A communication system networking method and communication system for power consumption information collection

Abstract

This application relates to a networking method and communication system for electricity consumption information collection. The communication system includes a base station, relay nodes, and terminal nodes. The base station periodically sends networking beacon frames. After powering on, the terminal nodes listen for networking beacon frames. If the terminal node receives a networking beacon frame sent by the base station within a preset superframe period, it determines that the terminal node is a directly connected node, reads the base station's synchronization message, obtains the competition for network access start time, initiates registration and network access, and transmits networking beacon frames downwards. After the terminal nodes complete networking, each node selects basic information data to evaluate the system's operating status. The subjective weights of the evaluation indicators are determined using the analytic hierarchy process (AHP), and the objective weights of the evaluation indicators are determined using the coefficient of variation method. Finally, the two methods are integrated to obtain a comprehensive weight for evaluating the quality of the communication lines. This achieves dynamic adjustment and routing optimization of the network structure, greatly improving network performance and stability.

Description

Technical Field

[0001] This application relates to the field of power distribution Internet of Things (IoT) resource allocation, and in particular to a communication system networking method and communication system for electricity consumption information collection. Background Technology

[0002] In the construction of smart grids, smart distribution networks are a key research focus, and communication technology is the foundation for realizing smart distribution networks. Without advanced communication networks, none of the advantages of smart distribution networks can be realized. Therefore, a crucial step in realizing smart grids is to establish a two-way, high-speed, and integrated communication network.

[0003] Currently, there are various communication system access methods for power distribution networks, including wired high-speed power line carrier communication (HPLC) technology, fiber optic communication technology, and wireless wireless communication technologies such as ZigBee, WiMAX, and GPRS. However, wired and wireless communication technologies each have their advantages and disadvantages: wireless is less expensive and suitable for areas that are difficult to access; wired is relatively stable and has higher reliability. With the large-scale integration of distributed power sources into the grid and the increasing demands of users for power quality, the system's requirements for grid reliability are increasing, and existing power distribution network communication systems cannot cope with the new power supply and consumption patterns and demands. Different communication methods are suitable for different environments and regions; to establish an efficient and reliable intelligent power distribution network communication system, it is necessary to use several communication methods in combination according to the actual situation. Summary of the Invention

[0004] This application provides a networking method and communication system for electricity consumption information collection. The technical solution of this application is as follows:

[0005] According to a first aspect of the embodiments of this application, a networking method for a communication system for collecting electricity consumption information is provided, the communication system including a base station, relay nodes, and terminal nodes, characterized in that the method includes:

[0006] After powering on, the terminal nodes and relay nodes listen for the network beacon frames;

[0007] If the node receives a network beacon frame sent by the base station within the preset superframe period, it determines that the terminal node or relay node is a local direct connection node, reads the synchronization message of the base station, obtains the competition network access start time, starts the registration network access, and transmits the network beacon frame downwards.

[0008] If the node does not receive a network beacon frame sent by the base station within the preset superframe period, it is determined that the terminal node or relay node is a non-directly connected node and enters the intelligent routing state.

[0009] Optionally, when a terminal node in the intelligent routing state receives wireless networking beacon frames from multiple potential relay nodes, it evaluates the wireless communication performance of the current network after accessing the potential relay nodes through its local edge computing module, and selects the relay node with the largest wireless communication performance value as the wireless relay node.

[0010] Furthermore, when a relay node receives carrier networking beacon frames from multiple potential relay nodes, it evaluates the carrier communication performance of the current network after accessing the potential relay nodes, selects the node with the largest carrier communication performance value as the quasi-carrier relay node, and if the quasi-carrier relay node does not change within a preset time, then the quasi-carrier relay node becomes the carrier relay node.

[0011] Optionally, after the terminal node is determined to be a directly connected node, the terminal node registers with the network within the time interval given by the network beacon frame.

[0012] Optionally, after the terminal node initiates registration and network access, it specifically includes:

[0013] By performing edge computing on the basic information data of each terminal node, the wireless communication performance of the current network after accessing a potential relay node is evaluated to determine the route. The basic information data includes at least one of power, communication speed, hop count, remaining energy, bit error rate, packet loss rate, and latency.

[0014] If the wireless communication performance value is lower than the evaluation threshold, the terminal node is identified as a problem node and enters the intelligent routing state.

[0015] Problem nodes perform minimum hop count synchronization to obtain the minimum number of hops that can reach the base station;

[0016] Once the hop count of the problematic node is synchronized, the relay node of the problematic node is determined.

[0017] After the relay node is determined, the problem node obtains the optimal route to the base station through a repeated route selection process, updates the routing table, and transmits the updated route to the base station.

