Base station equipment management system based on Internet of Things technology

Abstract

According to the base station equipment management system based on the Internet of Things technology, self-adaptive compatibility of multiple mainstream Internet of Things protocols is achieved through the self-adaptive protocol adaptation gateway, and flexibility is high; a clustering multiple access mechanism is adopted, a TDMA and FDMA hybrid multiple access technology is combined, and a conflict detection and avoidance mechanism is matched, so that channel blockage and a conflict rate of simultaneous access of multiple terminals are remarkably reduced; a layered authentication mode is adopted to replace a traditional single-terminal one-by-one verification mode, so that the terminal access success rate is improved; through a roaming authentication cooperation module and a distributed authentication information sharing center, real-time synchronization of authentication information between adjacent base station networks is realized, and a roaming authentication fracture problem is avoided. The authentication interaction process is optimized, the interaction frequency is reduced, a low-power-consumption scheduling mechanism is matched, and the terminal authentication calculation energy consumption and the continuous to-be-authenticated energy consumption are reduced; unified management of the terminal and the base station network is realized through the equipment management platform, and the operation and maintenance management efficiency of the system is improved in combination with a visual interface.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things device management and communication, and in particular to a base station device management system based on Internet of Things technology. BACKGROUND

[0002] With the rapid development of Internet of Things technology, the demand for massive terminal devices accessing the network is growing, and Internet of Things technology, as the core hub of terminal access, plays a crucial role in the base station device management system, directly determining the system's running efficiency and stability. The existing base station device management system based on Internet of Things technology has many defects in actual application, mainly in the following aspects: (1) Insufficient protocol adaptation flexibility: the existing technology mostly adopts a fixed protocol adaptation mode, which is difficult to compatible with different manufacturers and different types of terminal communication protocols, resulting in poor terminal access compatibility and limited system scalability; (2) High terminal access conflict rate: using polling authentication or random access mechanism, terminals need to queue and wait for response when accessing the network, and when multiple terminals send authentication requests at the same time, it is easy to cause channel congestion and significantly increase the access conflict rate; (3) Low success rate of massive terminal access: the authentication process adopts a single-terminal verification mode, and when massive terminals access densely, the network processing pressure increases dramatically, resulting in long access delay and even access failure, and low access success rate; (4) Cross-base station roaming authentication break: when terminals roam between different base station network coverage areas, the existing system lacks an effective cross-base station authentication information sharing mechanism, and needs to perform a complete authentication process again, which causes authentication break and affects communication continuity; (5) Shortened terminal endurance: due to the long time-consuming of terminal access authentication, terminals need to maintain high-power communication state, resulting in rapid consumption of battery energy and significantly shortening the terminal device battery endurance time.

[0003] In view of the above problems, there is currently no base station device management scheme that can fully consider protocol adaptation flexibility, low access conflict, high access success rate, continuous roaming authentication and low power consumption, therefore, it is urgent to design a new base station device management system based on Internet of Things technology to solve the defects of the existing technology. SUMMARY

[0004] The present application aims to overcome the defects of the prior art base station device management system, such as inflexible protocol adaptation, high access conflict rate, low success rate of massive terminal access, cross-base station roaming authentication break and short terminal endurance, and provides a base station device management system based on Internet of Things technology with adaptive protocol adaptation capability, low conflict access, efficient authentication, continuous roaming and low power consumption.

[0005] To achieve the above object, the technical scheme adopted by the present application is: The present application provides a base station equipment management system based on Internet of Things technology, comprising a terminal layer, a base station layer and a core network layer, the base station layer being in communication connection with the terminal layer and the core network layer respectively; The terminal layer comprises a plurality of Internet of Things terminals for data interaction, each Internet of Things terminal being internally provided with a lightweight authentication module and a protocol adaptation proxy module; The base station layer is used for receiving the sensing data collected by the terminal and forwarding it to the core network layer, and also for issuing control instructions from the core network layer to the terminal; The base station layer comprises an adaptive protocol adaptation gateway for realizing multi-protocol adaptive compatibility, a clustering multiple access management module for optimizing terminal access mechanism, a hierarchical authentication module for realizing efficient terminal authentication, a roaming authentication collaboration module for realizing continuity of cross-base station network roaming authentication, and an energy consumption optimization module for reducing terminal energy consumption; The core network layer is used for receiving information transmitted by the base station layer to generate control instructions and global scheduling strategies, and also for performing full life cycle management on the terminal layer and the base station layer; the core network layer comprises a distributed authentication information sharing center and a device management platform.

[0006] As a preference, the adaptive protocol adaptation gateway comprises a protocol identification unit, a protocol conversion unit and a dynamic configuration unit; The protocol identification unit is used for analyzing and identifying the communication protocol type of the access terminal based on deep packet inspection technology; The protocol conversion unit is used for converting data packets of different protocols into a unified intermediate protocol format within the base station network, so as to realize standardization of data interaction; The dynamic configuration unit is used for remotely updating protocol adaptation rules, generating corresponding adaptation rules according to the received unknown protocol feature template, and transmitting the adaptation rules to the core network layer for issuing configuration parameters.

