A device data collection method and system based on Internet of Things control platform

Abstract

The present invention discloses a device data collection method and system based on an Internet of Things control platform, which relates to the technical field of the Internet of Things, including obtaining device information and classifying it into a cloud resource directory; defining data sources and designing data output requirements; dynamically selecting data transmission paths; enabling multi-path redundancy mechanisms to transmit data when the network is not good; and reorganizing and verifying data after it arrives. The present invention improves resource organization efficiency through fine device classification and automated management; customized data collection strategies ensure the pertinence and efficiency of data processing; adaptive compression and intelligent path selection mechanisms ensure data transmission speed; multi-path redundancy design further enhances transmission stability and reduces the risk of data loss; data reorganization and verification mechanisms ensure data integrity and credibility, which overall improves the efficiency, reliability and security of Internet of Things data collection and provides strong support for data applications.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a device data acquisition method and system based on an Internet of Things control platform. Background Art

[0002] The rapid development of Internet of Things technology has promoted the leap forward of smart cities and industrial automation, but the resulting surge in devices and massive amounts of data pose severe challenges to existing data collection technologies. At present, the limitations of single-path transmission, static data processing strategies, and manual equipment classification management have led to unstable data transmission, low efficiency, and difficulties in resource management. Intelligent, adaptive, and innovative strategies are urgently needed.

[0003] In response to the above problems, the present invention aims to innovate the data collection method through the Internet of Things control platform, integrate intelligent equipment classification, dynamic data source management and adaptive transmission optimization, and add multi-path redundancy guarantee to achieve efficient and reliable equipment data collection and processing, fundamentally breaking through the bottleneck of traditional technology. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a device data collection method and system based on an Internet of Things control platform to solve the problems of unstable data transmission, low efficiency and difficult resource management.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a device data collection method based on an Internet of Things control platform, which includes obtaining device information of the Internet of Things control platform, and classifying the devices to form a cloud resource directory;

[0008] Define data sources for each type of device in the cloud resource catalog, bind them to the corresponding IoT devices, and design data output requirements;

[0009] Design data collection schemes based on data sources and output requirements, add adaptive compression strategies to data collection schemes, and detect network conditions to dynamically select data transmission paths;

[0010] During data transmission, if the network condition is poor, the multi-path redundancy mechanism is enabled to indirectly transmit data;

[0011] After the data reaches the IoT platform, it is reorganized and verified to realize the collection of device data.

[0012] As a preferred solution of the device data acquisition method based on the Internet of Things control platform of the present invention, wherein: the device classification refers to classifying device information according to device functions, creating a multi-level directory structure on the cloud platform, and automatically entering the device information into the corresponding directory by using cloud platform tools to obtain a cloud resource directory.

[0013] As a preferred solution of the device data acquisition method based on the Internet of Things control platform of the present invention, wherein: the defined data sources include the frequency of data acquisition, data format definition, and device attribute mapping, and the output requirements include determining the data output format, selecting a transmission protocol, defining the data destination, and defining a storage strategy.

[0014] As a preferred solution of the device data acquisition method based on the Internet of Things control platform of the present invention, wherein: adding an adaptive compression strategy to the data acquisition scheme and detecting the network condition to dynamically select a data transmission path includes

[0015] expanding the single bandwidth and delay into the weighted average of multiple data sources, and introducing a time exponential decay function and an adaptive compression strategy to form a final network condition scoring model, expressed as:

[0016] ;

[0017] In the formula, n represents the number of data sources, represents the weight of the i-th data source, represents the bandwidth of the i-th data source, represents the delay of the i-th data source, is the weight of the network condition score, is the bandwidth frequency constant, is the bandwidth function after adaptive compression, represents the congestion factor of the i-th data source at time t, represents the packet loss rate of the i-th data source, represents the packet loss rate adjustment coefficient, is the time decay coefficient, t is the time variable, represents the gain coefficient of the compression strategy, represents the bandwidth compression threshold, T represents the observation time period;

[0018] Set a network status threshold V. When NCS≥V, the network condition is defined as good. When NCS<V, the network condition is defined as poor;

[0019] When the network status is good, select a direct-connected device for direct transmission, and the device directly transmits data to the cloud platform through the standard protocol HTTP;

[0020] When the network status is not good, choose non-direct connection devices for indirect transmission. The data is first processed through a multi-path redundancy mechanism and then indirectly transmitted to the platform through the HTTPS security protocol.

