A real-time sensing method supporting high-concurrency and flexible access of mass devices

Abstract

The application provides a real-time sensing method for supporting high-concurrent and elastic access of mass devices, relates to the technical field of data real-time sensing, and comprises the following steps: establishing a communication service of a bottom-layer communication framework of Netty; setting different service nodes; setting a collection device for each power-using device; setting a "twin device n" for the collection device; the "twin device n" encapsulates the collected physical device information and sends the information to the service node; when the access information suddenly increases, the communication service is dynamically expanded; by setting the "twin device n" for the collection device, when the information access amount suddenly increases, the encapsulated information can be sent to multiple different temporary service nodes by continuously sending instructions to the message mailbox of the "twin device n", so that the communication service is dynamically expanded, and the problem of low data transmission efficiency when mass user power data is processed is solved.

Description

Technical Field

[0001] This invention relates to the field of real-time data sensing technology, and in particular to a real-time sensing method that supports high-concurrency, elastic access for a massive number of devices. Background Technology

[0002] The number and types of distributed power monitoring equipment, distribution network operation monitoring equipment, electric vehicle charging piles, energy storage equipment, and smart home devices for customers are increasing day by day. The data is also showing massive volume, polymorphism, and correlation. The electricity information collection system manages tens of millions of collection objects (electricity meters, terminals, etc.), and these collection objects will generate massive amounts of event messages.

[0003] The continuous development of existing technologies, such as intelligent metering, edge computing, and new sensing technologies, has helped to achieve low-voltage user electricity management. As the types of user electrical equipment increase, electricity consumption data is also growing rapidly. However, at present, the sensing level at the end of the electricity consumption chain only reaches the user's electricity meter, which cannot sense the massive number of user electrical equipment. When processing massive amounts of user electricity data, it is often necessary to start and stop existing services, which cannot effectively improve data transmission efficiency. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a real-time sensing method that supports high-concurrency, elastic access for massive numbers of devices. By setting up a "twin device n" for the data acquisition device, when the amount of information access surges, instructions can be continuously sent to the message mailbox of the "twin device n" to send encapsulated information to multiple different temporary service nodes. This achieves the purpose of dynamically expanding the communication service without having to start or stop the original service, allowing new devices to be smoothly and stably distributed on temporary service nodes, ensuring stable device access and elastic expansion capabilities. This solves the problem in existing technologies where, when processing massive amounts of user electricity data, it is often necessary to start or stop the original service, resulting in low data transmission efficiency.

[0005] To achieve the above objectives, the present invention provides a real-time sensing method supporting high-concurrency, elastic access for massive numbers of devices, characterized in that the method comprises:

[0006] Step S1: Establish the underlying communication framework for Netty's communication services;

[0007] Step S2: Divide the entire power network into multiple service areas and set up different service nodes for each service area;

[0008] Step S3: Set up a data acquisition device for each electrical device. The data acquisition device is used to collect the power consumption data of the electrical device; set up a "twin device n" Actor for the data acquisition device;

[0009] Step S4: The "twin device n" Actor encapsulates the collected physical device information and sends it to the service node;

[0010] Step S5: The service node is used to receive encapsulation information. When the number of encapsulation information received suddenly increases, the communication service is dynamically expanded.

[0011] Further, step S2 includes the following sub-steps:

[0012] Step S201: Obtain electricity consumption data from the electricity network, wherein the electricity consumption data includes: number of users, user electricity consumption and electricity address;

[0013] Step S202: Based on the electricity usage address, divide the users into grids to obtain the pre-electricity usage grid;

[0014] Step S203: Count the number of users and their electricity consumption within each electricity grid, and set the product of the number of users and their electricity consumption as the grid user count.

[0015] When the number of grid users exceeds the grid threshold, the pre-power grid is re-divided to obtain two first power grids;

[0016] When the number of grid users is less than or equal to the grid threshold, the pre-used grid is set as the second grid.

[0017] Step S204: Set all first and second power grids as service areas, and set up a service node in each service area. The service node is used to receive power consumption information in the service area it is in.

[0018] Further, step S3 includes the following sub-steps:

[0019] Step S301: Obtain all electrical devices and their types in the service area. Based on the types of electrical devices in the area, set up a data acquisition device n for each electrical device in the service area, where n is a positive integer. The data acquisition device n is used to collect the power consumption data of the electrical devices.