[0018] Optionally, after registration is completed at the endpoint node, the method further includes:

[0019] Basic information data of terminal nodes is collected, a judgment matrix N is established, and the relative weights between each indicator are obtained by comparing factors in the evaluation factor set pairwise. The largest eigenvalue of the judgment matrix N is then calculated to obtain the eigenvector. Thus, the subjective weights are determined, and the judgment matrix is ​​constructed as follows:

[0020]

[0021] in, Characterization and evaluation factors right The relative importance values ​​are expressed using a scale of 1 to 9 and their reciprocals;

[0022] Assuming there are n terminal nodes, and each terminal node has m evaluation indicators, the eigenvector is obtained using the coefficient of variation method. This allows us to determine objective weights. The greater the variability of an indicator, the greater its importance in the evaluation object, and thus the greater its weight value.

[0023] Based on the multiplicative synthesis method that integrates subjective and objective weights, the final comprehensive weight vector is: ;

[0024] In an n x m matrix, rank transformation is performed to convert dimensional statistics into dimensionless rank sum ratios (RSRs). Then, parametric statistical analysis of the RSR distribution and sorting of the RSR values ​​are used to obtain the status of each evaluation object.

[0025] Take n evaluation objects and their corresponding m indicators, arrange the resulting indicators into an original data table of n rows and m columns, and then calculate the rank of each indicator for each evaluation object.

[0026] When different indicators have different weight values, the weighted RSR of the i-th evaluation object is calculated using the following formula:

[0027]

[0028] in, Let be the rank of the element in the i-th row and j-th column;

[0029] Following the rule of ascending from smallest to largest, Arrange identical values ​​in a column and compile... Frequency distribution, listing the frequency in each column. Calculate the frequency of each column. Determine each column The corresponding ranks and average ranks are respectively Calculate frequency and Using standard normal deviation Calculate the probability unit value Probit corresponding to p;

[0030] by As the dependent variable, The corresponding Probit grouping independent variable was used to calculate the linear regression equation:

[0031]

[0032] in, The intercept constant is... The tangent constant;

[0033] according to The values ​​are sorted into categories.

[0034] Optionally, assuming there are n terminal nodes, each with m evaluation indicators, the feature vector can be obtained using the coefficient of variation method. This determines the objective weights, specifically including:

[0035] Assume there are n terminal nodes, each terminal node has m evaluation metrics, and the evaluation metric vector of the i-th device is: The evaluation matrix is ​​then constructed as follows:

[0036]

[0037] Elements in the evaluation matrix This represents the value of the j-th index of the i-th objects. ;

[0038] The indicators are aligned:

[0039]

[0040] in, As a positive indicator, It is a negative indicator. This represents the maximum value of the element in the column containing the index vector;

[0041] Dedimensionalization of indicators:

[0042] Calculate the average and average difference of each indicator:

[0043]

[0044]

[0045] Calculate the coefficient of variation for each indicator:

[0046]

[0047] The coefficients of variation of each indicator were normalized to obtain the final indicators:

[0048]

[0049] Optionally, the registration of the terminal node on the network specifically includes:

[0050] The terminal node randomly selects a network access time slot and checks whether the channel is occupied.

[0051] If it is not occupied, a network access request frame Net_REQ is sent;

[0052] After receiving the network access request frame Net_REQ, the base station sends a reply frame Net_RSP to the terminal node. The reply frame Net_RSP contains the terminal node ID number and the terminal node address information. The terminal node ID number is assigned by the base station to the terminal nodes in the order in which the terminal nodes sent their network access applications.

[0053] When a terminal node receives a reply frame Net_RSP, it switches its local status flag to "on the network," records the terminal node ID number assigned to it by the base station, and adjusts the latency to complete synchronization based on the ranging information contained in the network access request frame Net_REQ and the reply frame Net_RSP.

[0054] Optionally, the method of the terminal node randomly selecting a network access time slot and detecting whether the channel is occupied further includes:

[0055] If the slot is occupied, the binary exponential backoff algorithm is used to randomly back off for a number of time slots before resending the network access request frame.

[0056] Optionally, the process of registering the terminal node on the network further includes:

[0057] In the control time frame, the base station establishes a dynamic time slot allocation table based on the number of nodes in the network, distributes data time slots equally to each terminal node, and sends the dynamic time slot table to each terminal node through broadcast.

[0058] The terminal node receives the dynamic time slot allocation table, looks up the data upload time slot corresponding to the dynamic time slot allocation table according to the terminal node ID number, and sends data according to the time slot corresponding to the terminal node.

[0059] According to a second aspect of the embodiments of this application, a communication system for collecting electricity consumption information is provided, characterized in that the communication system includes:

[0060] Base stations are used to periodically send network beacon frames in order to obtain the status of the network channels of the communication system.

[0061] Terminal nodes and relay nodes are used to listen for network beacon frames after power-on; and,

[0062] If the terminal node or relay node receives a network beacon frame sent by the base station within the preset superframe period, it is determined to be a local directly connected node, reads the synchronization message of the base station, obtains the competition for network access start time, starts the registration and network access, and transmits the network beacon frame downwards.

[0063] If the terminal node or relay node does not receive a network beacon frame sent by the base station within the preset superframe period, it is determined to be a non-directly connected node and enters the intelligent routing state.

