Large-capacity data transmission method of Internet of Things with Lora communicated with cable television network

Abstract

The invention relates to an internet of things large-capacity data transmission method with Lora communicated with a cable television network, which comprises the following steps of: S1, deploying an edge computing node at a data source of internet of things equipment to preprocess large-capacity data; s2, classifying the preprocessed data by using a fuzzy logic algorithm, judging through an established fuzzy rule base, and dividing large-capacity data into high-priority data, medium-priority data and low-priority data; and S3, establishing an intermediate layer of the Lora and cable television network fused by the heterogeneous network, and adjusting the transmission of the large-capacity data through the intermediate layer. According to the invention, data transmission, transfer-in and reception are carried out by means of the intermediate layer fused by heterogeneous networks, the data transmission security is enhanced, the utilization efficiency of network resources is improved, and smooth transition and efficient transmission of data among different networks are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things data transmission, and in particular to a method for transmitting large-capacity data of the Internet of Things by connecting a Lora network to a cable television network. Background Art

[0002] As the Internet of Things (IoT) is developing rapidly, the massive amounts of data generated by a large number of devices require efficient and reliable transmission methods. With the continuous expansion of application scenarios such as smart cities and industrial IoT, IoT devices are widely distributed and the data they generate is diverse and large in capacity. For example, in intelligent transportation systems, sensors on the road continuously collect data such as traffic volume, speed, and vehicle type; on industrial production lines, various types of equipment upload information such as production parameters and equipment status in real time. These data are crucial for real-time monitoring, intelligent decision-making, and optimized operation of the system, but traditional IoT data transmission methods have gradually exposed many problems when facing large-capacity data transmission.

[0003] Traditional IoT data transmission technology mainly relies on a single network architecture, such as using only wireless sensor networks or conventional mobile communication networks. Taking wireless sensor networks as an example, their nodes usually use low-power, low-bandwidth transmission methods, which are extremely inefficient when transmitting large amounts of data. Due to the limited communication capabilities of the nodes, the data transmission delay is high, packet loss is prone to occur, and it is impossible to effectively distinguish and prioritize data based on its importance and urgency. For example, in environmental monitoring applications, when sudden pollution incidents occur, critical monitoring data may not be able to reach the control center in time due to transmission delays, resulting in the inability to take timely countermeasures. This single network architecture transmission technology lacks flexibility and reliability when dealing with complex IoT data transmission requirements, which seriously limits the performance improvement of IoT systems.

[0004] Some existing improved technologies attempt to solve the problem of large-capacity data transmission by increasing network bandwidth or optimizing transmission protocols. For example, some solutions use higher frequency bands and more advanced modulation technologies in mobile communication networks to increase transmission rates. However, these technologies often require high hardware costs and complex network deployment, and are not applicable to some resource-constrained IoT application scenarios. Moreover, they still do not fundamentally solve the problem of how to perform intelligent transmission based on the characteristics of data. They simply improve the overall transmission capacity and lack differentiated processing for different types of data. For example, in a smart home system, the user's real-time control commands and the device's periodic status reports are not reasonably distinguished during the transmission process, which may cause control command response delays and affect the user experience.

[0005] Therefore, this application innovatively proposes a method for large-capacity data transmission of the Internet of Things that connects Lora and the cable TV network, which effectively solves many problems of the prior art. By deploying edge computing nodes at the data source to pre-process and classify the data, it is possible to accurately identify the time sensitivity, importance and data type of the data, and prioritize them accordingly to ensure the rapid transmission of critical data. For example, in the electric power Internet of Things, real-time monitoring data of power grid faults can be quickly identified as high priority and transmitted first to ensure the safe and stable operation of the power system. This application realizes the efficient transmission and conversion of data between different networks, giving full play to the advantages of the Lora network and the cable TV network, while improving transmission efficiency and reducing costs, providing a more intelligent, reliable and economical solution for large-capacity data transmission in the Internet of Things, and greatly promoting the application and development of Internet of Things technology in various fields. Summary of the invention

[0006] The present invention provides a method for transmitting large-capacity data of the Internet of Things by connecting Lora and a cable TV network, comprising the following steps:

[0007] S1. Deploy edge computing nodes at the data source of IoT devices to pre-process large-capacity data;

[0008] S2. Classify the pre-processed data using fuzzy logic algorithm, and make judgments through the established fuzzy rule base to divide the large-capacity data into high, medium and low priority data;

[0009] S3. Establish an intermediate layer between the heterogeneous network fusion Lora and the cable TV network, and adjust the transmission of large-capacity data through the intermediate layer.

