Ad-hoc network, monitoring method and topology maintenance method based on power wireless private network

Abstract

The invention provides an ad hoc network based on a power wireless private network, a monitoring method and a maintenance method, and belongs to the technical field of operation and maintenance of the power wireless private network, and the method comprises the following steps: realizing efficient sharing of channel resources of the ad hoc network through channel resource optimization, data frame effective bearing and channel dynamic management; based on time synchronization of the cellular ad hoc network, multi-hop networking and route dynamic updating of the cellular ad hoc network are realized; the local networking scene selection equipment is in a low-power-consumption and miniaturized mode, and the remote blind compensation scene selection equipment is in a mode that the transmitting power can be rapidly and dynamically adjusted; network dynamic monitoring based on heterogeneous network multilayer modeling, heterogeneous network coverage simulation and node position optimization, and node health degree and topology anomaly analysis and early warning; and based on network topology maintenance of network data intelligent acquisition, rapid database retrieval, ad hoc network edge performance improvement, ad hoc network equipment hierarchical management and ad hoc network routing tracking, a flexible ad hoc network of a power wireless private network is constructed, and flexible resource configuration and dynamic network monitoring are realized.

Description

Technical Field

[0001] This invention belongs to the field of power wireless private network operation and maintenance technology, specifically a method for self-organizing network, monitoring and topology maintenance based on power wireless private network. Background Technology

[0002] The field of power wireless private network self-organizing network communication and network monitoring is constantly advancing, but it still faces some major problems: (1) Design challenges of low-energy communication protocols: At present, power wireless private networks mainly focus on the design of low-energy and high-efficiency communication protocols. However, how to maintain a stable communication connection under low energy consumption, and how to minimize communication energy consumption in multi-channel and multi-device scenarios, are still obstacles. (2) Privacy and security issues of network monitoring data: With the continuous development of network monitoring technology, a large amount of real-time data is collected and transmitted, but there is currently a lack of effective data encryption and privacy protection mechanisms, and there may be network attack threats in large-scale data transmission. (3) Insufficient collaborative mechanisms for channel sharing: Power wireless private networks usually face limited spectrum resources, so efficient sharing of channel resources is particularly important. In the current dynamic electromagnetic environment, intelligent spectrum sensing and collaborative sharing have not yet been realized, as well as the problem of spectrum competition and interference between nodes has not been solved. The lack of effective channel resource sharing protocols and mechanisms limits the overall performance of the network. Summary of the Invention

[0003] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0004] A method for self-organizing networks, monitoring, and topology maintenance based on a power wireless private network, comprising:

[0005] Step 1: Through channel resource optimization, effective data frame carrying, and dynamic channel management, efficient sharing of channel resources in the self-organizing network based on unified broadcast signaling is achieved, laying the foundation for the construction of the self-organizing network of the power wireless private network;

[0006] Step 2: Based on the efficient shared self-organizing network channel in Step 1, and combined with the time synchronization of the cellular self-organizing network, multi-hop networking and dynamic route updates of the cellular self-organizing network are realized.

[0007] Step 3: For the multi-hop networking and dynamic routing updates in Step 2, select the corresponding dynamic power control method for the two different application scenarios. For the local networking scenario, select the low power consumption and miniaturization method of the device. For the remote blind spot filling scenario, select the method of the device's transmission power can be quickly and dynamically adjusted.

[0008] Step 4: For the self-organizing network of the power wireless private network constructed in Step 3, dynamic network monitoring is carried out based on heterogeneous network multi-layer modeling, heterogeneous network coverage simulation and node location optimization, node health and topology anomaly analysis and early warning.

[0009] Step 5: Maintain the topology of the self-organizing network based on intelligent acquisition of network data, rapid database retrieval, performance improvement of the self-organizing network edge, hierarchical management of self-organizing network devices, and self-organizing network route tracing.

[0010] Furthermore, channel resource optimization includes efficient channel resource allocation, optimal channel coding rules, and agile channel multiplexing;

[0011] Efficient channel resource allocation includes multi-frame control as well as the fragmentation and reassembly of signaling and data;

[0012] The optimal channel coding rule is selected from RCPC convolutional codes, CRC check codes, and Turbo codes based on the signal-to-noise ratio, bit error rate requirements, and encoding / decoding performance indicators of the application scenario.

[0013] Agile channel multiplexing includes frequency division multiplexing, time division multiplexing, code division multiplexing, space division multiplexing, and channel communication rules for channel access control.

[0014] Furthermore, the effective data frame bearer includes signaling multiplexing, channel information micro-bearer, and initial channel linkage.

[0015] Furthermore, dynamic channel management includes the linkage between hop count and channel resources, as well as channel resource optimization.

[0016] Among them, channel resource optimization is achieved by using or combining channel allocation algorithms, adaptive modulation and coding, cooperative communication and cooperative multiple access, non-orthogonal multiple access, priority scheduling and multipath transmission.

[0017] Furthermore, multi-hop networking includes broadcast signaling agile networking, node information micro-bearer, optimal routing decision and control center self-switching;

[0018] Dynamic route updates include adaptive periodic configuration of network signaling and intelligent waiting for forwarding.

[0019] Furthermore, in local networking scenarios, low power consumption and miniaturization of devices are achieved by selecting low-power CPUs, adjusting CPU operating frequencies, converting static memory to dynamic memory, using efficient storage technologies and low-power I / O devices, limiting program execution time or adjusting CPU sleep cycles, using interrupts to handle control flow, and reducing redundant calculations and memory usage.

[0020] Furthermore, the equipment's transmission power can be quickly and dynamically adjusted, including multi-level transmission and dual-network power adaptation;

[0021] Multi-stage launch includes:

[0022] Multi-level output power is achieved by employing an adjustable power amplifier;

[0023] Radio frequency switches select different power levels by switching signal paths, achieving rapid switching.

[0024] The power control circuit and intelligent control unit monitor the network status in real time and intelligently adjust the transmission power according to communication needs;

[0025] Digital signal processing dynamically adjusts the amplitude of a signal in the digital domain to achieve more precise power control;

[0026] Dynamic power management is used to adjust the supply voltage and current to match the current power demand and achieve higher energy efficiency.

[0027] Analyze the energy consumption distribution at different power levels to provide a reference for optimized design;

[0028] Dual-network power adaptation includes:

[0029] Real-time monitoring of network quality, congestion, and signal strength; adjusting device power levels under different network environments.

[0030] By evaluating the characteristics of the current communication channel in real time, the power level can be adjusted more accurately to adapt to changes in the channel.

[0031] Based on real-time monitoring of network conditions and channel estimation results, the system intelligently adjusts the transmission power to maximize communication quality while reducing power consumption.

[0032] Critical tasks or services should be prioritized during power adjustments;

[0033] Balancing communication performance and power consumption, and making reasonable trade-offs between different power levels to meet data transmission requirements while minimizing power consumption;

[0034] Based on historical data and predictive models, network changes can be anticipated in advance, allowing power to be adjusted more proactively to meet future communication needs.

[0035] Furthermore, multi-layer modeling of heterogeneous networks needs to meet the requirements of data format uniformity, protocol compatibility, and device frequency consistency;

[0036] Node health assessment metrics include connectivity, energy consumption, task execution quality, load balancing, security, latency analysis, environmental awareness, and node health prediction.

[0037] Intelligent data acquisition includes real-time streaming data processing, log file analysis, and power load forecasting.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] 1) A self-organizing network communication protocol based on power cellular private networks is proposed. Based on the redefinition of bit fields in cellular control frames, a method for carrying and forwarding networking and routing frames is designed, and the configuration of the transmission cycle of broadcast signaling is optimized. At the same time, the optimal routing transmission and forwarding strategy is adopted to transmit routes in real time, realizing a routing transmission mechanism with minimal bit field occupation. By utilizing inter-frequency transceiver, the requirement for real-time route updates of dynamic nodes is met. A method for rapid dynamic adjustment of transmit power is proposed to realize flexible blind spot coverage of self-organizing networks based on cellular private networks. A low-power, integrated communication module is designed to realize local self-organizing network applications based on cellular private networks.