[0007] Among them, analyzing and identifying the communication protocol type of the access terminal based on deep packet inspection technology comprises: The network interface of the base station is used to capture the access request data packet sent by the terminal in real time, and record the basic transmission information such as transmission time sequence, source address and destination address of the data packet; A protocol hierarchical analysis mechanism is adopted to analyze the data link layer frame header, network layer and application layer header of the data packet respectively, and extract the time sequence feature and protocol feature; A protocol classification model is constructed based on the random forest algorithm combined with the LSTM algorithm, the time sequence feature and the protocol feature are taken as inputs, and the protocol type is determined through multiple rounds of branch matching data packet; The data packets that are not matched successfully are subjected to clustering analysis based on a DBSCAN algorithm, and unknown protocol feature templates are formed according to the feature similarity of the data packets and are transmitted to a dynamic configuration unit.

[0008] As preferred, when capturing the access request data packet sent by the terminal, for the non-standard scene with field variation and incomplete data packet, a CNN algorithm is introduced to input the byte sequence of the data packet as a two-dimensional matrix, capture the local correlation features between the bytes through convolution operation on the two-dimensional matrix, generate a multi-channel feature map, and map the feature map to a one-dimensional feature vector after pooling processing, so as to automatically learn the implicit global features of the protocol data.

[0009] As preferred, the cluster-based multiple access management module comprises a terminal clustering unit, a time slot allocation unit and a conflict detection unit. The terminal clustering unit is configured to divide the Internet of Things terminals in the coverage range into a plurality of terminal clusters according to the geographical position, signal strength and communication frequency of the terminals, and elect a cluster head terminal for each terminal cluster by using a K-means clustering algorithm. The time slot allocation unit is configured to allocate an independent communication frequency band for each terminal cluster by using a TDMA and FDMA hybrid multiple access technology, and allocate a dedicated access time slot for each terminal in the terminal cluster, so as to realize parallel processing of terminal access. The conflict detection unit is configured to monitor the channel state in real time by using a carrier sensing technology, trigger a dynamic time slot adjustment mechanism when an access conflict is detected, and allocate an access time slot for the conflict terminal again, and synchronize a conflict avoidance instruction through the cluster head terminal.

[0010] As preferred, the hierarchical authentication module comprises a base station-cluster head pre-authentication unit, a cluster head-terminal batch authentication unit and an authentication cache unit. The base station-cluster head pre-authentication unit is configured to perform pre-authentication of the base station and the cluster head terminal by using an ECC algorithm, generate a session key and an authentication credential, and the authentication credential comprises a cluster head identifier, a coverage range and a valid time length. The cluster head-terminal batch authentication unit is configured to perform batch identity verification of the terminals in the cluster by the cluster head terminal according to the authentication credential generated by the pre-authentication, and the terminal that passes the verification feeds back an authentication result to the base station through the cluster head terminal. The authentication cache unit is configured to cache the identity information of the terminal that has passed the authentication, and when the terminal accesses again, the cached information can be directly called to complete fast authentication.

[0011] As preferred, the roaming authentication cooperation module comprises an authentication information synchronization unit and a roaming fast access unit. The authentication information synchronization unit realizes real-time synchronization of authentication information between adjacent base station networks in cooperation with a distributed authentication information sharing center of a core network layer, and the authentication information includes terminal identity, authentication state and session key digest; The roaming quick access unit is configured to perform a simplified authentication process based on historical authentication information.

[0012] Preferably, the energy consumption optimization module comprises an authentication duration optimization unit and a low-power scheduling unit, and the energy consumption optimization module works in cooperation with a lightweight authentication module of the terminal layer to reduce terminal energy consumption. The authentication duration optimization unit is configured to optimize the authentication interaction process and reduce the number of authentication interactions between the base station network and the terminal. The low-power scheduling unit is configured to allocate different communication priorities and sleep cycles to the terminal according to the access demand and battery power of the terminal, and the terminal automatically enters a low-power sleep state after authentication is completed, and wakes up only at a preset wake-up time slot or when data transmission is required, thereby reducing the energy consumption of the terminal in a continuous authentication state.

[0013] Preferably, the distributed authentication information sharing center adopts a blockchain technology to build a distributed storage architecture for storing authentication information of each base station, terminal roaming records and protocol adaptation rules, thereby guaranteeing the security and consistency of the authentication information and supporting fast information query and synchronization of each base station. The device management platform is configured to uniformly manage the terminal and the base station network, including terminal state monitoring, base station network parameter configuration, protocol adaptation rule updating and authentication log statistics, and provides a visual management interface to enable operation and maintenance personnel to real-time master the system operation state.

[0014] Preferably, the terminal state is monitored by dynamically collecting multi-dimensional state data, and the multi-dimensional state data includes connection state, transmission quality, energy consumption state, protocol interaction state, hardware state and environment state.

