Iot device management method and system combined with blockchain

Abstract

The present disclosure provides a kind of management method and system of internet of things equipment combined with blockchain, it is related to internet of things technical field, method includes: based on edge processing division rule and multiple index analysis results, group identification is carried out to internet of things equipment, obtains cloud processing equipment group and edge processing equipment group;The computing power resources required for all devices in edge processing equipment group are predicted in turn;The computing power resource prediction result sequence is judged, and the computing power deviation sequence is obtained;Based on the computing power deviation sequence, identify the associated computing power processing block, and construct edge processing unit;Based on the cloud processing unit in cloud processing equipment group and edge processing unit, the collaborative management of internet of things equipment is carried out.It can solve the technical problems that the existing internet of things equipment management method has lower equipment response speed and poorer data transmission security, can improve the response speed of internet of things equipment and the security of data processing, to improve the user experience effect of internet of things equipment.

Description

Technical Field

[0001] This disclosure relates to the field of Internet of Things (IoT) technology, and more specifically, to an IoT device management method and system incorporating blockchain. Background Technology

[0002] IoT devices are devices that connect to a network and can achieve functions such as intelligent identification, monitoring, and management. They transmit data through network connections and can remotely control multiple devices. Currently, when remotely managing IoT devices, data processing is typically done through a cloud computing platform. Since data processing and transmission take time, as the amount of data to be processed increases, the request response time increases, leading to a deterioration in user experience.

[0003] The shortcomings of existing IoT device management methods are that they suffer from slow device response speed and poor data security. Summary of the Invention

[0004] Therefore, in order to solve the above-mentioned technical problems, the technical solutions adopted in the embodiments of this disclosure are as follows:

[0005] The IoT device management method incorporating blockchain includes the following steps: connecting to an IoT device management system to obtain an IoT device set and a device information set, wherein the device information set includes device type, device function, and device computing power; based on the device type and device function, sequentially performing multi-indicator analysis on the IoT devices in the IoT device set to generate multiple indicator analysis results; based on edge processing partitioning rules and the multiple indicator analysis results, grouping and identifying the IoT device set to obtain a cloud processing device group and an edge processing device group; and sequentially predicting the computing power resources required by all devices in the edge processing device group based on historical device usage scenarios to obtain a computing power resource prediction. The sequence of predicted computing power resources is determined based on the actual computing power of the edge processing device group. When the predicted computing power resources do not meet the actual computing power of the corresponding device, a computing power deviation sequence is obtained based on the computing power deviation value, where the computing power deviation value is the difference between the actual computing power of the device and the predicted computing power resources. A data transmission security requirement analysis is performed on the computing power deviation sequence to obtain a data transmission security coefficient sequence. Computing power processing blocks that meet the data transmission security coefficient are identified as associated computing power processing blocks. An edge processing unit is constructed based on the associated computing power processing blocks. Collaborative management of IoT devices is performed based on the cloud processing unit in the cloud processing device group and the edge processing unit.

[0006] The IoT device management system integrating blockchain includes: a device information set acquisition module, which connects to the IoT device management system to acquire an IoT device set and a device information set, wherein the device information set includes device type, device function, and device computing power; an indicator analysis result generation module, which performs multi-indicator analysis on the IoT devices in the IoT device set based on the device type and device function, generating multiple indicator analysis results; a device grouping and identification module, which groups and identifies the IoT device set based on edge processing partitioning rules and the multiple indicator analysis results, obtaining a cloud processing device group and an edge processing device group; and a computing power resource prediction module, which predicts the computing power resources required by all devices in the edge processing device group based on historical device usage scenarios. The system comprises: a prediction module for obtaining a sequence of predicted computing power resources; a computing power deviation sequence acquisition module, which judges the predicted computing power resources sequentially based on the actual computing power of the edge processing device group; when the predicted computing power resources do not meet the actual computing power of the corresponding device, a computing power deviation sequence is obtained based on the computing power deviation value, wherein the computing power deviation value is the difference between the actual computing power of the device and the predicted computing power resources; an edge processing unit construction module, which performs data transmission security requirement analysis on the computing power deviation sequence to obtain a data transmission security coefficient sequence, identifies computing power processing blocks that meet the data transmission security coefficient as associated computing power processing blocks, and constructs edge processing units based on the associated computing power processing blocks; and a device collaborative management module, which performs collaborative management of IoT devices based on the cloud processing units and the edge processing units in the cloud processing device group.

[0007] Due to the adoption of the above-mentioned technical methods, the technical advancements achieved by this disclosure compared to the prior art are as follows:

[0008] This method addresses the technical problems of low device response speed and poor data transmission security in existing IoT device management methods. By connecting to an IoT device management system, it acquires a set of IoT devices and a set of device information, including device type, device function, and device computing power. Based on the device type and function, it sequentially performs multi-indicator analysis on the IoT devices in the set, generating multiple indicator analysis results. Based on edge processing partitioning rules and the multiple indicator analysis results, it groups and identifies the IoT device set, resulting in a cloud processing device group and an edge processing device group. Finally, considering historical device usage scenarios, it sequentially allocates the computing power resources required by all devices in the edge processing device group. The process involves predicting computing power resources to obtain a sequence of predicted results. Based on the actual computing power of the edge processing device group, the predicted results are sequentially evaluated. When the predicted results do not meet the actual computing power of the corresponding device, a computing power deviation sequence is obtained based on the computing power deviation value, where the computing power deviation value is the difference between the actual computing power of the device and the predicted computing power resources. Data transmission security requirements are analyzed on the computing power deviation sequence to obtain a data transmission security coefficient sequence. Computing power processing blocks that meet the data transmission security coefficient are identified as associated computing power processing blocks. An edge processing unit is constructed based on the associated computing power processing blocks. Collaborative management of IoT devices is performed based on the cloud processing unit in the cloud processing device group and the edge processing unit. This approach can improve the response speed and data processing security of IoT devices, thereby enhancing the user experience of IoT devices. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0010] Figure 1 This application provides a flowchart illustrating an IoT device management method that incorporates blockchain.

