Data management system of Internet of Things platform based on cloud technology

Abstract

According to the data management system of the Internet of Things platform based on the cloud technology, the device data of the Internet of Things device is collected through the Internet of Things platform, the task priority and the task execution parameter of the to-be-executed task are determined through the cloud computing platform, and the to-be-executed task is executed according to the first performance parameter, the task priority and the task execution parameter of the to-be-executed task. Determining a first reward function value for executing the to-be-executed task by the edge node according to the first performance parameter of the edge node, the task priority and the task execution parameter, and determining a second reward function value for executing the to-be-executed task by the edge node according to the second performance parameter of the edge node, the task priority and the task execution parameter; according to the method, whether the to-be-executed task is executed by the cloud computing platform or the edge node is determined, so that factors such as task priority, execution time and execution accuracy are comprehensively considered, an execution main body is elastically determined, task execution is ensured, and effective data management is realized based on combination of the Internet of Things platform and the cloud computing platform.

Description

Technical Field

[0001] The present application relates to the field of cloud technology, and in particular to a data management system for an Internet of Things platform based on cloud technology. Background Art

[0002] In real-world Industrial IoT scenarios, the vast amount of device data involved in IoT devices makes managing this data challenging. For example, computing, processing, and storing this data present significant challenges. Currently, managing device data entirely on edge nodes offers fast processing and transmission speeds, but limited computing power leads to accuracy issues. Cloud-based processing, while highly accurate, also suffers from transmission delays, making effective data management difficult. Therefore, a flexible management solution is needed. Summary of the Invention

[0003] The present application provides a data management system for an Internet of Things platform based on cloud technology to solve at least one problem existing in related technologies. The technical solution is as follows:

[0004] In a first aspect, embodiments of the present application provide a data management system for an Internet of Things platform based on cloud technology, comprising:

[0005] IoT platform, used to respond to pending tasks and collect device data from IoT devices;

[0006] The cloud computing platform is configured to determine a task priority of a task to be executed and task execution parameters required for executing the task, determine a first reward function value for executing the task to be executed by the cloud computing platform itself based on its first performance parameter, task priority, and task execution parameters, and determine a second reward function value for executing the task to be executed by the edge node based on the second performance parameter, task priority, and task execution parameters of the edge node, wherein the first reward function value and the second reward function value are comprehensive values ​​reflecting task priority, execution time, and execution accuracy;

[0007] When the first reward function value is greater than the second reward function value, the task to be executed is executed using the task execution parameters. When the first reward function value is less than or equal to the second reward function value, the edge node is instructed to execute the task to be executed using the task execution parameters, and the execution result after the edge node executes the task to be executed is received and stored.

[0008] In one embodiment, the cloud computing platform includes a priority unit, which is configured to:

[0009] When the task to be executed is device control, the task priority is determined to be the first priority;

[0010] When the task to be executed is equipment monitoring, the task priority is determined to be the second priority;

[0011] When the task to be executed is an update or upgrade, the task priority is determined to be the third priority.

[0012] In one embodiment, the cloud computing platform includes a first unit, a second unit, and a third unit:

[0013] The first unit is used to determine the target priority value corresponding to the task priority, wherein the higher the task priority, the larger the corresponding target priority value;

[0014] The second unit is configured to determine a first objective optimization function for executing the task to be executed based on the first performance parameter and the task execution parameter;

[0015] The third unit is used to determine a first reward function value for executing the task to be executed based on the first objective optimization function and the target priority value.

[0016] In one embodiment, the first performance parameter includes the network bandwidth between the IoT platform, the first computing capability value, and the cloud computing accuracy; the task execution parameter includes the task computing amount and the task data amount; and the second unit is specifically configured to:

[0017] Determining a first ratio of a task computing amount to a first computing capability value, and determining a second ratio of a task data amount to a network bandwidth, and determining a cloud computing time consumption according to a sum of the first ratio and the second ratio;

[0018] Determine a first difference between the preset value and the cloud computing precision to obtain a cloud precision loss;

[0019] Determine a first product of cloud computing time and a first weight parameter and a second product of cloud precision loss and a second weight parameter, and determine a first objective optimization function for executing the task to be executed based on the sum of the first product and the second product;

[0020] The first value of the first weight parameter and the second value of the second weight parameter are obtained with the goal of minimizing the first objective optimization function.