[0064] Optionally, after the terminal node is determined to be a directly connected node, it registers on the network within the time interval given by the network beacon frame.

[0065] The terminal node includes an edge computing module, which is used to execute the following after the terminal node completes registration and is connected to the network:

[0066] Basic information data of terminal nodes is collected, a judgment matrix N is established, and the relative weights between each indicator are obtained by comparing factors in the evaluation factor set pairwise. The largest eigenvalue of the judgment matrix N is then calculated to obtain the eigenvector. Thus, the subjective weights are determined, and the judgment matrix is ​​constructed as follows:

[0067]

[0068] in, Characterization and evaluation factors right The relative importance values ​​are expressed using a scale of 1 to 9 and their reciprocals;

[0069] Assuming there are n terminal nodes, and each terminal node has m evaluation indicators, the eigenvector is obtained using the coefficient of variation method. This allows us to determine objective weights. The greater the variability of an indicator, the greater its importance in the evaluation object, and thus the greater its weight value.

[0070] Based on the multiplicative synthesis method that integrates subjective and objective weights, the final comprehensive weight vector is: ;

[0071] In an n x m matrix, rank transformation is performed to convert dimensional statistics into dimensionless rank sum ratios (RSRs). Then, parametric statistical analysis of the RSR distribution and sorting of the RSR values ​​are used to obtain the status of each evaluation object.

[0072] Take n evaluation objects and their corresponding m indicators, arrange the resulting indicators into an original data table of n rows and m columns, and then calculate the rank of each indicator for each evaluation object.

[0073] When different indicators have different weight values, the weighted RSR of the i-th evaluation object is calculated using the following formula:

[0074]

[0075] in, Let be the rank of the element in the i-th row and j-th column;

[0076] Following the rule of ascending from smallest to largest, Arrange identical values ​​in a column and compile... Frequency distribution, listing the frequency in each column. Calculate the frequency of each column. Determine each column The corresponding ranks and average ranks are respectively Calculate frequency and Using standard normal deviation Calculate the probability unit value Probit corresponding to p;

[0077] by As the dependent variable, The corresponding Probit grouping independent variable was used to calculate the linear regression equation:

[0078]

[0079] in, The intercept constant is... It is the tangent constant;

[0080] according to The values ​​are sorted into categories.

[0081] Beneficial effects:

[0082] The communication system networking method for electricity consumption information collection involved in this application includes a base station, relay nodes, and terminal nodes. The base station periodically sends networking beacon frames to obtain the status of the network channel of the communication system. After powering on, the terminal node listens for networking beacon frames. If the terminal node receives a networking beacon frame sent by the base station within a preset superframe period, it determines that the terminal node or relay node is a directly connected node, reads the synchronization message of the base station, obtains the competition for network access start time, starts registration and network access, and transmits the networking beacon frame downwards. If the terminal node does not receive a networking beacon frame sent by the base station within the preset superframe period, it determines that the terminal node or relay node is not a directly connected node and enters intelligent routing state. After the terminal nodes complete the network, each terminal node selects at least one of the following to evaluate the system's operating status: power, communication speed, hop count, remaining energy, bit error rate, packet loss rate, and latency. The subjective weights of the evaluation indicators are determined using the analytic hierarchy process (AHP) and the objective weights are determined using the coefficient of variation method. Finally, the two methods are combined to obtain a comprehensive weight for each indicator to evaluate the quality of the communication lines. Nodes with poor evaluation results will reselect their routing paths, thus realizing dynamic adjustment and optimization of the network structure and greatly improving the network's performance and stability.

[0083] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0084] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application, and do not constitute an undue limitation of this application.

[0085] Figure 1 This is a schematic diagram of a network topology for a communication system for collecting electricity consumption information, according to an exemplary embodiment.

[0086] Figure 2 This is a schematic diagram illustrating the networking process of a communication system for collecting electricity consumption information, according to an exemplary embodiment.

[0087] Figure 3 This is a schematic diagram illustrating the process of a terminal node registering on the network in a communication system for collecting electricity consumption information, according to an exemplary embodiment.

[0088] Figure 4 This is a schematic diagram illustrating the process of node decommissioning in a communication system terminal for electricity consumption information collection, according to an exemplary embodiment. Detailed Implementation

[0089] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0090] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of communication systems and methods consistent with some aspects of this application as detailed in the appended claims.

[0091] In electricity consumption information collection networks, smart meter distribution has the following characteristics: multiple meters are located in the same meter box, with the meters within the box close together and unobstructed; meters in different meter boxes are far apart and obstructed. This application, considering the distribution of smart meters, access methods, and the attenuation differences between low-power wireless and power line carrier signals, proposes a networking method for a communication system oriented towards electricity consumption information collection. This communication network is a dual-mode network combining high-speed power line carrier and low-power wireless. By comparing three networking technologies—power line carrier and wireless complementing each other, wireless as an extension of power line carrier communication, and power line carrier communication as an extension of wireless communication—it designs a fusion method of HPLC and low-power wireless based on a unified MAC layer.