[0040] 2) The self-organizing network communication protocol of the power private cellular network carries load, power distribution, management control and data acquisition business technical solutions, forming reliable communication capabilities of local and remote multi-domain networks, realizing the operation of local and remote communication on a single network, and unified management and control;

[0041] 3) Key technologies such as optimization of wireless communication channel resources based on power licensed frequency bands, micro-bearer of data frames, dynamic channel management, agile networking of broadcast signaling, optimal routing decision mechanism, intelligent waiting period mechanism, protocol multi-block joint peak-to-average ratio frame synchronization algorithm, fast synchronization method of local oscillator and external clock, low power consumption, miniaturization, and power adaptive control are implemented to achieve adaptive networking between nodes, real-time updating of routing information based on node location, and finding the optimal path for networking when a node initiates a service; self-scaling, adaptive, and dynamic routing to realize multi-hop power wireless private network self-organizing network; and relying on multi-layer management modeling, data acquisition, and efficient management of self-organizing network equipment to achieve dynamic monitoring and network topology maintenance of self-organizing network. Attached Figure Description

[0042] Figure 1 This is a flowchart of a self-organizing network, monitoring, and topology maintenance method for a power wireless private network according to an embodiment of the present invention;

[0043] Figure 2 This is a schematic diagram of the EPDT protocol architecture according to an embodiment of the present invention;

[0044] Figure 3 This is a schematic diagram of the uplink and downlink data and control burst format according to an embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of the CACH burst format according to an embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of the control signaling frame message structure according to an embodiment of the present invention;

[0047] Figure 6 This is a schematic diagram of the control signaling frame format according to an embodiment of the present invention;

[0048] Figure 7This is a schematic diagram of the two-byte arrangement of user data and padding bits in an embodiment of the present invention;

[0049] Figure 8 This is a schematic diagram illustrating the linear arrangement of user data and padding bytes in 8-bit words according to an embodiment of the present invention;

[0050] Figure 9 This is a schematic diagram illustrating the placement rules of the FM(x) coefficients in the CRC field according to an embodiment of the present invention;

[0051] Figure 10 This is a schematic diagram illustrating the sorting method of the CRC field in an embodiment of the present invention;

[0052] Figure 11 This is a schematic diagram of the embedded signaling BPTC format according to an embodiment of the present invention;

[0053] Figure 12 This is a schematic diagram of the CACH burst interleaver according to an embodiment of the present invention;

[0054] Figure 13 This is a flowchart of the PL / MAC signal processing process in a cellular network according to an embodiment of the present invention;

[0055] Figure 14 This is a flowchart illustrating the physical layer demodulation process according to an embodiment of the present invention.

[0056] Figure 15 This describes the relative positions of the self-organizing wireless private network communication modules in an embodiment of the present invention.

[0057] Figure 16 This is the self-organizing network formation process according to an embodiment of the present invention;

[0058] Figure 17 This is the data service logic processing flow according to an embodiment of the present invention;

[0059] Figure 18 This is the packet data multicast service processing flow according to an embodiment of the present invention;

[0060] Figure 19 This is a timing diagram of a self-organizing network according to an embodiment of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0062] Example

[0063] See Figures 1-19 The methods for self-organizing networks, monitoring, and topology maintenance based on power wireless private networks include:

[0064] I. Research on efficient channel resource sharing mechanisms for private network self-organizing networks

[0065] By optimizing channel resources, effectively carrying data frames, and dynamically managing channels, efficient sharing of channel resources in self-organizing networks based on unified broadcast signaling is achieved, laying the foundation for the construction of self-organizing networks for power wireless private networks.

[0066] 1) Self-organizing network protocol based on private cellular network

[0067] The first step is to study the PL protocol in the ad hoc network protocol.

[0068] 1. Protocol Architecture

[0069] The development and implementation of the self-organizing network protocol rely on the wireless private network cellular networking protocol, and the protocol architecture is as follows: Figure 2 As shown, following the general protocol layering architecture, the air interface protocol stack model is divided into three layers, including the physical layer (PL), the data link layer (DLL), and the call control layer (CCL).

[0070] 2. Physical layer functions

[0071] The first layer of the protocol is the physical layer, which is the lowest layer of the protocol. The physical layer is used to handle physical bursts and the construction of bits for transmission and reception, and its main functions include the following:

[0072] 1) Modulation and demodulation;

[0073] 2) Transmitter and receiver switching;

[0074] 3) RF characteristics;

[0075] 4) Definition of bit and sign;

[0076] 5) Frequency and symbol synchronization;

[0077] 6) Sudden establishment.

[0078] The protocol uses GMSK modulation with a roll-off factor of 0.3, a modulation rate of 16 kbit / s, and a maximum modulation frequency deviation of 4 kHz.

[0079] 3. Air Interface RF Performance Indicators

[0080] The air interface radio frequency supports 25K / 50K / 100K bandwidth and sets corresponding requirements for common indicators such as maximum transmit power, EVM (vector amplitude error), and frequency error on both the base station and terminal sides. The main common transmit requirements include: maximum transmit power: ≤50W (47dBm, base station); ≤5W (37dBm, terminal); EVM (vector amplitude error): ≤7% (base station), ≤10% (terminal); frequency error: ≤1×10⁻⁶ (base station); ≤1.5×10⁻⁶ (terminal). Other main transmit and receive requirements are as follows:

[0081] 1) RF performance requirements for a 25kHz transmitter:

[0082] (1) Modulation method: GMSK;

[0083] (2) Adjacent channel power ratio (ACPR, the extent to which the main power leaks into the adjacent channel): ≤-40dB (±25kHz); ≤-55dB (±50kHz); ≤-65dB (±75kHz);

[0084] 2) RF performance requirements for a 50kHz transmitter:

[0085] (1) Modulation method: Supports high-order modulation (8PSK, etc.);

[0086] (2) Adjacent channel power ratio (ACPR, the extent to which the main power leaks into the adjacent channel): ≤-35dB (±50kHz); ≤-50dB (±100kHz); ≤-60dB (±150kHz);

[0087] 3) RF performance requirements for a 100kHz transmitter:

[0088] (1) Modulation method: Supports high-order modulation (8PSK / 16QAM, etc.);

[0089] (2) Adjacent channel power ratio (ACPR, the extent to which the main power leaks into the adjacent channel): ≤-35dB (±100kHz); ≤-50dB (±200kHz); ≤-60dB (±300kHz);

[0090] 4) RF performance requirements for a 25kHz receiver:

[0091] (1) Static sensitivity (when the bit error rate is 5%): ≤-115dBm (base station); ≤-113dBm (terminal);

[0092] (2) Adjacent channel selectivity and dual signal selectivity: ≥45dB (±25kHz); ≥55dB (±50kHz); ≥65dB (±75kHz); ≥75dB (±1000kHz);

[0093] 5) RF performance requirements for a 50kHz receiver:

[0094] (1) Static sensitivity (when the bit error rate is 5%): ≤-105dBm (base station); ≤-103dBm (terminal);

[0095] (2) Adjacent channel selectivity and dual signal selectivity: ≥40dB (±50kHz); ≥50dB (±100kHz); ≥60dB (±150kHz); ≥70dB (±1000kHz);

[0096] 6) RF performance requirements for a 100kHz receiver:

[0097] (1) Static sensitivity (when the bit error rate is 5%): ≤-98dBm (base station); ≤-96dBm (terminal);

[0098] (2) Adjacent channel selectivity and dual signal selectivity: ≥40dB (±100kHz); ≥50dB (±200kHz); ≥60dB (±300kHz); ≥70dB (±1000kHz).

[0099] The second step is to study the MAC layer protocol in the ad hoc network protocol.

[0100] 1. Data Link Layer Functions

[0101] The MAC layer, located at the second layer of the protocol architecture—the data link layer (DLL)—primarily handles shared media among multiple users, managing logical connections and shielding the physical layer from higher layers. The data link layer mainly includes the following functions:

[0102] 1) Channel coding (FEC, CRC);

[0103] 2) Interleaving / deinterleaving and bit sorting;

[0104] 3) Response and retry mechanism;

[0105] 4) Media access control and channel management;

[0106] 5) Frame establishment and synchronization;

[0107] 6) Burst and parameter definition;

[0108] 7) Data-carrying services;

[0109] 8) Exchange signaling or user data with CCL.

[0110] 2. Data link layer burst format

[0111] Bursts include general data bursts and public broadcast channel bursts. Bursts carry user data and signaling embedded in Protocol Data Units (PDUs).

[0112] 1) Data and Control

[0113] Uplink and downlink data and control burst formats such as Figure 3 As shown.

[0114] A data synchronization word SYNC or an area containing embedded signaling information is provided in the middle of each control burst, with a total of 16 bits of "slot type" PDU on both sides, which indicates the data type of the 384 bits of information carried by the burst.

[0115] 2) Public broadcast channel burst

[0116] CACH is only present on the downlink channel. This channel does not belong solely to time slot 1 or time slot 2, but is shared by both time slots. The CACH burst format is as follows: Figure 4 As shown.

[0117] Each CACH burst consists of 32 bits, including 4 bits of information and 3 bits of checksum. This part is called the TDMA Access Channel Type (TACT) PDU, protected using Hamming (7,4) FEC, with 1 bit reserved. The remaining 24 bits are used to carry signaling. CACH does not provide FEC protection for this signaling area; the FEC or CRC protection is entirely defined by the signaling itself. CACH bursts should be sent every 30ms.

[0118] When the downlink channel is transmitting EPDT traffic, the AT bit in the CACH indicates to the terminal whether the uplink channel is "idle" or "busy". When there is activity on the uplink channel, the BS should set the AT bit to "busy". Additionally, when activity is expected on the uplink channel, the BS can also set the AT bit to "busy".