[0021] In one embodiment, the task execution parameter further includes a task deadline; the first value of the first weight parameter and the second value of the second weight parameter are obtained by the following steps:

[0022] Determine a first candidate value of the first weight parameter and a second candidate value corresponding to the second weight parameter when the first objective optimization function is minimized;

[0023] If there is only one first candidate value, the first candidate value is used as the first value and the second candidate value is used as the second value;

[0024] If there are multiple first candidate values, the urgency value is determined based on the task deadline and the cloud computing time. The smaller the urgency value, the more urgent it is.

[0025] If the urgency level value is less than or equal to the urgency threshold, determining the largest first candidate value as the first value, and determining the second candidate value corresponding to the largest first candidate value as the second value;

[0026] If the urgency level is greater than the urgency threshold, the largest second candidate value is determined as the second value, and the first candidate value corresponding to the largest second candidate value is determined as the first value.

[0027] In one embodiment, the third unit is specifically configured to:

[0028] Normalize the minimum value of the first objective optimization function and the target priority value respectively;

[0029] Determine a first reward function value for executing the task to be executed by itself according to a second difference between the normalized target priority value and the normalized minimum value of the first objective optimization function.

[0030] In one embodiment, the cloud computing platform further includes a fourth unit and a fifth unit:

[0031] A fourth unit is configured to determine a second objective optimization function for the edge node to execute the task to be executed based on the second performance parameter and the task execution parameter;

[0032] The fifth unit is used to determine a second reward function value for the edge node to perform the task to be performed according to the second objective optimization function and the target priority value.

[0033] In one embodiment, the second performance parameter includes a second computing capability value and an edge computing accuracy; and the fourth unit is specifically configured to:

[0034] Determine a third ratio of the task computing amount to the second computing capability value to obtain edge computing time;

[0035] Determine a third difference between the preset value and the edge calculation accuracy to obtain an edge accuracy loss;

[0036] Determine a third product of the edge computing time and the third weight parameter and a fourth product of the edge precision loss and the fourth weight parameter, and determine a second objective optimization function for the edge node to execute the task to be executed based on the sum of the third product and the fourth product;

[0037] The third value of the third weight parameter and the fourth value of the fourth weight parameter are obtained with the goal of minimizing the second objective optimization function.

[0038] In one embodiment, the fifth unit is specifically configured to:

[0039] Normalize the minimum value of the second objective optimization function and the target priority value respectively;

[0040] Determine a second reward function value for the edge node executing the task to be executed based on a fourth difference between the normalized target priority value and the normalized minimum value of the second objective optimization function.

[0041] In a second aspect, an embodiment of the present application provides another data management system for an Internet of Things platform based on cloud technology, comprising: a processor and a memory, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement the following method:

[0042] Respond to pending tasks through the IoT platform and collect device data from IoT devices;

[0043] Determine, through the cloud computing platform, a task priority of the task to be executed and task execution parameters required for executing the task, determine a first reward function value for executing the task to be executed by the edge node based on its own first performance parameter, task priority, and task execution parameters, and determine a second reward function value for executing the task to be executed by the edge node based on the edge node's second performance parameter, task priority, and task execution parameters, wherein the first reward function value and the second reward function value are comprehensive values ​​reflecting task priority, execution time, and execution accuracy;

[0044] When the first reward function value is greater than the second reward function value, the task to be executed is executed using the task execution parameters through the cloud computing platform. When the first reward function value is less than or equal to the second reward function value, the cloud computing platform instructs the edge node to execute the task to be executed using the task execution parameters, and the cloud computing platform receives the execution result of the edge node after executing the task to be executed and stores it.

[0045] The beneficial effects of the above technical solution include at least:

[0046] The device data of the Internet of Things devices is collected through the Internet of Things platform, and the task priority and task execution parameters of the task to be executed are determined through the cloud computing platform. The first reward function value of the task to be executed by itself is determined based on its own first performance parameters, task priority and task execution parameters, and the second reward function value of the edge node for executing the task to be executed is determined based on the second performance parameters, task priority and task execution parameters of the edge node. By comparing the sizes of the first reward function value and the second reward function value, it is determined whether the task to be executed is executed by the cloud computing platform or the edge node. Therefore, factors such as task priority, execution time and execution accuracy are comprehensively considered, and the execution subject is flexibly determined, which is conducive to ensuring the execution of the task. Effective data management is achieved based on the combination of the Internet of Things platform and the cloud computing platform.