[0015] Compared with the prior art, the present application has the following advantages: (1) High protocol adaptation flexibility: the adaptive protocol adaptation gateway realizes adaptive compatibility of multiple mainstream Internet of Things protocols, supports dynamic update of adaptation rules, and can extend the protocol adaptation type without modifying the hardware, thereby improving the compatibility and expansibility of the system. (2) Low access conflict rate: using a cluster multiple access mechanism, combining TDMA and FDMA hybrid multiple access technology, realizing parallel processing of terminal access, cooperating with conflict detection and avoidance mechanism, significantly reducing the channel congestion and conflict rate of multiple terminals accessing at the same time; (3) High mass terminal access success rate: using a hierarchical authentication mode, through base station-cluster head pre-authentication and cluster head-terminal batch authentication, replacing the traditional single-terminal one-by-one verification mode, reducing the authentication processing pressure of the Internet of Things, and improving the access success rate in the mass terminal dense access scenario; (4) Continuous cross-base station roaming authentication: through the roaming authentication cooperation module and the distributed authentication information sharing center, the real-time synchronization of authentication information between adjacent base station networks is realized, and the terminal only needs to perform a simplified authentication process when roaming, avoiding the problem of roaming authentication breakage, and ensuring the continuity of communication; (5) Long terminal battery life: by optimizing the authentication interaction process and reducing the number of interactions, cooperating with a low-power scheduling mechanism, reducing the terminal authentication calculation energy consumption and continuous authentication energy consumption, significantly prolonging the terminal battery life; (6) Efficient system management: through the device management platform of the core network layer, the terminal is managed uniformly, and combined with the visual interface, the system operation and management efficiency is improved, and the operation and maintenance cost is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a base station equipment management system block diagram based on the Internet of Things technology of the present application; Figure 2 is a protocol identification unit flow chart of the present application; Figure 3 is an implementation process flow chart of the present application. DETAILED DESCRIPTION

[0017] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined invention purpose, the specific implementation, structure, features and effects of the present application are described in detail below in combination with the drawings and preferred embodiments.

[0018] Please refer to Figure 1 The present application provides a base station equipment management system based on Internet of Things technology, which comprises a terminal layer, a base station layer and a core network layer, the base station layer is respectively connected with the terminal layer and the core network layer in communication; The terminal layer comprises a plurality of Internet of Things terminals for data interaction, each Internet of Things terminal is internally provided with a lightweight authentication module and a protocol adaptation agent module; The lightweight authentication module supports working with the hierarchical authentication module of the base station layer to complete efficient and low-power consumption authentication, adopts a lightweight encryption algorithm to process authentication data, and avoids high energy consumption of the terminal due to complex encryption calculation; in cooperation with the hierarchical authentication process, the batch identity verification of the cluster head terminal is accepted, direct one-by-one interaction with the base station is not needed, the authentication efficiency is improved, and the communication power consumption of the terminal is reduced.

[0019] The protocol adaptation agent module is used for realizing protocol compatible adaptation of the terminal and the base station network, that is, adapting the communication protocol (such as LoRa, NB-IoT, Modbus, etc.) of the terminal itself to the intermediate protocol format unified in the base station network; ensuring that the access request, authentication data and the like sent by the terminal can be correctly parsed by the base station network, while successfully receiving the instructions issued by the base station network, realizing smooth data interaction of the terminal and the base station network, and supporting the multi-protocol adaptive compatibility capability of the system.

[0020] The base station layer is configured to receive the sensing data collected by the terminal and forward the sensing data to the core network layer, and to issue control instructions of the core network layer to the terminal. The base station layer includes an adaptive protocol adaptation gateway for realizing multi-protocol adaptive compatibility, a clustering multiple access management module for optimizing the terminal access mechanism, a hierarchical authentication module for realizing efficient terminal authentication, a roaming authentication cooperation module for realizing the continuity of roaming authentication across base station networks, and an energy consumption optimization module for reducing the energy consumption of the terminal. The adaptive protocol adaptation gateway includes a protocol identification unit, a protocol conversion unit and a dynamic configuration unit. The protocol identification unit is configured to parse the communication protocol type of the access terminal based on a deep packet inspection technology, and supports identification of mainstream Internet of Things protocols such as LoRa, NB-IoT, ZigBee and Modbus. As shown in Figure 2 The protocol identification unit is configured to parse the communication protocol type of the access terminal based on a deep packet inspection technology, and supports identification of mainstream Internet of Things protocols such as LoRa, NB-IoT, ZigBee and Modbus. The network interface of the base station is used to capture the access request data packet sent by the terminal in real time, and record the basic transmission information of the data packet, such as transmission time sequence, source address and destination address. For non-standard scenes with field variation and incomplete data packets (such as data packet loss caused by signal attenuation during terminal movement), the CNN algorithm is introduced to input the byte sequence of the data packet as a two-dimensional matrix, capture the local correlation features between bytes through convolution operation on the two-dimensional matrix, generate a multi-channel feature map, and map it to a one-dimensional feature vector after pooling processing, automatically learn the implicit global features of the protocol data, such as the correlation law between fields and the encoding mode, without relying on artificial preset feature templates, and improve the compatible identification capability for non-standard data packets and new and rare protocols.