[0092] Figure 1 This is a schematic diagram of a network topology for a communication system for collecting electricity consumption information, according to an exemplary embodiment. Figure 1 As shown, the network includes a base station 301, multiple relay nodes 201 and 202, and multiple terminal nodes 101. These nodes can choose to access the network via low-voltage power line or low-power wireless communication. If there are other obstructions between nodes, power line communication is selected; if there are power line carrier noise sources between nodes, wireless communication is selected to establish a connection. Nodes can choose between power line or wireless communication methods according to different service requirements.

[0093] Terminal nodes include source nodes that act as data senders and target nodes that act as data receivers. When the distance between the source and target nodes is too large, a multi-hop relay method is used to establish a connection, thereby ensuring the reliability and effectiveness of the communication network. Among them, relay nodes 201 and 202 can receive signals and forward them, thereby realizing communication between base station 301 and multiple terminal nodes 101.

[0094] Figure 2 This is a flowchart illustrating a communication system networking method for collecting electricity consumption information, according to an exemplary embodiment. The communication system includes a base station, relay nodes, and terminal nodes. The method includes:

[0095] S11, The base station periodically sends network beacon frames in order to obtain the status of the network channel of the communication system;

[0096] S12, After the terminal node and relay node are powered on, they listen to the network beacon frame;

[0097] S13, if the terminal node receives a network beacon frame sent by the base station within the preset superframe period, then it is determined that the terminal node or relay node is a local direct connection node, reads the synchronization message of the base station, obtains the competition network access start time, starts registration network access, and transmits the network beacon frame downwards.

[0098] S14. If the terminal node does not receive a network beacon frame sent by the base station within the preset superframe period, the terminal node is determined to be a local non-directly connected node and enters the intelligent routing state.

[0099] Terminal nodes that are not directly connected to the base station cannot communicate directly with the base station.

[0100] In some embodiments, when a terminal node in intelligent routing state receives wireless networking beacon frames from multiple potential relay nodes, it first evaluates the wireless communication performance of the current network after accessing the potential relay nodes through an edge computing module, and selects the relay node with the highest wireless communication performance value as the wireless relay node. Simultaneously, when a relay node receives carrier networking beacon frames from multiple potential relay nodes, it first evaluates the carrier communication performance of the current network after accessing the potential relay nodes, and selects the node with the highest carrier communication performance value as the quasi-carrier relay node.

[0101] In some embodiments, the relay node begins to test the quasi-carrier relay node. If the quasi-carrier relay node does not change within a preset time, then the quasi-carrier relay node becomes the carrier relay node. If the quasi-carrier relay node changes, the relay node retests the new quasi-carrier relay node.

[0102] This application illustrates, according to an exemplary embodiment, the process of terminal node registration on the network in a communication system for electricity consumption information collection as follows: Figure 3 As shown. Within the time interval given by the network beacon frame, the process of the terminal node registering on the network specifically includes:

[0103] S21, The terminal node randomly selects a network access time slot and checks whether the channel is occupied;

[0104] S22, if not occupied, send a network access request frame Net_REQ;

[0105] S23, after receiving the network access request frame Net_REQ, the base station sends a reply frame Net_RSP to the terminal node. The reply frame Net_RSP contains the terminal node ID number and the terminal node address information. The terminal node ID number is assigned by the base station to the terminal nodes in the order in which the terminal nodes send their network access applications.

[0106] S24, when the terminal node receives the reply frame Net_RSP, it switches its local status flag to "on the network", records the terminal node ID number assigned to it by the base station, and adjusts the time delay to complete synchronization based on the ranging information contained in the network access request frame Net_REQ and the reply frame Net_RSP.

[0107] In some embodiments, upon receiving a reply frame Net_RSP, the terminal node completes authentication and switches its status flag to "on the network," thus completing network access. Simultaneously, it records the ID number assigned to it by the base station and adjusts the latency to complete synchronization based on the ranging information contained in Net_REQ and Net_RSP.

[0108] If S25 is occupied, the binary exponential backoff algorithm is used to randomly back off for a number of time slots before resending the network access request frame.

[0109] In some embodiments, if a terminal node does not receive a reply frame Net_RSP from the base station within the first request time of sending a network access request frame Net_REQ, it adopts a binary exponential backoff algorithm to randomly back off for several time slots and resends the network access request frame Net_REQ.

[0110] In some embodiments, after the terminal node is on the network, the process of registering the terminal node on the network further includes:

[0111] S26, In the control time frame, the base station establishes a dynamic time slot allocation table based on the number of nodes in the network, distributes data time slots equally to each terminal node, and sends the dynamic time slot table to each terminal node through broadcast.

[0112] S27, the terminal node receives the dynamic time slot allocation table, looks up the data upload time slot corresponding to the dynamic time slot allocation table according to the terminal node ID number, and sends data according to the time slot corresponding to the terminal node.