[0021] As a preferred solution of the device data collection method based on the Internet of Things control platform of the present invention, wherein: the multi-path redundancy mechanism includes:

[0022] The data set to be transmitted is stored in a data packet and sent simultaneously through multiple paths;

[0023] If the NCS score of a path drops significantly and falls below the preset threshold S, the path switching mechanism is triggered to quickly redirect the data flow originally assigned to the path to other paths with higher scores. The adaptive compression strategy is dynamically adjusted during this process. For data packets on low-scoring paths, a higher compression ratio is adopted to reduce transmission time and ensure that data is delivered intact.

[0024] As a preferred solution of the device data collection method based on the Internet of Things control platform of the present invention, wherein: the data reorganization and verification includes:

[0025] Parse the received data packets, check and remove duplicate data packets, use the checksum method to verify whether the data remains intact during transmission, and merge the data scattered in different data packets according to the device ID and time sequence to form complete data;

[0026] Check whether the reorganized data conforms to the predefined output format. If so, the device data collection is completed. If not, retransmission is required.

[0027] In a second aspect, the present invention provides a device data acquisition system based on an Internet of Things control platform, including a device information classification module for obtaining device information of the Internet of Things control platform and classifying the devices to form a cloud resource directory;

[0028] The data source definition module is used to define the data source for each type of device in the cloud resource directory, bind it to the corresponding IoT device, and design data output requirements;

[0029] Intelligent data collection module designs data collection schemes based on data sources and output requirements, adds adaptive compression strategies to data collection schemes, detects network conditions and dynamically selects data transmission paths;

[0030] Multipath redundancy mechanism module: during data transmission, if the network condition is not good, the multipath redundancy mechanism is enabled to indirectly transmit data;

[0031] The data verification module is used to reorganize and verify data after it reaches the IoT platform to realize the collection of device data.

[0032] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the device data collection method based on the Internet of Things control platform as described in the first aspect of the present invention is implemented.

[0033] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the device data collection method based on the Internet of Things control platform as described in the first aspect of the present invention is implemented.

[0034] The beneficial effects of the present invention are as follows: the present invention improves resource organization efficiency through fine equipment classification and automated management; customized data collection strategies ensure the pertinence and efficiency of data processing; adaptive compression and intelligent path selection mechanisms optimize network adaptability and reliability while ensuring data transmission speed; multi-path redundancy design further enhances transmission stability and reduces the risk of data loss; data reorganization and verification mechanisms ensure data integrity and credibility, and overall improve the efficiency, reliability and security of IoT data collection, providing strong support for data applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0036] Figure 1 This is a flow chart of the device data collection method based on the Internet of Things control platform in Example 1. DETAILED DESCRIPTION

[0037] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0038] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0039] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0040] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a device data collection method based on an Internet of Things control platform, comprising the following steps:

[0041] S1. Obtain the device information of the IoT control platform and classify the devices to form a cloud resource directory.

[0042] First, a classification system is established, with the first level classification being the major types of equipment, the second level classification being specific uses, and the third level classification being specific models or functional characteristics;

[0043] Then, a multi-level directory structure model is designed on the cloud platform to support the hierarchical division of devices from major categories to subcategories, including:

[0044] First level directory: equipment categories;

[0045] Secondary directory: equipment purpose or subcategory;

[0046] Level 3 directory: specific device models or functional groups;

[0047] Finally, integrate with the API interface of the IoT control platform to obtain the real-time device information of the device, and use the cloud platform tool to automatically enter the real-time device information into the corresponding directory to obtain the cloud resource directory.

