Space-time information database management system and method based on Internet of Things

Abstract

The invention discloses a spatio-temporal information database management system and method based on the Internet of Things, and relates to the technical field of electric digital data processing. Firstly, time-space information of each data acquisition point and operation and maintenance information of each edge computing node are acquired, so that network resources and computing resources can be distributed more accurately, a data transmission path is optimized, and the data transmission efficiency of a database management system is improved; and then preprocessing configuration is carried out on the spatio-temporal information sent by the target receiving data acquisition point cluster of each edge computing node, so that the overall data processing speed of the database can be improved, the operation data of each storage port can be synchronously called for analysis, possible storage performance problems can be found in time, storage optimization is carried out, and the storage efficiency is improved. And finally, distributed storage management is performed on the cluster spatio-temporal information, so that the data access load can be balanced, and the response speed and throughput of a spatio-temporal information database management system can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a spatiotemporal information database management system and method based on the Internet of Things. Background Art

[0002] With the rapid popularization of smart devices and the continuous development of network communication technology, it is crucial to efficiently and accurately manage and analyze massive spatiotemporal data. Spatiotemporal information data usually includes the status information of objects at a specific time and space position, and is widely used in smart cities, smart transportation, environmental monitoring and other fields. The spatiotemporal information database management system based on the Internet of Things can realize real-time collection, storage and analysis of spatiotemporal data by combining sensor data with geographic information system technology, thereby improving the application ability of the Internet of Things in intelligent management.

[0003] Prior art, such as the invention patent with announcement number: CN106528793B, discloses a space-time 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 space-time characteristics of the spatial data are fully considered, and the space-time information of the spatial data is integrated into the data sharding of the distributed database. When multiple database servers are queried in parallel, the server performance is fully utilized. At the same time, the changes in the interest conditions of the data are taken into account through the data migration step.

[0004] Prior art, such as the invention patent with announcement number: CN113609126B, discloses an integrated storage management method and system for crowd-source spatiotemporal data, wherein the method includes: analyzing and integrating the data structure of initial spatiotemporal data to obtain standard spatiotemporal data; constructing a crowd-source heterogeneous spatiotemporal database based on the standard spatiotemporal data after corresponding level of processing; storing the data processing logic lines in the crowd-source heterogeneous spatiotemporal database in correspondence with the corresponding standard spatiotemporal data and initial spatiotemporal data; retrieving the first newly added spatiotemporal data to obtain the first data processing logic line; processing the first newly added spatiotemporal data according to the first data processing logic line to obtain standard newly added spatiotemporal data; storing the standard newly added spatiotemporal data in the crowd-source heterogeneous spatiotemporal database.

[0005] Combined with the above solutions, it is found that in the field of spatiotemporal information database management technology, usually only the data query process is monitored and analyzed. However, there are data transmission problems in the process of spatiotemporal information data collection, which makes the spatiotemporal information unable to be effectively synchronized and updated. This not only affects the real-time and accuracy of the data, but also fails to receive key spatiotemporal data changes in time. It may also cause operation and maintenance information to lag, and there is a risk of data loss and processing delays, affecting the overall efficiency and reliability of the system. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a spatiotemporal 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 implemented through the following technical solutions: a spatiotemporal information database management system based on the Internet of Things, including a data acquisition module, which 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.

[0008] 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.

[0009] 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.

[0010] The distributed storage management module is used to synchronously retrieve the operating 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.

[0011] Furthermore, the spatiotemporal information of each data collection point includes a data collection frequency, a data volume and a data transmission rate of each data collection point.

[0012] 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.

[0013] Furthermore, the processing is performed based on the spatiotemporal information of each data collection point and the operation and maintenance information of each edge computing node. The specific process is: based on the spatiotemporal information of each data collection point, the basic characteristic values ​​of each data collection point are processed, and the basic characteristic values ​​of each data collection point are used to comprehensively quantify the data collection efficiency of each data collection point.