[0020] Step S302: Set a "twin device n" Actor for the acquisition device n, where n is a positive integer. The "twin device n" Actor acquires the power consumption data and device information collected by the acquisition device in real time and sets it as physical device information.

[0021] Furthermore, the "twin device n" Actor is also configured with its own status and message mailbox.

[0022] Further, step S4 includes the following sub-steps:

[0023] Step S401: The "twin device n" Actor encapsulates the collected physical device information and sends it to the service node at the first collection time interval;

[0024] Step S402: When the state of the "twin device n" Actor reaches the sending standard, the collected physical device information is encapsulated and sent to the service node;

[0025] Step S403: When the message mailbox of the "twin device n" Actor receives the sending instruction, the collected physical device information is encapsulated and sent to the service node.

[0026] Furthermore, in step S5, dynamic expansion includes the following sub-steps:

[0027] Step S501: Obtain the upper limit of the number of transmissions of the service node. When the number of encapsulated information accessed by the service node is greater than or equal to the upper limit of the number of transmissions of the service node, set up multiple temporary service nodes according to the number of encapsulated information, and send a diversion instruction to the message mailbox of the "twin device n" Actor. The "twin device n" Actor will send the subsequent encapsulated information to the temporary service nodes.

[0028] Step S502: Statistically analyze the encapsulation balance values ​​received by the temporary service nodes, and sort the temporary service nodes in descending order of the encapsulation balance values ​​received by the temporary service nodes.

[0029] Step S503: Upload the encapsulation information of the temporary service nodes to the system in order of sorting, and release the temporary service nodes whose encapsulation information has been uploaded to the system;

[0030] Step S504: When the remaining number of temporary service nodes is less than the node threshold, a normal instruction is sent to the message mailbox of the "twin device n" Actor, and the "twin device n" Actor sends the subsequent encapsulation information to the service node.

[0031] Further, step S501 includes the following sub-steps:

[0032] Step S50101: Obtain the number of encapsulation information to be accessed, set the upper limit of the transmission quantity of the existing service node to the upper limit of the total transmission quantity, and set the difference between the upper limit of the total transmission quantity and the number of encapsulation information as the load value.

[0033] Step S50102: When there is only one service node and the load value is greater than or equal to 0, it is determined that there is a sudden increase in encapsulation information; obtain the load value at this time and set a temporary service node;

[0034] Step S50103: Obtain the maximum total number of transmissions. When the load value exceeds the maximum total number of transmissions, set up a temporary service node.

[0035] Step S50104: Repeat step S50103 until the load value is less than or equal to the total transmission limit. Number all temporary service nodes in sequence as temporary service node 1, 2, 3, ..., m, where m is a positive integer.

[0036] Step S50105: Send a diversion instruction to the message mailbox of the "twin device n" Actor. The diversion instruction includes: sending the encapsulation information to the temporary service node m; the "twin device n" Actor sends the subsequent encapsulation information to the corresponding temporary service node m according to the received diversion instruction.

[0037] Furthermore, step S502 also includes calculating the cumulative similarity value of the physical device information of each package using a preset dynamic value algorithm.

[0038] Furthermore, the dynamic value algorithm is... Where F a f is the aforementioned packaging balance value. a (t d ) is a preset cumulative time mapping function that reflects the mapping relationship between the cumulative time it takes for the physical device information to be sent to the service node and the cumulative time impact value. d This reflects the generation time of the temporary service node. j Assign weights to the features corresponding to the j-th physical device feature in the physical device information, n j Let m be the number of physical device features that are identical to the j-th physical device feature in the preceding physical feature information, and let ε be the total number of physical device features in this physical device information. i f is the comparison similarity value of physical device information corresponding to the same i-th physical device feature, where the comparison similarity value reflects the number of identical physical device features in the two pieces of physical device information. b (t i ) is a preset similarity time mapping function, reflecting the mapping relationship between the time when the physical device information corresponding to the i-th physical device feature is uploaded to the system and the similarity time influence value, t i K represents the time when the physical device information corresponding to the i-th physical device feature is uploaded to the system. u The number of physical device information that is uploaded to the temporary service node and has a similar relationship with the physical device information is ΔK, where ΔK is a preset baseline similarity value, β1 is a preset cumulative time weight, β2 is a preset similarity time weight, and β3 is a preset similarity quantity weight.