[0021] Specifically, the introduction of CNN algorithm to identify protocol types includes: Non-standard data packet preprocessing: first, the captured incomplete / variant data packets are preprocessed, and invalid interference data is removed. The incomplete data packets are filled with 0 to complete the fixed length. The filling length is set according to the maximum frame length of the mainstream protocol of Internet of Things, usually 256 bytes, to ensure the uniformity of input data format. At the same time, the byte sequence of the data packet is converted into a numerical sequence, and each byte is mapped to the corresponding ASCII code or hexadecimal numerical value to form a one-dimensional numerical sequence. Sequence to two-dimensional matrix: the one-dimensional numerical sequence is reshaped into a two-dimensional matrix according to a fixed step size. The step size and the block size are set according to the field association of the protocol data. The row / column dimensions of the matrix correspond to the byte position and field segment of the data packet, respectively, and the position association information between the bytes is preserved. CNN model construction and feature learning: a CNN model containing an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer is constructed. The input layer receives the reshaped two-dimensional matrix. The convolutional layer uses multiple size convolution kernels to perform convolution operation on the matrix, captures local correlation features between bytes through local receptive field, and generates multi-channel feature maps. The pooling layer uses maximum pooling to downsample the feature maps, preserves key features, reduces data dimension, and reduces computational complexity. The fully connected layer maps the multi-dimensional features after pooling to a one-dimensional feature vector, automatically learns the implicit global features of the protocol data (such as hierarchical association between fields, overall encoding mode), and does not need to rely on artificial preset feature templates. Model training and optimization: based on the labeled non-standard data packet sample set (including incomplete / variant samples of different protocols, such as LoRa field misplacement samples, NB-IoT frame header missing samples, and new and rare protocol samples), the CNN model is trained. The cross-entropy loss function is used to calculate the prediction error, and the model parameters (such as convolution kernel weight and fully connected layer parameter) are iteratively optimized through the back propagation algorithm until the model recognition accuracy reaches the preset threshold (usually ≥95%). Non-standard protocol identification and output: the preprocessed two-dimensional matrix of non-standard data packets is input into the trained CNN model, and the model outputs the protocol type prediction result and confidence. When the confidence is ≥90%, the protocol type is directly determined. When the confidence is between 70% and 90%, the subsequent captured continuous data packets are verified again. When the confidence is <70%, it is transferred to the DBSCAN clustering process for unknown protocol cluster analysis. For new and rare protocols, the model can be quickly adapted through transfer learning, using the existing protocol training parameters, and only a small amount of new protocol sample fine-tuning is needed to complete the identification and adaptation.

[0022] The protocol layer analysis mechanism is adopted to analyze the data link layer frame header (such as the preamble and the synchronization word of LoRa, the RLC layer frame structure of NB-IoT), the network layer and the application layer header of the data packet respectively, and the timing characteristics and the protocol characteristics are extracted; The protocol characteristics are inherent characteristics of different Internet of Things protocols, for example, the spreading factor and the bandwidth parameter of the LoRa protocol, the cell identification (CI) field of the NB-IoT protocol, the network address field of the ZigBee protocol, the function code (such as 0x03 for reading a holding register and 0x06 for writing a single register) and the CRC check bit of the Modbus protocol, etc. The data packet sent by the Internet of Things terminal has timing continuity, such as the frame sequence of LoRa and the request-response interaction sequence of Modbus. The inherent characteristics and the timing characteristics of the protocol are combined and analyzed to avoid low accuracy of single feature recognition.

[0023] The protocol classification model is constructed based on the random forest algorithm combined with the LSTM algorithm, the timing characteristics and the protocol characteristics are taken as inputs, and the protocol type is determined through multiple rounds of branch matching of the data packet. The random forest algorithm can further integrate the classification results of multiple decision trees to reduce the recognition error caused by single feature mismatch and improve the recognition stability in the mixed access scene of multiple protocols. The LSTM algorithm can capture the timing dependency of the data packet, analyze the feature change of the continuous data packet, solve the recognition ambiguity problem caused by the insufficient features of a single data packet, and is especially suitable for the fragmented data packet recognition of intermittent transmission of low-power terminals.

[0024] The DBSCAN algorithm is used for clustering analysis of the data packets that are not matched successfully, unknown protocol characteristic templates are formed according to the feature similarity (such as the frame length distribution and the field repetition rule) of the data packet, and are transmitted to the dynamic configuration unit.

[0025] Specifically, the formation of the unknown protocol cluster includes: Data preprocessing: the data packets that are not matched successfully are denoised to eliminate the incomplete data packets (data packets with data integrity ≥80%) caused by signal interference and transmission errors, and the key fields of the data packet are standardized to avoid the influence of the order of magnitude difference on the clustering result; Feature extraction: the feature similarity index of the data packet is extracted, in addition to the frame length distribution and the field repetition rule, the field entropy (reflecting the randomness of the field), the frame interval time, the header field length and the check bit type are supplemented, and a multi-dimensional feature vector is constructed; Algorithm parameter configuration: set the core parameters of DBSCAN according to the transmission characteristics of the Internet of Things data packet, wherein the neighborhood radius ε is determined according to the characteristic distance statistical results of the same type of protocol data packet (usually 1.2-1.5 times the standard deviation of the characteristic vector), and the minimum point number MinPts is set according to the sample size of the data packet (5-8 when the sample size is greater than or equal to 100, and 3-5 when the sample size is less than 100); Density clustering process: based on the configured parameters, traverse all unmatched data packets, calculate the Euclidean distance between the characteristic vector of each data packet and other data packets, identify core points (points containing ≥MinPts data packets in the neighborhood ε), boundary points (data packet number < MinPts in the neighborhood ε but falling within the neighborhood of core points) and noise points (neither core points nor boundary points); the core points and boundary points with density reachability are classified into the same cluster, and multiple unknown protocol candidate clusters are formed; Cluster verification and merging: remove the noise clusters with a sample size < 3 (to avoid accidental data interference), calculate the average of the feature similarity of each candidate cluster, and merge the adjacent clusters with a feature similarity average ≥ 0.85 into the final unknown protocol cluster; Cluster feature output: extract the common features of each unknown protocol cluster (such as average frame length, core field repetition period, feature entropy range, etc.), form an unknown protocol feature template, and provide data support for updating protocol adaptation rules for the dynamic configuration unit.