[0010] Preferably, in said S1, the time sensitivity is determined by analyzing the correlation between data generation time and real-time for edge computing nodes through preprocessing, the importance of impact on the Internet of Things is determined, and the data type is divided according to source and purpose; the time sensitivity, importance and data type in large-capacity data are determined through preprocessing.

[0011] Preferably, the characteristic data of time sensitivity, importance and data type after preprocessing obtained in S2 are divided by a fuzzy logic algorithm; the time sensitivity is divided into three fuzzy sets of "high time sensitivity", "medium time sensitivity" and "low time sensitivity"; the importance is divided into three fuzzy sets of "high importance", "medium importance" and "low importance"; the data type is divided into real-time monitoring type, state feedback type, control instruction type and statistical analysis type data, and the classified data sets are delivered to the fuzzy rule library.

[0012] Preferably, the fuzzy rule base determines the priority membership by calculating the membership function to divide the data into high priority data, medium priority data and low priority data; the membership function includes a time sensitivity membership function and an importance membership function; the time sensitivity membership function formula is: Where x is the data timestamp, t1 and t2 are the set time thresholds; the importance membership function formula is: Where y is the data evaluation value, i1 and i2 are the set evaluation value thresholds; the priority membership obtained by the membership function output is converted into a priority value through defuzzification for division, and the defuzzification formula is: Where n is the number of fuzzy sets, x i is a fuzzy set element, is the priority membership; the priority value P obtained by i Real-time monitoring, status feedback, control instruction and statistical analysis data are combined to divide the data into high priority data, medium priority data and low priority data.

[0013] Preferably, the divided high-priority data, medium-priority data and low-priority data are classified and modulated; the high-priority data adopts a high-code rate and high-frequency modulation method, and is given high transmission priority and resource guarantee during transmission; the medium-priority data adopts a medium code rate and frequency modulation; the low-priority data is transmitted when network resources are relatively abundant, adopts a low code rate and frequency modulation, and compresses the low-priority data.

[0014] Preferably, in the S3, data transmission, transfer-in and reception are performed through an intermediate layer of heterogeneous network fusion; the data transmission is the transmission of initial data through the Lora network, the data transfer-in is the data transfer from the Lora network into the cable TV network, and the data reception is the data entering the receiving end of the cable TV network; the data transmission is adjusted when the data is transmitted, transferred-in and received through the intermediate layer.

[0015] Preferably, when the Lora network transmits initial data, the beam width of the transmitting and receiving antennas is dynamically adjusted using a beamforming algorithm according to the direction of data transmission and the interference of the surrounding environment through adaptive antenna array technology. The formula is: Where W0 is the initial beam width, W is the adjusted beam width, k is the adjustment coefficient, is the interference source direction vector, is the data transmission direction vector.

[0016] Preferably, when the data is transmitted from the Lora network to the cable TV network, the middle layer uses virtual private network VPN technology to create a secure tunnel for the data; during the VPN establishment process, an encryption key management mechanism is adopted, and the key update frequency is dynamically adjusted according to the priority of the data. For high-priority data, a high-frequency key update mechanism is adopted, otherwise, a medium or low-frequency key update mechanism is adopted.

[0017] Preferably, when the data enters the cable TV network, the data is adjusted through the traffic prediction and resource allocation mechanism of artificial intelligence; the data traffic trend in the future period is predicted by using the long short-term memory network by acquiring historical transmission data and the current network load situation, and the network resource allocation is adjusted according to the prediction result; in the allocation, the large-capacity data is divided into multiple sub-stream data through a multi-channel parallel transmission mechanism, and transmitted through different channels or frequency bands.

[0018] Preferably, the cable TV network uses distributed storage and distributed computing architecture at the receiving end, and stores the data in different storage nodes according to the received data category and processing requirements, and processes the data through a distributed computing engine; for high-priority data, a fast processing algorithm is used to ensure that it is quickly used by the application; for medium-priority data, it is processed in an orderly manner according to the load balancing situation and the urgency of data processing; for low-priority data, batch processing is performed when system resources are idle.

[0019] Compared with the prior art, the technical solution of this application has the following technical effects:

[0020] The present invention deploys edge computing nodes at the data source of the Internet of Things device to perform data preprocessing, and uses operations such as analyzing the correlation between data generation time and real-time to determine time sensitivity, determining the importance of the impact on the Internet of Things, and dividing data types according to source and purpose. This solves the problem that traditional transmission methods cannot effectively identify and classify large-capacity data features, and obtains a technical effect that can provide basic data information for subsequent precise transmission and make data processing more targeted, thereby improving the orderliness and rationality of the entire data transmission system.