[0014] The data collection points are classified according to the sensor type to obtain the data collection points corresponding to the sensor type. The basic characteristic values ​​of the data collection points corresponding to the sensor type are averaged to obtain the basic characteristic average value of the data collection points corresponding to the sensor type, which is recorded as the comprehensive characteristic value of the data collection points corresponding to the sensor type.

[0015] 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.

[0016] Furthermore, the target reception data collection point cluster of each edge computing node is obtained, and the specific process is: matching the data processing energy efficiency characterization value of each edge computing node 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 counting 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, and recording it as the data collection point target reception comprehensive characteristic value range of each edge computing node.

[0017] The comprehensive characteristic values ​​of the data collection points corresponding to each sensor type are matched with the target reception comprehensive characteristic value range of the data collection points 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 reception comprehensive characteristic value range of the data collection point of a certain edge computing node, then the sensor type is defined as the target reception sensor type of the edge computing node, and all target reception sensor types of each edge computing node and all data collection points corresponding to the target reception sensor types are traversed and counted in turn.

[0018] 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.

[0019] Furthermore, the spatiotemporal information sent by the target receiving data collection point cluster of each edge computing node is preprocessed and configured, and the specific process is: taking the average value based on the basic characteristic values ​​of each target receiving data collection point corresponding to each edge computing node to obtain the data transmission evaluation value of each edge computing node.

[0020] 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 node is preprocessed 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 node is preprocessed with the data preprocessing parameters preset in the information management database.

[0021] Furthermore, the cluster spatiotemporal information of each edge computing node is statistically uploaded to the storage port of the spatiotemporal information database after the preprocessing configuration. The specific process is: after the preprocessing configuration, a data upload feasible signal is output, the spatiotemporal information database receives the data upload feasible signal, and the cluster spatiotemporal information of each edge computing node is transmitted to the storage port of the spatiotemporal information database respectively.

[0022] Furthermore, the storage port of the spatiotemporal information database synchronously retrieves the operation data of each storage port for analysis. The specific process is: 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.

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

[0024] The cluster spatiotemporal information data of each edge computing node is counted, 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.

[0025] The cluster spatiotemporal information data of each edge computing node is comprehensively analyzed to obtain the cluster spatiotemporal information evaluation value of each edge computing node, and the cluster spatiotemporal information evaluation value of each edge computing node is used to comprehensively quantify the comprehensive performance of each edge computing node.

[0026] 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.

[0027] 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.

[0028] Furthermore, the storage performance benchmark value of each storage port is specifically analyzed under the following conditions:

[0029] ; 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.

[0030] A spatiotemporal information database management system based on the Internet of Things also includes: a load balancing module, which 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: based on the load parameters of each data storage node, the load evaluation value of each data storage node is processed, and the load balancing configuration of each data storage node is performed according to the load evaluation value of each data storage node.

[0031] Furthermore, the second aspect of the present invention provides a method for managing a spatiotemporal information database based on the Internet of Things, including: counting each data collection point and each edge computing node connected to the Internet of Things, and obtaining the spatiotemporal information of each data collection point and the operation and maintenance information of each edge computing node.

[0032] Based on the spatiotemporal information of each data collection point and the operation and maintenance information of each edge computing node, the target receiving data collection point cluster of each edge computing node is obtained.

[0033] The spatiotemporal information sent by the target receiving data collection point cluster of each edge computing node is preprocessed and configured, and 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.

[0034] 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.

[0035] The present invention has the following beneficial effects:

[0036] (1) The present invention provides a spatiotemporal information database management system and method based on the Internet of Things. First, the target receiving data collection point cluster of each edge computing node is processed to improve the efficiency and orderliness of data stream transmission, and then the sent spatiotemporal information is pre-processed and configured, which helps to improve the data transmission rate. Then, the cluster spatiotemporal information is statistically uploaded to the storage port of the spatiotemporal information database, which can realize centralized management of data. Finally, the cluster spatiotemporal information is distributedly stored and managed, which improves the reliability of data storage.