[0113] After a terminal node is connected to the network, the base station, in the control frame, establishes a dynamic timeslot allocation table based on the number of connected nodes, distributing data timeslots evenly to each node. This table is then broadcast. Terminal nodes first check their own status; if connected, they receive the table; otherwise, they discard it. Connected nodes, upon receiving the table, use the ID number in the table to find their corresponding data upload timeslot. Each terminal node then sends data in its assigned timeslot, avoiding conflicts.

[0114] Figure 4 This is a schematic diagram illustrating the process of a terminal node decommissioning from the network in a communication system terminal for electricity consumption information collection, according to an exemplary embodiment. Figure 4 As shown, the specific methods for terminal node decommissioning include:

[0115] S31, the base station periodically broadcasts network beacon frames in each superframe network time frame;

[0116] S32, after receiving the network beacon frame, the terminal node sends a network decommissioning request Net_SSoffREQ to the base station;

[0117] S33, after receiving the network exit request Net_SSoffREQ sent by the terminal node, the base station sends a removal instruction Net_SSoffSP to the terminal, disconnects the connection with the terminal, and deletes the terminal node's information from the local and dynamic time slot tables.

[0118] The base station periodically broadcasts network beacon frames in each superframe network time frame for new terminal access and for existing terminal decommissioning. When a terminal needs to voluntarily leave the system for some reason, it sends a decommissioning request to the base station. Upon receiving the decommissioning request, the base station sends a removal command to the terminal, then disconnects from the terminal, removes the terminal's information from its local and dynamic time slot tables, and releases the radio resources it occupies for allocation to other new terminal nodes. Simultaneously, after receiving the removal command, the terminal will only respond to the base station's broadcasts (excluding synchronization information).

[0119] In addition to proactive network shutdown, an exemplary embodiment of this application illustrates a method for a base station to force a terminal node to shut down. For forced shutdown, if the base station sends three consecutive network beacon frames without receiving a response from the terminal, or if the received data from the endpoint node is corrupted, the connection with the terminal node is forcibly disconnected, the time slot information of the terminal is deleted from the dynamic time slot table, the radio resources occupied by the terminal are released, and the time slot status is set to idle to facilitate resource allocation for newly accessed terminals.

[0120] In some embodiments of this application, to address the increased information transmission delay and bandwidth caused by the expansion of the distribution network and the surge in inspection information, this application introduces an edge computing-based smart distribution network operation status assessment based on the concept of power Internet of Things edge computing. By using edge computing modules on base stations, relay nodes, and terminal nodes to perform edge computing and analysis on the system's network information, the on-network status of terminal nodes can be assessed. This solves the problems of poor adaptability and low real-time performance in dual-mode communication networks and improves reliability. Optionally, the edge computing module can be a smart sensor.

[0121] In some embodiments, the system's operational status is evaluated after each endpoint node has completed its registration and is online.

[0122] S41, Collect basic information data of terminal nodes, establish a judgment matrix N, and obtain the relative weights between each indicator by comparing factors in the evaluation factor set pairwise, and calculate the maximum eigenvalue of the judgment matrix N to obtain the eigenvector. This allows us to determine subjective weights.

[0123] In some embodiments, the basic information data includes at least one of power, communication speed, hop count, remaining energy, bit error rate, packet loss rate, and latency. Two of the basic information data are used as evaluation factors and compared pairwise to obtain the relative weights between each indicator.

[0124] In some embodiments, the judgment matrix is ​​constructed as follows:

[0125]

[0126] in, Characterization and evaluation factors right The relative importance values ​​are represented by scales 1 through 9 and their reciprocals. Finally, the largest eigenvalue of matrix N is calculated, yielding its eigenvector. ,in, The weights of each evaluation indicator correspond to the elements in this vector.

[0127] In some embodiments, subjective weights are determined, and qualitative and quantitative analysis are combined using the analytic hierarchy process (AHP). To obtain the weights of each indicator, the AHP first establishes a judgment matrix; then, it compares factors in the evaluation factor set pairwise to obtain the relative weights between each indicator; and finally, to ensure the reliability of the obtained data, the consistency index of the judgment matrix is ​​checked.

[0128] S42: Assuming there are n terminal nodes, each terminal node has m evaluation indicators, the eigenvector is obtained using the coefficient of variation method. This allows us to determine objective weights. The greater the variability of an indicator, the greater its importance in the evaluation object, and thus the greater its weight value.

[0129] In some embodiments, the objective weights are determined using the coefficient of variation method, specifically including:

[0130] S421, assuming there are n terminal nodes, each terminal node has m evaluation indicators, and the evaluation indicator vector of the i-th device is... The evaluation matrix is ​​then constructed as follows:

[0131]

[0132] Elements in the evaluation matrix This represents the value of the j-th index of the i-th objects. ;

[0133] S422, Indicators are aligned:

[0134]

[0135] in, As a positive indicator, It is a negative indicator. This represents the maximum value of the element in the column containing the index vector.

[0136] In some embodiments, a smaller negative index value and a larger positive index value indicate better communication performance.