[0021] The present invention uses a fuzzy logic algorithm in combination with an established fuzzy rule base to classify data, and divides high, medium and low priority data according to characteristics such as time sensitivity, importance and data type, thereby solving the problem that data priority was unclear in the previous transmission process and resources could not be reasonably allocated according to the importance of the data, and achieved the technical effect of ensuring priority transmission of important data, rationally utilizing network resources, and reducing delays in key data transmission, thereby improving the reliability and timeliness of Internet of Things data transmission.

[0022] The present invention uses the intermediate layer of heterogeneous network fusion to transmit, transfer and receive data, and uses adaptive antenna array technology to dynamically adjust the transmitting and receiving antenna beam widths according to the data transmission direction and interference conditions when the Lora network transmits initial data, thereby solving the problems of poor transmission stability and susceptibility to interference of the Lora network in complex environments, and achieving the technical effects of enhancing the anti-interference ability of Lora network transmission, ensuring the stability of the data transmission link, and improving the success rate of data transmission, effectively improving the quality of IoT data in the wireless transmission link.

[0023] The present invention adopts virtual private network VPN technology when data is transferred from the Lora network to the cable TV network and dynamically adjusts the encryption key update frequency according to the data priority, and uses artificial intelligence traffic prediction and resource allocation mechanism to make adjustments when the data enters the cable TV network, thereby solving the security risks of data in the process of heterogeneous network conversion and the unreasonable allocation of network resources, and achieving the technical effects of ensuring data transmission security, improving network resource utilization efficiency, and realizing smooth transition and efficient transmission of data between different networks, which effectively promotes the security and efficiency of heterogeneous network fusion transmission of the Internet of Things.

[0024] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application so that it can be implemented in accordance with the contents of the specification, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following is a detailed description of the preferred embodiments of the present application in conjunction with the accompanying drawings as follows.

[0025] Based on the detailed description of the specific embodiments of the present application in combination with the accompanying drawings below, those skilled in the art will become more aware of the above and other objects, advantages and features of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings without creative work. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual scale.

[0027] Figure 1 , a flow chart of a method for transmitting large-capacity data of the Internet of Things by connecting Lora with a cable TV network;

[0028] Figure 2 , a large-capacity data partitioning structure diagram of the Internet of Things connecting Lora and cable TV network in the present invention. DETAILED DESCRIPTION

[0029] To make the purpose, technical scheme and advantages of the embodiment of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the present application. Obviously, the described embodiment is a part of the embodiment of the present application, rather than all of the embodiments. In the following description, specific details such as specific configuration and components are provided only to help fully understand the embodiments of the present application. Therefore, it should be clear to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. In addition, for clarity and brevity, the description of known functions and structures is omitted in the embodiment.

[0030] It should be understood that the references to "one embodiment" or "this embodiment" throughout the specification mean that the specific features, structures, or characteristics associated with the embodiment are included in at least one embodiment of the present application. Therefore, the references to "one embodiment" or "this embodiment" appearing throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0031] In addition, the present application may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplicity and clarity, and does not in itself indicate the relationship between the various embodiments and / or settings discussed.

[0032] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist at the same time. The term " / and" in this article describes another type of association object relationship, indicating that there can be two relationships. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the previous and next associated objects are in an "or" relationship.

[0033] The term "at least one" in this article is merely a description of the association relationship of associated objects, indicating that there may be three relationships. For example, at least one of A and B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0034] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusions.

[0035] Example 1

[0036] This embodiment mainly describes a method for transmitting large-capacity data of the Internet of Things in communication between Lora and a cable TV network. Figure 1 As shown, the following steps are included:

[0037] S1. Deploy edge computing nodes at the data source of IoT devices to pre-process large-capacity data;

[0038] S2. Classify the pre-processed data using fuzzy logic algorithm, and make judgments through the established fuzzy rule base to divide the large-capacity data into high, medium and low priority data;

[0039] S3. Establish an intermediate layer between the heterogeneous network fusion Lora and the cable TV network, and adjust the transmission of large-capacity data through the intermediate layer.

[0040] Furthermore, in S1, preprocessing is used to determine the time sensitivity of edge computing nodes by analyzing the correlation between data generation time and real-time, determine the importance of the impact on the Internet of Things, and divide data types according to source and purpose; time sensitivity, importance and data type in large-capacity data are determined through preprocessing.

