatio-temporal information evaluation value of each edge computing node helps the system allocate storage resources more accurately.

[0103] The storage performance verification value of each edge computing node is obtained by matching according to the cluster spatio-temporal information evaluation value of each edge computing node.

[0104] It should be noted that the process of obtaining the storage performance verification value of each edge computing node by matching according to the cluster spatio-temporal information evaluation value of each edge computing node is as follows: the cluster spatio-temporal information evaluation value of each edge computing node is matched with the storage performance verification value corresponding to the interval of each cluster spatio-temporal information evaluation value stored in the information management database, and the storage performance verification value corresponding to the interval where the cluster spatio-temporal information evaluation value of each edge computing node is located is counted, which is recorded as the storage performance verification value of each edge computing node.

[0105] It should be added that obtaining the storage performance verification value of each edge computing node can more accurately evaluate the required storage performance of each edge computing node, thereby improving the allocation accuracy of the edge computing node and the storage port, and ensuring that the cluster spatio-temporal information of the edge computing node is allocated to the most reliable storage port in terms of performance.

[0106] Compare the storage performance benchmark value of each storage port with the storage performance verification value of each edge computing node. If the storage performance benchmark value of a certain storage port is higher than and closest to the storage performance verification value of a certain edge computing node, then record this storage port as the target storage port of this edge computing node. Traverse in turn to obtain the target storage port of each edge computing node, and transmit the cluster spatio-temporal information of each edge computing node to the corresponding target storage port.

[0107] It should be noted that transmitting the cluster spatio-temporal information of each edge computing node to the corresponding target storage port optimizes the data storage process. According to the comparison result between the storage performance verification value and the storage performance benchmark value, storage resources are dynamically allocated, which helps to improve the flexibility and efficiency of data management, reduces resource waste, and improves the overall utilization rate of storage resources.

[0108] An spatio-temporal information database management system based on the Internet of Things further includes: a load balancing module, which is used to count each data storage node in the spatio-temporal information database management system and obtain the load parameters of each data storage node for load balancing configuration. The specific process is as follows: based on the load parameters of each data storage node, the load evaluation value of each data storage node is processed, and load balancing configuration is performed on each data storage node according to the load evaluation value of each data storage node.

[0109] It should be noted that the load parameters of each data storage node include the CPU utilization rate, data read / write speed, and memory utilization rate of each data storage node.

[0110] It should be noted that the specific process of performing load balancing configuration on each data storage node according to the load evaluation value of each data storage node is as follows: compare the load evaluation value of each data storage node with the set first load evaluation threshold. If the load evaluation value of a certain data storage node is lower than the set first load evaluation threshold, then mark this data storage node as a low-load storage node, and thus obtain each low-load storage node. If the load evaluation value of a certain data storage node is higher than or equal to the set first load evaluation threshold, then compare the load evaluation value of this data storage node with the set second load evaluation threshold. If the load evaluation value of this data storage node is lower than the set second load evaluation threshold, then mark this data storage node as a normal-load storage node, and thus obtain each normal-load storage node. If the load evaluation value of this data storage node is higher than or equal to the set second load evaluation threshold, then mark this data storage node as a high-load storage node, and thus obtain each high-load storage node. Allocate the data traffic of the high-load storage node to the low-load storage node for data traffic regulation.

[0111] It should be noted that the second load evaluation threshold is higher than the first load evaluation threshold. Allocate the data traffic of the high-load storage node to the low-load storage node for data traffic regulation, apply a speed limit policy to the high-load storage node to ensure that the high-load storage node will not be overloaded, and at the same time use a dynamic routing protocol to import the excess traffic into the low-load storage node according to the load situation of the high-load storage node.

[0112] It should be added that in the process of analyzing and obtaining the load evaluation values of each data storage node, the CPU utilization rate, data read / write speed, and memory utilization rate of each data storage node are all de-unified.