[0137] S423, Dimensionless Measurement of Indicators:

[0138] S424, calculate the average and average difference of each indicator:

[0139]

[0140]

[0141] S425, Calculate the coefficient of variation for each indicator:

[0142]

[0143] S426, normalize the coefficients of variation of each indicator to obtain the final indicators:

[0144]

[0145] S43, based on the multiplicative synthesis method, integrates subjective weights and objective weights to determine the comprehensive weight.

[0146] In some embodiments, to combine the strengths of subjective and objective indicator weights and obtain more reasonable indicator weights, this paper uses a multiplicative synthesis method to fuse the subjective and objective indicator weights. The weight vectors obtained by the analytic hierarchy process (AHP) and the coefficient of variation method are as follows: , The comprehensive weight of the j-th indicator obtained after fusion is:

[0147]

[0148] The final comprehensive weight vector is: .

[0149] S44. In an n x m matrix, rank transformation is performed to convert dimensional statistics into dimensionless rank-sum-ratios (RSR). Then, parametric statistical analysis of the RSR distribution and sorting of the RSR values ​​are used to obtain the state of each evaluation object.

[0150] In some embodiments, the rank-sum ratio comprehensive evaluation method is used for evaluation.

[0151] S45, take n evaluation objects and their corresponding m indicators, arrange the obtained indicators into an original data table of n rows and m columns, and then calculate the rank of each indicator for each evaluation object.

[0152] In some embodiments, when ranking, low-priority indicators are ranked from largest to smallest, high-priority indicators are ranked from smallest to largest, and if the data for the same indicator are the same, they are assigned to the average rank.

[0153] S46, When different weight values ​​are given to each indicator, the weighted RSR of the i-th evaluation object is calculated using the following formula:

[0154]

[0155] in, Let be the rank of the element in the i-th row and j-th column.

[0156] S47, Probability unit calculation, following the rule of ascending order, Arrange identical values ​​in a column and compile... Frequency distribution, listing the frequency in each column. Calculate the frequency of each column. Determine each column The corresponding ranks and average ranks are respectively Calculate frequency and Using standard normal deviation Calculate the probability unit value Probit corresponding to p.

[0157] S48, with As the dependent variable, The corresponding Probit grouping independent variable was used to calculate the linear regression equation:

[0158]

[0159] in, The intercept constant is... It is the tangent constant;

[0160] S49, according to The values ​​are sorted into categories.

[0161] In some embodiments, the evaluation status is divided into 3-5 levels. The optimal level is one where the variance of each level is consistent and the difference between them is significant. The resulting level is the evaluation status of each node. Problem nodes with poor evaluations will reselect a routing path.

[0162] In some embodiments, when the evaluation result (wireless communication performance value) of a terminal node is lower than the evaluation threshold, the terminal node is identified as a problem node and enters intelligent routing state to adjust the path structure of the wireless communication system, specifically including:

[0163] S51, the problem node performs minimum hop count synchronization to obtain the minimum hop count that can reach the base station.

[0164] S52, After the hop count of the problem node is synchronized, determine the relay node of the problem node.

[0165] In some embodiments, the network generates relay nodes and prospective relay nodes in preparation for path selection.

[0166] S53, After determining the relay node, the problem node obtains the optimal route to the base station through a repeated route selection process, and updates the routing table and transmits it to the base station to determine the path.

[0167] This application introduces a smart distribution network operation status assessment method based on edge computing. After the network is completed, each node selects at least one of the following to evaluate the system's operation status: power, communication speed, hop count, remaining energy, bit error rate, packet loss rate, and latency. The subjective weights of the evaluation indicators are determined using the analytic hierarchy process (AHP) and the objective weights are determined using the coefficient of variation method. Finally, the two methods are integrated to obtain a comprehensive weight for each indicator to assess the quality of the communication lines. Nodes with poor evaluation results will reselect their routing paths, realizing dynamic adjustment and optimization of the network structure, which greatly improves the network's performance and stability.

[0168] In summary, the network method for a dual-mode communication system in a distribution substation for electricity information collection, as described in this invention, constructs a dual-tree network combining tree and mesh networks for HPLC and wireless converged communication. Based on the concept of edge computing in the power Internet of Things, it introduces edge computing-based intelligent distribution network operation status assessment. By collecting basic information and other data from each terminal node, the subjective weights of the assessment indicators are determined using the analytic hierarchy process (AHP), and the objective weights are determined using the coefficient of variation method. Finally, the two are fused to obtain the comprehensive weight of each indicator, and nodes requiring changes in routing paths are dynamically adjusted. This application uses intelligent sensor terminals on base stations, relay nodes, and terminal nodes to perform edge computing and analysis of the system's network information, achieving a preliminary assessment of the on-network status of terminal nodes. This solves the problems of poor adaptability and low real-time performance in dual-mode communication networking and improves reliability.

[0169] The communication system provided in this application embodiment can be specifically used to perform the above-described... Figure 2 In accordance with the solution provided in the corresponding method embodiments, specifically, the communication system includes a base station, a relay node, and a terminal node.