[0041] Furthermore, the characteristic data of time sensitivity, importance and data type obtained after preprocessing in S2 are divided through fuzzy logic algorithm; time sensitivity is divided into three fuzzy sets of "high time sensitivity", "medium time sensitivity" and "low time sensitivity"; importance is divided into three fuzzy sets of "high importance", "medium importance" and "low importance"; data type is divided into real-time monitoring, state feedback, control instruction and statistical analysis data, and the classified data sets are sent to the fuzzy rule base.

[0042] Furthermore, the fuzzy rule base determines the priority membership by calculating the membership function to divide the data into high priority data, medium priority data and low priority data; the membership function includes the time sensitivity membership function and the importance membership function; the time sensitivity membership function formula is: Where x is the data timestamp, t1 and t2 are the set time thresholds; the importance membership function formula is: Where y is the data evaluation value, i1 and i2 are the set evaluation value thresholds; the priority membership output by the membership function is obtained and converted into priority values ​​through defuzzification for division. The defuzzification formula is: Where n is the number of fuzzy sets, x i is a fuzzy set element, is the priority membership; the priority value P obtained by iReal-time monitoring, status feedback, control instruction and statistical analysis data are combined to divide the data into high priority data, medium priority data and low priority data.

[0043] Furthermore, the high-priority data, medium-priority data and low-priority data are classified and modulated after division; high-priority data is modulated with high bit rate and high frequency, and is given high transmission priority and resource guarantee during transmission; medium-priority data is modulated with medium bit rate and frequency; low-priority data is transmitted when network resources are relatively abundant, with low bit rate and frequency modulation, and the low-priority data is compressed.

[0044] Furthermore, in S3, data transmission, transfer and reception are performed through the intermediate layer of heterogeneous network fusion; data transmission is the transmission of initial data through the Lora network, data transfer is the transfer of data from the Lora network to the cable TV network, and data reception is the data entering the receiving end of the cable TV network; data transmission is adjusted when data is transmitted, transferred and received through the intermediate layer.

[0045] Furthermore, when the Lora network transmits initial data, it uses the adaptive antenna array technology to dynamically adjust the beam width of the transmitting and receiving antennas according to the direction of data transmission and the interference of the surrounding environment using the beamforming algorithm. The formula is: Where W0 is the initial beam width, W is the adjusted beam width, k is the adjustment coefficient, is the interference source direction vector, is the data transmission direction vector.

[0046] Furthermore, when data is transferred from the Lora network to the cable TV network, the middle layer uses virtual private network VPN technology to create a secure tunnel for the data; during the VPN establishment process, an encryption key management mechanism is adopted, and the key update frequency is dynamically adjusted according to the priority of the data. For high-priority data, a high-frequency key update mechanism is adopted, otherwise, a medium or low-frequency key update mechanism is adopted.

[0047] Furthermore, when data enters the cable TV network, the data is adjusted through the traffic prediction and resource allocation mechanism of artificial intelligence; by obtaining historical transmission data and current network load conditions, the long short-term memory network is used to predict the data traffic trend in the future, and the network resource allocation is adjusted according to the prediction results; during the allocation, the large-capacity data is divided into multiple sub-stream data through a multi-channel parallel transmission mechanism, and transmitted through different channels or frequency bands.

[0048] Furthermore, the cable TV network uses distributed storage and distributed computing architecture at the receiving end. According to the received data category and processing requirements, the data is stored in different storage nodes and processed through a distributed computing engine. For high-priority data, a fast processing algorithm is used to ensure that it is quickly used by applications. For medium-priority data, it is processed in an orderly manner according to the load balancing situation and the urgency of data processing. For low-priority data, batch processing is performed when system resources are idle.

[0049] This embodiment describes in detail that the present application performs data preprocessing by setting up edge computing nodes at the data source, accurately analyzes data characteristics, and uses fuzzy logic algorithms and fuzzy rule bases to classify and prioritize data, thereby ensuring fast and reliable transmission of high-priority data such as emergency control instructions; the adaptive antenna technology of the heterogeneous network fusion middle layer and the Lora network realizes intelligent resource allocation and multi-channel transmission optimization efficiency, and promotes the efficient operation of IoT data transmission in all aspects.