[0113] In this embodiment, the load evaluation values of each data storage node are used to comprehensively quantify the workload of each data storage node and can be obtained through the following analysis method. The specific analysis conditions are as follows:

[0114] ;

[0115] In the formula, represents the load evaluation value of the s-th data storage node, represents the CPU utilization rate of the s-th data storage node, represents the weight factor corresponding to the set CPU utilization rate, represents the data read / write speed of the s-th data storage node, represents the weight factor corresponding to the set data read / write speed, represents the memory utilization rate of the s-th data storage node, represents the weight factor corresponding to the set memory utilization rate. s represents the number of each data storage node, , represents the total number of data storage nodes, and e represents the natural constant.

[0116] It should be added that in this embodiment, the weight factor corresponding to the preset CPU utilization rate, the weight factor corresponding to the data read / write speed, and the weight factor corresponding to the memory utilization rate are obtained from the information management database.

[0117] It should be explained that the weight factors corresponding to the CPU utilization rate, the data read / write speed, and the memory utilization rate are respectively used to adjust the importance of the CPU utilization rate, the data read / write speed, and the memory utilization rate of each data storage node in the process of analyzing and obtaining the load evaluation value. There is a preset mapping relationship between the real-time load parameters of each data storage node and the corresponding weight factors in the information management database. Through the preset mapping relationship, the weight factors corresponding to the real-time load parameters of each data storage node can be matched. For example, the CPU utilization rate, the data read / write speed, and the memory utilization rate of each data storage node are respectively matched with the preset mapping relationship to obtain the weight factors corresponding to the CPU utilization rate, the data read / write speed, and the memory utilization rate of each data storage node.

[0118] In this implementation, the load parameters of each data storage node include the CPU utilization rate, data read / write speed, and memory utilization rate of each data storage node, which are related and do not exist independently. For example, when the CPU utilization rate increases, the data read / write speed and memory utilization rate will also increase, indicating that the node is processing a large amount of data and may face performance bottlenecks. By comprehensively analyzing the load evaluation values of each data storage node, the actual load status and performance bottlenecks of the data storage node can be accurately obtained, providing a data basis for resource optimization and system adjustment.

[0119] It should be noted that an Internet of Things-based spatio-temporal information database management system and method also includes an information management database, which is used to store the weight factors corresponding to the data collection frequency obtained by analyzing historical data, the weight factors corresponding to the data volume, the weight factors corresponding to the data transmission rate, the reference memory utilization rate of each edge computing node, the reference bandwidth utilization rate of each edge computing node, the reference data packet processing rate of each edge computing node, the reference CPU utilization rate of each edge computing node, the data transmission evaluation threshold, the reference memory utilization rate of each storage port, the correction factor corresponding to the transmission delay duration, the correction factor corresponding to the data read / write speed, the correction factor corresponding to the data transmission rate, the compensation factor corresponding to the data processing energy efficiency characterization value, the compensation factor corresponding to the number of target received data collection points, the first load evaluation threshold, the second load evaluation threshold, the weight factor corresponding to the CPU utilization rate, the weight factor corresponding to the data read / write speed, the weight factor corresponding to the memory utilization rate, the range of the target received comprehensive characteristic values of data collection points corresponding to each data processing energy efficiency characterization value interval, the data preprocessing parameters corresponding to each data transmission evaluation value interval, and the storage performance verification values corresponding to each cluster spatio-temporal information evaluation value interval.

[0120] Such as Figure 2 , the second aspect of the present invention provides an Internet of Things-based spatio-temporal information database management method, including: counting each data collection point and each edge computing node connected to the Internet of Things, and obtaining the spatio-temporal information of each data collection point and the operation and maintenance information of each edge computing node.

[0121] Based on the spatio-temporal information of each data collection point and the operation and maintenance information of each edge computing node, processing is performed to obtain the target received data collection point clusters of each edge computing node.

[0122] Perform preprocessing configuration on the spatio-temporal information sent by the target received data collection point clusters of each edge computing node, and after the preprocessing configuration, count the cluster spatio-temporal information of each edge computing node and upload it to the spatio-temporal information database waiting storage port.

[0123] Synchronously retrieve and analyze the operation data of each storage port through the spatio-temporal information database waiting storage port, and perform distributed storage management on the cluster spatio-temporal information.

[0124] It should be noted that in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0125] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention.