[0037] (2) The present invention obtains the target receiving data collection point cluster of each edge computing node through processing, and distributes data according to the actual processing capacity of the edge computing node. It can accurately distribute suitable data collection points to the edge computing node with the greatest processing capacity, and can maximize the use of the processing capacity of each edge computing node, thereby improving the data processing efficiency of the edge computing node, reducing excessive load and inefficient resource usage, and optimizing the utilization of computing resources of the edge computing node.

[0038] (3) The present invention can reduce the redundancy of data transmission and alleviate the network burden by pre-processing the spatiotemporal information sent by the target receiving data collection point cluster of each edge computing node, thereby speeding up the data transmission. After effective processing, the spatiotemporal information of the data collection point can better reflect the actual status and environmental changes of the device, reduce errors and losses in the data transmission process, and improve the reliability of data transmission.

[0039] (4) The present invention uses the storage port of the spatiotemporal information database to synchronously retrieve the operating data of each storage port for analysis, and performs distributed storage management on the cluster spatiotemporal information, which can optimize data storage and allocation, reduce the waste of storage resources, optimize data flow and storage scheduling process, and improve the overall data transmission efficiency of the system. Accurate spatiotemporal information and data collection point cluster analysis can arrange storage resources more reasonably.

[0040] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the connection of the system modules of the present invention.

[0042] Figure 2 It is a schematic diagram of the process of the present invention.

[0043] Figure 3 This is an example graph of how the amount of data at a data collection point changes over time. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] See also Figure 1 As shown, an embodiment of the present invention provides a technical solution: a spatiotemporal information database management system based on the Internet of Things, including a data acquisition module, which 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.

[0046] 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.

[0047] 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.

[0048] The distributed storage management module is used to synchronously retrieve the operating 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.

[0049] Specifically, the spatiotemporal information of each data collection point includes the data collection frequency, data volume and data transmission rate of each data collection point.

[0050] It should be noted that Figure 3 This is an example diagram of the data volume of the data collection point changing over time, such as Figure 3 As shown, the horizontal axis represents the time of data collection, the vertical axis represents the data volume of the data collection point, and the corresponding curve label is a.

[0051] It should be noted that the spatiotemporal information of each data collection point also includes time information (such as timestamp, collection period), spatial information (such as geographic coordinates, altitude) and environmental condition information (such as temperature, humidity).

[0052] It should be noted that the data collection frequency and data volume of each data collection point can be measured by the system log of the data collection system, and the data transmission rate of each data collection point can be measured by a network traffic monitoring tool.

[0053] 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.

[0054] It should be noted that the memory utilization of each edge computing node reflects the consumption of memory resources when the edge computing node processes data, while the bandwidth utilization of each edge computing node reflects the efficiency of data transmission of the edge computing node. The packet processing rate of each edge computing node shows the speed at which the edge computing node processes data. The CPU utilization of each edge computing node is a direct measure of the computing power of the edge computing node. The resource monitoring tools provided by the operating system can be used to measure the memory utilization, bandwidth utilization, packet processing rate and CPU utilization of each edge computing node. For example, the free command in the Linux operating system can measure the memory utilization of each edge computing node, and the iptraf command in the Linux operating system can measure the bandwidth utilization and packet processing rate of each edge computing node.

[0055] Specifically, processing is performed based on the spatiotemporal information of each data collection point and the operation and maintenance information of each edge computing node. The specific process is: based on the spatiotemporal information of each data collection point, the basic characteristic values ​​of each data collection point are processed, and the basic characteristic values ​​of each data collection point are used to comprehensively quantify the data collection efficiency of each data collection point.

[0056] It should be added that, in the process of analyzing and obtaining the basic characteristic values ​​of each data collection point, the data collection frequency, data volume and data transmission rate of each data collection point were de-normalized.

[0057] In this embodiment, the basic characteristic value of each data collection point can be obtained by the following analysis method, and the specific analysis conditions are as follows:

[0058] ;

[0059] In the formula, represents the basic eigenvalue of the jth data collection point, represents the data collection frequency of the jth data collection point, Indicates the weight factor corresponding to the set data collection frequency, represents the data volume of the jth data collection point, Indicates the weight factor corresponding to the set amount of data, 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, , Indicates the total number of data collection points.