[0026] The protocol conversion unit is configured to convert data packets of different protocols into a unified intermediate protocol format within the base station network, and realize the standardization of data interaction. The dynamic configuration unit is configured to remotely update the protocol adaptation rules, generate corresponding adaptation rules according to the received unknown protocol feature template, and transmit the adaptation rules to the core network layer for configuration parameter distribution.

[0027] It should be noted that when the dynamic configuration unit receives the unknown protocol feature template, it first performs validity verification, filters out effective unknown protocol clusters with stable protocol features, and generates adaptation rules for the unknown protocol cluster features that pass the verification, such as protocol conversion rules (defining the mapping relationship between unknown protocol data packets and the unified intermediate protocol within the base station) and dynamic adaptation parameter rules (configuring parameters such as access time slot priority, communication frequency range, and authentication interaction timing for the protocol). After simulating and verifying the generated adaptation rules and optimizing the parameters, the rules are distributed to all base stations in the network through the device management platform of the core network layer, and the running data uploaded by each base station network is continuously received to monitor the running state and iteratively optimize the rules. When a new terminal protocol type is added, the protocol adaptation extension can be completed by only distributing configuration parameters through the device management platform of the core network layer.

[0028] The cluster-based multiple access management module includes a terminal clustering unit, a time slot allocation unit, and a collision detection unit. The terminal clustering unit is configured to divide the Internet of Things terminals in the coverage range into a plurality of terminal clusters according to the geographical positions, signal strengths and communication frequencies of the terminals, and to elect a cluster head terminal for each terminal cluster; The time slot allocation unit is configured to allocate an independent communication frequency band for each terminal cluster by using a hybrid multiple access technology of TDMA (Time Division Multiple Access) and FDMA (Frequency Division Multiple Access), and to allocate a dedicated access time slot for each terminal in the terminal cluster, so as to realize parallel processing of terminal access; The conflict detection unit is configured to monitor the channel state in real time by using a carrier sensing technology, and to trigger a dynamic time slot adjustment mechanism to re-allocate an access time slot for the conflicting terminals and to synchronize a conflict avoidance instruction through the cluster head terminal when detecting an access conflict.

[0029] The hierarchical authentication module includes a base station-cluster head pre-authentication unit, a cluster head-terminal batch authentication unit and an authentication cache unit; The base station-cluster head pre-authentication unit is configured to perform pre-authentication of the base station and the cluster head terminal by using an ECC algorithm, to generate a session key and an authentication credential, and to include the cluster head identifier, the coverage range and the valid time length in the authentication credential; It should be noted that the ECC (Elliptic Curve Cryptography) algorithm is improved in the embodiment: a lightweight elliptic curve more suitable for Internet of Things terminals is selected, such as an SM2 curve conforming to the national standard, the key length is simplified from the traditional 256bit to 192bit on the premise of ensuring 128bit security strength, the key generation / signature calculation amount of the cluster head is reduced, the traditional ECC "4-round interaction of base station signature→cluster head verification→cluster head signature→base station verification" is simplified to 1-time two-way authentication interaction, that is, the base station sends "its own public key and random challenge value", the cluster head returns "its own public key and challenge value signature" once, the base station can complete the identity verification and key negotiation of the cluster head at the same time, the communication time consumption is reduced, the base station and all cluster heads are pre-configured with the same elliptic curve base parameters, the transmission of parameters is avoided every time the pre-authentication is performed, the terminal storage and bandwidth resources are saved, the ECC key exchange (ECDH) and the authentication process are combined, the session key is derived synchronously in the pre-authentication process, and a separate key negotiation step is not needed, so that the process efficiency is improved.

[0030] The cluster head-terminal batch authentication unit is configured to perform batch identity verification of the terminals in the cluster by the cluster head terminal according to the authentication credential generated by the pre-authentication, and to feed back the authentication result to the base station network by the cluster head terminal for the terminals passing the verification; the base station network does not need to perform authentication interaction with each terminal one by one; In the embodiment, the ECC batch authentication algorithm based on group signature can optimize the batch authentication efficiency of the cluster head terminal to the terminals in the cluster. By assigning a unique group public key to each cluster, the terminal generates a group private key when accessing the cluster. The cluster head terminal only needs to verify the group signature of the cluster terminal (verify the validity of the signature of multiple terminals at a time), without the need for individual interaction, further reducing the number of interactions for authentication of terminals in the cluster.