[0011] Figure 2 This application provides a flowchart illustrating the process of generating multiple indicator analysis results in an IoT device management method that incorporates blockchain.

[0012] Figure 3 This application provides a schematic diagram of the structure of an IoT device management system that incorporates blockchain.

[0013] Figure 4 This is a schematic diagram of the structure of a computer device provided in this application.

[0014] Explanation of reference numerals in the attached diagram: 01 Device information set acquisition module, 02 Index analysis result generation module, 03 Device group identification module, 04 Computing power resource prediction module, 05 Computing power deviation sequence acquisition module, 06 Edge processing unit construction module, 07 Device collaborative management module, 100 Electronic device, 101 Processor, 102 Memory, 103 Bus. Detailed Implementation

[0015] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0016] Example 1

[0017] Based on the above description, such as Figure 1 As shown, this disclosure provides a method for managing IoT devices using blockchain, including:

[0018] The method provided in this application is used to intelligently manage IoT devices by combining blockchain technology, thereby improving the response speed of IoT devices and the security of data processing. The method is specifically implemented in an IoT device management system that combines blockchain.

[0019] Connect to the IoT device management system to obtain a set of IoT devices and a set of device information, wherein the set of device information includes device type, device function, and device computing power;

[0020] In this embodiment, firstly, an IoT device management system is connected, and an IoT device set and a device information set are obtained based on the IoT device management system. The IoT device set includes multiple devices remotely controlled via the IoT, and the device information set contains information about multiple IoT devices, including device type, device function, and device computing power, where device computing power refers to the maximum data processing capability of the IoT device. Obtaining the device information set provides data support for the next step of IoT device analysis.

[0021] Based on the device type and the device function, the IoT devices in the IoT device set are sequentially subjected to multi-indicator analysis to generate multiple indicator analysis results.

[0022] In this embodiment of the application, based on the device type and the device function, the IoT devices in the IoT device set are sequentially subjected to multi-index performance analysis to obtain multiple index analysis results, wherein the index analysis results and the IoT devices have a corresponding relationship.

[0023] like Figure 2 As shown, in one embodiment, the method further includes:

[0024] The multiple indicators include response speed, processing latency, and data security.

[0025] Select a first IoT device from the set of IoT devices, and obtain the first device type and first device function of the first IoT device;

[0026] Based on the multiple indicators, the first device type and the first device function are analyzed sequentially to obtain the first indicator analysis result, wherein the first indicator analysis result includes the first response speed, the first processing delay, and the first data security.

[0027] Multi-indicator analysis is performed on all IoT devices in the IoT device set in sequence to obtain multiple indicator analysis results.

[0028] In this embodiment, the multiple indicators include response speed, processing latency, and data security. First, a first IoT device is selected from the set of IoT devices. The first IoT device can be any IoT device in the set of IoT devices, and a first device type and a first device function of the first IoT device are obtained. Then, the required performance analysis is performed on the first device type and the first device function according to the multiple indicators to obtain the first indicator analysis result, wherein the first indicator analysis result includes a first response speed, a first processing latency, and a first data security.

[0029] Using the same method as the one used to obtain the first indicator analysis result, multi-indicator analysis was sequentially performed on all IoT devices in the IoT device set, resulting in multiple indicator analysis results. Obtaining multiple indicator analysis results provides data support for the next step of device segmentation.

[0030] Based on the edge processing partitioning rules and the analysis results of the multiple indicators, the IoT device set is grouped and identified to obtain the cloud processing device group and the edge processing device group.

[0031] In this embodiment, edge processing partitioning rules are obtained, and then the analysis results of multiple indicators are partitioned according to the edge processing partitioning rules to obtain multiple edge indicator analysis results and multiple cloud indicator analysis results. The IoT devices corresponding to the multiple edge indicator analysis results are marked as edge processing devices, and the IoT devices corresponding to the multiple cloud indicator analysis results are marked as cloud processing devices, thus obtaining multiple cloud processing devices and multiple edge processing devices. Cloud processing devices refer to devices that perform data processing in the cloud, including cloud computers, cloud control platforms, etc. Edge processing devices refer to devices that IoT terminals can directly process data. Then, cloud processing devices are extracted from the IoT device set to form a cloud processing device group, and edge processing devices are extracted from the IoT device set to form an edge processing device group, resulting in a cloud processing device group and an edge processing device group.

[0032] In one embodiment, the method further includes:

[0033] The edge processing partitioning rules include response speed threshold, processing latency threshold, and data security threshold.

[0034] The analysis results of the multiple indicators that meet any one of the following thresholds—the response speed threshold, the processing delay threshold, and the data security threshold—are marked as edge indicator analysis results, thereby obtaining multiple edge indicator analysis results.