[0060] It should be noted that the greater the data collection frequency of each data collection point, the more data is collected per unit time, and a higher data processing speed and greater processing power are required to process the data in real time. The larger the basic eigenvalue is, the larger the data volume of each data collection point is, the more information needs to be stored and processed, the demand for data processing power increases, the larger the basic eigenvalue is, the greater the data transmission rate of each data collection point is, the faster the data transmission speed is, more bandwidth and data processing power are required to receive and process data, and the larger the basic eigenvalue is.

[0061] It should be added that, in this embodiment, the weight factor corresponding to the preset data collection frequency, the weight factor corresponding to the data volume, and the weight factor corresponding to the data transmission rate are obtained from the information management database.

[0062] It should be explained that the weight factors corresponding to the data collection frequency, data volume and data transmission rate are respectively used to adjust the importance of the data collection frequency, data volume and data transmission rate of each data collection point in the process of analyzing and obtaining the basic characteristic values. There is a preset mapping relationship between the real-time spatiotemporal information of each data collection point and the corresponding weight factors in the information management database. The weight factors corresponding to the real-time spatiotemporal information of each data collection point can be matched through the preset mapping relationship. For example, the data collection frequency, data volume and data transmission rate of each data collection point are respectively matched with the preset mapping relationship to obtain the weight factors corresponding to the data collection frequency, data volume and data transmission rate of each data collection point.

[0063] In this implementation scheme, the data collection frequency, data volume and data transmission rate of each data collection point are correlated and do not exist independently. For example, the data collection frequency directly affects the amount of data generated per unit time, and the increase in data volume has a higher demand for data transmission rate. Data collection points with high data collection frequency will generate a large amount of data, requiring a higher data transmission rate to ensure real-time data transmission. A low data transmission rate may lead to data backlog and delay. Comprehensive analysis to obtain the basic characteristic values ​​of each data collection point can improve the accuracy of the actual workload and performance bottleneck analysis of the data collection point, thereby providing data support for optimizing the data collection method.

[0064] The data collection points are classified according to the sensor type to obtain the data collection points corresponding to the sensor type. The basic characteristic values ​​of the data collection points corresponding to the sensor type are averaged to obtain the basic characteristic average value of the data collection points corresponding to the sensor type, which is recorded as the comprehensive characteristic value of the data collection points corresponding to the sensor type.

[0065] It should be added that the comprehensive characteristic values ​​of the data collection points corresponding to each sensor type can reflect the average level of data collection efficiency of each sensor type.

[0066] It should be noted that sensor types include but are not limited to satellite positioning sensors, environmental sensors (such as temperature and humidity sensors, gas sensors, pressure sensors, etc.), visual sensors (such as cameras, lidar, etc.) and acoustic sensors (such as microphones, sound wave sensors, etc.).

[0067] 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.

[0068] In this embodiment, the data processing energy efficiency characterization value of each edge computing node can be obtained by the following analysis method, and the specific analysis conditions are as follows:

[0069] ;

[0070] In the formula, represents the data processing energy efficiency characterization value of the kth edge computing node, represents the memory utilization of the kth edge computing node, Indicates the reference memory utilization of the kth edge computing node. represents the bandwidth utilization of the kth edge computing node, represents the reference bandwidth utilization of the kth edge computing node. represents the packet processing rate of the kth edge computing node, represents the reference packet processing rate of the kth edge computing node. represents the CPU utilization of the kth edge computing node, Indicates the reference CPU utilization of the kth edge computing node, where k represents the number of each edge computing node. , Indicates the total number of edge computing nodes.