[0031] The authentication cache unit is configured to cache the terminal identity information that has passed the authentication, so that when the terminal accesses again, the cached information can be directly called to complete the fast authentication.

[0032] The roaming authentication cooperation module comprises an authentication information synchronization unit and a roaming fast access unit. The authentication information synchronization unit cooperates with the distributed authentication information sharing center of the core network layer to realize real-time synchronization of authentication information between adjacent base stations. The authentication information comprises a terminal identity, an authentication state and a session key digest. The roaming fast access unit is configured to perform a simplified authentication process based on the historical authentication information.

[0033] When the Internet of Things terminal roams from the current base station coverage to the adjacent base station coverage, the adjacent base station obtains the historical authentication information of the terminal through the roaming authentication cooperation module, performs a simplified authentication process based on the historical authentication information, and only needs to verify the validity of the session key of the terminal to complete the access authentication, without the need to perform a complete authentication process again.

[0034] The energy consumption optimization module comprises an authentication duration optimization unit and a low-power consumption scheduling unit. The energy consumption optimization module cooperates with the lightweight authentication module of the terminal layer to reduce the energy consumption of the terminal. The authentication duration optimization unit is configured to optimize the authentication interaction process and reduce the number of authentication interactions between the base station network and the terminal. The number of interactions in the traditional single-terminal authentication process is reduced from N times (N is the number of terminals) to N / K times (K is the number of terminal clusters). Specifically, according to the terminal clustering result of the cluster-based multiple access management module, the authentication duration optimization unit causes the base station to perform 1 pre-authentication interaction with the cluster head terminal of each cluster to generate an authentication credential, and then the cluster head terminal performs batch identity verification on all terminals in the cluster based on the authentication credential, without the need for the base station to participate, thereby simplifying the number of interactions. For example, 1000 terminals are divided into 20 clusters. In the traditional single-terminal authentication mode, the base station needs to interact with 1000 terminals 1 time each, for a total of 1000 interactions. After optimization in the embodiment, the base station only needs to interact with 20 cluster heads 1 time each, for a total of 20 interactions, and the number of interactions is directly reduced to 1 / 50 of the original number.

[0035] In addition, the authentication duration optimization unit links the authentication cache unit of the hierarchical authentication module to cache the terminal identity information (such as terminal ID and session key) that has passed the authentication (the validity period can be set to 24 hours). When the terminal accesses again, it does not need to perform the complete authentication process again, but directly calls the cached information to complete the quick access, and the number of interactions is reduced from multiple to zero (no new interaction).

[0036] The low-power scheduling unit is configured to allocate different communication priorities and sleep cycles to terminals according to their access requirements and battery power, and after authentication, the terminal automatically enters a low-power sleep state and wakes up only at a preset wake-up time slot or when data transmission is required, thereby reducing the energy consumption of the terminal in the standby authentication state.

[0037] For example, a smart meter with sufficient power (which transmits data only once a day) is assigned a low communication priority, and the sleep cycle is set to 23 hours and 50 minutes, and it only wakes up to transmit data at a fixed time every day; while a smoke alarm with low power (which needs to be monitored in real time) is assigned a high priority, but it also maintains a low-power sleep state at ordinary times and wakes up immediately only when smoke is detected. After the terminal authentication is completed, it will automatically sleep and does not need to be on standby for messages, greatly reducing the high-power state in the standby authentication state.

[0038] The core network layer is configured to receive the information generated by the base station layer to generate control instructions and global scheduling strategies, and is also configured to manage the terminal layer and the base station layer throughout their life cycles; the core network layer includes a distributed authentication information sharing center and a device management platform.

[0039] It should be noted that the core network layer formulates unified coordination rules based on global information such as the access state of terminals in the entire network, the load of base stations, protocol adaptation requirements, and terminal energy consumption data, so that the resource (channel, computing power) allocation of the entire system is more reasonable and the operation is more efficient, avoiding congestion and excessive energy consumption of local base stations / terminals. The main content includes a base station load balancing scheduling strategy (used to balance the terminal access pressure of different base stations), a clustering parameter dynamic adjustment strategy (based on the distribution density and communication frequency of terminals in the entire network, the clustering rule of the base station is adjusted), a terminal energy consumption differentiation scheduling strategy (combined with the battery power and service requirements of the terminal, the energy consumption parameters are optimized), and a global synchronization strategy for protocol adaptation rules (for unknown protocol clusters generated by DBSCAN, the strategy will uniformly coordinate the protocol adaptation rhythm of the base stations in the entire network).

[0040] The distributed authentication information sharing center uses a distributed storage architecture based on blockchain technology to store the authentication information of each base station, terminal roaming records, and protocol adaptation rules, ensuring the security and consistency of the authentication information, while supporting fast information query and synchronization of each base station; The device management platform is used for unified management of terminals and base station networks, including terminal state monitoring, base station network parameter configuration, protocol adaptation rule updating, authentication log statistics function, while providing a visual management interface, supporting operation and maintenance personnel to master the system running state in real time.