[0039] Furthermore, the feature configuration weights corresponding to the physical device features are obtained by looking up a pre-built feature configuration weight table.

[0040] The beneficial effects of the present invention are as follows: The present invention first encapsulates the collected data by setting up "twin devices n" for the data acquisition device, and each "twin device n" is configured with a message mailbox, which provides basic support for the "device intelligent autonomy" of the acquisition system;

[0041] When the amount of information access surges, instructions can be continuously sent to the message mailbox of the "twin device n" to send the encapsulated information to multiple different temporary service nodes, so as to achieve the purpose of dynamically expanding the communication service without having to start or stop the original service. This allows the new devices to be smoothly and stably distributed on the temporary service nodes, ensuring stable access for the devices and providing elastic expansion capabilities.

[0042] Advantages of additional aspects of the invention will be set forth in part in the detailed description of the invention below, and in part will be obvious from the description or may be learned by practice of the invention. Attached Figure Description

[0043] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0044] Figure 1 This is a flowchart illustrating the steps of the method of the present invention;

[0045] Figure 2 This is a schematic diagram illustrating the dynamic expansion of the present invention. Detailed Implementation

[0046] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0047] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0048] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0049] Please see Figure 1As shown, this invention provides a real-time sensing method that supports high-concurrency elastic access for massive numbers of devices. It provides a method for setting up a "twin device n" for the data acquisition device. When the amount of information access surges, instructions can be continuously sent to the message mailbox of the "twin device n" to send encapsulated information to multiple different temporary service nodes, thereby achieving dynamic expansion of communication services without needing to start or stop the original services. This allows newly added devices to be smoothly and stably distributed on temporary service nodes, ensuring stable device access and elastic expansion capabilities. This solves the problem in existing technologies where, when processing massive user electricity data, it is often necessary to start and stop the original services, which cannot effectively improve data transmission efficiency.

[0050] Specifically, a real-time sensing method supporting high-concurrency, elastic access for massive numbers of devices is characterized by comprising:

[0051] Step S1: Establish the underlying communication framework for Netty's communication services. In the specific implementation, Netty is based on NIO. NIO's characteristic is that it can utilize a single thread to concurrently handle multiple connections, also known as I / O multiplexing. A single thread in the server can non-blockingly handle I / O requests from multiple clients. This reduces the number of threads and improves thread utilization, achieving I / O multiplexing. Netty utilizes this non-blocking I / O to enable a single thread to concurrently handle multiple connections. Using Netty to build the underlying communication framework can improve the efficiency of data transmission and processing.

[0052] Step S2: Divide the entire power network into multiple service areas and set up different service nodes for each service area; Step S2 includes the following sub-steps:

[0053] Step S201: Obtain electricity consumption data from the electricity network, wherein the electricity consumption data includes: number of users, user electricity consumption and electricity address;

[0054] Step S202: Based on the electricity usage address, divide the users into grids to obtain the pre-electricity usage grid;

[0055] Step S203: Count the number of users and their electricity consumption within each electricity grid, and set the product of the number of users and their electricity consumption as the grid user count.

[0056] When the number of grid users exceeds the grid threshold, the pre-consumption grid is further divided into two first-consumption grids. In the specific implementation process, the greater the grid electricity consumption within a grid, the more electricity consumption data there is within that grid. Re-dividing the grid with more grid data can reduce the load on service nodes and improve data transmission efficiency. The grid threshold is set as the average grid electricity consumption of users in the electricity consumption network.

[0057] When the number of grid users is less than or equal to the grid threshold, the pre-used grid is set as the second grid.

[0058] Step S204: Set all first and second power grids as service areas, and set up a service node in each service area. The service node is used to receive power consumption information in the service area it is in.

[0059] In the specific implementation process, dividing the power network into service areas can avoid the problem of unbalanced load on regional service nodes and excessive load on some service nodes due to excessive power consumption and electrical equipment in some areas, which can easily lead to power transmission failures for users in the area.

[0060] Step S3: Set up a data acquisition device for each electrical device. The data acquisition device is used to collect the power consumption data of the electrical device; set up a "twin device n" Actor for the data acquisition device;

[0061] Step S3 includes the following sub-steps:

[0062] Step S301: Obtain all electrical devices and their types within the service area. Based on the types of electrical devices in the area, assign a data collection device n to each electrical device in the service area, where n is a positive integer. The data collection device n is used to collect the electricity consumption data of the electrical devices. In specific implementation, the electrical devices include distributed power monitoring devices, distribution network operation monitoring devices, electric vehicle charging piles, energy storage devices, smart home devices for customers, etc. The data collection devices are numbered according to the type of electrical device. For example, the data collection device for energy storage devices is numbered as data collection device 1, and the data collection device for electric vehicle charging piles is numbered as data collection device 2.