[0071] It should be noted that the larger 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 larger the absolute value of the difference between the memory utilization of the edge computing node and the reference memory utilization, the smaller the data processing energy efficiency characterization value of the edge computing node. It may be that excess memory leads to resource waste, or insufficient memory leads to limited processing power, thereby reducing the data processing energy efficiency characterization value of the edge computing node. The larger the absolute value of the difference between the bandwidth utilization of the edge computing node and the reference bandwidth utilization, the larger the bandwidth of the node may not be effectively utilized. It may be that excess bandwidth leads to increased costs, or insufficient bandwidth leads to data transmission delays, which will also cause the data processing energy efficiency characterization value of the edge computing node to decrease. 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 capability of the edge computing node.

[0072] In this implementation scheme, the memory utilization, bandwidth utilization, packet processing rate and CPU utilization of each edge computing node are correlated and do not exist independently. For example, since high-load computing tasks often require more memory support, the CPU load will increase. Therefore, memory utilization and CPU utilization usually show a certain positive correlation. High bandwidth utilization indicates that the node needs to process higher-frequency data streams, which may cause bottlenecks in the memory and CPU when processing network data. Excessive memory utilization leads to increased CPU utilization, while low bandwidth utilization limits the improvement of packet processing rate. Comprehensive analysis obtains the data processing energy efficiency characterization value of each edge computing node, which can reflect the comprehensive data processing capabilities of the edge computing node in actual applications, and provide a data basis for improving the overall performance and stability of the system.

[0073] Specifically, a target reception data collection point cluster of each edge computing node is obtained. The specific process is: matching the data processing energy efficiency characterization value of each edge computing node with the target reception comprehensive characteristic value range of the data collection point corresponding to each data processing energy efficiency characterization value interval stored in the information management database, and counting the target reception comprehensive characteristic value range 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 target reception comprehensive characteristic value range of the data collection point of each edge computing node.

[0074] The comprehensive characteristic values ​​of the data collection points corresponding to each sensor type are matched with the target reception comprehensive characteristic value range of the data collection points 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 reception comprehensive characteristic value range of the data collection point of a certain edge computing node, then the sensor type is defined as the target reception sensor type of the edge computing node, and all target reception sensor types of each edge computing node and all data collection points corresponding to the target reception sensor types are traversed and counted in turn.

[0075] 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.

[0076] In this implementation, based on the basic characteristic values ​​of each data collection point and the data processing energy efficiency characterization values ​​of each edge computing node, a comprehensive analysis is performed to obtain the target receiving data collection point cluster of each edge computing node, which can improve the resource utilization efficiency of the system, optimize the real-time and accuracy of data processing, and ensure that the edge computing node can receive data according to the data processing capability of the node, thereby improving the performance and reliability of the entire system.

[0077] Specifically, the spatiotemporal information sent by the target receiving data collection point cluster of each edge computing node is preprocessed and configured. The specific process is: taking the average value of the basic characteristic values ​​of each target receiving data collection point corresponding to each edge computing node to obtain the data transmission evaluation value of each edge computing node.

[0078] It should be noted that by analyzing the data transmission evaluation value of each edge computing node, the data collection amount 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.

[0079] 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 node is preprocessed 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 node is preprocessed with the data preprocessing parameters preset in the information management database.

[0080] It should be noted that the data preprocessing parameters include data transmission bandwidth and data compression ratio. The data transmission evaluation value of each edge computing node is matched with the data preprocessing parameters corresponding to each data transmission evaluation value interval stored in the information management database. The data preprocessing parameters corresponding to the interval in which the data transmission evaluation value of each edge computing node is located are counted and recorded as preset data preprocessing parameters.

[0081] In this implementation scheme, the basic characteristic values ​​of each target receiving data collection point corresponding to each edge computing node are analyzed to obtain the data transmission evaluation value of each edge computing node, and then pre-processing configuration is performed. The system can respond to real-time needs of data processing more quickly, help optimize network bandwidth and data transmission rate, and reduce data transmission delays caused by network congestion.

[0082] Specifically, 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: after the preprocessing configuration, a data upload feasible signal is output, the spatiotemporal information database receives the data upload feasible signal, and the cluster spatiotemporal information of each edge computing node is transmitted to the storage port of the spatiotemporal information database respectively.