[0170] The base station periodically sends network beacon frames in order to obtain the status of the network channel of the communication system;

[0171] After powering on, the terminal nodes and relay nodes listen for the network beacon frames;

[0172] If the terminal node receives a network beacon frame sent by the base station within the preset superframe period, it determines that the terminal node or relay node is a local direct connection node, reads the synchronization message of the base station, obtains the competition network access start time, starts the registration network access, and transmits the network beacon frame downwards.

[0173] If the terminal node does not receive a network beacon frame sent by the base station within the preset superframe period, it is determined that the terminal node or relay node is a non-directly connected node and enters the intelligent routing state.

[0174] In some embodiments, the terminal node and relay node each include an edge computing module. The edge computing module is used by the terminal node to perform the following after it has completed registration on the network:

[0175] Basic information data of terminal nodes is collected, a judgment matrix N is established, and the relative weights between each indicator are obtained by comparing factors in the evaluation factor set pairwise. The largest eigenvalue of the judgment matrix N is then calculated to obtain the eigenvector. Thus, the subjective weights are determined, and the judgment matrix is ​​constructed as follows:

[0176]

[0177] in, Characterization and evaluation factors right The relative importance values ​​are expressed using a scale of 1 to 9 and their reciprocals;

[0178] Assuming there are n terminal nodes, and each terminal node has m evaluation indicators, the eigenvector is obtained using the coefficient of variation method. This allows us to determine objective weights. The greater the variability of an indicator, the greater its importance in the evaluation object, and thus the greater its weight value.

[0179] Based on the multiplicative synthesis method that integrates subjective and objective weights, the final comprehensive weight vector is: ;

[0180] In an n x m matrix, rank transformation is performed to convert dimensional statistics into dimensionless rank sum ratios (RSRs). Then, parametric statistical analysis of the RSR distribution and sorting of the RSR values ​​are used to obtain the status of each evaluation object.

[0181] Take n evaluation objects and their corresponding m indicators, arrange the resulting indicators into an original data table of n rows and m columns, and then calculate the rank of each indicator for each evaluation object.

[0182] When different indicators have different weight values, the weighted RSR of the i-th evaluation object is calculated using the following formula:

[0183]

[0184] in, Let be the rank of the element in the i-th row and j-th column;

[0185] Following the rule of ascending from smallest to largest, Arrange identical values ​​in a column and compile... Frequency distribution, listing the frequency in each column. Calculate the frequency of each column. Determine each column The corresponding ranks and average ranks are respectively Calculate frequency and Using standard normal deviation Calculate the probability unit value Probit corresponding to p;

[0186] by As the dependent variable, The corresponding Probit grouping independent variable was used to calculate the linear regression equation:

[0187]

[0188] in, The intercept constant is... The tangent constant;

[0189] according to The values ​​are sorted into categories.

[0190] The application scenarios described in this application are for the purpose of more clearly illustrating the technical solutions of this application, and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the emergence of new application scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0191] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0192] In some possible implementations, the electronic device according to this application may include at least one processor and at least one memory. The memory stores program code that, when executed by the processor, causes the processor to perform the operational data management methods according to the various exemplary embodiments of this application described above. For example, the processor may perform steps such as those in the operational data management method.

[0193] It should be noted that although several units or sub-units of the communication system have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0194] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, or one step may be broken down into multiple steps.

[0195] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0196] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable image scaling device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable image scaling device, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A communication system that specifies the functions in one or more boxes.