[0050] Based on Example 1, the time sensitivity and importance of large-capacity data are obtained. In determining the time sensitivity, the edge computing node will deeply analyze the relationship between the data generation time and real-time performance. For data with strict time requirements, such as the following preferred example, for sensor data on certain industrial automation production lines, if it is not processed for a very short time, resulting in interruption of the production process or damage to product quality, the node will make a judgment based on the system preset time window. For example, in a high-precision machining process, if the temperature monitoring data of a key component needs to be updated every 10 milliseconds, once new data is not obtained for more than 15 milliseconds, it will be marked as high time sensitivity; for some environmental monitoring data, although instantaneous response is not required, it must be updated within a certain time interval (such as 1 hour) to reflect the trend of environmental changes. This type of data is classified as medium time sensitivity; and some statistical historical data, whose timeliness requirements are relatively loose, can be processed within a few days or even weeks, and belong to low time sensitivity; by analyzing the time characteristics of different data sources, the time sensitivity level of data is divided;

[0051] In terms of determining importance, the data is differentiated based on the degree of impact on the key operating links and decision-making processes of the IoT system. Data that directly affects system security and core functions is high-importance data, data that has a certain effect on auxiliary functions is medium-importance data, and data that has little impact on system operation is low-importance data.

[0052] This embodiment introduces in detail how to obtain time sensitivity and importance by giving preferred examples. By distinguishing the time sensitivity and importance of data and accurately defining time sensitivity, it can ensure that data with high time sensitivity can be processed and transmitted as soon as possible to avoid losses due to delays. As for importance, it can ensure that data that plays a key role in the core links of the Internet of Things system, such as key parameters related to equipment safety, obtains sufficient resources and attention, effectively improving the overall reliability and operating efficiency of the system.

[0053] Based on Example 1, a large volume of data types are obtained and classified, and the data priority is changed in combination with time sensitivity and importance, specifically:

[0054] The data types obtained after preprocessing are divided into real-time monitoring, state feedback, control instruction and statistical analysis data through fuzzy logic algorithm;

[0055] For real-time monitoring data, if the calculation result of its time sensitivity membership function shows high time sensitivity and the calculation result of its importance membership function shows high importance, it will be determined as high priority data in the fuzzy rule base; if the time sensitivity membership is medium or below and the importance membership is low importance, it will be determined as low priority data, but when the key indicators of the real-time monitoring data show abnormal changes, the priority enhancement mechanism will be triggered to ensure that it will receive higher priority in subsequent transmission;

[0056] For status feedback data, the priority is determined based on the importance and time sensitivity of the status feedback. If the feedback is about the core status and the time sensitivity reaches the set high threshold, it is classified as high priority data in the fuzzy rule base; if it is general status feedback and the time sensitivity and importance are both at a low level, it is processed according to the conventional priority classification and transmission process;

[0057] For control instruction data, due to its direct impact on system operation, the default importance is high. If the result of its time-sensitive membership function calculation is high, it is directly determined as high-priority data in the fuzzy rule base, and a high-bitrate and high-frequency modulation method and high-priority transmission guarantee measures are adopted during transmission; for non-urgent conventional control instructions, their priority and transmission parameters are reasonably adjusted in combination with the current data transmission load and network resource status of the system;

[0058] For statistical analysis data, since it is mainly used for long-term system performance analysis and trend research, its importance is relatively low. It is usually classified as low-priority data in the fuzzy rule base and transmitted during periods of abundant network resources. Moderate data compression can be performed to improve transmission efficiency. However, when the system initiates a specific in-depth analysis task and this type of data plays a key supporting role in the analysis results, its priority can be increased accordingly based on the urgency and importance of the analysis task.

[0059] This embodiment determines the priority of data types based on the classification of time sensitivity and importance, and explains the specific rules of priority determination and subsequent processing of different data types in the fuzzy rule base, making full use of the role of data type classification.

[0060] Based on Example 1, when large-capacity data is transferred from the Lora network to the cable TV network according to the priority order, the middle layer implements encrypted transmission of the data, specifically including:

[0061] When data is transferred from the Lora network to the cable TV network, the middle layer uses virtual private network VPN technology to create a secure tunnel. At the connection point between the Lora network and the cable TV network, the middle layer starts the VPN protocol and wraps the original data in a secure protocol header through encryption and encapsulation operations; such as using the IPsec protocol to encapsulate the data and add a new IP header, so that the source address and destination address of the data are hidden during transmission, and the external network cannot directly spy on the true source and destination of the data; at the same time, VPN technology will establish a logical dedicated channel on the transmission path, which is based on the Internet infrastructure but independent of ordinary network traffic; by configuring VPN policies on the routers and gateway devices in the middle layer, the passing data packets are screened and guided to ensure that only authorized data that complies with VPN rules can enter this secure tunnel, thereby ensuring the security and privacy of data in the process of transferring from the Lora network to the cable TV network, and preventing data from being stolen or tampered with during transmission.