[0083] Specifically, the storage port of the spatiotemporal information database synchronously retrieves the operation data of each storage port for analysis. The specific process is: 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.

[0084] It should be noted that the transmission delay of each storage port can be measured using network testing tools (such as ping, iperf), and the data read and write speed of each storage port can be measured using storage performance testing software (such as CrystalDiskMark).

[0085] Based on the operation data of each storage port, a storage performance benchmark value of each storage port is processed and obtained, and the storage performance benchmark value of each storage port is used to comprehensively quantify the storage capacity utilization degree of each storage port.

[0086] It should be added that, in the process of analyzing and obtaining the storage performance benchmark value of each storage port, the transmission delay time, data reading and writing speed, and data transmission rate of each storage port are all de-normalized.

[0087] Specifically, the storage performance benchmark value of each storage port is analyzed under the following conditions:

[0088] ;

[0089] 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.

[0090] It should be noted that the smaller the absolute value of the difference between the memory utilization of each storage port and the reference memory utilization is, the more effectively the memory resources are utilized. The larger the storage performance benchmark value of the storage port, the shorter the transmission delay time, 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 faster the data read and write speed, and the more data the storage port can read or write per unit time. A higher data read and write speed indicates that the storage port can process a large amount of data faster, thereby having a greater data storage capacity. The larger the storage performance benchmark value of the storage port, the faster the data transmission rate, and 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. The data storage capacity of the storage port is usually stronger, and the larger the storage performance benchmark value of the storage port is.

[0091] It should be added that, in this embodiment, the correction factor corresponding to the preset transmission delay duration, the correction factor corresponding to the data reading and writing speed, and the correction factor corresponding to the data transmission rate are obtained from the information management database.

[0092] It should be explained that the correction factors corresponding to the transmission delay duration, data read and write speed, and data transmission rate are respectively used to adjust the importance of the transmission delay duration, data read and write speed, and data transmission rate of each storage port in the process of analyzing and obtaining the storage performance benchmark value. There is a pre-set mapping relationship between the real-time operation data of each storage port and the corresponding correction factors in the information management database. The correction factors corresponding to the real-time operation data of each storage port can be matched through the pre-set mapping relationship. For example, the transmission delay duration, data read and 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 and write speed, and data transmission rate of each storage port.

[0093] In this implementation scheme, the memory utilization, transmission delay time, data read and write speed and data transmission rate of each storage port are correlated and do not exist independently. For example, memory utilization reflects the resource consumption level of the storage port under a specific load. High memory utilization usually means that the storage port is heavily loaded, which may lead to an increase in transmission delay time, while affecting data read and write speed and data transmission rate. When memory usage is close to full load, the system requires more time for data processing and buffering, which leads to an increase in transmission delay time, which in turn affects the stability of data read and write speed. Comprehensive analysis of the storage performance benchmark value of each storage port can more accurately reflect the actual data storage capacity of the storage port and help optimize system design and resource allocation.

[0094] The cluster spatiotemporal information data of each edge computing node is counted, 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.

[0095] The cluster spatiotemporal information data of each edge computing node is comprehensively analyzed to obtain the cluster spatiotemporal information evaluation value of each edge computing node, and the cluster spatiotemporal information evaluation value of each edge computing node is used to comprehensively quantify the comprehensive performance of each edge computing node.

[0096] In this embodiment, the cluster spatiotemporal information evaluation value of each edge computing node can be obtained by the following analysis method, and the specific analysis conditions are as follows:

[0097] ;

[0098] In the formula, represents the cluster spatiotemporal information evaluation value of the kth edge computing node, represents the data processing energy efficiency characterization value of the kth edge computing node, Indicates 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 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 a natural constant.

[0099] 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.

[0100] It needs to be explained that the compensation factors corresponding to the data processing energy efficiency characterization value and 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 spatiotemporal information evaluation value. There is a pre-set mapping relationship between the real-time cluster spatiotemporal information data of each edge computing node and the corresponding compensation factor in the information management database. The real-time cluster spatiotemporal information data of each edge computing node can be matched to obtain the compensation factor corresponding to the pre-set mapping relationship. 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.