[0047] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present application will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar components or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in this application and should not be construed as limiting the scope of this application.

[0049] Figure 1 This is a block diagram of a data management system for an Internet of Things platform based on cloud technology according to an embodiment of the present application;

[0050] Figure 2 This is a structural block diagram of a data management system of an Internet of Things platform based on cloud technology in another embodiment of the present application. DETAILED DESCRIPTION

[0051] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present application. Therefore, the drawings and description are to be regarded as illustrative in nature and not restrictive.

[0052] Reference Figure 1 , showing a block diagram of a data management system of an Internet of Things platform based on cloud technology according to an embodiment of the present application. The data management system of the Internet of Things platform based on cloud technology may include at least an Internet of Things platform, a cloud computing platform and an edge node.

[0053] The IoT platform is used to respond to pending tasks and collect device data from IoT devices.

[0054] The cloud computing platform is configured to determine a task priority and task execution parameters of a task to be executed, determine a first reward function value for executing the task to be executed by the cloud computing platform itself based on its first performance parameter, task priority, and task execution parameters, and determine a second reward function value for executing the task to be executed by the edge node based on the second performance parameter, task priority, and task execution parameters of the edge node, wherein the first reward function value and the second reward function value are comprehensive values ​​reflecting task priority, execution time, and execution accuracy;

[0055] When the first reward function value is greater than the second reward function value, the task to be executed is executed using the task execution parameters. When the first reward function value is less than or equal to the second reward function value, the edge node is instructed to execute the task to be executed using the task execution parameters, and the execution result after the edge node executes the task to be executed is received and stored.

[0056] The technical solution of the embodiment of the present application collects device data of the Internet of Things devices through the Internet of Things platform, determines the task priority and task execution parameters of the task to be executed through the cloud computing platform, determines the first reward function value of its own execution of the task to be executed based on its own first performance parameters, task priority and task execution parameters, and determines the second reward function value of the edge node execution of the task to be executed based on the second performance parameters, task priority and task execution parameters of the edge node, and determines whether the task to be executed is executed by the cloud computing platform or the edge node by comparing the sizes of the first reward function value and the second reward function value, thereby comprehensively considering factors such as task priority, execution time and execution accuracy, and flexibly determining the execution subject, which is conducive to ensuring the execution of the task and realizing effective data management based on the combination of the Internet of Things platform and the cloud computing platform.

[0057] In one embodiment, the Internet of Things platform, the cloud computing platform and the edge nodes communicate with each other. Users can log in to the Internet of Things platform or the cloud computing platform and then publish tasks to be executed. If the cloud computing platform publishes, it will be sent to the Internet of Things platform. The tasks to be executed will respond through the Internet of Things platform and then collect the device data of the Internet of Things devices involved in the execution of the tasks. It should be noted that the type of Internet of Things devices is not specifically limited. In the embodiment of this application, production equipment is taken as an example. The tasks to be executed include but are not limited to equipment control, equipment monitoring and updates and upgrades. Equipment control is to obtain equipment data such as the start, stop, tool movement, and robot movement of the production equipment by obtaining equipment data such as the start, stop, tool movement, and robot movement of the production equipment, so as to switch the start, stop, tool movement, and robot movement of the production equipment when switching is required (for example, monitoring abnormality, manual switching); equipment monitoring can obtain and monitor equipment data such as temperature, air pressure, voltage, etc.; updates and upgrades can be to obtain equipment data such as the version number of various software or hardware to determine the specific parts that need to be updated and perform corresponding updates.

[0058] In an embodiment of the present application, for tasks waiting to be executed, such as equipment control, equipment monitoring, and update and upgrade, the task priorities corresponding to different tasks to be executed can be set in advance in the cloud computing platform; at the same time, after the tasks to be executed are determined, the cloud computing platform can analyze the determined tasks to be executed and determine the task execution parameters required to execute the tasks, including but not limited to the task calculation amount, task data amount, task deadline, etc.