[0062] During the VPN establishment process, the middle layer generates one or more key pairs, including public keys and private keys. Before data transmission, the sender uses the receiver's public key to encrypt the data, and the receiver uses its own private key to decrypt. If the RSA algorithm is used to generate the key pair, it has high security. At the same time, in order to ensure the security of the key, a key exchange protocol, such as the Diffie-Hellman protocol, is used to exchange keys securely in an insecure network environment. In addition, the middle layer will also set up a key storage and backup mechanism to prevent the loss of keys from causing data to be unable to be decrypted. And the key will be verified and updated regularly to ensure the integrity and validity of the key. Through these measures, a strict encryption system is built to ensure the secure transmission of data in the VPN tunnel.

[0063] The key update frequency is dynamically adjusted according to the priority of the data. For high-priority data, a high-frequency key update mechanism is adopted due to its importance and sensitivity. For example, in the transmission process of financial transaction data or key instructions of industrial control systems, the key is updated every few minutes or even shorter. This is because once the key of high-priority data is cracked, it will cause huge economic losses or system failures. For medium-priority data, such as general enterprise business data, the update frequency can be appropriately reduced to once every few hours, because its importance is relatively low, but it still requires certain security guarantees. For low-priority data, such as some non-critical equipment log data, the update frequency can be lower, updated once a day or several days, while ensuring a certain security, avoiding the additional computing and network resource consumption caused by frequent key updates, and realizing differentiated security guarantees based on data priority.

[0064] This embodiment describes in detail the encryption of priority data, thereby protecting the data, and at the same time uses the key update frequency to implement different frequency updates for data of different priorities, thereby avoiding additional calculations and network resource consumption caused by frequent key updates.

[0065] Based on Example 1, when large-capacity data enters the cable TV network according to the priority order, the middle layer implements data adjustment, specifically including:

[0066] When using the long short-term memory network (LSTM) to predict data traffic trends, a large amount of historical transmission data is obtained. The data covers data traffic information in different time periods, different business types, and different network load conditions. These data are pre-processed and input into the LSTM network. The LSTM network captures the time series characteristics and long-term dependencies in the data through the gating structure. Through the learning and training of historical data, the changing patterns of data traffic on different time scales are analyzed, such as daily, weekly or monthly traffic peak and trough periods. At the same time, combined with the current network load situation, it can update the prediction model in real time, thereby more accurately predicting the data traffic trend in the future period;

[0067] Adjust network resource allocation based on the data traffic trend predicted by the LSTM network. When a traffic peak is predicted in a certain period in the future, increase the bandwidth resources allocated to high-priority data in advance to ensure that its transmission is not affected. For example, for industrial control instruction data with extremely high real-time requirements, sufficient bandwidth will be reserved to ensure its fast transmission; for medium-priority data, its transmission rate and resource allocation ratio will be appropriately adjusted according to the traffic situation to ensure a certain transmission efficiency while avoiding excessive resource occupation; for low-priority data, its transmission bandwidth may be further restricted during peak traffic periods, and even the transmission of some low-priority data may be suspended, giving priority to high- and medium-priority data. During the trough period of traffic, some resources can be appropriately released to low-priority data to achieve dynamic balance and efficient use of network resources.

[0068] In the multi-channel parallel transmission mechanism, large-capacity data will first be segmented according to certain rules. For example, it can be divided into multiple sub-stream data according to the type, priority or order of data blocks. During the segmentation process, the integrity and independence of each sub-stream data must be ensured so that the original data can be accurately restored later. Then, according to the channel or frequency band resources of the cable TV network, these sub-stream data are allocated to different channels or frequency bands for transmission. During the allocation process, factors such as the bandwidth, interference and data priority of the channel or frequency band will be considered; for high-priority sub-stream data, it will be preferentially allocated to high-quality channels or frequency bands with wider bandwidth and less interference; while low-priority sub-stream data may be allocated to relatively narrow or slightly more interference channels or frequency bands that still meet the transmission requirements. At the same time, during the transmission process, each sub-stream data will be monitored and managed to ensure that they can be accurately and efficiently transmitted to the destination, and correctly reorganized and restored at the receiving end, so as to achieve fast and reliable transmission of large-capacity data.

[0069] This embodiment describes in detail how to use a long short-term memory network to predict traffic trends, which can reasonably plan resources for data of different priorities in advance, ensuring that key information can be smoothly transmitted during peak hours to avoid congestion; at the same time, the multi-channel parallel transmission mechanism effectively improves the transmission efficiency of large-capacity data and reduces the risk of data loss. Through resource allocation and efficient transmission mode, the data processing capability of the Internet of Things system in the cable TV network is enhanced to ensure stable and efficient operation of the system.