[0101] It should be added that a comprehensive analysis of the data processing energy efficiency characterization value of each edge computing node and the number of target receiving data collection points can be performed to obtain the cluster spatiotemporal information evaluation value of each edge computing node, which will help the system to allocate storage resources more accurately.

[0102] 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.

[0103] It should be noted that 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 specific process is to match the cluster spatiotemporal information evaluation value of each edge computing node with the storage performance verification value corresponding to the interval of each cluster spatiotemporal information evaluation value stored in the information management database, and count the storage performance verification value corresponding to the interval of the cluster spatiotemporal information evaluation value of each edge computing node, which is recorded as the storage performance verification value of each edge computing node.

[0104] It should be added that matching 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 accuracy of the allocation of edge computing nodes and storage ports, and ensuring that the cluster spatiotemporal information of the edge computing nodes is allocated to the storage port with the most reliable performance.

[0105] 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.

[0106] It should be noted that the cluster spatiotemporal information of each edge computing node is transmitted to the corresponding target storage port, which optimizes the data storage process. Storage resources are dynamically allocated based on the comparison results of the storage performance verification value and the storage performance benchmark value, which helps to improve the flexibility and efficiency of data management, reduce resource waste, and improve the overall utilization of storage resources.

[0107] A spatiotemporal information database management system based on the Internet of Things also includes: a load balancing module, which 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: based on the load parameters of each data storage node, the load evaluation value of each data storage node is processed, and the load balancing configuration of each data storage node is performed according to the load evaluation value of each data storage node.

[0108] It should be noted that the load parameters of each data storage node include the CPU utilization, data reading and writing speed, and memory utilization of each data storage node.

[0109] It should be noted that the specific process of load balancing configuration of each data storage node according to the load evaluation value of each data storage node is to compare the load evaluation value of each data storage node with the set first load evaluation threshold. If the load evaluation value of a data storage node is lower than the set first load evaluation threshold, the data storage node is marked as a low-load storage node, thereby obtaining each low-load storage node. If the load evaluation value of a data storage node is higher than or equal to the set first load evaluation threshold, the load evaluation value of the data storage node is compared with the set second load evaluation threshold. If the load evaluation value of the data storage node is lower than the set second load evaluation threshold, the data storage node is marked as a normal-load storage node, thereby obtaining each normal-load storage node. If the load evaluation value of the data storage node is higher than or equal to the set second load evaluation threshold, the data storage node is marked as a high-load storage node, thereby obtaining each high-load storage node, and the data flow of the high-load storage node is distributed to the low-load storage node for data flow regulation.

[0110] It should be noted that the second load assessment threshold is higher than the first load assessment threshold. The data traffic of the high-load storage node is distributed to the low-load storage node for data traffic regulation, and a speed limit strategy is imposed on the high-load storage node to ensure that the high-load storage node is not overloaded. At the same time, a dynamic routing protocol is used to direct excess traffic to the low-load storage node according to the load conditions of the high-load storage node.

[0111] It should be added that, in the process of analyzing and obtaining the load evaluation value of each data storage node, the CPU utilization, data reading and writing speed, and memory utilization of each data storage node are all de-normalized.

[0112] In this embodiment, the load evaluation value of each data storage node is used to comprehensively quantify the workload of each data storage node, which can be obtained by the following analysis method. The specific analysis conditions are as follows:

[0113] ;

[0114] In the formula, represents the load evaluation value of the sth data storage node, Indicates the CPU utilization of the sth data storage node, Indicates the weight factor corresponding to the set CPU utilization. Indicates the data read and write speed of the sth data storage node, Indicates the weight factor corresponding to the set data read and write speed. represents the memory utilization of the sth data storage node, Indicates the weight factor corresponding to the set memory utilization, s indicates the number of each data storage node, , represents the total number of data storage nodes, and e represents a natural constant.