[0041] The terminal state is monitored by dynamically collecting multi-dimensional state data, including connection state, transmission quality, energy consumption state, protocol interaction state, hardware state and environment state; the connection state includes online / offline, signal strength RSSI, signal-to-noise ratio SNR; the transmission quality includes packet loss rate, time delay, retransmission times; the energy consumption state includes battery voltage, remaining power, energy consumption rate; the protocol interaction state includes message format, interaction frequency; the hardware state includes terminal temperature, module working state; the environment state includes terminal deployment environment temperature, humidity.

[0042] A dynamic collection strategy is formulated, and the base station dynamically adjusts the collection frequency according to the terminal type and business demand through an AI engine. For example, for high-change-rate terminals (such as industrial sensors), the collection frequency is 100 ms / time, ensuring that key data is not lost; for medium-change-rate terminals (such as smart home devices), the collection frequency is 1 s / time, balancing data integrity and resource consumption; for low-change-rate terminals (such as water meters and electricity meters), the collection frequency is 5 min / time, reducing resource waste.

[0043] Please refer to Figure 3 The specific workflow of the application is as follows: S1: After the Internet of Things terminal is started, the protocol adaptation proxy module sends an access request to the base station, and the access request contains a terminal identifier and a communication protocol type; S2: The adaptive protocol adaptation gateway at the base station layer parses the protocol type in the access request through a protocol identification unit, completes protocol conversion through a protocol conversion unit, and loads the corresponding adaptation rules through a dynamic configuration unit; S3: The cluster multi-access management module performs clustering processing on the terminal requesting access through a terminal clustering unit, elects a cluster head terminal, and allocates a communication frequency band and an access time slot for each terminal cluster and the terminal within the cluster through a time slot allocation unit; S4: The hierarchical authentication module completes the pre-authentication of the base station and the cluster head terminal through a base station-cluster head pre-authentication unit, generates an authentication credential; the cluster head terminal performs batch authentication on the terminals within the cluster based on the authentication credential through a cluster head-terminal batch authentication unit, and feeds back the authentication result to the base station; S5: The terminal that passes the authentication accesses the system, enters a low-power sleep state according to the sleep period allocated by the energy consumption optimization module; the terminal that fails the authentication is fed back the reason for the authentication failure by the cluster head terminal, and the access authentication process is triggered again; S6: When the terminal roams across base stations, the roaming authentication cooperation module acquires the historical authentication information of the terminal from the distributed authentication information sharing center through the authentication information synchronization unit, and completes the simplified authentication through the roaming quick access unit, thereby realizing the quick access; S7: The device management platform of the core network layer monitors the terminal access state, the base station running parameter and the authentication log in real time, and guarantees the synchronous update of the authentication information between the base stations through the distributed authentication information sharing center.

[0044] The above embodiments only describe the preferred embodiments of the present application, and do not limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements of the technical solutions of the present application made by the ordinary engineering technicians in the art shall fall within the protection scope determined by the claims of the present application.

Claims

1. A base station equipment management system based on Internet of Things (IoT) technology, characterized in that, It includes a terminal layer, a base station layer, and a core network layer, wherein the base station layer is communicatively connected to the terminal layer and the core network layer, respectively. The terminal layer includes several IoT terminals for data interaction, and each IoT terminal has a built-in lightweight authentication module and a protocol adaptation proxy module. The base station layer is used to receive the sensing data collected by the terminal and forward it to the core network layer, and is also used to send the control commands of the core network layer to the terminal; The base station layer includes an adaptive protocol adaptation gateway for achieving multi-protocol adaptive compatibility, a clustered multiple access management module for optimizing terminal access mechanisms, a layered authentication module for achieving efficient terminal authentication, a roaming authentication coordination module for achieving cross-base station network roaming authentication continuity, and an energy consumption optimization module for reducing terminal energy consumption. The core network layer is used to receive information transmitted from the base station layer to generate control commands and global scheduling strategies, and is also used to perform full lifecycle management of the terminal layer and the base station layer; the core network layer includes a distributed authentication information sharing center and a device management platform.

2. The base station equipment management system based on Internet of Things (IoT) technology according to claim 1, characterized in that, The adaptive protocol adaptation gateway includes a protocol identification unit, a protocol conversion unit, and a dynamic configuration unit; The protocol identification unit is used to parse and identify the communication protocol type of the access terminal based on deep packet inspection technology; The protocol conversion unit is used to convert data packets of different protocols into a unified intermediate protocol format within the base station network, thereby achieving standardization of data interaction. The dynamic configuration unit is used to remotely update the protocol adaptation rules, generate corresponding adaptation rules based on the received unknown protocol feature templates, and transmit the adaptation rules to the core network layer for the distribution of configuration parameters.