[0070] Based on Example 1, the cable TV network uses a distributed storage and distributed computing architecture at the receiving end, according to the type of received data and processing requirements, specifically including:

[0071] In the distributed storage architecture of the cable TV network receiving end, efficient data management is achieved according to data categories and processing requirements. When data flows in, it is classified and identified according to pre-set rules. For example, for a large amount of image data of video surveillance, it will be subdivided according to time sequence, monitoring area and other factors. Then, these data are dispersed and stored on different storage nodes. These nodes can be storage units located in different geographical locations or different hardware devices. Through this distributed storage, the risk of data loss caused by single point failure is effectively avoided, and the availability and reliability of data are improved. Even if a storage node has a problem, other nodes still save part of the data, ensuring the integrity of the data of the entire system. In addition, when retrieving data, the storage node where the required data is located can be quickly located through distributed indexing technology, which greatly improves the efficiency of data query.

[0072] When processing data, the distributed computing architecture uses a fast processing algorithm for high-priority data. At the moment of data reception, the algorithm will quickly identify and extract key information, discard irrelevant or redundant data parts, and reduce the amount of calculation; when high-priority data arrives, such as fault alarm data in the power system, the distributed computing engine will immediately start a fast processing algorithm specifically for this type of data, complete complex computing tasks in a very short time, and ensure that the data can be quickly used by the application program so that timely countermeasures can be taken. During the calculation process, the distributed computing engine will dynamically allocate computing resources and concentrate superior computing power to process high-priority data, ensuring its efficient and timely processing and avoiding serious consequences due to processing delays.

[0073] For medium-priority data, it is processed in order according to the load balancing situation and the urgency of data processing. In a distributed computing environment, the load of each computing node is monitored in real time. When medium-priority data is received, such as general equipment performance monitoring data, the busyness of the computing nodes in the current network will be evaluated first. If a node has a light load and the processing urgency of the data is high, the data will be assigned to this node for processing first. At the same time, according to the type of data and processing logic, the processing order is reasonably arranged to ensure that the data is processed in a certain priority order, which not only ensures the timeliness of data processing, but also makes full use of computing resources, and realizes efficient and stable operation of the system.

[0074] For low-priority data, batch processing when resources are idle is an effective strategy to optimize resource utilization. Low-priority data, such as some daily equipment operation log data, does not need to be processed in real time. The distributed computing architecture will continuously monitor the use of system resources. When it detects that the system has a lot of idle computing resources and storage resources, it will start the batch processing program for low-priority data. In the processing process, some batch processing algorithms are used to uniformly analyze, compress and store a large amount of low-priority data, making full use of idle system resources and improving resource utilization. It can also prevent low-priority data from occupying too much network bandwidth and computing power during transmission and processing, ensuring that the processing efficiency of high-priority and medium-priority data is not affected.

[0075] This embodiment describes the distributed storage and distributed computing architecture in detail. Distributed storage disperses data storage according to data characteristics, reduces the risk of data loss, improves data availability and reliability, and makes retrieval more efficient. Distributed computing flexibly processes data of different priorities. High-priority data is processed quickly to ensure real-time performance; medium-priority data is processed in an orderly manner according to load balancing to optimize resource utilization; low-priority data is processed in batches when idle to avoid resource waste, thereby enhancing the data processing capability and stability of the cable TV network receiving end.

[0076] The above are only preferred embodiments of the present invention, which do not limit the scope of protection of the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any changes, modifications, replacements, integrations and parameter changes to these embodiments within the spirit and principles of the present invention through conventional substitutions or without departing from the principles and spirit of the present invention fall within the scope of protection of the present invention.

Claims

1. A method for transmitting large-capacity data of the Internet of Things by connecting Lora and a cable TV network, characterized in that: The following steps are involved: S1. Deploy edge computing nodes at the data source of IoT devices to pre-process large-capacity data; S2. Classify the pre-processed data using fuzzy logic algorithm, and make judgments through the established fuzzy rule base to divide the large-capacity data into high, medium and low priority data; S3. Establish an intermediate layer between the heterogeneous network fusion Lora and the cable TV network, and adjust the transmission of large-capacity data through the intermediate layer.

2. The method for transmitting large-capacity data of the Internet of Things by connecting Lora and a cable TV network according to claim 1, characterized in that: In the S1, the edge computing node determines the time sensitivity by analyzing the correlation between the data generation time and real-time through preprocessing, determines the importance of the impact on the Internet of Things, and divides the data type according to the source and purpose; The preprocessing determines the time sensitivity, importance and data type in the large volume of data.