[0119] The third step is to study the CCL layer protocol in the ad hoc network protocol.

[0120] 1. Call control layer functions

[0121] The call control layer and packet data layer are entities that support EPDT services, located above the second-layer protocol, and mainly include the following functions:

[0122] 1) Base station activation / deactivation;

[0123] 2) Establishing, maintaining, and terminating business operations;

[0124] 3) Destination addressing (EPDT ID or gateway);

[0125] 4) Broadcast announcement signaling;

[0126] 5) Initiation and acceptance of unicast or multicast data services.

[0127] 2. Call control layer protocol frame format

[0128] The protocol's call control layer supports ad hoc network services by using control channel and packet channel uplink / downlink signaling, and indicates the PDU type through data type information elements in the PDU. For control channel uplink / downlink signaling, the frame types mainly include link control frames, control signaling frames, idle messages, multi-frame control messages, and end-to-end data encryption control frames. For packet data uplink / downlink signaling, the frame types mainly include header frames and data frames. Taking a control signaling frame as an example, the control signaling frame message structure is as follows: Figure 5 As shown.

[0129] The control signaling frame (CSBK) is 96 bits long and consists of LB, PF, CSBK command code (CSBKO), Function Set ID (FID), 8 bytes of data, and CSBK CRC. Two 96-bit CSBK frames (80 bits + 16 bits CRC) are protected by RCPC (2,1,7) FEC. The control signaling frame format is as follows: Figure 6 As shown.

[0130] II) Research on channel resource optimization methods for ad hoc networks

[0131] The first step is to study methods for efficient allocation of channel resources.

[0132] 1. Multi-frame control method

[0133] If a large amount of control information needs to be carried, and a single CSBK cannot carry it, then a Multi-Frame Control Message (MBC) is required. The basic format of MBC is based on the CSBK message structure. The message contains an MBC header frame, 0 to 2 MBC intermediate frames, and an MBC end frame.

[0134] The content of the data area carried by the MBC is determined by the CSBK command code and the function set ID, and conforms to the following rules:

[0135] A) The MBC should transmit continuously in a certain time slot;

[0136] B) The MBC header frame carries 64 bits, the middle frame carries 95 bits, and the end frame carries 79 bits;

[0137] C) If three data frames are appended after the MBC header frame, the MBC can carry a maximum of 333 bits.

[0138] The MBC header frame uses a 16-bit CRC check to verify the data carried in the header frame. The 16-bit CRC check of the end frame is used to check all MBC frames except the header frame.

[0139] The MBC header frame, intermediate frames, and end frame are all protected using RCPC(2,1,7) FEC encoding. All MBC header frames, intermediate frames, and end frames can carry information. Setting the data type information unit in the "Slot Type" PDU to "MBC Header Frame" results in an MBC header frame; setting it to "MBC Subsequent Frame" results in an MBC intermediate or end frame.

[0140] 2. Fragmentation and reassembly of signaling and data

[0141] The air interface protocol supports the over-the-air transmission of IP datagrams, data fragmentation and reassembly, error correction and detection, data transmission with acknowledgments, and encrypted data transmission.

[0142] A long IP datagram is broken down into several fragments, and each fragment is further broken down into data packets consisting of multiple data frames. Each packet is preceded by one or two header frames, and subsequent data frames are identified by numbers from 1 to m. Each data frame is protected by its own FEC.

[0143] There is no maximum limit to the number of data packets that can be split. The length of each packet can vary, but it cannot exceed the specified maximum number of bytes, N_DFragMax. Terminals and base stations require at least N_DFragMax bytes of storage capacity. Each packet is sequentially split into several data frames, and the carrying capacity of each data frame depends on the service type.

[0144] In the header frame, there are 4 bits for the fragment sequence number (FSN) for fragment reassembly. In a multi-fragment acknowledged data transmission, the highest bit of the fragment sequence number is used to indicate whether it is the final fragment, and the lowest 3 bits are used to represent the fragment sequence number, starting from 0002 and then cycling from 0012 to 1112. 0002 is only used for the first fragment of data.

[0145] If the communication module needs to carry a large amount of control information, making a single CSBK insufficient, multi-frame control messages (MBC) are required. The basic format of MBC is based on the CSBK message structure, and the message contains an MBC header frame, 0-2 MBC intermediate frames, and an MBC end frame. The communication module's control signaling fragmentation module divides the control signaling into multiple frames for transmission based on the signaling length and the frame structure's carrying capacity.

[0146] The second step is to study the optimal channel coding rule.

[0147] This study investigates the principles of encoding and decoding methods such as RCPC convolutional codes, CRC check codes, and turbo codes, and determines the encoding and decoding algorithms based on indicators such as signal-to-noise ratio, bit error rate requirements, and encoding and decoding performance in the application scenario.

[0148] 1. RCPC convolutional code

[0149] The generator polynomial is:

[0150]

[0151] The octal form is [171133]. The RCPC217 code is a tail-biting TBCC code, meaning that the state machine initialization is the last 6 bits of the current frame.

[0152] 2. 32-bit Cyclic Redundancy Check (CRC)

[0153] The CRC of message data is a 32-bit Cyclic Redundancy Check (CRC) code that encodes all user data and padding bytes in intermediate and final data frames. The CRC of message data does not include the sequence number of the data frame and the 9-bit CRC. The calculation process is as follows:

[0154] User data and padding bits are arranged according to Figure 7 Arranged in 16-bit units, each 16-bit word contains a most significant byte (MSO) and a least significant byte (LSO).

[0155] The first byte of user data should be placed in the most significant byte of the 0th word, and the second byte should be placed in the least significant byte of the 0th word. The third byte should be placed in the most significant byte of the 1st word, and so on, until the last byte of user data is placed in the least significant byte of the k / 16-1th word.

[0156] k is the total number of user data and padding bits that need to be calculated for the CRC message. Figure 8 The format is arranged into 16-bit words to calculate a 32-bit CRC.

[0157] Treat the k data bits as coefficients of a (k-1)th degree polynomial M(x), where: the highest bit of the 0th byte corresponds to the coefficient of the xk-1 term, and the lowest bit (LSB) of the last byte corresponds to the coefficient of the x0 term. The generator polynomial of the 32-bit cyclic redundancy check (CRC) code is:

[0158]

[0159] The CRC redundancy information polynomial FM(x) of the message data is calculated as follows:

[0160] F M (x)=x32 M(x) mod G M (x) (3)

[0161] All calculations are modulo-2 calculations in GF(2). For the calculation of the 32-bit cyclic redundancy check (CRC) code, the CRC remainder register should be initialized to 00000000. 16 .

[0162] The placement rules for FM(x) coefficients in the CRC field are as follows: Figure 9 As shown, where:

[0163] 1) The highest bit of the 0th byte of the CRC corresponds to the coefficient of the x31 term;

[0164] 2) The highest bit of the first byte of the CRC corresponds to the coefficient of the x23 term;

[0165] 3) The highest bit of the second byte of the CRC corresponds to the coefficient of the x15 term;

[0166] 4) The highest bit of the third byte of the CRC corresponds to the coefficient of the x7 term.

[0167] The CRC field places the least significant byte before the most significant byte. The sorting method of the CRC field is as follows: Figure 10 As shown.

[0168] 3. Variable-length packet product Turbo for broadcast channel signaling

[0169] Embedded signaling uses BPTC encoding for protection. Its row encoding uses Hamming (16,11,4) codes, and its column encoding uses simple parity checking. The embedded signaling BPTC format is as follows: Figure 11 As shown, as the number of information bits increases, new rows can be added for encoding, and the basic format of the decoder remains unchanged regardless of the number of information bits. Padding bits or parity bits can be added to the information bits as needed to fill the 11-bit length.

[0170] Figure 10 It also includes an embedded signaling interleaving scheme. The BPTC forward error correction coding matrix includes signaling information, Hamming parity bits, and parity check bits. The data in the coding matrix is ​​read from top to bottom and left to right, and then written into the transmission matrix in the same order to obtain the interleaved bits used for transmission. Each row of the transmission matrix is ​​32 bits long and is placed in the embedded signaling field of a continuous burst.

[0171] The interleaving method of the 32-bit CACH for the broadcast channel is shown in [link to documentation]. Figure 12To combat fading, the Access Bit (AT), Time Slot Number Bit (TC), Frame Flag Bit (LCSS), and 3 Hamming Check Bits (H), along with the Reserved Bit (RSV), are distributed throughout the CACH. The 24 payloads in the CACH are placed into the gaps sequentially.

[0172] The third step is to study agile channel multiplexing strategies.

[0173] This study investigates the principles of channel multiplexing strategies such as frequency division multiplexing, time division multiplexing, and code division multiplexing, and determines the channel multiplexing strategy based on specific application scenarios.