[0063] Step S302: Set a "twin device n" Actor for the acquisition device n, where n is a positive integer. The "twin device n" Actor acquires the power consumption data and device information collected by the acquisition device in real time and sets it as physical device information.

[0064] The "twin device n" Actor is also configured with its own status and message mailbox; simple transaction processing is achieved through message passing; in the specific implementation process, by configuring the "twin device n" Actor with its own status and message mailbox, it is convenient to use message passing to achieve simple transaction processing of the "twin device n" Actor, providing basic support for the "device intelligent autonomy" of the acquisition system;

[0065] Step S4: The "twin device n" Actor encapsulates the collected physical device information and sends it to the service node; Step S4 includes the following sub-steps:

[0066] Step S401: The "twin device n" Actor encapsulates and sends the collected physical device information to the service node at the first collection time interval; in the specific implementation, the first collection time is 60 minutes.

[0067] Step S402: When the state of the "twin device n" Actor reaches the sending standard, the collected physical device information is encapsulated and sent to the service node; in specific implementation, the sending standard is that the stored physical device information reaches the storage limit of the "twin device n" Actor.

[0068] Step S403: When the message mailbox of the "twin device n" Actor receives the sending instruction, the collected physical device information is encapsulated and sent to the service node;

[0069] Step S5: The service node is used to receive encapsulation information. When the number of encapsulation information received suddenly increases, the communication service is dynamically expanded.

[0070] Please see Figure 2 As shown, in step S5, dynamic expansion includes the following sub-steps:

[0071] Step S501: Obtain the upper limit of the number of transmissions of the service node. When the number of encapsulated information received by the service node is greater than or equal to the upper limit of the number of transmissions of the service node, multiple temporary service nodes are set according to the number of encapsulated information. A diversion command is sent to the message mailbox of the "twin device n" Actor. The "twin device n" Actor sends the subsequent encapsulated information to the temporary service nodes. The number of temporary service nodes is set according to the number of encapsulated information and the upper limit of the transmission of the temporary service nodes. In the specific implementation process, all temporary service nodes have the same transmission speed as the service node. By setting multiple temporary service nodes to divert the access information, each encapsulated information can be smoothly and stably distributed to the expanded service nodes, ensuring stable access of the device.

[0072] Step S501 includes the following sub-steps:

[0073] Step S50101: Obtain the number of encapsulation information to be accessed, set the upper limit of the transmission quantity of the existing service node to the upper limit of the total transmission quantity, and set the difference between the upper limit of the total transmission quantity and the number of encapsulation information as the load value.

[0074] Step S50102: When there is only one service node and the load value is greater than or equal to 0, it is determined that there is a sudden increase in encapsulation information; obtain the load value at this time and set a temporary service node;

[0075] Step S50103: Obtain the maximum total number of transmissions. When the load value exceeds the maximum total number of transmissions, set up a temporary service node.

[0076] Step S50104: Repeat step S50103 until the load value is less than or equal to the total transmission limit. Number all temporary service nodes in sequence as temporary service node 1, 2, 3, ..., m, where m is a positive integer.

[0077] Step S50105: Send a diversion instruction to the message mailbox of the "twin device n" Actor. The diversion instruction includes: sending the encapsulation information to the temporary service node m; the "twin device n" Actor sends the subsequent encapsulation information to the corresponding temporary service node m according to the received diversion instruction; number the multiple temporary service nodes, and send the diversion instruction to the message mailbox of the "twin device n" Actor according to the number, so that each encapsulation information can be accurately connected to the expanded service node, ensuring the accuracy of device access;

[0078] Step S502: Statistically analyze the encapsulation balance values ​​received by the temporary service nodes, and sort the temporary service nodes in descending order of the encapsulation balance values ​​received by the temporary service nodes.

[0079] Step S503: Upload the encapsulation information of temporary service nodes to the system in order of sorting, and release the temporary service nodes whose encapsulation information has been uploaded to the system; in the specific implementation process, prioritize releasing service nodes with large encapsulation balance values, which can quickly reduce the total amount of encapsulation information of service nodes.