[0115] It should be added that, in this embodiment, the preset weight factor corresponding to the CPU utilization, the weight factor corresponding to the data read and write speed, and the weight factor corresponding to the memory utilization are obtained from the information management database.

[0116] It needs to be explained that the weight factors corresponding to the CPU utilization, data read and write speed, and memory utilization are respectively used to adjust the importance of the CPU utilization, data read and write speed, and memory utilization of each data storage node in the process of analyzing and obtaining the load assessment value. There is a pre-set mapping relationship between the real-time load parameters of each data storage node and the corresponding weight factors in the information management database. The pre-set mapping relationship can be used to match the weight factors corresponding to the real-time load parameters of each data storage node. For example, the CPU utilization, data read and write speed, and memory utilization of each data storage node are respectively matched with the pre-set mapping relationship to obtain the weight factors corresponding to the CPU utilization, data read and write speed, and memory utilization of each data storage node.

[0117] In this implementation scheme, the load parameters of each data storage node include the CPU utilization, data read and write speed and memory utilization of each data storage node, which are correlated and do not exist independently. For example, when the CPU utilization increases, the data read and write speed will increase and the memory utilization will also increase, indicating that the node is processing a large amount of data and may face performance bottlenecks. Comprehensive analysis of the load evaluation value of each data storage node can accurately obtain the actual load status and performance bottleneck of the data storage node, thereby providing data basis for resource optimization and system adjustment.

[0118] It should be noted that a spatiotemporal information database management system and method based on the Internet of Things also includes an information management database, which is used to store weight factors corresponding to data collection frequency, weight factors corresponding to data volume, weight factors corresponding to data transmission rate, reference memory utilization of each edge computing node, reference bandwidth utilization of each edge computing node, reference data packet processing rate of each edge computing node, reference CPU utilization of each edge computing node, data transmission evaluation threshold, reference memory utilization of each storage port, correction factor corresponding to transmission delay duration, correction factor corresponding to data read and write speed, correction factor corresponding to data transmission rate, compensation factor corresponding to data processing energy efficiency characterization value, compensation factor corresponding to the number of target receiving data collection points, first load evaluation threshold, second load evaluation threshold, weight factor corresponding to CPU utilization, weight factor corresponding to data read and write speed, weight factor corresponding to memory utilization, target reception comprehensive characteristic value range of data collection points corresponding to each data processing energy efficiency characterization value interval, data preprocessing parameters corresponding to each data transmission evaluation value interval, and storage performance verification value corresponding to each cluster spatiotemporal information evaluation value interval.

[0119] like Figure 2 The second aspect of the present invention provides a method for managing a spatiotemporal information database based on the Internet of Things, including: counting each data collection point and each edge computing node connected to the Internet of Things, and obtaining the spatiotemporal information of each data collection point and the operation and maintenance information of each edge computing node.

[0120] Based on the spatiotemporal information of each data collection point and the operation and maintenance information of each edge computing node, the target receiving data collection point cluster of each edge computing node is obtained.

[0121] The spatiotemporal information sent by the target receiving data collection point cluster of each edge computing node is preprocessed and configured, and 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.

[0122] 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.

[0123] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0124] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well.

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; The distributed storage management module is used to synchronously retrieve the operating 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.

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 is based on the spatiotemporal information of each data collection point and the operation and maintenance information of each edge computing node. The specific process is as follows: Based on the spatiotemporal information of each data collection point, a basic characteristic value of each data collection point is obtained by processing, and the basic characteristic value of each data collection point is used to comprehensively quantify the data collection efficiency of each data collection point; 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: 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 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 node is preprocessed 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 node is preprocessed with the data preprocessing parameters preset in the information management database.

6. 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.

7. 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.

8. The spatiotemporal information database management system based on the Internet of Things according to claim 7, 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.

9. A spatiotemporal information database management system based on the Internet of Things, further comprising: 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.

10. A method for managing a spatiotemporal information database based on the Internet of Things, 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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