[0197] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable image scaling device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction communication system implemented in the process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0198] These computer program instructions can also be loaded onto a computer or other programmable image scaling device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0199] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0200] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A networking method for a communication system for collecting electricity consumption information, the communication system comprising a base station, relay nodes, and terminal nodes, characterized in that, The method includes: The base station periodically sends network beacon frames in order to obtain the status of the network channel of the communication system; After powering on, the terminal nodes and relay nodes listen for the network beacon frames; If the terminal node receives a network beacon frame sent by the base station within the preset superframe period, it determines that the terminal node or relay node is a local direct connection node, reads the synchronization message of the base station, obtains the competition network access start time, starts the registration network access, and transmits the network beacon frame downwards. If the terminal node does not receive the network beacon frame sent by the base station within the preset superframe period, it is determined that the terminal node or relay node is a non-directly connected node and enters the intelligent routing state. After the terminal node initiates registration and network access, the specific steps include: By performing edge computing on the basic information data of each terminal node, the wireless communication performance of the current network after accessing a potential relay node is evaluated to determine the route. The basic information data includes at least one of power, communication speed, hop count, remaining energy, bit error rate, packet loss rate, and latency. If the wireless communication performance value is lower than the evaluation threshold, the terminal node is identified as a problem node and enters the intelligent routing state. Problem nodes perform minimum hop count synchronization to obtain the minimum number of hops that can reach the base station; Once the hop count of the problematic node is synchronized, the relay node of the problematic node is determined. After the relay nodes are determined, the problem node obtains the optimal route to the base station through a repeated route selection process, updates the routing table, and transmits the updated route to the base station to determine the route. After registration and networking are completed at the endpoint node, the method further includes: Basic information data of terminal nodes is collected, a judgment matrix N is established, and the relative weights between each indicator are obtained by comparing factors in the evaluation factor set pairwise. The largest eigenvalue of the judgment matrix N is then calculated to obtain the eigenvector. Thus, the subjective weights are determined, and the judgment matrix is ​​constructed as follows: in, Characterization and evaluation factors right The relative importance values ​​are expressed using a scale of 1 to 9 and their reciprocals; Assuming that there are n terminal nodes, each of which has m evaluation indexes, a characteristic vector is obtained by using a coefficient of variation method Thus, objective weights are determined, wherein the greater the variation degree of an index is, the greater the importance of the index in the evaluation object is, and thus the greater the weight value is; The final comprehensive weight vector is obtained by fusing the subjective weight and the objective weight based on the multiplication synthesis method: ; By performing rank transformation on an n x m matrix, dimensional statistics are converted into dimensionless rank sum ratios (RSRs). Then, by using parametric statistical analysis to analyze the RSR distribution and sorting the RSR values, the state of each evaluation object can be obtained. Take n evaluation objects and their corresponding m indicators, arrange the resulting indicators into an original data table of n rows and m columns, and then calculate the rank of each indicator for each evaluation object. When different indicators have different weight values, the weighted RSR of the i-th evaluation object is calculated using the following formula: in, Let be the rank of the element in the i-th row and j-th column; Following the rule of ascending from smallest to largest, Arrange identical values ​​in a column and compile... Frequency distribution, listing the frequency in each column. Calculate the frequency of each column. Determine each column The corresponding ranks and average ranks are respectively Calculate frequency and Using standard normal deviation Calculate the probability unit value Probit corresponding to p; by As the dependent variable, The corresponding Probit grouping independent variable was used to calculate the linear regression equation: wherein is an intercept constant, is a slope constant; According to the values are binned.

2. The method of claim 1, wherein, After the terminal node is identified as a directly connected node, it registers with the network within the time interval given by the network beacon frame.

3. The method of claim 1, wherein, Assuming there are n terminal nodes, and each terminal node has m evaluation indicators, the eigenvector is obtained using the coefficient of variation method. This determines the objective weights, specifically including: Assume there are n terminal nodes, each terminal node has m evaluation metrics, and the evaluation metric vector of the i-th device is: The evaluation matrix is ​​then constructed as follows: elements in the evaluation matrix the value of the jth indicator of the ith object, ; The indicators are aligned: wherein, is a positive indicator, is a negative indicator, denotes the maximum value of the elements in the column of the indicator vector. Index de-dimensioning: Calculate the average and average difference of each indicator: Calculate the coefficient of variation for each indicator: The coefficients of variation of each indicator were normalized to obtain the final indicators: 。 4. The method of claim 2, wherein, The registration of the terminal node on the network specifically includes: The terminal node randomly selects a network access time slot and checks whether the channel is occupied. If it is not occupied, a network access request frame Net_REQ is sent; After receiving the network access request frame Net_REQ, the base station sends a reply frame Net_RSP to the terminal node. The reply frame Net_RSP contains the terminal node ID number and the terminal node address information. The terminal node ID number is assigned by the base station to the terminal nodes in the order in which the terminal nodes sent their network access applications. When a terminal node receives a reply frame Net_RSP, it switches its local status flag to "on the network," records the terminal node ID number assigned to it by the base station, and adjusts the latency to complete synchronization based on the ranging information contained in the network access request frame Net_REQ and the reply frame Net_RSP.

5. The method of claim 4, wherein, The method of randomly selecting a network access time slot and detecting whether the channel is occupied by the terminal node also includes: If the slot is occupied, the binary exponential backoff algorithm is used to randomly back off for a number of time slots before resending the network access request frame.

6. The method of claim 5, wherein, The process of registering the terminal node on the network further includes: In the control time frame, the base station establishes a dynamic time slot allocation table based on the number of nodes in the network, distributes data time slots equally to each terminal node, and sends the dynamic time slot table to each terminal node through broadcast. The terminal node receives the dynamic time slot allocation table, looks up the data upload time slot corresponding to the dynamic time slot allocation table according to the terminal node ID number, and sends data according to the time slot corresponding to the terminal node.

7. A communication system for power consumption information collection, employing the networking method of any one of claims 1-6, characterized in that, The communication system includes: Base stations are used to periodically send network beacon frames in order to obtain the status of the network channels of the communication system. Terminal nodes and relay nodes are used to listen for network beacon frames after power-on; and, If the terminal node or relay node receives a network beacon frame sent by the base station within the preset superframe period, it is determined to be a local directly connected node, reads the synchronization message of the base station, obtains the competition for network access start time, starts the registration and network access, and transmits the network beacon frame downwards. If the terminal node or relay node does not receive a network beacon frame sent by the base station within the preset superframe period, it is determined to be a non-directly connected node and enters the intelligent routing state.

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