[0048] It should be noted that by automatically collecting and classifying device information into a cloud resource directory, the efficiency and accuracy of device management are improved, and resources are organized and structured, which facilitates subsequent data processing and analysis tasks.

[0049] S2. Define data sources for each type of device in the cloud resource directory, bind them to the corresponding IoT devices, and design data output requirements.

[0050] Defining data sources includes the frequency of data collection, data format definition, and device attribute mapping. The frequency of data collection is determined based on the device type and application requirements. The data format definition is to define standard data fields for each device type, including timestamp, device ID, data value, and field data type and range restrictions. Device attribute mapping refers to establishing mapping rules between device attributes and their corresponding fields in the data format to ensure that the data collection software can accurately identify and fill in data.

[0051] Output requirements include determining the data output format, selecting the transmission protocol, defining the data destination, and defining the storage strategy. The data output format refers to determining the final format of the output data based on the needs of the receiving end. Selecting the transmission protocol refers to selecting the most appropriate transmission protocol based on network conditions, data sensitivity, and real-time requirements. MQTT is suitable for low-power, high-latency networks, and HTTPS is suitable for security-sensitive scenarios. Defining the data destination refers to clarifying the final destination of the data. Defining the storage strategy refers to selecting the appropriate storage solution based on the data volume, access frequency, and cost budget, such as hot storage, cold storage, or hybrid storage, setting the data retention period, and planning data archiving or deletion strategies to optimize the use of storage resources.

[0052] It should be noted that defining data sources and output requirements enables standardization and customization of data collection, ensuring the accuracy and pertinence of data. The binding mechanism between devices and templates simplifies the configuration process and enhances the flexibility of data collection.

[0053] S3. Based on the data source and output requirements, design a data collection plan, add an adaptive compression strategy to the data collection plan, detect the network status and dynamically select the data transmission path.

[0054] The single bandwidth and delay are expanded into a weighted average of multiple data sources to form a preliminary network status scoring model, which is expressed as:

[0055] ;

[0056] in, represents the weight of the i-th data source, represents the bandwidth of the i-th data source, represents the delay of the i-th data source;

[0057] Based on the changes in network status, a time exponential decay function is introduced to form an improved network status scoring model, which is expressed as:

[0058] ;

[0059] in, is the time attenuation coefficient, t is the time variable, and T represents the observation time period;

[0060] An adaptive compression strategy is introduced to adjust the bandwidth through a dynamic function, which is expressed as:

[0061] ;

[0062] in, represents the gain coefficient of the compression strategy, Indicates the bandwidth compression threshold;

[0063] Define the impact function of packet loss rate and congestion factor on network status, expressed as,

[0064] ;

[0065] in, Indicates the packet loss rate adjustment coefficient, represents the packet loss rate of the i-th data source;

[0066] Based on the improved network status scoring model, dynamic function and influence function, the final network status scoring model is formed, which is expressed as:

[0067] ;

[0068] In the formula, n represents the number of data sources, represents the weight of the i-th data source, represents the bandwidth of the i-th data source at time t, represents the delay of the i-th data source at time t, The weight used to score the network status. is the bandwidth frequency constant, is the bandwidth function after adaptive compression, represents the congestion factor of the i-th data source at time t;

[0069] The network status threshold V is set to 0.3. When NCS ≥ 0.3, the network status is defined as good, and when NCS < 0.3, the network status is defined as poor;

[0070] When the network status is good, choose direct connection type device for direct transmission. The device directly transmits data to the cloud platform through the standard protocol HTTP.

[0071] When the network status is not good, choose non-direct connection devices for indirect transmission. The data is first processed through a multi-path redundancy mechanism and then indirectly transmitted to the platform through the HTTPS security protocol.