[0059] In an embodiment of the present application, the cloud computing platform can obtain its own first performance parameter and the second performance parameter of the edge node to perform one of the analysis factors of performance analysis, determine the first reward function value of the cloud computing platform itself executing the task to be executed and the second reward function value of the edge node executing the task to be executed, and finally determine whether the task to be executed is executed on the cloud computing platform or the edge node, and the first reward function value and the second reward function value take into account factors such as task priority, execution time and execution accuracy, and are comprehensive values ​​reflecting task priority, execution time and execution accuracy, because it is necessary to consider that the execution accuracy of the cloud computing platform is higher than that of the edge node, but the execution time of the cloud computing platform is slower than that of the edge node, and a "balance" needs to be made.

[0060] In one embodiment, the cloud computing platform includes a priority unit, which is configured to:

[0061] When the task to be executed is device control, the task priority is determined to be the first priority;

[0062] When the task to be executed is equipment monitoring, the task priority is determined to be the second priority;

[0063] When the task to be executed is an update or upgrade, the task priority is determined to be the third priority.

[0064] The first priority is greater than the second priority, and the second priority is greater than the third priority.

[0065] In one embodiment, the cloud computing platform includes a first unit, a second unit, and a third unit:

[0066] The first unit is used to determine the target priority value corresponding to the task priority.

[0067] Optionally, the cloud computing platform sets corresponding preset priority values ​​based on different task priorities in advance. Therefore, after the task priority is currently determined, the corresponding target priority value can be determined, where the higher the task priority, the larger the corresponding target priority value.

[0068] The second unit is used to determine a first target optimization function for executing the task to be executed based on the first performance parameter and the task execution parameter.

[0069] Optionally, the first performance parameter includes the network bandwidth between the IoT platform, the first computing capability value, and the cloud computing accuracy. The second unit is specifically configured to:

[0070] 1. Determine the task computational load C task (Unit: FLOPS) and the first computing capability value P cloud The first ratio (unit: FLOP / S) And determine the task data volume D task (Unit: MB) and network bandwidth B net Second ratio (unit: Mbps) Determine the cloud computing time based on the sum of the first ratio and the second ratio

[0071] 2. Determine the preset value and cloud computing accuracy A cloud The first difference is the cloud precision loss.

[0072] For example, taking the preset value as 1, the first difference, that is, the cloud accuracy loss, is: 1-A cloud .

[0073] 3. Determine the first product of cloud computing time and the first weight parameter α1 and the second product of cloud precision loss and the second weight parameter γ1. Based on the sum of the first product and the second product, determine the first objective optimization function for executing the task to be executed. The specific formula is:

[0074] α1×T cloud +γ1×(1-A cloud )

[0075] Among them, α1×T cloud Equivalent to delay penalty, γ1×(1-A cloud ) is equivalent to the precision loss penalty.

[0076] The first value of the first weight parameter α1 and the second value of the second weight parameter γ1 are obtained by minimizing the first objective optimization function, that is, min(α1×T cloud +γ1×(1-A cloud )).

[0077] In one embodiment, the first value of the first weight parameter and the second value of the second weight parameter are obtained by the following steps:

[0078] First, determine the first objective optimization function to be the minimum value min(α1×T cloud +γ1×(1-A cloud )), the first candidate value of the first weight parameter α1 and the second candidate value corresponding to the second weight parameter γ1.

[0079] Next, if there is only one first candidate value, the first candidate value is used as the first value and the second candidate value is used as the second value.

[0080] Then, if there are multiple first candidate values, the urgency value is determined according to the task deadline and the cloud computing time. The smaller the urgency value, the more urgent it is.

[0081] It should be noted that when the first objective optimization function is minimized, there may be one or more values ​​of the first weight parameter α1 and the second weight parameter γ1, that is, there are multiple sets of first candidate values ​​and second candidate values ​​that make the first objective optimization function the minimum value. If there are multiple sets, further analysis is required to determine which set will be more accurate.

[0082] Therefore, if there is only one first candidate value, the first candidate value is used as the first value and the second candidate value is used as the second value.

[0083] If there are multiple first candidate values, according to the task deadline T deadline (For example, the task to be executed must be completed at a certain time, and then calculate the length of time between this time and the current time) and the cloud computing time T cloud , determine the urgency value u:

[0084]

[0085] Therefore, it can be seen that the smaller the urgency value u is, the more urgent it is.