[0035] The IoT devices corresponding to the analysis results of the multiple edge indicators are identified as edge processing devices to obtain an edge processing device group;

[0036] The analysis results of the multiple indicators, excluding the edge indicator analysis results, are marked as cloud indicator analysis results, and the IoT devices corresponding to the cloud indicator analysis results are identified as cloud processing devices, thus obtaining a cloud processing device group.

[0037] In this embodiment of the application, the edge processing classification rules include a response speed threshold, a processing latency threshold, and a data security threshold. Those skilled in the art can set the response speed threshold, processing latency threshold, and data security threshold according to actual conditions. Edge processing devices with higher response speed requirements are such as target detection and face recognition devices. Edge processing devices with lower processing latency requirements are such as autonomous driving and remote surgery devices. Edge processing devices with higher data processing security requirements are such as information acquisition devices.

[0038] Then, based on the response speed threshold, processing latency threshold, and data security threshold, the analysis results of the multiple indicators are judged. Indicator analysis results that are greater than any one of the response speed threshold and / or less than the processing latency threshold and / or greater than the data security threshold are marked as edge indicator analysis results, thus obtaining multiple edge indicator analysis results. Then, the IoT devices corresponding to the multiple edge indicator analysis results are set as edge processing devices, thus obtaining multiple edge processing devices, and an edge processing device group is formed based on these multiple edge processing devices. Indicator analysis results other than edge indicator analysis results are marked as cloud indicator analysis results, and the IoT devices corresponding to the cloud indicator analysis results are identified as cloud processing devices, thus obtaining multiple cloud processing devices, and a cloud processing device group is formed based on these multiple cloud processing devices.

[0039] Based on historical equipment usage scenarios, the computing power resources required by all devices in the edge processing device group are predicted sequentially to obtain a sequence of computing power resource prediction results.

[0040] In this embodiment of the application, the historical device usage scenarios of the edge processing devices in the edge processing device group are obtained, and the historical data processing records of the edge processing devices are called according to the historical device usage scenarios. Then, the computing power resources required by all devices in the edge processing device group are predicted sequentially according to the historical data processing records, and a computing power resource prediction result sequence is generated based on multiple computing power resource prediction results.

[0041] In one embodiment, the method further includes:

[0042] In the edge processing device group, a processing device is randomly selected as the first edge processing device, and based on IoT big data, historical computing power scheduling data and historical scheduling scenarios of the same source devices of the first edge processing device are obtained;

[0043] The scheduling scenario with the highest frequency of occurrence in the historical scheduling scenarios is selected as the regular scheduling scenario.

[0044] In this embodiment, firstly, a processing device is randomly selected from the edge processing device group as the first edge processing device, where the first edge processing device is any one of the edge processing devices. Then, based on IoT big data technology, information retrieval is performed using the first edge processing device as the search condition. IoT big data refers to a method of IoT data mining using big data technology to obtain historical computing power scheduling data and historical scheduling scenarios of devices of the same type and model as the first edge processing device. Then, the frequency of occurrence of the historical scheduling scenarios is statistically analyzed, and the scheduling scenario with the highest frequency among the historical scheduling scenarios is selected as the regular scheduling scenario.

[0045] In one embodiment, the method further includes:

[0046] The frequency of occurrence of the historical scheduling scenarios is counted according to the first preset time window, and multiple first scenario scheduling sequences are constructed based on the frequency count results;

[0047] A gradual weight is set based on the time distance of historical scheduling scenarios, and the multiple first scenario scheduling sequences are weighted according to the gradual weight to obtain a comprehensive scenario scheduling sequence.

[0048] The first N scenarios in the comprehensive scenario scheduling sequence are selected as regular scheduling scenarios, where N is an integer greater than 3.

[0049] In this embodiment of the application, firstly, a first preset time window is obtained. Those skilled in the art can set the first preset time window according to the actual situation. For example, the first preset time window is set to one month. Then, the frequency of occurrence of the historical scheduling scenarios is statistically analyzed according to the first preset time window, and multiple first scenario scheduling sequences are constructed according to the frequency statistical results. Each first scenario scheduling sequence corresponds to a preset time window, that is, the frequency statistical results of multiple months of historical scheduling scenarios are obtained.

[0050] Then, a gradual weight is set based on the time distance of historical scheduling scenarios. The closer to the current time, the larger the corresponding weight value; the farther away from the current time, the smaller the corresponding weight value. The weight value can be set using the existing coefficient of variation method, which is a commonly used weight setting method by those skilled in the art and will not be elaborated here. Then, the multiple first scenario scheduling sequences are weighted according to the gradual weight to obtain the comprehensive frequency of each scenario scheduling, and arranged in descending order of comprehensive frequency to obtain a comprehensive scenario scheduling sequence. Finally, the first N scenario schedulings in the comprehensive scenario scheduling sequence are selected as regular scheduling scenarios, where N is an integer greater than 3. The specific value of N can be set by those skilled in the art according to the actual situation.

[0051] The average value of multiple historical computing power scheduling data under the conventional scheduling scenario is calculated, and the average value calculation result is set as the first computing power resource prediction result of the first edge processing device.

[0052] The computing power resources of all devices in the edge processing device group are predicted sequentially to obtain a sequence of computing power resource prediction results.