[0072] The core feature of direct-connect devices is to reduce the intermediate links in the communication path and directly establish the communication link, thereby improving efficiency, reducing latency or adapting to specific environmental restrictions;

[0073] The core feature of non-directly connected devices is that they do not communicate directly with each other or with the target system, but rather transmit data through one or more intermediate devices.

[0074] Direct-connected device direct transmission means that two devices establish a point-to-point connection to exchange data without going through any intermediate nodes, achieving fast and efficient data transmission;

[0075] Indirect transmission between non-directly connected devices refers to the process of data being relayed between the sender and the receiver through one or more relay devices to achieve communication across different networks;

[0076] It should be noted that the adaptive compression strategy and dynamic path selection intelligently adjust data transmission parameters according to network conditions, optimize data transmission efficiency, reduce bandwidth requirements, and improve the stability and reliability of data transmission.

[0077] S4. During data transmission, if the network condition is not good, the multi-path redundancy mechanism is enabled to indirectly transmit data.

[0078] The data set to be transmitted is stored in a data packet and sent simultaneously through multiple paths;

[0079] If the NCS score of a path drops significantly and falls below the preset threshold S, the path switching mechanism is triggered to quickly redirect the data flow originally assigned to the path to other paths with higher scores. The adaptive compression strategy is dynamically adjusted during this process. For data packets on low-scoring paths, a higher compression ratio is adopted to reduce transmission time and ensure that data is delivered intact.

[0080] It should be noted that the multi-path redundancy mechanism further enhances the robustness of data transmission and ensures continuous data transmission even in unstable network conditions. Data integrity is ensured through path switching and dynamic compression.

[0081] S5. After the data reaches the IoT platform, it is reorganized and verified to realize the collection of device data.

[0082] Parse the received data packets, check and remove duplicate data packets, use the checksum method to verify whether the data remains intact during transmission, and merge the data scattered in different data packets according to the device ID and time sequence to form complete data;

[0083] Check whether the reorganized data conforms to the predefined output format. If so, the device data collection is completed. If not, retransmission is required.

[0084] It should be noted that data reorganization and verification ensure the accuracy of data during transmission. Whether it is direct or indirect transmission, the data integrity can be verified through the verification mechanism, which improves the credibility of data collection.

[0085] This embodiment also provides a device data collection system based on the Internet of Things control platform, including a device information classification module for obtaining device information of the Internet of Things control platform and classifying the devices to form a cloud resource directory;

[0086] The data source definition module is used to define the data source for each type of device in the cloud resource directory, bind it to the corresponding IoT device, and design data output requirements;

[0087] Intelligent data collection module designs data collection schemes based on data sources and output requirements, adds adaptive compression strategies to data collection schemes, detects network conditions and dynamically selects data transmission paths;

[0088] Multipath redundancy mechanism module: during data transmission, if the network condition is not good, the multipath redundancy mechanism is enabled to indirectly transmit data;

[0089] The data verification module is used to reorganize and verify data after it reaches the IoT platform to realize the collection of device data.

[0090] This embodiment also provides a computer device, which is suitable for the device data collection method based on the Internet of Things control platform, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the device data collection method based on the Internet of Things control platform proposed in the above embodiment.

[0091] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0092] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the device data collection method based on the Internet of Things control platform proposed in the above embodiment is implemented; 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 (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0093] In summary, the beneficial effects of the present invention are as follows: through fine equipment classification and automated management, the efficiency of resource organization is improved; customized data collection strategies ensure the pertinence and efficiency of data processing; adaptive compression and intelligent path selection mechanisms optimize network adaptability and reliability while ensuring data transmission speed; multi-path redundancy design further enhances transmission stability and reduces the risk of data loss; data reorganization and verification mechanisms ensure data integrity and credibility, which overall improves the efficiency, reliability and security of IoT data collection, and provides strong support for data applications.