[0174] 1. Frequency Division Multiplexing

[0175] Frequency division multiplexing (FDM) refers to optimizing the spectrum of each signal at the transmitting end using carriers of different frequencies to form various frequency bands, thereby achieving multiplexing. In FDM, the signals do not overlap in frequency, allowing for timely and efficient transmission by aggregating them into a single channel. Upon reaching the receiving end, various bandpass filters within the center frequency are used to separate the signals.

[0176] 2. Time-division multiplexing

[0177] Time division multiplexing (TDM) divides time into different time slots, with each user or node sending or receiving data within its assigned time slot to avoid time conflicts. TDM primarily addresses the time slots occupied by sampled values ​​from multiple signals, enabling smooth transmission of each signal within a single channel. The advantages of TDM are: both combining and decomposing multiplexed signals primarily utilize digital circuits, resulting in ease of operation and high reliability. In contrast, frequency division multiplexing (FDM) systems suffer from low intermodulation fidelity and frequent harmonics due to channel nonlinearity, leading to mutual interference between signals. Therefore, FDM places particular emphasis on the accuracy and reliability of channel nonlinearity. TDM systems, however, do not have such stringent requirements for nonlinearity accuracy.

[0178] 3. Code Division Multiplexing

[0179] Different users use different coding sequences, allowing them to communicate on the same frequency and time, and the signals of different users are distinguished by coding. Code division multiplexing (CDM) uses code division multiplexing technology, in which the code symbols of several signals in the spectrum and in time are in an aliased state, but the codewords representing several signals are in an orthogonal state.

[0180] 4. Space division multiplexing

[0181] By utilizing spatial resources, such as directional antennas or spatial separation, signals of the same frequency and time can be spatially separated to reduce interference.

[0182] 5. Channel Access Control

[0183] When a communication module transmits on a dual-band base station channel, it must comply with channel access rules and procedures. The channel access rules design different levels of "avoidance transmission" and consider coexistence with analog services and other digital protocol services on the same carrier frequency. They also specify how the base station should perform channel access control during uplink channel activity and during channel suspension periods.

[0184] The channel access process is described using the SDL language. It supports three mechanisms: "no avoidance", "avoidance of its own color code", and "avoidance of all".

[0185] The communication module can request to transmit from different states; for example, it can start channel access from the Out_of_Sync_Channel_-Monitored state. For non-real-time applications, the terminal can wait for the channel to become idle before transitioning to the Holdoff state. These states are defined as follows:

[0186] Out_of_Sync_Channel_Monitored state: If synchronization cannot be achieved on this channel after detecting RSSI for a sufficiently long time, the terminal transitions from the Out_of_Sync state to this state. The RSSI detection duration is limited by timer T_Monitor. When this timer expires, the terminal should consider that there is no EPDT activity on this channel. In this state, the terminal continues to detect RSSI and search for synchronization.

[0187] Holdoff state: If a non-real-time service transmission is requested on a busy channel, the terminal should switch to this state and queue the transmission request. When a random holdoff is requested, the terminal will start a random T_Holdoff timer.

[0188] The channel access control module of the communication module mainly controls the access process of this terminal based on the CACH broadcast sent by the base station. Random access by the terminal depends on the UP, SF, and MASK conditions of C_ALOHA, representing user level, service category, and address range, respectively. These three conditions are ANDed, meaning that random access can only be performed if all three conditions are met. The terminal can perform random access based on C_ALOHA and the TACT (AT=0) of the CACH. In addition, the terminal can also reserve access based on the TACT (AT=1) of the CACH.

[0189] III. Research on effective methods for carrying data frames in ad hoc networks

[0190] The first step is to study the signaling multiplexing mechanism and the method of micro-bearer of channel information.

[0191] Taking the CSBK frame as an example, the receive frequency number field is set in the CSBK frame. The service channel transmit frequency number of each hop = (service channel receive frequency number + 1)%3. The self-organizing network uses four frequencies for forwarding by default. If more frequency resources are available, the valid bits of the frequency bit field can be expanded.

[0192] The bit field discussion takes the CSBK frame as an example, including but not limited to various valid frames specified in the protocol. The definitions of important bit fields carried by the CSBK frame channel information are shown in Table 1.

[0193] Table 1 Definition of Important Bit Fields Carried by CSBK Frame Channel Information

[0194]

[0195] The second step is to study the linkage mechanism between the transmitting and receiving channels.

[0196] This study investigates the characteristics of ad hoc network transceiver channels and aims to improve communication quality and efficiency by coordinating the configuration of transmit and receive channels.

[0197] The design of the transceiver channels takes several aspects into consideration. For example, adaptive modulation technology is used to adjust the modulation method according to the channel quality, ensuring that the transmitting and receiving ends operate under the same modulation mode, thus improving communication reliability. For certain service scenarios, half-duplex communication is considered, where transmitting and receiving operations occur at different times to reduce the possibility of channel collisions. The linkage between the transceiver channels is considered during the communication protocol design phase to ensure that the protocol supports dynamic channel configuration and optimization strategies to adapt to different communication needs. Intelligent communication scheduling algorithms are introduced to comprehensively consider the channel states of both transmission and reception, dynamically adjusting communication time and frequency to optimize the overall communication system performance. An effective feedback mechanism is established to monitor channel quality and performance parameters in real time and adjust the transmission and reception channel configurations promptly to adapt to the actual network conditions.

[0198] IV) Research on dynamic channel management rules for ad hoc networks

[0199] The first step is to study the linkage mechanism between hop count and channel resources.

[0200] The study investigates the interaction between different routing hop counts and channel resources, that is, to fully consider the path selection and channel resource allocation for different routing hop counts in order to achieve more efficient and balanced data transmission.

[0201] A path optimization algorithm based on channel resource conditions comprehensively considers hop count and channel quality, selecting paths with sufficient channel resources for data transmission. It monitors network channel resource usage in real time and dynamically adjusts routes accordingly. During routing decisions, paths with more abundant channel resources are prioritized, ensuring a balance between hop count optimization and channel resource availability.

[0202] The second step is to study channel resource optimization strategies.

[0203] The study examines the channel quality of available channels in a self-organizing network and selects the optimal channel as the transmission channel.

[0204] 1) Channel allocation algorithm: Use intelligent channel allocation algorithms, such as dynamic frequency allocation or dynamic time division multiplexing, to adjust channel allocation according to real-time network conditions and needs;

[0205] 2) Adaptive modulation and coding: Adaptively select the modulation method and coding scheme according to the channel quality to maximize the data transmission rate;

[0206] 3) Cooperative communication and cooperative multiple access: Allows nodes to communicate cooperatively and share information to improve channel utilization, while using multiple access technology to support simultaneous transmission by multiple users;

[0207] 4) Non-orthogonal multiple access: Multiple users are allowed to share the channel on the same frequency and time, achieving efficient utilization of multi-user resources in a non-orthogonal manner;

[0208] 5) Priority scheduling: Set priorities for different services or users, and use intelligent scheduling algorithms to ensure that high-priority tasks get better channel resources;

[0209] 6) Multipath transmission: Multipath transmission technology allows data to be transmitted through multiple paths simultaneously, improving channel capacity and reliability.

[0210] The above channel resource optimization strategies should be selected and combined according to the actual application scenario and network conditions to achieve optimized utilization of channel resources.

[0211] II. Research on route update and multi-hop technology for private network self-organizing networks

[0212] Based on the efficient shared self-organizing network channel and combined with the time synchronization of cellular self-organizing network, multi-hop networking and dynamic route updates of cellular self-organizing network are realized.

[0213] 1) Research on time synchronization technology for cellular self-organizing networks based on private cellular network self-organizing protocols.

[0214] The input signal is first filtered by the channel, then enters the serial-to-parallel converter, and then despins. Frame synchronization is then performed separately, providing a time reference. This embodiment uses a threshold decision based on the combined peak-to-average power ratio (PAPR) of multiple blocks to find the frame synchronization start point and complete frame synchronization and bit synchronization. When an external clock (1 PPS) is available, the local time slot clock needs to be calibrated with the external clock in real time, for example, every 6 seconds.

[0215] II) Research on multi-hop networking of cellular ad hoc networks

[0216] The first step is to study agile networking methods using broadcast signaling.

[0217] Broadcast signaling agile networking achieves agile networking of self-organizing networks by issuing broadcast signaling, mainly including three stages: the networking information broadcast signaling stage, the routing information broadcast stage, and the idle stage.

[0218] The relative positions of the self-organizing wireless private network communication modules are as follows: Figure 15 As shown.

[0219] After the converged node (which can automatically switch) is powered on, the main control unit sends a network broadcast signaling message. Other nodes then send their own network broadcast signaling messages in sequence. After one network broadcast signaling message transmission cycle (3 broadcast signaling message transmission cycles, the number of cycles is configurable), each node knows which nodes can reach the converged node. The next cycle of routing information broadcasting then begins, which can also be sent 3 times (configurable). After this phase, the converged wireless private network communication module can see the location information of all nodes and all reachable paths. At this point, the system enters an idle phase, during which each node can initiate service requests. The self-organizing network formation process is as follows: Figure 16 As shown

[0220] When a node sends packet data, it first sends a packet data service request broadcast signaling. This broadcast signaling contains routing information and carries the service channel information used by each node. This broadcast signaling propagates within the network, and intermediate nodes forward the broadcast signaling. Afterwards, all nodes enter the designated service channel for reception or forwarding (time slots 1 and 2 operate on different frequencies), ensuring that packet data services can effectively reach the convergence node. The control packet data flow from the convergence node to each node within the network is the same.