Claims

1. A spatiotemporal information database management system based on the Internet of Things, characterized in that: include: The data collection module is used to count the data collection points and edge computing nodes connected to the Internet of Things, and obtain the spatiotemporal information of each data collection point and the operation and maintenance information of each edge computing node; The target receiving data analysis module is used to process the spatiotemporal information of each data collection point and the operation and maintenance information of each edge computing node to obtain the target receiving data collection point cluster of each edge computing node; The spatiotemporal information processing and transmission module is used to pre-process and configure the spatiotemporal information sent by the target receiving data collection point cluster of each edge computing node, and after the pre-processing configuration, the cluster spatiotemporal information of each edge computing node is counted and uploaded to the storage port of the spatiotemporal information database; A distributed storage management module is used to synchronously retrieve the operation data of each storage port through the storage port of the spatiotemporal information database for analysis, and to perform distributed storage management of the cluster spatiotemporal information; The specific process of preprocessing and configuring the spatiotemporal information sent by the target receiving data collection point cluster of each edge computing node is as follows: The data transmission evaluation value of each edge computing node is obtained by taking the average value of the basic characteristic values ​​of each target receiving data collection point corresponding to each edge computing node; The basic characteristic value is obtained based on the spatiotemporal information processing of the data collection point, and the basic characteristic value of the data collection point is used to comprehensively quantify the data collection efficiency of the data collection point; The data transmission evaluation value of each edge computing node is compared with the set data transmission evaluation threshold. If the data transmission evaluation value of the edge computing node is lower than the set data transmission evaluation threshold, the data of the edge computing node is preprocessed and configured with the current data preprocessing parameters. If the data transmission evaluation value of the edge computing node is higher than or equal to the set data transmission evaluation threshold, the data of the edge computing node is preprocessed and configured with the data preprocessing parameters preset in the information management database.

2. According to claim 1, a spatiotemporal information database management system based on the Internet of Things is characterized by: The spatiotemporal information of each data collection point includes the data collection frequency, data volume and data transmission rate of each data collection point; The operation and maintenance information of each edge computing node includes the memory utilization, bandwidth utilization, data packet processing rate and CPU utilization of each edge computing node.

3. A spatiotemporal information database management system based on the Internet of Things according to claim 2, characterized in that: The processing based on the spatiotemporal information of each data collection point and the operation and maintenance information of each edge computing node includes: Classify each data collection point according to the sensor type to obtain each data collection point corresponding to each sensor type, average the basic characteristic values ​​of each data collection point corresponding to each sensor type, and obtain the basic characteristic average value of the data collection point corresponding to each sensor type, which is recorded as the comprehensive characteristic value of the data collection point corresponding to each sensor type; A comprehensive analysis is performed on the operation and maintenance information of each edge computing node to obtain a data processing energy efficiency characterization value of each edge computing node, and the data processing energy efficiency characterization value of each edge computing node is used to comprehensively quantify the data processing capability of each edge computing node.

4. The spatiotemporal information database management system based on the Internet of Things according to claim 3, characterized in that: The specific process of obtaining the target receiving data collection point cluster of each edge computing node is as follows: The data processing energy efficiency characterization value of each edge computing node is matched with the data collection point target reception comprehensive characteristic value range corresponding to each data processing energy efficiency characterization value interval stored in the information management database, and the data collection point target reception comprehensive characteristic value range corresponding to the data processing energy efficiency characterization value interval of each edge computing node is counted, and recorded as the data collection point target reception comprehensive characteristic value range of each edge computing node; The comprehensive characteristic value of the data collection point corresponding to each sensor type is matched with the target receiving comprehensive characteristic value range of the data collection point of each edge computing node. If the comprehensive characteristic value of the data collection point corresponding to a certain sensor type is within the target receiving comprehensive characteristic value range of the data collection point of a certain edge computing node, then the sensor type is defined as the target receiving sensor type of the edge computing node, and all the target receiving sensor types of each edge computing node and all the data collection points corresponding to the target receiving sensor types are traversed and counted in turn; The data collection points corresponding to all target receiving sensor types of each edge computing node are uniformly recorded as the target receiving data collection points corresponding to each edge computing node, thereby integrating the target receiving data collection point clusters of each edge computing node.