[0094] Example 2, referring to Table 1, is the second example of the present invention. To further verify the advancement of the present invention, experimental simulation data of a device data acquisition method based on an Internet of Things control platform is provided.

[0095] This example aims to verify a device data collection method based on an IoT control platform, especially its data transmission efficiency and data integrity maintenance capabilities in a complex network environment. Six different types of IoT devices were selected as research objects, including smart thermostats, environmental monitoring sensors, smart door locks, remote cameras, industrial automation controllers, and smart wearable devices. These devices were installed in environments with different network conditions to simulate the network diversity of the real world, such as high-speed Wi-Fi in the city center, low-speed mobile networks in the suburbs, and wired networks inside factories.

[0096] First, the six devices were divided into three categories according to their functions: environmental monitoring (smart thermostats, environmental monitoring sensors), security control (smart door locks, remote cameras), and human-computer interaction (industrial automation controllers, smart wearable devices). Then, a cloud platform tool was used to automatically create a multi-level directory structure and archive the device information to the corresponding directory to form a cloud resource directory.

[0097] Next, define the data source for each type of device, including data collection frequency (such as every 5 minutes, every hour, real-time), data format (such as JSON, CSV), device attribute mapping, etc., and set data output requirements, such as using the MQTT protocol, storing data in a cloud database, and defining a data retention period.

[0098] When designing the data collection scheme, an adaptive compression strategy was specially added. The above formula NCS is used to dynamically evaluate the network status and select the optimal data transmission path accordingly. In the specific implementation, the network status threshold V=0.7 is set to distinguish between good and bad network conditions. In order to simulate this process, network status data for a consecutive week was collected, including bandwidth ),Delay , congestion factor and packet loss rate , and calculate the NCS value for each day.

[0099] The details are shown in Table 1 below:

[0100] Table 1 Experimental record table

[0101] Device Name Data source type Collection frequency Data Format Data size before compression (KB) Compressed data size (KB) NCS score Data integrity verification Smart thermostat Temperature, humidity Every 5 minutes JSON 20 5 0.92 pass Environmental Monitoring Sensors PM2.5, Noise per hour CSV 15 4 0.88 pass Smart door lock Switch status, power level real time MQTT 8 2 0.95 pass Remote Camera Video Streaming real time H.264 500 150 0.78 pass Industrial Automation Controller Motor status, pressure Every 10 minutes JSON 30 8 0.85 pass Smart Wearable Devices Heart rate, steps real time XML 6 1 0.90 pass

[0102] Through the above data comparison and analysis, it can be seen that the adaptive compression strategy effectively reduces the amount of data transmission under different network conditions and significantly improves the data transmission efficiency. For example, the original video stream data volume of the remote camera is compressed from 500KB to 150KB, with a compression ratio of up to 3.33 times, which can ensure smooth data transmission even when the network condition is poor (NCS=0.78). In addition, the data integrity verification of all devices is "passed", indicating that the data can still remain intact after compression and multi-path redundant transmission, verifying the effectiveness of the data reorganization and verification links.

[0103] Compared with the prior art, the present invention significantly improves the success rate of data transmission when network conditions are poor, especially in data transmission between remote cameras and industrial automation controllers, and reduces bandwidth usage, showing obvious innovation and practicality. In particular, under the implementation of the multi-path redundancy mechanism, even if the NCS score of an individual path drops significantly, the system can quickly redirect the data flow to ensure reliable data delivery, reflecting the robustness and flexibility of the system.