[0221] The business logic processing flow of grouped data is as follows Figure 17 As shown.

[0222] The packet data multicast service processing flow is as follows: Figure 18 As shown.

[0223] The timing relationship of packet data multicast services is as follows: Figure 19 As shown.

[0224] The second step is to study the micro-bearer method for information transmission between self-organizing network nodes.

[0225] Taking the CSBK frame as an example, the CSBK frame sets the source address, destination address, first hop node address, second hop node address, third hop node address, fourth hop node address, and service initiator address. If there is a need to expand the scale of the self-organizing network or expand services, the valid bits of the node information bit field can be dynamically expanded.

[0226] The bit field discussion takes the CSBK frame as an example, including but not limited to various valid frames specified in the cellular private network communication protocol. The definitions of important bit fields carried by the CSBK frame node information are shown in Table 2.

[0227] Table 2 Definitions of Important Bit Fields Carried by CSBK Frame Node Information

[0228]

[0229] The third step is to study the optimal routing decision mechanism.

[0230] In ad hoc networks, different service scenarios require different routing decision mechanisms. Therefore, it is necessary to comprehensively consider multiple principles to formulate the optimal routing decision mechanism. The priority order of routing decision mechanisms is as follows:

[0231] 1. For upstream services, the routing selection principle is as follows:

[0232] 1) Group call: Based on the principle of the furthest path that can reach the control center;

[0233] 2) Single call: The principle of the shortest path to the control center.

[0234] 2. For downlink services, the routing selection principle is as follows:

[0235] 1) Group call: The principle of the furthest path to the node;

[0236] 2) Single call: The principle of the shortest path to the node.

[0237] 3. Stable path principle.

[0238] 4. The principle of maximizing RSSI.

[0239] The fourth step is to study the self-switching method of the control center.

[0240] Peripheral nodes continuously send network broadcast signals during the network broadcast signaling cycle. These signals carry information such as "whether the control center is reachable," "how many nodes to reach the control center," and "which nodes to pass through to reach the control center." Peripheral nodes monitor the real-time information of all nodes and adjust routing information accordingly. When the control center fails and cannot function, a backup reachable node can be selected as the control center according to the control center sub-switching method, achieving automatic control center handover.

[0241] (iii) Research on the dynamic route update mechanism of cellular ad hoc networks

[0242] The first step is to study the adaptive periodic configuration mechanism of network signaling.

[0243] When setting up a self-organizing network, the distribution cycle of broadcast signaling during the network information broadcast signaling phase and the routing information broadcast signaling phase needs to be considered. The signaling distribution cycle length should be configured according to the specific usage environment, including the broadcast signaling cycle, the routing broadcast cycle, and the idle cycle. After each cycle, the routing information is updated to the optimal path information from the previous cycle. Node status and the size of the self-organizing network nodes will affect the signaling distribution cycle length; therefore, the cycle length needs to be designed based on the node movement speed and the number of nodes in the network.

[0244] The second step is to study the intelligent waiting mechanism for forwarding.

[0245] The system intelligently adjusts the waiting period based on current network conditions, load, and device requirements to achieve more efficient energy management and response time.

[0246] By dynamically adjusting latency based on sensor data, network conditions, and user behavior, devices can intelligently adjust their latency cycles according to network activity, data transmission needs, and device status. They can adaptively select to enter sleep mode based on varying network activity levels to reduce energy consumption. Furthermore, they can intelligently choose when to enter a latency mode by sensing network conditions. For example, when the network is under low load, devices can extend latency to reduce power consumption. Using sensor technology or user interaction information, they can intelligently detect user activity to determine whether to shorten the latency cycle. If the user is active, the device can reduce latency to provide faster response. Based on historical data and models, devices can predict future network activity and intelligently adjust latency cycles in advance to balance energy efficiency and response performance. In multi-device environments, devices can collaborate and share information to determine when to enter and exit latency states. By analyzing network traffic in real time, devices can adjust latency cycles based on current network activity to avoid excessive energy consumption when the network is idle.

[0247] By intelligently adjusting the device's waiting period using the above strategies, the optimal balance between performance and energy efficiency can be achieved under different scenarios.

[0248] III. Research on dynamic power control technology for private network self-organizing networks

[0249] For multi-hop networking and dynamic route updates, corresponding dynamic power control methods are selected for two different application scenarios: for local networking scenarios, low power consumption and miniaturization of devices are selected, and for remote blind spot filling scenarios, the device's transmit power can be quickly and dynamically adjusted.

[0250] 1) Low power consumption and miniaturization of the equipment

[0251] 1. The proposed low-power technologies for hardware are as follows:

[0252] 1) CPU: In embedded systems, selecting a low-power CPU or adjusting the CPU's operating frequency can effectively reduce system power consumption.

[0253] 2) Memory: Memory is also an important indicator of power consumption in embedded systems. Converting static memory to dynamic memory can effectively reduce power consumption in idle states;

[0254] 3) Storage and I / O: Memory and input / output devices account for a large proportion of the system. Using efficient storage technologies and low-power I / O devices is also an effective way to reduce power consumption.

[0255] 2. In terms of software, the following low-power technologies are proposed:

[0256] 1) At the application level, limiting runtime or adjusting sleep cycles can reduce the energy consumed by the CPU when it is idle;

[0257] 2) The control flow is handled by interrupts, which avoids the problem of CPU resources being occupied for a long time;

[0258] 3) Optimizing code, using efficient algorithms and data structures, and reducing redundant calculations and memory usage are also effective ways to reduce power consumption.

[0259] (ii) The equipment's transmission power can be quickly and dynamically adjusted.

[0260] The first step is to study the design technology of multi-level transmission hardware circuits.

[0261] This research focuses on multi-level transmission hardware circuit design technology to achieve flexible transmission at different power levels, adapting to different communication needs and environmental conditions.

[0262] First, by employing an adjustable power amplifier, the system can achieve multiple levels of output power at the hardware level. Furthermore, the introduction of an RF switch allows for rapid switching between different power levels by changing the signal path.

[0263] In terms of control, by utilizing power control circuits and intelligent control units, the system can monitor network conditions in real time and intelligently adjust the transmission power according to communication requirements. The introduction of digital signal processing enables the system to dynamically adjust the signal amplitude in the digital domain, achieving more precise power control.

[0264] To improve efficiency, dynamic power management technology is used to adjust the supply voltage and current to match current power demand, achieving higher energy efficiency. Energy consumption analysis, as a key step in the design process, helps to understand the energy consumption distribution at different power levels, providing a reference for optimizing the design.

[0265] Furthermore, considering system stability and reliability, thermal design is essential, especially at high power levels, where effective heat dissipation is necessary to prevent hardware overheating. Testing and calibration mechanisms ensure the system responds accurately when switching between different power levels, maintaining data transmission quality.

[0266] The second step is to study the dual-network power adaptive software control strategy.

[0267] In a dual-network mode, the study proposes a software-based method to achieve adaptive power control, thereby optimizing device performance and minimizing power consumption while achieving optimal QoS.

[0268] On the software side, an integrated network condition monitoring mechanism can be used to monitor the quality, congestion, and signal strength of various networks in real time, adjusting the power levels of devices under different network environments. Channel estimation technology can be used to evaluate the characteristics of the current communication channel in real time, allowing for more accurate power level adjustments to adapt to channel changes. Software algorithms can intelligently identify the current communication mode to adjust power and optimize network performance. Dynamic power control algorithms can be developed to intelligently adjust transmission power based on real-time network conditions and channel estimation results, maximizing communication quality while minimizing power consumption. Considering quality of service requirements, software control can ensure that critical tasks or services are prioritized during power adjustments to maintain user experience. Energy efficiency optimization algorithms can be introduced to balance communication performance and power consumption, making reasonable trade-offs between different power levels to meet data transmission needs while minimizing power consumption. Based on historical data and predictive models, predictive algorithms can be developed to anticipate network changes, allowing for more proactive power adjustments to adapt to future communication needs.

[0269] IV. Research on network dynamic monitoring and network topology maintenance technologies for private network self-organizing networks

[0270] For the self-organizing network of the constructed power wireless private network, dynamic network monitoring is based on heterogeneous network multi-layer modeling, heterogeneous network coverage simulation and node location optimization, node health and topology anomaly analysis and early warning.

[0271] 1) Research on multi-layer management modeling methods for ad hoc network devices

[0272] The first step is to study the multilayer modeling requirements of heterogeneous networks.