[0053] In this embodiment, the average of multiple historical computing power scheduling data under the conventional scheduling scenario is calculated, and the result of the average calculation of the historical computing power scheduling data is set as the first computing power resource prediction result of the first edge processing device. By using the average computing power scheduling under the conventional scheduling scenario as the computing power resource prediction result, the accuracy and rationality of the computing power resource prediction result setting can be improved. Finally, using the same method as obtaining the first computing power resource prediction result, the computing power resources of all devices in the edge processing device group are predicted sequentially to obtain multiple computing power resource prediction results. Then, a computing power resource prediction result sequence is constructed based on the multiple computing power resource prediction results. By obtaining the computing power resource prediction result sequence, data support is provided for the next step of computing power deviation analysis.

[0054] Based on the actual computing power of the edge processing device group, the sequence of computing power resource prediction results is judged sequentially. When the computing power resource prediction result does not meet the actual computing power of the corresponding device, a computing power deviation sequence is obtained based on the computing power deviation value, wherein the computing power deviation value is the difference between the actual computing power of the device and the computing power resource prediction result.

[0055] In this embodiment of the application, firstly, the computing power resource prediction results corresponding to the computing power resource prediction results in the computing power resource prediction result sequence are judged according to the actual computing power of the edge processing devices in the edge processing device group. When the computing power resource prediction result is less than the actual computing power of the corresponding device, it indicates that the computing power of the current device does not meet the current usage conditions. Then, the actual computing power of the corresponding device is subtracted from the corresponding computing power resource prediction result, and the difference between the two is taken as the computing power deviation to obtain multiple computing power deviation values. Then, a computing power deviation sequence is constructed based on the multiple computing power deviation values ​​to obtain the computing power deviation sequence.

[0056] Data transmission security requirements are analyzed on the computing power deviation sequence to obtain a data transmission security coefficient sequence. The computing power processing blocks that meet the data transmission security coefficient are identified as associated computing power processing blocks. An edge processing unit is constructed based on the associated computing power processing blocks.

[0057] In this embodiment, firstly, multiple computing power processing blocks are matched according to the computing power deviation sequence, wherein the computing power processing blocks and the computing power deviation values ​​have a one-to-one correspondence; then, the data transmission security factor required by the edge processing device in the computing power deviation sequence is obtained, and the computing power processing blocks that meet the data transmission security factor are identified as the associated computing power processing blocks of the edge processing device, and a mapping association between the edge processing device and the computing power processing blocks is established, wherein the mapping association includes a first mapping association and a second mapping association, and an edge processing unit is constructed based on the mapping association.

[0058] In one embodiment, the method further includes:

[0059] Based on the computing power deviation sequence, the device type and device function of the edge processing device are analyzed to determine the data transmission security requirements and obtain the data transmission security coefficient sequence.

[0060] Based on the computing power deviation sequence and the data transmission security coefficient sequence, a computing power processing block sequence is set, and a first mapping association between the edge processing device group and the computing power processing block sequence is established, wherein the edge processing device and the computing power processing block correspond one-to-one in the first mapping association;

[0061] Select a second edge processing device from the plurality of edge processing devices, and obtain the first data transmission security factor of the second edge processing device;

[0062] The plurality of computing power processing blocks are filtered according to the first data transmission security coefficient, and the computing power processing blocks that meet the first data transmission security coefficient are identified as the second associated computing power processing blocks, thus obtaining the second associated computing power processing block set;

[0063] Based on the second set of associated computing power processing blocks, the associated computing power processing block set of the edge processing device is obtained by sequential analysis, and a second mapping relationship between the edge processing device and the associated computing power processing block set is established, wherein the second mapping relationship between the edge processing device and the associated computing power processing block is a one-to-many relationship;

[0064] An edge processing unit is constructed based on the first mapping relationship and the second mapping relationship.

[0065] In this embodiment, firstly, data transmission security requirements are analyzed based on the device type and function of the edge devices corresponding to multiple computing power deviation values ​​in the computing power deviation sequence. Higher device privacy corresponds to higher data transmission security requirements and a larger required data transmission security coefficient, resulting in multiple data transmission security coefficients. Then, a data transmission security coefficient sequence is constructed based on these coefficients. Next, multiple computing power processing blocks are set according to the multiple computing power deviation values ​​and the multiple data transmission security coefficients. These computing power processing blocks are those that meet the corresponding computing power deviation values ​​and data transmission security levels. Data interaction between the computing power processing blocks and the edge processing devices is achieved through a blockchain protocol. Finally, a first mapping association is established between the edge processing devices and the computing power processing blocks, where the first mapping association is a one-to-one mapping association.

[0066] A second edge processing device is selected from the plurality of edge processing devices, wherein the second edge processing device is any one of the plurality of edge processing devices, and a first data transmission security coefficient of the second edge processing device is obtained. Then, the plurality of computing power processing blocks are filtered according to the first data transmission security coefficient, and the computing power processing blocks among the plurality of computing power processing blocks whose data transmission security coefficient is greater than or equal to the first data transmission security coefficient are identified as second associated computing power processing blocks, thereby obtaining a set of second associated computing power processing blocks.