[0080] Step S504: When the remaining number of temporary service nodes is less than the node threshold, a normal instruction is sent to the message mailbox of the "twin device n" Actor, and the "twin device n" Actor sends the subsequent encapsulation information to the service node. In the specific implementation process, temporary service nodes are released in a timely manner to ensure the stability of the number of service nodes in the service area and avoid the situation of too many service nodes and excessive and redundant data.

[0081] Step S502 further includes calculating the cumulative similarity value of the physical device information of each package using a preset dynamic value algorithm.

[0082] Where F a f is the aforementioned packaging balance value. a (t d ) is a preset cumulative time mapping function that reflects the mapping relationship between the cumulative time it takes for the physical device information to be sent to the service node and the cumulative time impact value. d This reflects the generation time of the temporary service node. j Assign weights to the features corresponding to the j-th physical device feature in the physical device information, n jLet m be the number of physical device features that are identical to the j-th physical device feature in the preceding physical feature information, and let ε be the total number of physical device features in this physical device information. i f is the comparison similarity value of physical device information corresponding to the same i-th physical device feature, where the comparison similarity value reflects the number of identical physical device features in the two pieces of physical device information. b (t i ) is a preset similarity time mapping function, reflecting the mapping relationship between the time when the physical device information corresponding to the i-th physical device feature is uploaded to the system and the similarity time influence value, t i K represents the time when the physical device information corresponding to the i-th physical device feature is uploaded to the system. u The value of ΔK represents the number of physical device information entries uploaded to the temporary service node that are similar to the original physical device information. ΔK is a preset baseline similarity value, β1 is a preset cumulative time weight, β2 is a preset similarity time weight, and β3 is a preset similarity quantity weight. The dynamic value algorithm can balance the order in which the temporary service node sends the corresponding physical device information, ensuring that subsequent similar physical device information can be uploaded more quickly, thus resolving system congestion.

[0083] The feature configuration weights corresponding to the physical device features are obtained by looking up a pre-built feature configuration weight table.

[0084] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] The above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and are not intended to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the claims.

Claims

1. A real-time sensing method supporting high-concurrency, elastic access for massive numbers of devices, characterized in that, The method includes: Step S1: Establish the underlying communication framework for Netty's communication services; Step S2: Divide the entire power network into multiple service areas and set up different service nodes for each service area; Step S3: Set up a data acquisition device for each electrical device. The data acquisition device is used to collect the power consumption data of the electrical device; set up a "twin device n" Actor for the data acquisition device; Step S4: The "twin device n" Actor encapsulates the collected physical device information and sends it to the service node; Step S5: The service node is used to receive encapsulation information. When the number of encapsulation information received suddenly increases, the communication service is dynamically expanded. When there is only one service node and the load value is greater than or equal to 0, it is determined that the number of encapsulation information has suddenly increased. In step S5, dynamic expansion includes the following sub-steps: Step S501: Obtain the upper limit of the number of transmissions of the service node. When the number of encapsulated information accessed by the service node is greater than or equal to the upper limit of the number of transmissions of the service node, set up multiple temporary service nodes according to the number of encapsulated information, send a diversion instruction to the message mailbox of the "twin device n" Actor, and the "twin device n" Actor sends the subsequent encapsulated information to the temporary service nodes. Step S502: Statistically analyze the encapsulation balance values ​​received by the temporary service nodes, and sort the temporary service nodes in descending order of the encapsulation balance values ​​received by the temporary service nodes. Step S503: Upload the encapsulation information of the temporary service nodes to the system in order of sorting, and release the temporary service nodes whose encapsulation information has been uploaded to the system; Step S504: When the remaining number of temporary service nodes is less than the node threshold, a normal instruction is sent to the message mailbox of the "twin device n" Actor, and the "twin device n" Actor sends the subsequent encapsulation information to the service node.

2. The real-time sensing method supporting high-concurrency elastic access of massive devices according to claim 1, characterized in that, Step S2 includes the following sub-steps: Step S201: Obtain electricity consumption data from the electricity network, wherein the electricity consumption data includes: number of users, user electricity consumption and electricity address; Step S202: Based on the electricity usage address, divide the users into grids to obtain the pre-electricity usage grid; Step S203: Count the number of users and their electricity consumption within each electricity grid, and set the product of the number of users and their electricity consumption as the grid user count. When the number of grid users exceeds the grid threshold, the pre-power grid is re-divided to obtain two first power grids; When the number of grid users is less than or equal to the grid threshold, the pre-used grid is set as the second grid. Step S204: Set all first and second power grids as service areas, and set up a service node in each service area. The service node is used to receive power consumption information in the service area it is in.