[0086] Furthermore, if the urgency value u is less than or equal to the urgency threshold, the largest first candidate value is determined as the first value, and the second candidate value corresponding to the largest first candidate value is determined as the second value, because this represents time priority and the first weight parameter α1 needs to be considered in particular.

[0087] Finally, if the urgency u is greater than the urgency threshold, the largest second candidate value is determined as the second value, and the first candidate value corresponding to the largest second candidate value is used as the first value. Therefore, it means that time is not urgent at this time, and calculation accuracy is given priority, that is, γ1 is considered in particular.

[0088] The third unit is used to determine a first reward function value for executing the task to be executed based on the first objective optimization function and the target priority value.

[0089] Optionally, the third unit is specifically configured to:

[0090] 1. Normalize the minimum value of the first objective optimization function and the target priority value respectively to facilitate subsequent calculations.

[0091] 2. Based on the second difference between the normalized target priority value and the normalized minimum value of the first objective optimization function, determine the first reward function value R1 of the cloud computing platform itself for executing the task to be executed. That is, in the first reward function value R1, it is equivalent to taking the normalized target priority value as the positive gain, and taking the delay penalty part and the accuracy loss penalty in the first objective optimization function as negative gains. Therefore, the larger the first reward function value R1, the better.

[0092] In one embodiment, the cloud computing platform further includes a fourth unit and a fifth unit including:

[0093] The fourth unit is used to determine a second objective optimization function for the edge node to execute the task to be executed according to the second performance parameter and the task execution parameter.

[0094] The second performance parameter includes a second computing capability value and edge computing accuracy. The fourth unit is specifically used to:

[0095] 1. Determine the task computational load C task (Unit: FLOPS) and the second computing capability value P edge The third ratio (unit: FLOP / S) Get edge computing time T edge .

[0096] 2. Determine the preset value and edge computing accuracy A edge The third difference is the edge accuracy loss.

[0097] For example, if the preset value is 1, the third difference is the edge accuracy loss: 1-A edge , where A cloud >A edge .

[0098] 3. Determine the edge computing time T edge The third product of the edge precision loss and the third weight parameter α2 and the fourth product of the edge precision loss and the fourth weight parameter γ2 are used to determine the second objective optimization function for the edge node to perform the task to be performed according to the sum of the third product and the fourth product. The specific formula is:

[0099] α2×T edge +γ2×(1-A edge )

[0100] Among them, α2×T edge Equivalent to delay penalty, γ2×(1-A edge ) is equivalent to the precision loss penalty.

[0101] The third value of the third weight parameter and the fourth value of the fourth weight parameter are obtained by minimizing the second objective optimization function, similarly by calculating min(α2×T edge +γ2×(1-A edge )) to determine the first candidate value of the third weight parameter α2 that makes the second objective optimization function the minimum value and the fourth candidate value corresponding to the second weight parameter γ2.

[0102] Next, if there is only one third candidate value, the third candidate value is used as the third value and the fourth candidate value is used as the fourth value.

[0103] Then, if there are multiple third candidate values, according to the task deadline and edge computing time T edge , determine the urgency value, the smaller the urgency value, the more urgent it is, such as the calculation principle of u above.

[0104] The fifth unit is used to determine a second reward function value for the edge node to perform the task to be performed according to the second objective optimization function and the target priority value.

[0105] Specifically, the minimum value of the second objective optimization function and the target priority value are normalized respectively, and then the second reward function value R2 of the edge node for executing the task to be executed is determined based on the fourth difference between the normalized target priority value and the normalized minimum value of the second objective optimization function. The calculation principle is similar to that of the first reward function value R1 in the third unit and will not be repeated here.

[0106] In an embodiment of the present application, the first reward function value is compared with the second reward function value. When the first reward function value is greater than the second reward function value, it means that after comprehensive analysis, the cloud computing platform itself will obtain better results by processing the task to be executed, specifically using the task execution parameters to execute the task to be executed; and when the first reward function value is less than or equal to the second reward function value, it means that after comprehensive analysis, the cloud computing platform will instruct the edge node to execute the task to be executed using the task execution parameters. After execution, the cloud computing platform receives the execution result of the edge node after executing the task to be executed and stores it.