[0067] Based on the second set of associated computing power processing blocks, a set of associated computing power processing blocks for multiple edge processing devices is obtained through sequential analysis. A second mapping relationship is then established between the edge processing devices and the sets of associated computing power processing blocks, wherein the second mapping relationship is a one-to-many mapping relationship between the edge processing devices and the associated computing power processing blocks. Finally, an edge processing unit is constructed based on the first mapping relationship and the second mapping relationship.

[0068] The collaborative management of IoT devices is based on the cloud processing unit and the edge processing unit in the cloud processing device group.

[0069] In this embodiment of the application, the collaborative management of IoT devices is achieved through the cloud processing unit and the edge processing unit in the cloud processing device group, wherein the cloud processing unit refers to the cloud processing platform.

[0070] In one embodiment, the method further includes:

[0071] The data processing mode of the edge processing unit is set, wherein the data processing mode includes a standalone operation mode and a collaborative operation mode;

[0072] The individual operation mode is set based on the first mapping association, and the collaborative operation mode is set based on the second mapping association.

[0073] When the computing power resources of the edge processing devices in the edge processing device group all meet the current computing power requirements, the data processing of the edge processing devices is carried out based on the individual operation mode.

[0074] When the computing power resources of any edge processing device in the edge processing device group are insufficient to meet the current computing power requirements, the data processing of the edge processing device is carried out in collaboration between the cloud processing unit and the edge processing unit based on the collaborative operation mode.

[0075] In this embodiment, firstly, the data processing mode of the edge processing unit is set, wherein the data processing mode includes a standalone operation mode and a collaborative operation mode. The standalone operation mode is an operation mode set based on the first mapping association, that is, multiple edge processing devices perform data processing independently without data interaction; the collaborative operation mode is an operation mode set based on the second mapping association.

[0076] When the computing power resources of multiple edge processing devices in the edge processing device group are all greater than or equal to the current computing power requirement, data processing of the edge processing devices is performed based on the individual operation mode. When the computing power resources of any edge processing device in the edge processing device group are less than the current computing power requirement, in order to improve data processing efficiency and response speed, data processing of the edge processing devices is performed collaboratively by the cloud processing unit and the edge processing unit based on the collaborative operation mode. By setting up individual operation mode and collaborative operation mode for collaborative management of IoT devices, the utilization rate of computing power resources and data processing efficiency can be improved, thereby improving device response speed.

[0077] The above methods can solve the technical problems of low device response speed and poor data security in existing IoT device management methods, improve the response speed of IoT devices and the security of data processing, thereby improving the user experience of IoT devices.

[0078] Example 2

[0079] In one embodiment, such as Figure 3 The diagram illustrates an IoT device management system incorporating blockchain technology, comprising: a device information set acquisition module 01, an indicator analysis result generation module 02, a device grouping and identification module 03, a computing power resource prediction module 04, a computing power deviation sequence acquisition module 05, an edge processing unit construction module 06, and a device collaborative management module 07, wherein:

[0080] Device information set acquisition module 01 is used to connect to the Internet of Things (IoT) device management system to acquire IoT device set and device information set, wherein the device information set includes device type, device function and device computing power.

[0081] The indicator analysis result generation module 02 is used to perform multi-indicator analysis on the IoT devices in the IoT device set based on the device type and the device function, and generate multiple indicator analysis results.

[0082] The device grouping identification module 03 is used to group and identify the IoT device set based on the edge processing partitioning rules and the analysis results of the multiple indicators, so as to obtain the cloud processing device group and the edge processing device group.

[0083] The computing power resource prediction module 04 is used to predict the computing power resources required by all devices in the edge processing device group in sequence by combining historical device usage scenarios, and obtain a sequence of computing power resource prediction results.

[0084] The computing power deviation sequence acquisition module 05 is used to judge the computing power resource prediction result sequence sequentially based on the actual computing power of the edge processing device group. When the computing power resource prediction result does not meet the actual computing power of the corresponding device, the computing power deviation sequence is obtained based on the computing power deviation value, wherein the computing power deviation value is the difference between the actual computing power of the device and the computing power resource prediction result.

[0085] Edge processing unit construction module 06 is used to perform data transmission security requirement analysis on the computing power deviation sequence, obtain data transmission security coefficient sequence, and identify the computing power processing blocks that meet the data transmission security coefficient as associated computing power processing blocks, and construct edge processing units based on the associated computing power processing blocks;

[0086] Device Collaborative Management Module 07 performs collaborative management of IoT devices based on the cloud processing unit and the edge processing unit in the cloud processing device group.

[0087] In one embodiment, the system further includes:

[0088] The multi-indicator module refers to a module in which the multiple indicators include response speed, processing latency, and data security.

[0089] The first IoT device selection module is used to select a first IoT device from the IoT device set and obtain the first device type and first device function of the first IoT device.

[0090] The first indicator analysis result acquisition module is used to analyze the first device type and the first device function sequentially based on the multiple indicators to obtain the first indicator analysis result, wherein the first indicator analysis result includes the first response speed, the first processing delay, and the first data security.

[0091] The indicator analysis result acquisition module is used to sequentially perform multi-indicator analysis on all IoT devices in the IoT device set to obtain multiple indicator analysis results.

[0092] In one embodiment, the system further includes:

[0093] An edge processing partitioning rule module, wherein the edge processing partitioning rules include a response speed threshold, a processing latency threshold, and a data security threshold;

[0094] An edge indicator analysis result acquisition module is used to mark the indicator analysis results that meet any one of the response speed threshold, the processing delay threshold, and the data security threshold among the multiple indicator analysis results as edge indicator analysis results, thereby obtaining multiple edge indicator analysis results;

[0095] An edge processing device identification module is used to identify the IoT devices corresponding to the multiple edge indicator analysis results as edge processing devices, thereby obtaining an edge processing device group.