3. The base station equipment management system based on Internet of Things technology according to claim 2, characterized in that, Based on deep packet inspection technology, the communication protocol types of access terminals are analyzed and identified, including: The network interface of the base station is used to capture access request data packets sent by the terminal in real time, and record basic transmission information such as the transmission sequence, source address, and destination address of the data packets. A protocol layered parsing mechanism is adopted to parse the data link layer frame header, network layer, and application layer header of the data packets respectively, and extract timing features and protocol features. A protocol classification model is constructed based on the random forest algorithm combined with the LSTM algorithm. The protocol type is determined by multi-round branch matching of data packets, using the time-series features and protocol features as input. The DBSCAN algorithm is used to perform cluster analysis on unmatched data packets, and unknown protocol feature templates are formed based on the feature similarity of the data packets and transmitted to the dynamic configuration unit.

4. The base station equipment management system based on Internet of Things technology according to claim 3, characterized in that, When capturing access request data packets sent by the terminal, for non-standard scenarios with field variations and incomplete data packets, a CNN algorithm is introduced to take the byte sequence of the data packet as a two-dimensional matrix input. By performing convolution operations on the two-dimensional matrix, the local correlation features between bytes are captured, generating a multi-channel feature map. After pooling, it is mapped to a one-dimensional feature vector, automatically learning the implicit global features of the protocol data.

5. A base station equipment management system based on Internet of Things (IoT) technology according to claim 1, characterized in that, The clustering multiple access management module includes a terminal clustering unit, a time slot allocation unit, and a conflict detection unit; The terminal clustering unit is used to divide the IoT terminals within the coverage area into several terminal clusters based on the terminal's geographical location, signal strength, and communication frequency using the K-means clustering algorithm, and to elect a cluster head terminal for each terminal cluster. The time slot allocation unit uses a hybrid TDMA and FDMA multiple access technology to allocate an independent communication frequency band to each terminal cluster, and at the same time allocates a dedicated access time slot to each terminal within the terminal cluster to achieve parallel processing of terminal access. The conflict detection unit monitors the channel status in real time using carrier sensing technology. When an access conflict is detected, it triggers a dynamic time slot adjustment mechanism to reallocate access time slots to the conflicting terminals and synchronizes conflict avoidance instructions through the cluster head terminal.

6. A base station equipment management system based on Internet of Things (IoT) technology according to claim 1, characterized in that, The hierarchical authentication module includes a base station-cluster head pre-authentication unit, a cluster head-terminal batch authentication unit, and an authentication cache unit; The base station-cluster head pre-authentication unit uses the ECC algorithm to perform pre-authentication between the base station and the cluster head terminal, and generates a session key and authentication credentials. The authentication credentials include the cluster head identifier, coverage area and validity period. The cluster head-terminal batch authentication unit is used to perform batch identity verification of terminals within the cluster by the cluster head terminal based on the authentication credentials generated by pre-authentication. Terminals that pass the verification report the authentication result to the base station network through the cluster head terminal. The authentication caching unit is used to cache the identity information of the terminal that has been authenticated. When the terminal connects again, it can directly call the cached information to complete the fast authentication.

7. A base station equipment management system based on Internet of Things (IoT) technology according to claim 1, characterized in that, The roaming authentication collaboration module includes an authentication information synchronization unit and a roaming fast access unit; The authentication information synchronization unit, in collaboration with the distributed authentication information sharing center of the core network layer, realizes real-time synchronization of authentication information between adjacent base station networks. The authentication information includes terminal identity identifier, authentication status, and session key digest. The roaming fast access unit is used to perform a simplified authentication process based on historical authentication information.

8. A base station equipment management system based on Internet of Things (IoT) technology according to claim 1, characterized in that, The energy consumption optimization module includes an authentication time optimization unit and a low-power scheduling unit; the energy consumption optimization module works in conjunction with the lightweight authentication module of the terminal layer to reduce terminal energy consumption; The authentication time optimization unit is used to optimize the authentication interaction process and reduce the number of authentication interactions between the base station network and the terminal. The low-power scheduling unit is used to allocate different communication priorities and sleep cycles to the terminal according to the terminal's access requirements and battery power. After authentication, the terminal automatically enters a low-power sleep state and is only woken up in a preset wake-up time slot or when there is a data transmission requirement, thereby reducing the energy consumption of the terminal in the continuous waiting authentication state.

9. A base station equipment management system based on Internet of Things (IoT) technology according to claim 1, characterized in that, The distributed authentication information sharing center uses blockchain technology to build a distributed storage architecture to store authentication information, terminal roaming records and protocol adaptation rules of each base station, ensuring the security and consistency of authentication information, while supporting rapid information query and synchronization of each base station. The device management platform is used for unified management of terminals and base station networks, including terminal status monitoring, base station network parameter configuration, protocol adaptation rule updates, and authentication log statistics. It also provides a visual management interface to support maintenance personnel in monitoring the system's operating status in real time.

10. A base station equipment management system based on Internet of Things (IoT) technology according to claim 9, characterized in that, The terminal status is monitored by dynamically collecting multi-dimensional status data, including connection status, transmission quality, power consumption status, protocol interaction status, hardware status, and environmental status. The connection status includes online / offline status, signal strength, and signal-to-noise ratio. The transmission quality includes packet loss rate, latency, and retransmission count. The power consumption status includes battery voltage, remaining battery power, and power consumption rate. The protocol interaction status includes message format and interaction frequency. The hardware status includes terminal temperature and module operating status. The environmental status includes the temperature and humidity of the terminal deployment environment.

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