[0107] It should be noted that the execution results of the edge node after executing the task can be stored in the cloud computing platform for statistics. The cloud computing platform has data storage and management functions, which can realize data management and query on the Internet platform.

[0108] Reference Figure 2 , shows a structural block diagram of a data management system for an Internet of Things platform based on cloud technology according to an embodiment of the present application. The data management system for an Internet of Things platform based on cloud technology includes: a memory 310 and a processor 320. The memory 310 stores instructions that can be executed on the processor 320. The processor 320 loads and executes the instructions to implement the following method:

[0109] Respond to pending tasks through the IoT platform and collect device data from IoT devices;

[0110] Determine, through the cloud computing platform, a task priority of the task to be executed and task execution parameters required for executing the task, determine a first reward function value for executing the task to be executed by the edge node based on its own first performance parameter, task priority, and task execution parameters, and determine a second reward function value for executing the task to be executed by the edge node based on the edge node's second performance parameter, task priority, and task execution parameters, wherein the first reward function value and the second reward function value are comprehensive values ​​reflecting task priority, execution time, and execution accuracy;

[0111] When the first reward function value is greater than the second reward function value, the task to be executed is executed using the task execution parameters through the cloud computing platform. When the first reward function value is less than or equal to the second reward function value, the cloud computing platform instructs the edge node to execute the task to be executed using the task execution parameters, and the cloud computing platform receives the execution result of the edge node after executing the task to be executed and stores it.

[0112] The number of memories 310 and processors 320 may be one or more, for example, they may include processors and memories in a cloud computing platform, processors and memories in an Internet of Things platform, processors and memories in an edge node, etc.

[0113] In one embodiment, the data management system of the cloud-based Internet of Things platform further includes a communication interface 330 for communicating with external devices and performing data exchange and transmission.

[0114] In the description of this specification, the reference terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0115] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, features specified as "first" or "second" may explicitly or implicitly include at least one of such features. In the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0116] Any process or method description in a flow chart or otherwise described herein can be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process. The scope of the preferred embodiments of the present application includes additional implementations in which the functions may be performed in a different order than shown or discussed, including in a substantially simultaneous manner or in a reverse order depending on the functions involved.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein may be considered, for example, as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device).

[0118] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0119] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the aforementioned integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a magnetic disk, or an optical disk, etc.

[0120] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included within the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A data management system for an Internet of Things platform based on cloud technology, characterized in that: include: IoT platform, used to respond to pending tasks and collect device data from IoT devices; The cloud computing platform is configured to determine a task priority of a task to be executed and task execution parameters required for executing the task, determine a first reward function value for executing the task to be executed by the cloud computing platform itself based on its first performance parameter, task priority, and task execution parameters, and determine a second reward function value for executing the task to be executed by the edge node based on the second performance parameter, task priority, and task execution parameters of the edge node, wherein the first reward function value and the second reward function value are comprehensive values ​​reflecting task priority, execution time, and execution accuracy; When the first reward function value is greater than the second reward function value, the task to be executed is executed using the task execution parameters. When the first reward function value is less than or equal to the second reward function value, the edge node is instructed to execute the task to be executed using the task execution parameters, and the execution result after the edge node executes the task to be executed is received and stored.

2. The data management system of the Internet of Things platform based on cloud technology according to claim 1, characterized in that: The cloud computing platform includes a priority unit, which is used to: When the task to be executed is device control, the task priority is determined to be the first priority; When the task to be executed is equipment monitoring, the task priority is determined to be the second priority; When the task to be executed is an update or upgrade, the task priority is determined to be the third priority.

3. The data management system of the Internet of Things platform based on cloud technology according to claim 1 or 2, characterized in that: The cloud computing platform includes a first unit, a second unit and a third unit: The first unit is used to determine the target priority value corresponding to the task priority, wherein the higher the task priority, the larger the corresponding target priority value; The second unit is configured to determine a first objective optimization function for executing the task to be executed based on the first performance parameter and the task execution parameter; The third unit is used to determine a first reward function value for executing the task to be executed based on the first objective optimization function and the target priority value.