[0096] A cloud processing device group acquisition module is used to mark the indicator analysis results (excluding edge indicator analysis results) among the multiple indicator analysis results as cloud indicator analysis results, and identify the IoT devices corresponding to the cloud indicator analysis results as cloud processing devices, thereby obtaining a cloud processing device group.

[0097] In one embodiment, the system further includes:

[0098] The first edge processing device selection module is used to randomly select a processing device as the first edge processing device from the edge processing device group, and to obtain historical computing power scheduling data and historical scheduling scenarios of the same source devices of the first edge processing device based on IoT big data.

[0099] A regular scheduling scenario setting module is used to select the scheduling scenario with the highest frequency of occurrence in the historical scheduling scenarios as the regular scheduling scenario.

[0100] The first computing power resource prediction result acquisition module is used to calculate the average of multiple historical computing power scheduling data under the conventional scheduling scenario, and set the average calculation result as the first computing power resource prediction result of the first edge processing device.

[0101] The computing power resource prediction result sequence acquisition module is used to predict the computing power resources of all devices in the edge processing device group in sequence to obtain a computing power resource prediction result sequence.

[0102] In one embodiment, the system further includes:

[0103] The first scenario scheduling sequence construction module is used to perform frequency statistics on the occurrence of the historical scheduling scenarios according to a first preset time window, and construct multiple first scenario scheduling sequences based on the frequency statistics results.

[0104] A comprehensive scene scheduling sequence obtaining module is used to set gradual weights based on the time distance of historical scheduling scenes, and to perform weighted calculations on the multiple first scene scheduling sequences according to the gradual weights to obtain a comprehensive scene scheduling sequence.

[0105] A regular scheduling scenario setting module is used to select the first N scenarios in the comprehensive scenario scheduling sequence as regular scheduling scenarios, where N is an integer greater than 3.

[0106] In one embodiment, the system further includes:

[0107] A data transmission security requirement analysis module is used to perform data transmission security requirement analysis based on the device type and device function of the edge processing device according to the computing power deviation sequence, and obtain a data transmission security coefficient sequence.

[0108] The first mapping association establishment module is used to set a computing power processing block sequence based on the computing power deviation sequence and the data transmission security coefficient sequence, and to establish a first mapping association between the edge processing device group and the computing power processing block sequence, wherein the edge processing device and the computing power processing block correspond one-to-one in the first mapping association;

[0109] The first data transmission security factor acquisition module is used to select a second edge processing device among the plurality of edge processing devices and acquire the first data transmission security factor of the second edge processing device.

[0110] The second associated computing power processing block set obtaining module is used to filter the plurality of computing power processing blocks according to the first data transmission security coefficient, and identify the computing power processing blocks that meet the first data transmission security coefficient as the second associated computing power processing blocks, thereby obtaining the second associated computing power processing block set.

[0111] The second mapping relationship establishment module is used to sequentially analyze and obtain the associated computing power processing block set of the edge processing device according to the second associated computing power processing block set, and establish a second mapping relationship between the edge processing device and the associated computing power processing block set, wherein the second mapping relationship between the edge processing device and the associated computing power processing block is a one-to-many relationship;

[0112] An edge processing unit construction module is used to construct an edge processing unit based on the first mapping relationship and the second mapping relationship.

[0113] In one embodiment, the system further includes:

[0114] A data processing mode setting module is used to set the data processing mode of the edge processing unit, wherein the data processing mode includes a standalone operation mode and a collaborative operation mode.

[0115] A job mode setting module is used to set the individual job mode based on the first mapping association and the collaborative job mode based on the second mapping association.

[0116] A separate task processing module is used to process data from the edge processing devices based on the separate task mode when the computing power resources of all edge processing devices in the edge processing device group meet the current computing power requirements.

[0117] The collaborative operation processing module is used to process data from the edge processing devices in collaboration with the cloud processing unit and the edge processing unit based on the collaborative operation mode when the computing power resources of any one of the edge processing devices in the edge processing device group do not meet the current computing power requirements.

[0118] Example 3

[0119] Figure 4 This is a schematic diagram based on the third embodiment of the present disclosure, as shown below. Figure 4 As shown, the computer device 100 in this disclosure may include a processor 101 and a memory 102.

[0120] Memory 102 is used to store programs. Memory 102 may include volatile memory, such as random access memory (RAM), such as static random access memory (SRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), etc.; memory may also include non-volatile memory, such as flash memory. Memory 102 is used to store computer programs (such as application programs, functional modules, etc. that implement the above methods), computer instructions, etc. The computer programs, computer instructions, etc., can be partitioned and stored in one or more memories 102. Furthermore, the computer programs, computer instructions, data, etc., can be accessed by processor 101.

[0121] The aforementioned computer programs and instructions can be stored in one or more partitions of memory 102. Furthermore, the aforementioned computer programs and instructions can be invoked by processor 101.

[0122] The processor 101 is configured to execute the computer program stored in the memory 102 to implement the various steps in the methods described in the above embodiments.

[0123] For details, please refer to the relevant descriptions in the preceding method embodiments.