[0273] The following requirements exist for data fusion from heterogeneous devices:

[0274] 1. Data Format Consistency Requirement: In the development of power cellular networks, data exchange between devices is usually based on power private network protocols. However, when cellular networks and ad hoc networks are operating simultaneously, the formats of device data may differ.

[0275] 2. Protocol compatibility requirements: Devices in cellular networks communicate based on power grid standards and protocols, such as EPDT, while ad hoc networks use extended protocols of the power grid. The incompatibility between protocols will prevent different devices from interacting directly, let alone achieving integration.

[0276] 3. Equipment Frequency Consistency Requirements: The inconsistent frequencies of equipment in power cellular networks also pose a significant challenge to data fusion between the two networks. Due to different tasks and usage scenarios, the data generation frequency of each device may vary greatly, which complicates the data fusion task.

[0277] The second step is to study the data fusion modeling of heterogeneous devices on dual networks.

[0278] Faced with the aforementioned challenges, how to perform data fusion modeling for heterogeneous devices on dual networks has become a very worthwhile research question. By establishing a unified data fusion model, data redundancy can be avoided, data processing efficiency can be improved, and richer data parsing tasks can be accomplished.

[0279] 1. Modeling elements

[0280] When modeling data fusion from heterogeneous devices, the following elements need to be considered:

[0281] 1) Sensors: Sensors are various detection components installed in the equipment according to requirements, used to monitor various data in the dual network in real time. The data captured by the sensors is used as input for modeling to build a data model of the environment.

[0282] 2) Physical objects: Physical objects are the objects being monitored, such as PLC controllers, circuit breakers, power transmission lines, etc. Physical objects are the foundation of modeling; by modeling physical objects, the characteristics and behaviors of the physical environment can be inferred.

[0283] 3) Environment: The environment describes the physical environment, which includes multi-dimensional information such as physical objects, location, time, and behavior. By constructing an environment model from the data captured by sensors, the behavioral patterns of the environment can be inferred.

[0284] 2. Modeling process

[0285] 1) Data acquisition: Data is acquired from sensors and preprocessed such as data cleaning and deduplication;

[0286] 2) Data normalization: Since different sensors use different data formats, it is necessary to convert the data format to make the data in different memory locations consistent;

[0287] 3) Data analysis: Data analysis involves analyzing collected data to derive valuable information. Data mining techniques, such as machine learning algorithms, can be used to automate data analysis.

[0288] 4) Modeling: Based on the results of data analysis, build a model;

[0289] 5) Data visualization: Transforming the model into a graphical form to facilitate observation and use by human users.

[0290] The third step is to study the simulation technology for heterogeneous network coverage and the optimization model technology for node location.

[0291] 1. Site layout

[0292] In the design process of heterogeneous network site layout, base station site selection is a dynamic process. Site selection is continuously optimized based on the results of site surveys and coverage simulation calculations until finally usable sites are determined. The specific steps are as follows:

[0293] 1) Site survey to obtain actual base station conditions information, and based on this, to initially set various base station parameters, such as antenna position, antenna height, antenna type, and transmission power;

[0294] 2) Make coverage predictions for the coverage area using planning software, and improve the planning simulation results by optimizing settings, and use the simulation results as a reference for network design;

[0295] 3) Determine site locations by comparing simulation results with coverage targets. If the simulation results show weak coverage in some areas or poor RSSINR values, it is necessary to add or modify sites, and provide an overall site modification plan based on the comparison results. For newly added or modified sites, it is necessary to return to the steps to conduct site surveys, determine the actual base station conditions on-site, and return to the steps to conduct simulation calculations again to determine the coverage effect. Repeat this process until satisfactory results are obtained before determining the final site construction plan.

[0296] 2. Coverage Simulation

[0297] The map used for simulation should be an unbiased projection survey map (not a biased projection public map such as Baidu Maps). For areas C, D, and E, a 20–50m accuracy 3D map is recommended; for areas A and B, a 5–20m accuracy 3D map is recommended. The map should include at least terrain elevation information, feature information, and vector boundary information. For areas A+, a 5m or higher accuracy 3D vector map is recommended, with added building vector edge and height information. The propagation model should be selected based on map accuracy and operating frequency band. Simulations using high-precision maps should employ a ray tracing propagation model; when using low-precision maps, the COST-231HATA propagation model is recommended for LTE 1800M.

[0298] Different propagation models have different applicable ranges. The appropriate propagation model should be selected based on the terrain characteristics of different coverage areas in the ad hoc network, and propagation loss should be calculated using digital maps and mobile communication network planning software. The calculation results of the coverage simulation should at least provide coverage effect diagrams for parameters such as RSRP and RS-SINR, as well as detailed sub-item coverage statistics.

[0299] The fourth step is to study node health and topology anomaly analysis and early warning model technology.

[0300] Ad hoc network node health analysis is the process of evaluating and monitoring the status of each node in an ad hoc network to ensure the reliability and performance of the network. The health of ad hoc network nodes is analyzed from the following dimensions:

[0301] 1. Connectivity: It is necessary to monitor the connectivity and communication capabilities between ad hoc network nodes, including the sending and receiving of power data packets. Packet loss rate, latency, and bandwidth utilization are important indicators for evaluating node connectivity.

[0302] 2. Energy consumption: For nodes in low-power self-organizing networks such as wireless sensor networks, monitoring the energy consumption of nodes is crucial. Low-power self-organizing network nodes may affect the stability of the entire network.

[0303] 3. Task execution quality: For self-organizing network nodes that execute specific tasks, the status and efficiency of the task execution process can be monitored, and the task completion status, execution time and resource utilization can be checked.

[0304] 4. Load balancing: It is necessary to consider the load balancing of self-organizing network nodes to prevent some nodes from being overloaded, which would lead to a decrease in node performance. This includes monitoring the task load and cache usage of nodes.

[0305] 5. Security: The analysis of node health also includes network security assessment of nodes to detect potential attacks, malicious nodes, or data tampering, in order to protect the integrity and privacy of the ad hoc network.

[0306] 6. Latency Analysis: Evaluate the communication latency between nodes to ensure timely communication between nodes. Increased latency may affect the transmission of real-time data streams and the performance of collaborative tasks.

[0307] 7. Environmental awareness: For some communication devices, the environmental awareness of a node is also an important factor affecting the node's health. Environmental parameters such as temperature and humidity may affect the node's performance and lifespan.

[0308] 8. Node health prediction: Utilize historical node data for trend analysis to understand the evolution trend of node performance, predict potential problems, and take preventive measures.

[0309] II) Research on methods for acquiring networking data of ad hoc network devices

[0310] This system enables self-organizing network topology maintenance based on intelligent network data acquisition, rapid database retrieval, improved performance at the self-organizing network edge, hierarchical management of self-organizing network devices, and self-organizing network route tracing.

[0311] The first step is to study the intelligent data acquisition mechanism.

[0312] 1. Real-time streaming data processing

[0313] Real-time processing of data streams transmitted via power grid wireless private networks is used to obtain real-time information. Real-time streaming data processing technology mainly includes three parts: real-time data acquisition, real-time data computation, and efficient data storage.

[0314] 1) Real-time data acquisition. Utilizing the distributed message queue Kafka, real-time acquisition of multi-source power data is achieved, forming a large-scale power grid data stream. The real-time power big data stream computing and processing platform needs to establish a buffer between data acquisition and data processing. The buffer is established by combining memory caching and file caching, which can effectively balance processing performance and data security.

[0315] 2) Real-time data computation. Based on a real-time stream computing processing platform, a sliding window approach is used for power data stream input. A multi-data stream collaborative computation method is employed to optimize asynchronous collaboration between data streams, enabling real-time processing of multiple data streams.

[0316] 3) Efficient Data Storage. When the results of real-time processing of multiple data streams are relatively small and of low importance, they can be directly stored in a distributed columnar database. The drawback of this method is insufficient data storage stability, potentially leading to data loss. When the results of real-time processing of multiple data streams are large and of high importance, they can be stored in a distributed columnar database using a distributed message queue like Kafka. The advantage of this method is that it fully utilizes Kafka's caching mechanism, ensuring that data is not lost.

[0317] 2. Log file analysis

[0318] Analyze log files generated by systems, applications, or devices to extract valuable data.

[0319] 3. Power load forecasting

[0320] By using machine learning and data analytics, historical power load data can be analyzed to predict future power load changes, thereby optimizing power system planning and resource allocation.

[0321] The second step is to study fast database retrieval algorithms.

[0322] The amount of data generated by power grid operation and maintenance is growing exponentially. Power data is characterized by its massive volume, diverse data types, rapid changes, low data value density, and the inclusion of a large amount of multidimensional spatiotemporal data, relational data, and real-time response data. Therefore, based on the characteristics of existing power data, enabling fast and accurate database retrieval of collected power data is the foundation and key to the construction of a management system.