4. The data management system of the Internet of Things platform based on cloud technology according to claim 3, characterized in that: The first performance parameter includes the network bandwidth between the IoT platform, the first computing capability value, and the cloud computing accuracy; the task execution parameter includes the task computing amount and the task data amount; the second unit is specifically used to: Determining a first ratio of a task computing amount to a first computing capability value, and determining a second ratio of a task data amount to a network bandwidth, and determining a cloud computing time consumption according to a sum of the first ratio and the second ratio; Determine a first difference between the preset value and the cloud computing precision to obtain a cloud precision loss; Determine a first product of cloud computing time and a first weight parameter and a second product of cloud precision loss and a second weight parameter, and determine a first objective optimization function for executing the task to be executed based on the sum of the first product and the second product; The first value of the first weight parameter and the second value of the second weight parameter are obtained with the goal of minimizing the first objective optimization function.

5. The data management system of the Internet of Things platform based on cloud technology according to claim 4, characterized in that: The task execution parameters also include a task deadline; the first value of the first weight parameter and the second value of the second weight parameter are obtained by the following steps: Determine a first candidate value of the first weight parameter and a second candidate value corresponding to the second weight parameter when the first objective optimization function is minimized; If there is only one first candidate value, the first candidate value is used as the first value and the second candidate value is used as the second value; If there are multiple first candidate values, the urgency value is determined based on the task deadline and the cloud computing time. The smaller the urgency value, the more urgent it is. If the urgency level value is less than or equal to the urgency threshold, determining the largest first candidate value as the first value, and determining the second candidate value corresponding to the largest first candidate value as the second value; If the urgency level is greater than the urgency threshold, the largest second candidate value is determined as the second value, and the first candidate value corresponding to the largest second candidate value is determined as the first value.

6. The data management system of the Internet of Things platform based on cloud technology according to claim 4, characterized in that: The third unit is specifically used for: Normalize the minimum value of the first objective optimization function and the target priority value respectively; Determine a first reward function value for executing the task to be executed by itself according to a second difference between the normalized target priority value and the normalized minimum value of the first objective optimization function.

7. The data management system of the Internet of Things platform based on cloud technology according to claim 4, characterized in that: The cloud computing platform further includes a fourth unit and a fifth unit: A fourth unit is configured to determine a second objective optimization function for the edge node to execute the task to be executed based on the second performance parameter and the task execution parameter; The fifth unit is used to determine a second reward function value for the edge node to perform the task to be performed according to the second objective optimization function and the target priority value.

8. The data management system of the Internet of Things platform based on cloud technology according to claim 7, characterized in that: The second performance parameter includes a second computing capability value and an edge computing accuracy; and the fourth unit is specifically configured to: Determine a third ratio of the task computing amount to the second computing capability value to obtain edge computing time; Determine a third difference between the preset value and the edge calculation accuracy to obtain an edge accuracy loss; Determine a third product of the edge computing time and the third weight parameter and a fourth product of the edge precision loss and the fourth weight parameter, and determine a second objective optimization function for the edge node to execute the task to be executed based on the sum of the third product and the fourth product; The third value of the third weight parameter and the fourth value of the fourth weight parameter are obtained with the goal of minimizing the second objective optimization function.

9. The data management system of the Internet of Things platform based on cloud technology according to claim 7, characterized in that: The fifth unit is specifically configured to: Normalize the minimum value of the second objective optimization function and the target priority value respectively; Determine a second reward function value for the edge node executing the task to be executed based on a fourth difference between the normalized target priority value and the normalized minimum value of the second objective optimization function.

10. A data management system for an Internet of Things platform based on cloud technology, characterized in that: include: A processor and a memory, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement the following method: Respond to pending tasks through the IoT platform and collect device data from IoT devices; Determine, through the cloud computing platform, a task priority of the task to be executed and task execution parameters required for executing the task, determine a first reward function value for executing the task to be executed by the edge node based on its own first performance parameter, task priority, and task execution parameters, and determine a second reward function value for executing the task to be executed by the edge node based on the edge node's second performance parameter, task priority, and task execution parameters, wherein the first reward function value and the second reward function value are comprehensive values ​​reflecting task priority, execution time, and execution accuracy; When the first reward function value is greater than the second reward function value, the task to be executed is executed using the task execution parameters through the cloud computing platform. When the first reward function value is less than or equal to the second reward function value, the cloud computing platform instructs the edge node to execute the task to be executed using the task execution parameters, and the cloud computing platform receives the execution result of the edge node after executing the task to be executed and stores it.

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