[0124] The processor 101 and the memory 102 can be independent structures or integrated structures. When the processor 101 and the memory 102 are independent structures, the memory 102 and the processor 101 can be coupled together via the bus 103.

[0125] The computer device in this embodiment can execute the technical solution in the above method. Its specific implementation process and technical principles are the same, and will not be repeated here.

[0126] According to embodiments of the present disclosure, the present disclosure also provides a computer-readable storage medium storing a computer program that, when executed, implements the steps provided in any of the above embodiments.

[0127] In summary, compared with the prior art, the embodiments of this disclosure have the following technical effects:

[0128] (1) By using cloud processing units and edge processing units to collaboratively manage IoT devices, the response speed and data processing security of IoT devices can be improved, thereby enhancing the user experience of IoT devices.

[0129] (2) By setting up individual operation mode and collaborative operation mode to manage IoT devices collaboratively, the utilization rate of computing resources and data processing efficiency can be improved, thereby improving the device response speed.

[0130] The embodiments described above are merely illustrative of several implementations of this disclosure and should not be construed as limiting the scope of the invention. Therefore, those skilled in the art can make various types of substitutions, modifications, and alterations without departing from the scope of the concept as defined by the appended claims, and all such substitutions, modifications, and alterations fall within the protection scope of this disclosure.

Claims

1. A method for managing IoT devices using blockchain, characterized in that: The method includes: Connect to the IoT device management system to obtain a set of IoT devices and a set of device information, wherein the set of device information includes device type, device function, and device computing power; Based on the device type and the device function, the IoT devices in the IoT device set are sequentially subjected to multi-indicator analysis to generate multiple indicator analysis results. Based on the edge processing partitioning rules and the analysis results of the multiple indicators, the IoT device set is grouped and identified to obtain the cloud processing device group and the edge processing device group. Based on historical equipment usage scenarios, the computing power resources required by all devices in the edge processing device group are predicted sequentially to obtain a sequence of computing power resource prediction results. Based on the actual computing power of the edge processing device group, the sequence of computing power resource prediction results is judged sequentially. When the computing power resource prediction result does not meet the actual computing power of the corresponding device, a computing power deviation sequence is obtained based on the computing power deviation value, wherein the computing power deviation value is the difference between the actual computing power of the device and the computing power resource prediction result. Data transmission security requirements are analyzed on the computing power deviation sequence to obtain a data transmission security coefficient sequence. The computing power processing blocks that meet the data transmission security coefficient are identified as associated computing power processing blocks. An edge processing unit is constructed based on the associated computing power processing blocks. The collaborative management of IoT devices is based on the cloud processing unit and the edge processing unit in the cloud processing device group. The process involves performing data transmission security requirement analysis on the computing power deviation sequence to obtain a data transmission security coefficient sequence, and identifying computing power processing blocks that meet the data transmission security coefficient as associated computing power processing blocks. An edge processing unit is then constructed based on these associated computing power processing blocks, including: Based on the computing power deviation sequence, the device type and device function of the edge processing device are analyzed to determine the data transmission security requirements and obtain the data transmission security coefficient sequence. Based on the computing power deviation sequence and the data transmission security coefficient sequence, a computing power processing block sequence is set, and a first mapping association between the edge processing device group and the computing power processing block sequence is established, wherein the edge processing device and the computing power processing block correspond one-to-one in the first mapping association; Select a second edge processing device from among multiple edge processing devices, and obtain the first data transmission security factor of the second edge processing device; Multiple computing power processing blocks are filtered according to the first data transmission security coefficient, and the computing power processing blocks that meet the first data transmission security coefficient are identified as second associated computing power processing blocks, thus obtaining a set of second associated computing power processing blocks; Based on the second set of associated computing power processing blocks, the associated computing power processing block set of the edge processing device is obtained by sequential analysis, and a second mapping relationship between the edge processing device and the associated computing power processing block set is established, wherein the second mapping relationship between the edge processing device and the associated computing power processing block is a one-to-many relationship; An edge processing unit is constructed based on the first mapping association and the second mapping relationship.

2. The method as described in claim 1, characterized in that, Based on the device type and device function, the IoT devices in the IoT device set are sequentially subjected to multi-indicator analysis to generate multiple indicator analysis results. include: The multiple indicators include response speed, processing latency, and data security. Select a first IoT device from the set of IoT devices, and obtain the first device type and first device function of the first IoT device; Based on the multiple indicators, the first device type and the first device function are analyzed sequentially to obtain the first indicator analysis result, wherein the first indicator analysis result includes the first response speed, the first processing delay, and the first data security. Multi-indicator analysis is performed on all IoT devices in the IoT device set in sequence to obtain multiple indicator analysis results.

3. The method as described in claim 1, characterized in that, Based on the edge processing partitioning rules and the analysis results of the multiple indicators, the IoT device set is grouped and identified to obtain a cloud processing device group and an edge processing device group; including: The edge processing division rules include response speed threshold, processing latency threshold, and data security threshold. The analysis results of the multiple indicators that meet any one of the following thresholds—the response speed threshold, the processing delay threshold, and the data security threshold—are marked as edge indicator analysis results, thereby obtaining multiple edge indicator analysis results. The IoT devices corresponding to the analysis results of the multiple edge indicators are identified as edge processing devices to obtain an edge processing device group; The analysis results of the multiple indicators, excluding the edge indicator analysis results, are marked as cloud indicator analysis results, and the IoT devices corresponding to the cloud indicator analysis results are identified as cloud processing devices, thus obtaining a cloud processing device group.