[0104] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A device data collection method based on an Internet of Things control platform, characterized in that: including Obtain the device information of the Internet of Things control platform and classify the devices to form a cloud resource directory; Define data sources for each type of device in the cloud resource directory, bind them to the corresponding Internet of Things devices, and design data output requirements; Based on the data sources and output requirements, design a data collection scheme, add an adaptive compression strategy to the data collection scheme, and detect the network status to dynamically select the data transmission path; When transmitting data, if the network status is poor, enable the multi-path redundancy mechanism to indirectly transmit the data; After the data arrives at the Internet of Things platform, perform data reorganization and verification to achieve the collection of device data; The adding of the adaptive compression strategy to the data collection scheme and the detection of the network status to dynamically select the data transmission path includes Expanding the single bandwidth and delay into the weighted average of multiple data sources, and introducing a time exponential decay function and an adaptive compression strategy to form a final network status scoring model, expressed as: ； In the formula, n represents the number of data sources, represents the weight of the i-th data source, represents the bandwidth of the i-th data source, represents the delay of the i-th data source, The weight used to score the network status. is the bandwidth frequency constant, is the bandwidth function after adaptive compression, represents the congestion factor of the ith data source at time t, represents the packet loss rate of the i-th data source, Indicates the packet loss rate adjustment coefficient, is the time attenuation coefficient, t is the time variable, represents the gain coefficient of the compression strategy, represents the bandwidth compression threshold, T represents the observation time period; Set the network status threshold V. When NCS≥V, the network status is defined as good. When NCS<V, the network status is defined as poor; When the network status is good, select a direct-connected device for direct transmission, and the device directly transmits data to the cloud platform through the standard protocol HTTP; When the network status is poor, select a non-direct-connected device for indirect transmission. The data is first processed by the multi-path redundancy mechanism and then indirectly transmitted to the platform through the HTTPS security protocol; The multi-path redundancy mechanism includes Store the data set to be transmitted in a data packet and send it through multiple paths simultaneously; If the NCS score of a certain path drops significantly and is lower than the preset threshold S, trigger the path switching mechanism, quickly redirect the data stream originally assigned to this path to other paths with higher scores, and the adaptive compression strategy is dynamically adjusted during this process. For the data packets on the low-score paths, a higher compression ratio is adopted to reduce the transmission time and ensure the complete delivery of the data.

2. The device data collection method based on the Internet of Things control platform according to claim 1, characterized in that: The device classification refers to classifying the device information according to the device functions, creating a multi-level directory structure on the cloud platform based on the classification results, and automatically entering the device information into the corresponding directory using cloud platform tools to obtain the cloud resource directory.

3. The device data collection method based on the Internet of Things control platform as claimed in claim 2, characterized in that: The definition of the data source includes the data collection frequency, data format definition, and device attribute mapping. The output requirements include determining the data output format, selecting the transmission protocol, defining the data destination, and defining the storage strategy.

4. The device data collection method based on the Internet of Things control platform as claimed in claim 3 is characterized in that: The performing of data reorganization and verification includes Parse the received data packets, check and remove duplicate data packets, use the checksum method to verify whether the data remains complete during transmission, and merge the data scattered in different data packets according to the device ID and time series to form complete data; Check whether the reorganized data conforms to the predefined output format. If it conforms, the device data collection is completed. If it does not conform, request retransmission.

5. A device data acquisition system based on an Internet of Things control platform, based on the device data acquisition method based on an Internet of Things control platform according to any one of claims 1 to 4, characterized in that: including A device information classification module, which is used to obtain the device information of the Internet of Things control platform and classify the devices to form a cloud resource directory; A data source definition module, which is used to define data sources for each type of device in the cloud resource directory, bind them to the corresponding Internet of Things devices, and design data output requirements; Intelligent data collection module designs data collection schemes based on data sources and output requirements, adds adaptive compression strategies to data collection schemes, detects network conditions and dynamically selects data transmission paths; Multipath redundancy mechanism module: during data transmission, if the network condition is not good, the multipath redundancy mechanism is enabled to indirectly transmit data; The data verification module is used to reorganize and verify data after it reaches the IoT platform to realize the collection of device data.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the device data collection method based on the Internet of Things control platform described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the device data collection method based on the Internet of Things control platform described in any one of claims 1 to 4 are implemented.

Images