3. The method for transmitting large-capacity data of the Internet of Things by connecting Lora and a cable TV network according to claim 2, characterized in that: The characteristic data of time sensitivity, importance and data type obtained in S2 after preprocessing are divided by fuzzy logic algorithm; the time sensitivity is divided into three fuzzy sets of "high time sensitivity", "medium time sensitivity" and "low time sensitivity"; the importance is divided into three fuzzy sets of "high importance", "medium importance" and "low importance"; the data type is divided into real-time monitoring type, state feedback type, control instruction type and statistical analysis type data, and the classified data sets are transmitted to the fuzzy rule library.

4. The method for transmitting large-capacity data of the Internet of Things by connecting Lora and a cable TV network according to claim 3 is characterized in that: The fuzzy rule base determines the priority membership by calculating the membership function to divide the data into high priority data, medium priority data and low priority data; the membership function includes a time sensitivity membership function and an importance membership function; the time sensitivity membership function formula is: Where x is the data timestamp, t1 and t2 are the set time thresholds; the importance membership function formula is: Where y is the data evaluation value, i1 and i2 are the set evaluation value thresholds; the priority membership obtained by the membership function output is converted into a priority value through defuzzification for division, and the defuzzification formula is: Where n is the number of fuzzy sets, x i is a fuzzy set element, is the priority membership; the priority value P obtained by i Real-time monitoring, status feedback, control instruction and statistical analysis data are combined to divide the data into high priority data, medium priority data and low priority data.

5. The method for transmitting large-capacity data of the Internet of Things by connecting Lora and a cable TV network according to claim 4, characterized in that: After the division, the high priority data, medium priority data and low priority data are classified and modulated; the high priority data adopts a high code rate and high frequency modulation method, and is given high transmission priority and resource guarantee during the transmission process; the medium priority data adopts a medium code rate and frequency modulation; the low priority data is transmitted when network resources are relatively abundant, adopts a low code rate and frequency modulation, and compresses the low priority data.

6. The method for transmitting large-capacity data of the Internet of Things by connecting Lora and a cable TV network according to claim 1, characterized in that: In the S3, data transmission, transfer and reception are performed through the intermediate layer of heterogeneous network fusion; the data transmission is the transmission of initial data through the Lora network, the data transfer is the data transfer from the Lora network to the cable TV network, and the data reception is the data entering the receiving end of the cable TV network; the data transmission is adjusted when the data is transmitted, transferred and received through the intermediate layer.

7. The method for transmitting large-capacity data of the Internet of Things by connecting Lora and a cable TV network according to claim 6, characterized in that: When the Lora network transmits initial data, it uses the adaptive antenna array technology to dynamically adjust the beam width of the transmitting and receiving antennas according to the direction of data transmission and the interference of the surrounding environment using the beamforming algorithm. The formula is: Where W0 is the initial beam width, W is the adjusted beam width, k is the adjustment coefficient, is the interference source direction vector, is the data transmission direction vector.

8. The method for transmitting large-capacity data of the Internet of Things by connecting Lora and a cable TV network according to claim 6, characterized in that: When the data is transmitted from the Lora network to the cable TV network, the middle layer uses virtual private network VPN technology to create a secure tunnel for the data; during the VPN establishment process, an encryption key management mechanism is adopted, and the key update frequency is dynamically adjusted according to the priority of the data. For high-priority data, a high-frequency key update mechanism is adopted, otherwise, a medium or low-frequency key update mechanism is adopted.

9. The method for transmitting large-capacity data of the Internet of Things by connecting Lora and a cable TV network according to claim 6, characterized in that: When the data enters the cable TV network, the data is adjusted through the traffic prediction and resource allocation mechanism of artificial intelligence; by obtaining historical transmission data and current network load conditions, the long short-term memory network is used to predict the data traffic trend in the future, and the network resource allocation is adjusted according to the prediction results; in the allocation, a multi-channel parallel transmission mechanism is used to divide the large-capacity data into multiple sub-stream data, and transmit them through different channels or frequency bands.

10. The method for transmitting large-capacity data of the Internet of Things by connecting Lora and a cable TV network according to claim 9, characterized in that: The cable TV network uses distributed storage and distributed computing architecture at the receiving end, and stores the data in different storage nodes according to the received data category and processing requirements, and processes the data through a distributed computing engine; for high-priority data, a fast processing algorithm is used to ensure that it is quickly used by applications; for medium-priority data, it is processed in an orderly manner according to the load balancing situation and the urgency of data processing; for low-priority data, batch processing is performed when system resources are idle.

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