5. The spatiotemporal information database management system based on the Internet of Things according to claim 1, characterized in that: After the preprocessing configuration, the cluster spatiotemporal information of each edge computing node is counted and uploaded to the storage port of the spatiotemporal information database. The specific process is as follows: After the preprocessing configuration, a data upload feasible signal is output, the spatiotemporal information database receives the data upload feasible signal, and transmits the cluster spatiotemporal information of each edge computing node to the storage port of the spatiotemporal information database.

6. The spatiotemporal information database management system based on the Internet of Things according to claim 1, characterized in that: The storage port of the spatiotemporal information database synchronously retrieves the operation data of each storage port for analysis, and the specific process is as follows: The operation data of each storage port includes the memory utilization rate, transmission delay time, data reading and writing speed and data transmission rate of each storage port; Based on the operation data of each storage port, a storage performance benchmark value of each storage port is obtained by processing, wherein the storage performance benchmark value of each storage port is used to comprehensively quantify the storage capacity utilization degree of each storage port; Counting cluster spatiotemporal information data of each edge computing node, wherein the cluster spatiotemporal information data of each edge computing node includes a data processing energy efficiency characterization value of each edge computing node and the number of target receiving data collection points; Comprehensively analyzing the cluster spatiotemporal information data of each edge computing node to obtain a cluster spatiotemporal information evaluation value of each edge computing node, wherein the cluster spatiotemporal information evaluation value of each edge computing node is used to comprehensively quantify the comprehensive performance of each edge computing node; The storage performance verification value of each edge computing node is obtained by matching the cluster spatiotemporal information evaluation value of each edge computing node; The storage performance benchmark value of each storage port is compared with the storage performance verification value of each edge computing node. If the storage performance benchmark value of a storage port is higher than and closest to the storage performance verification value of a certain edge computing node, then the storage port is recorded as the target storage port of the edge computing node. The target storage ports of each edge computing node are traversed in turn, and the cluster spatiotemporal information of each edge computing node is transmitted to the corresponding target storage port.

7. The spatiotemporal information database management system based on the Internet of Things according to claim 6, characterized in that: The storage performance benchmark value of each storage port is specifically analyzed under the following conditions: ； In the formula, represents the storage performance benchmark value of the i-th storage port, represents the memory utilization of the i-th storage port, represents the reference memory utilization of the set i-th storage port, represents the transmission delay of the i-th storage port, Indicates the correction factor corresponding to the set transmission delay time. represents the data read and write speed of the i-th storage port, Indicates the correction factor corresponding to the set data reading and writing speed. represents the data transmission rate of the i-th storage port, Indicates the correction factor corresponding to the set data transmission rate, i indicates the number of each storage port, , n represents the total number of storage ports, and e represents a natural constant.

8. The spatiotemporal information database management system based on the Internet of Things according to claim 1, characterized in that: Also includes: The load balancing module is used to count the data storage nodes in the spatiotemporal information database management system and obtain the load parameters of each data storage node for load balancing configuration. The specific process is as follows: Based on the load parameters of each data storage node, a load evaluation value of each data storage node is obtained by processing, and load balancing configuration of each data storage node is performed according to the load evaluation value of each data storage node.

9. A method for managing a spatiotemporal information database based on the Internet of Things, applied to a spatiotemporal information database management system based on the Internet of Things as claimed in any one of claims 1 to 8, comprising: Count the data collection points and edge computing nodes connected to the Internet of Things, and obtain the spatiotemporal information of each data collection point and the operation and maintenance information of each edge computing node; Based on the spatiotemporal information of each data collection point and the operation and maintenance information of each edge computing node, a target receiving data collection point cluster of each edge computing node is obtained; Preprocess and configure the spatiotemporal information sent by the target receiving data collection point cluster of each edge computing node, and after the preprocessing configuration, count the cluster spatiotemporal information of each edge computing node and upload it to the storage port of the spatiotemporal information database; The operation data of each storage port is synchronously retrieved through the storage port of the spatiotemporal information database for analysis, and the cluster spatiotemporal information is distributedly stored and managed.

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