[0323] 1. Fast Information Retrieval Algorithm Based on Decision Tree

[0324] First, mutual information is used as an efficient attribute evaluation criterion. Similar to information entropy, mutual information is also derived from the concept of information theory; it can represent a measure of the interdependence between two variables. Mutual information is used to determine the inclusion relationship between different attributes and to select a subset of features with low redundancy, giving it a significant advantage in feature selection in the field of data mining. When calculating mutual information, the probability distribution of the two related variables needs to be considered; therefore, average mutual information is used, and a weighting factor is added. The weighting factor C... i -) for:

[0325]

[0326] Where, n i m0 represents the amount of data for attribute i; m0 is a manual constant that can be fine-tuned based on actual conditions.

[0327] For a sample attribute, mutual information represents its correlation with the category, and the mutual information MI(x) is:

[0328]

[0329] Where 1 / C is the weight factor, determined by the class numbers; MI(x) represents the mutual information between attribute x and class, p(x) i ,y i Let p(x) be the joint probability. i ) represents the marginal probability, p(y) i)' represents the marginal probability of the category.

[0330] The data filtering process using a decision tree model based on mutual information is as follows:

[0331] 1) Input: Candidate dataset (D data points), index keywords (S keywords);

[0332] 2) Construct a decision tree model T based on the index structure;

[0333] 3) Update the weight matrix C according to formula (5);

[0334] 4) Calculate MI(x) and sort the index in descending order;

[0335] 5) Filter out irrelevant data to obtain subcategories D. n ;

[0336] 6) Simplify irrelevant branches to obtain a simplified decision tree T. n ;

[0337] 7) Iterative calculation to obtain the final dataset Output.

[0338] Decision tree models based on mutual information can achieve data classification and data filtering. During the retrieval process, the greater the mutual information of the data, the higher the probability of filtering out more useless data, which can effectively reduce irrelevant information and improve retrieval speed and accuracy.

[0339] 2. MapReduce-based methods for retrieving massive amounts of data

[0340] MapReduce is a core component of Hadoop. It employs a parallel computing model, breaking down complex tasks into several simpler ones and distributing them across multiple computers to work collaboratively. The core of MapReduce consists of the Map and Reduce functions. Map is responsible for parallel processing of complex computations and passing the result set to Reduce, while Reduce is responsible for statistical analysis of the result set and outputting the results.

[0341] The MapReduce execution phase is divided into the task allocation phase, the Map processing phase, the Combine phase, the Shuffle phase, the Reduce phase, and the output phase.

[0342] During the task allocation phase, the Master node divides the dataset into several parts according to the block size set in Hadoop's HDFS, and assigns each part to the corresponding Map. In this process, the Master node does not need to consider the rewriting process and internal logic structure of the Map function; it directly splits the dataset and then allocates it to the corresponding Map.

[0343] During the Map processing phase, users need to modify the Map function. Specifically, users must modify the Map function according to project requirements. After receiving the slice allocated by the Master node, the Map function will process the data according to the user-defined function and generate a set of ordered key-value pairs.<key,value> Dataset.

[0344] In the Combine phase, the output of the Map processing phase is...<key,value> Merging key-value pairs reduces the large number of intermediate result sets generated in the Map phase and alleviates network communication pressure.

[0345] During the Shuffle phase, the intermediate results generated in the Map phase are allocated to the Reduce phase. In this phase, the intermediate results are merged and organized, and the organized results are stored in memory or on the disk of the child nodes. Finally, the corresponding file for Reduce is generated and sent to Reduce.

[0346] Similar to the Map phase, in the Reduce phase, users need to modify the Reduce function according to project requirements. In this phase, the system executes the user-modified Reduce function to process the intermediate results.

[0347] During the output phase, after the Reduce task is completed, the data result set is stored in HDFS as a file. After all tasks are completed, the Master node will wake up the program to continue executing the previous tasks.

[0348] The k-NN algorithm calculates the distance between each training sample and the test sample in the dataset, and then returns the k closest samples. The class of the k samples with the highest frequency is used as the predicted classification for the test data. The distance between samples is calculated using Euclidean distance, which is:

[0349]

[0350] Since the k-NN algorithm has linear complexity, using the MapReduce parallel programming model can effectively improve the algorithm's execution efficiency.

[0351] The fast retrieval process based on MapReduce is as follows:

[0352] 1) Establish a keyword library for the collected power data, establish keyword templates based on professional knowledge of power, and adopt the MapReduce parallel computing model, referencing the K-NN algorithm. Use the established keyword templates as the training set for the K-NN algorithm, rewrite the Map and Reduce functions of MapReduce, and output the keyword library under MapReduce parallel computing.

[0353] 2) The user enters search keywords;

[0354] 3) Traverse the keyword database, search and compare the user-input search keywords, and check for keywords with high similarity.

[0355] 4) Determine if the traversal has been completed. If it has, take the text data corresponding to the keyword in the keyword database as the result set, and return the result set to the user in reverse order of similarity.

[0356] 3. Data retrieval method based on manifold sorting

[0357] Manifold sorting is a special case of semi-supervised learning. The manifold sorting process can be intuitively described as describing the data as vectors in Euclidean space and building a weighted graph model in the corresponding feature space. Positive values ​​are assigned to labeled nodes, and zero values ​​are assigned to other nodes to be sorted. Then, all nodes propagate their scores to neighboring nodes through the weighted graph until the entire network reaches a balanced state, and finally sorts them according to their scores.

[0358] The third step is to study algorithms for improving the edge performance of ad hoc networks.

[0359] 1. The strategy and approach for carrying services in the power wireless private network is to prioritize coverage and control services, while also considering data acquisition and multimedia services within the coverage area. Based on engineering construction and technical practice experience of power wireless private networks, the network edge performance requirements for power wireless private networks are proposed. According to Q / GDW 11664-2017 "Planning and Design Guidelines for Power Wireless Private Networks", the network performance of power wireless private networks should meet the following requirements:

[0360] 1) The performance of the power wireless private network should meet the service types, scale, target coverage area, coverage area, capacity and performance targets specified in the plan;

[0361] 2) The power wireless private network should meet the requirements of business systems such as dispatch automation, distribution automation, and electricity consumption information collection for the security, reliability, real-time performance, and carrying capacity of the communication network.

[0362] 3) The power wireless private network should be able to be combined with fiber optic private networks, power line carriers, wireless public networks, etc., mainly to achieve full coverage of the "three remote" and "two remote" terminals of power distribution automation and the business terminals such as electricity consumption information collection in the region;

[0363] 4) Under the conditions of co-frequency networking and actual users in neighboring areas occupying 50% of network resources, the overall coverage rate of the wireless network in the planned target coverage area of ​​A+ and A-class areas should meet the following requirements: RSRP≥-115dBm and RS SNR≥-3dB, not less than 95% in locations with RSRP≥-115dBm and RS SNR≥-3dB, not less than 90% in B and C-class areas, and service terminals should be able to access the network 99% of the time.

[0364] 5) The area should be covered by outdoor thin coverage with an edge rate of at least 128 kbit / s.

[0365] 2. Based on the above network edge performance requirements, the following methods can be used to improve the network edge performance of ad hoc networks:

[0366] 1) Clustering Algorithm

[0367] Ad hoc networks typically assume that each mobile node has an equal status within the network, resulting in poor scalability. In large networks, routing overhead increases dramatically, significantly degrading network performance. Clustering algorithms can effectively address the poor scalability problem of ad hoc networks. Clustering algorithms elect cluster heads according to certain rules. Nodes within the effective transmission range of the cluster head form clusters, and nodes connected by wireless links between different clusters act as gateway nodes. The cluster head is responsible for maintaining information on all cluster members and link status within its cluster, exchanging cluster information with neighboring clusters, and calculating routes based on this information. Gateways are responsible for the transmission of information and data between clusters.

[0368] 2) Load balancing algorithm

[0369] Traditional wireless ad hoc network routing algorithms, based on the shortest path principle, can lead to uneven load distribution within the network. This load imbalance can cause serious consequences such as increased end-to-end latency, decreased transmission ratio, and reduced network lifetime. Therefore, load balancing algorithms are needed to address these issues. Existing load balancing strategies can be categorized into single-path and multi-path methods. The single-path method generally offers better performance. The algorithm divides the load of ad hoc network nodes into immediate load and past load. Immediate load reflects the current busy level of a node, while past load reflects the total load a node has carried in the past. The node's interface queue length reflects the immediate load, while the node's remaining energy reflects the past load. The algorithm emphasizes the impact of bottleneck nodes on network performance, proposing minimum remaining energy and maximum queue length indices for each path. These are recorded in relevant entries in the control packet and routing tables. During route selection, the algorithm tends to choose paths with larger minimum remaining energy and lower congestion levels, avoiding heavily loaded bottleneck nodes. This reduces network latency, increases transmission ratio, lowers average packet energy consumption, and improves overall network performance.

[0370] 3) Security Algorithm

[0371] Security algorithms are introduced into self-organizing edge nodes to ensure the confidentiality and integrity of power data transmission and prevent malicious attacks in the network.