3. The real-time sensing method supporting high-concurrency elastic access of massive devices according to claim 2, characterized in that, Step S3 includes the following sub-steps: Step S301: Obtain all electrical devices and their types in the service area. Based on the types of electrical devices in the area, set up a data acquisition device n for each electrical device in the service area, where n is a positive integer. The data acquisition device n is used to collect the power consumption data of the electrical devices. Step S302: Set a "twin device n" Actor for the acquisition device n, where n is a positive integer. The "twin device n" Actor acquires the power consumption data and device information collected by the acquisition device in real time and sets it as physical device information.

4. The real-time sensing method supporting high-concurrency elastic access of massive devices according to claim 3, characterized in that, The "twin device n" Actor is also configured with its own status and message mailbox.

5. The real-time sensing method supporting high-concurrency elastic access of massive devices according to claim 4, characterized in that, Step S4 includes the following sub-steps: Step S401: The "twin device n" Actor encapsulates the collected physical device information and sends it to the service node at the first collection time interval; Step S402: When the state of the "twin device n" Actor reaches the preset transmission standard, the collected physical device information is encapsulated and sent to the service node. Step S403: When the message mailbox of the "twin device n" Actor receives the sending instruction, the collected physical device information is encapsulated and sent to the service node.

6. The real-time sensing method supporting high-concurrency elastic access of massive devices according to claim 1, characterized in that, Step S501 includes the following sub-steps: Step S50101: Obtain the number of encapsulation information to be accessed, set the upper limit of the transmission quantity of the existing service node to the upper limit of the total transmission quantity, and set the difference between the upper limit of the total transmission quantity and the number of encapsulation information as the load value. Step S50102: When the amount of encapsulated information suddenly increases; obtain the load value at this time and set up a temporary service node; Step S50103: Obtain the maximum total number of transmissions. When the load value exceeds the maximum total number of transmissions, set up a temporary service node. Step S50104: Repeat step S50103 until the load value is less than or equal to the total transmission limit. Number all temporary service nodes in sequence as temporary service node 1, 2, 3, ..., m, where m is a positive integer. Step S50105: Send a diversion instruction to the message mailbox of the "twin device n" Actor. The diversion instruction includes: sending the encapsulation information to the temporary service node m. The "twin device n" Actor sends the subsequent encapsulation information to the corresponding temporary service node m based on the received diversion instruction.

7. The real-time sensing method supporting high-concurrency elastic access of massive devices according to claim 1, characterized in that, Step S502 further includes calculating the cumulative similarity value of the physical device information of each package using a preset dynamic value algorithm.

8. A real-time sensing method supporting high-concurrency elastic access of massive devices according to claim 7, characterized in that, Dynamic value algorithm is ,in The aforementioned packaging balance value, This is a preset cumulative time mapping function that reflects the mapping relationship between the cumulative time it takes for the physical device information to be sent to the service node and the cumulative time impact value. This reflects the generation time of the temporary service node. Assign weights to the features corresponding to the j-th physical device feature in the physical device information. Let m be the number of physical device features that are identical to the j-th physical device feature in the preceding physical feature information, and m be the total number of physical device features in this physical device information. The similarity value is the comparison value of physical device information corresponding to the same i-th physical device feature. The similarity value reflects the number of identical physical device features in the two pieces of physical device information. This is a pre-defined similarity time mapping function that reflects the mapping relationship between the time when the physical device information corresponding to the i-th physical device feature is uploaded to the system and the similarity time influence value. The time when the physical device information corresponding to the i-th physical device feature is uploaded to the system. This refers to the number of physical device information entries that are uploaded to the temporary service node and have a similar relationship to the physical device information. The preset benchmark similarity value, The preset cumulative time weight, For the preset similarity time weight, The preset similarity weights.

9. A real-time sensing method supporting high-concurrency elastic access of massive devices according to claim 8, characterized in that, The feature configuration weights corresponding to the physical device features are obtained by looking up a pre-built feature configuration weight table.

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