4. The method as described in claim 1, characterized in that, The method involves combining historical device usage scenarios to sequentially predict the computing power resources required by all devices in the edge processing device group, obtaining a sequence of computing power resource prediction results, including: In the edge processing device group, a processing device is randomly selected as the first edge processing device, and based on IoT big data, historical computing power scheduling data and historical scheduling scenarios of the same source devices of the first edge processing device are obtained; The scheduling scenario with the highest frequency of occurrence in the historical scheduling scenarios is selected as the regular scheduling scenario. The average value of multiple historical computing power scheduling data under the conventional scheduling scenario is calculated, and the average value calculation result is set as the first computing power resource prediction result of the first edge processing device. The computing power resources of all devices in the edge processing device group are predicted sequentially to obtain a sequence of computing power resource prediction results.

5. The method as described in claim 4, characterized in that, The step of selecting the scheduling scenario with the highest frequency in the historical scheduling scenarios as the regular scheduling scenario includes: The frequency of occurrence of the historical scheduling scenarios is counted according to the first preset time window, and multiple first scenario scheduling sequences are constructed based on the frequency count results; A gradual weight is set based on the time distance of historical scheduling scenarios, and the multiple first scenario scheduling sequences are weighted according to the gradual weight to obtain a comprehensive scenario scheduling sequence. The first N scenarios in the comprehensive scenario scheduling sequence are selected as regular scheduling scenarios, where N is an integer greater than 3.

6. The method as described in claim 1, characterized in that, The collaborative management of IoT devices based on the cloud processing unit and the edge processing unit in the cloud processing device group includes: The data processing mode of the edge processing unit is set, wherein the data processing mode includes a standalone operation mode and a collaborative operation mode; The individual operation mode is set based on the first mapping association, and the collaborative operation mode is set based on the second mapping relationship; When the computing power resources of the edge processing devices in the edge processing device group all meet the current computing power requirements, the data processing of the edge processing devices is carried out based on the individual operation mode. When the computing power resources of any edge processing device in the edge processing device group are insufficient to meet the current computing power requirements, the data processing of the edge processing device is carried out in collaboration between the cloud processing unit and the edge processing unit based on the collaborative operation mode.

7. An IoT device management system incorporating blockchain, characterized in that, The system comprises the steps for performing any one of the IoT device management methods incorporating blockchain as described in claims 1-6, wherein the system includes: A device information set acquisition module is used to connect to the Internet of Things (IoT) device management system to acquire IoT device sets and device information sets, wherein the device information set includes device type, device function, and device computing power. The indicator analysis result generation module is used to perform multi-indicator analysis on the IoT devices in the IoT device set based on the device type and the device function, and generate multiple indicator analysis results. The device grouping and identification module is used to group and identify the IoT device set based on the edge processing partitioning rules and the analysis results of the multiple indicators, so as to obtain the cloud processing device group and the edge processing device group. The computing power resource prediction module is used to predict the computing power resources required by all devices in the edge processing device group in sequence by combining historical device usage scenarios, and obtain a sequence of computing power resource prediction results. The computing power deviation sequence acquisition module is used to judge the computing power resource prediction result sequence sequentially based on the actual computing power of the edge processing device group. When the computing power resource prediction result does not meet the actual computing power of the corresponding device, the computing power deviation sequence is obtained based on the computing power deviation value, wherein the computing power deviation value is the difference between the actual computing power of the device and the computing power resource prediction result. An edge processing unit construction module is used to perform data transmission security requirement analysis on the computing power deviation sequence, obtain a data transmission security coefficient sequence, and identify the computing power processing blocks that meet the data transmission security coefficient as associated computing power processing blocks, and construct an edge processing unit based on the associated computing power processing blocks; A device collaboration management module, which performs collaborative management of IoT devices based on the cloud processing unit and the edge processing unit in the cloud processing device group; A data transmission security requirement analysis module is used to perform data transmission security requirement analysis based on the device type and device function of the edge processing device according to the computing power deviation sequence, and obtain a data transmission security coefficient sequence. The first mapping association establishment module is used to set a computing power processing block sequence based on the computing power deviation sequence and the data transmission security coefficient sequence, and to establish a first mapping association between the edge processing device group and the computing power processing block sequence, wherein the edge processing device and the computing power processing block correspond one-to-one in the first mapping association; The first data transmission security factor acquisition module is used to select a second edge processing device among multiple edge processing devices and acquire the first data transmission security factor of the second edge processing device. The second associated computing power processing block set obtaining module is used to filter multiple computing power processing blocks according to the first data transmission security coefficient, and identify the computing power processing blocks that meet the first data transmission security coefficient as the second associated computing power processing blocks, thereby obtaining the second associated computing power processing block set. The second mapping relationship establishment module is used to sequentially analyze and obtain the associated computing power processing block set of the edge processing device according to the second associated computing power processing block set, and establish a second mapping relationship between the edge processing device and the associated computing power processing block set, wherein the second mapping relationship between the edge processing device and the associated computing power processing block is a one-to-many relationship; The edge processing unit construction module is also used to construct an edge processing unit based on the first mapping association and the second mapping relationship.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

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