[0372] 4) Topology control algorithm

[0373] Using topology control algorithms to maintain the network topology structure to better reflect the actual node locations and connection states helps improve routing efficiency.

[0374] 5) Multi-path routing mechanism

[0375] The algorithm supports multi-path routing, which improves bandwidth utilization and reliability by using multiple paths to transmit data simultaneously.

[0376] 6) Self-organizing maintenance

[0377] Introducing a self-organizing mechanism enables the network to automatically adapt to changes without centralized management, thereby improving the system's flexibility.

[0378] (iii) Researching efficient management methods for ad hoc networks

[0379] The first step is to study the QoS measurement method for ad hoc networks.

[0380] 1. End-to-end delay measurement: Measure the data transmission delay from the sending end to the receiving end;

[0381] 2. Packet Loss Rate: Records the rate at which data packets are lost during transmission. Packet loss rate is an indicator for evaluating network stability and reliability.

[0382] 3. Throughput Measurement: By measuring the actual amount of data transmitted in the network, the network throughput is obtained, and the data transmission rate is calculated.

[0383] 4. Network signal strength measurement: Measure the signal strength and quality between ad hoc network nodes;

[0384] 5. Link quality measurement: Evaluate the link quality between nodes, including signal strength, interference and fading, and assess the stability and reliability of data transmission;

[0385] 6. Service Availability Measurement: Evaluate the availability of ad hoc network services to ensure that the ad hoc network can provide consistent services under different environmental conditions;

[0386] 7. Routing performance measurement: Measure the performance of the route, including routing latency, stability, and adaptability, to ensure an effective data transmission path.

[0387] The second step is to study the routing tracing method for ad hoc networks.

[0388] Power Grid wireless private networks (WBDNs) are characterized by large data volumes, numerous nodes, and rapid data changes. Finding a path from the source node to the destination node that meets QoS requirements within such a large-scale WBDN is challenging. Therefore, it is necessary to research WBDN route tracing methods to rationally allocate and utilize routing resources within the WBDN, ensuring effective data transmission and preventing performance degradation or network congestion caused by overloading certain nodes. The main WBDN route tracing methods include:

[0389] 1. Load balancing algorithm: Design an effective load balancing algorithm that dynamically adjusts the data transmission path based on the load status of nodes and the network topology to ensure a relatively balanced load among nodes.

[0390] 2. Clustering and Centralized Management: In ad hoc networks, by clustering nodes and having each cluster managed by a cluster head, a certain degree of load balancing can be achieved. Centralized management can effectively coordinate communication between nodes.

[0391] 3. Weight-based routing: Assign weights to each node and dynamically adjust route selection based on node load and performance to achieve a relatively balanced network traffic.

[0392] 4. Load-aware routing: Implement routing algorithms that can sense node load and adjust routes in a timely manner to ensure load balancing throughout the network;

[0393] 5. Node Mobility Awareness: Considering node mobility, the network topology and routing are adjusted by sensing changes in node positions to adapt to changes in node locations;

[0394] 6. Multi-path routing: Utilizing multi-path routing technology, data is transmitted through multiple paths simultaneously, distributing the load and improving network bandwidth utilization;

[0395] 7. Self-organization: Adding a self-organization mechanism enables the network to automatically adjust routes without central management to adapt to the network's dynamic nature.

[0396] Based on the actual business scenarios of power wireless private network self-organizing networks, combining the above methods can effectively achieve route balancing in self-organizing networks and improve network performance and stability.

[0397] Finally, it should be noted that the above embodiments are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit them. Any modifications, improvements, or other modifications or equivalent substitutions made by those skilled in the art to the technical solutions of the present invention, as long as they do not depart from the spirit and scope of the technical solutions of the present invention, should be included within the protection scope of the present invention.

Claims

1. A method for self-organizing networks, monitoring, and topology maintenance based on a power wireless private network, characterized in that, include: Step 1: Through channel resource optimization, effective data frame carrying, and dynamic channel management, efficient sharing of channel resources in the self-organizing network based on unified broadcast signaling is achieved, laying the foundation for the construction of the self-organizing network of the power wireless private network; Step 2: Based on the efficient shared self-organizing network channel in Step 1, and combined with the time synchronization of the cellular self-organizing network, multi-hop networking and dynamic route updates of the cellular self-organizing network are realized. Step 3: For the multi-hop networking and dynamic routing updates in Step 2, select the corresponding dynamic power control method for the two different application scenarios. For the local networking scenario, select the low power consumption and miniaturization method of the device. For the remote blind spot filling scenario, select the method of the device's transmission power can be quickly and dynamically adjusted. Step 4: For the self-organizing network of the power wireless private network constructed in Step 3, dynamic network monitoring is carried out based on heterogeneous network multi-layer modeling, heterogeneous network coverage simulation and node location optimization, node health and topology anomaly analysis and early warning. Step 5: Maintain the topology of the self-organizing network based on intelligent acquisition of network data, rapid database retrieval, performance improvement of the self-organizing network edge, hierarchical management of self-organizing network devices, and self-organizing network route tracing.

2. The method for self-organizing network, monitoring, and topology maintenance based on power wireless private network according to claim 1, characterized in that: Channel resource optimization includes efficient channel resource allocation, optimal channel coding rules, and agile channel multiplexing; Efficient channel resource allocation includes multi-frame control as well as the fragmentation and reassembly of signaling and data; The optimal channel coding rule is selected from RCPC convolutional codes, CRC check codes, and Turbo codes based on the signal-to-noise ratio, bit error rate requirements, and encoding / decoding performance indicators of the application scenario. Agile channel multiplexing includes frequency division multiplexing, time division multiplexing, code division multiplexing, space division multiplexing, and channel communication rules for channel access control.

3. The method for self-organizing network, monitoring, and topology maintenance based on power wireless private network according to claim 1, characterized in that: The effective data frame bearer includes signaling multiplexing, channel information micro-bearer, and initial channel linkage.

4. The self-organizing network, monitoring, and maintenance method based on a power wireless private network according to claim 1, characterized in that: Dynamic channel management includes linking hop count with channel resources and optimizing channel resources; Among them, channel resource optimization is achieved by using or combining channel allocation algorithms, adaptive modulation and coding, cooperative communication and cooperative multiple access, non-orthogonal multiple access, priority scheduling and multipath transmission.

5. The method for self-organizing network, monitoring, and topology maintenance based on power wireless private network according to claim 1, characterized in that: Multi-hop networking includes broadcast signaling agile networking, node information micro-bearer, optimal routing decision and control center self-switching; Dynamic route updates include adaptive periodic configuration of network signaling and intelligent waiting for forwarding.

6. The method for self-organizing network, monitoring, and topology maintenance based on power wireless private network according to claim 1, characterized in that: In local networking scenarios, low power consumption and miniaturization of devices are achieved by selecting low-power CPUs, adjusting CPU operating frequencies, converting static memory to dynamic memory, using efficient storage technologies and low-power I / O devices, limiting program execution time or adjusting CPU sleep cycles, using interrupts to handle control flow, and reducing redundant calculations and memory usage.

7. The method for self-organizing network, monitoring, and topology maintenance based on power wireless private network according to claim 1, characterized in that: The equipment's transmission power can be quickly and dynamically adjusted, including multi-level transmission and dual-network power adaptation; Multi-stage launch includes: Multi-level output power is achieved by employing an adjustable power amplifier; Radio frequency switches select different power levels by switching signal paths, achieving rapid switching. The power control circuit and intelligent control unit monitor the network status in real time and intelligently adjust the transmission power according to communication needs; Digital signal processing dynamically adjusts the amplitude of a signal in the digital domain to achieve more precise power control; Dynamic power management is used to adjust the supply voltage and current to match the current power demand and achieve higher energy efficiency. Analyze the energy consumption distribution at different power levels to provide a reference for optimized design; Dual-network power adaptation includes: Real-time monitoring of network quality, congestion, and signal strength; adjusting device power levels under different network environments. By evaluating the characteristics of the current communication channel in real time, the power level can be adjusted more accurately to adapt to changes in the channel. Based on real-time monitoring of network conditions and channel estimation results, the system intelligently adjusts the transmission power to maximize communication quality while reducing power consumption. Critical tasks or services should be prioritized during power adjustments; Balancing communication performance and power consumption, and making reasonable trade-offs between different power levels to meet data transmission requirements while minimizing power consumption; Based on historical data and predictive models, network changes can be anticipated in advance, allowing power to be adjusted more proactively to meet future communication needs.

8. The method for self-organizing network, monitoring, and topology maintenance based on power wireless private network according to claim 1, characterized in that: Multilayer modeling of heterogeneous networks must meet the requirements of data format uniformity, protocol compatibility, and device frequency consistency; Node health assessment metrics include connectivity, energy consumption, task execution quality, load balancing, security, latency analysis, environmental awareness, and node health prediction. Intelligent data acquisition includes real-time streaming data processing, log file analysis, and power load forecasting.

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