Distributed remote electric power data centralized reading system

Abstract

The invention relates to the technical field of intelligent electric meter communication, and discloses a distributed remote electric power data centralized reading system, which comprises a message synchronization module used for a master node to send a synchronization message to a slave node, and the slave node inquires to obtain locally stored electric power data after receiving the synchronization message; the data slicing module is used for slicing the power data of the a slave nodes to obtain b sliced data; the data transmission scheme generation module is used for generating a data transmission scheme through a whale optimization algorithm; the data consistency verification module is used for verifying the consistency of the power data by the main node; according to the method, the electric power data of the multiple intelligent electric meters are sliced, multi-channel transmission of the electric power data is completed through the whale optimization algorithm, so that the data transmission efficiency is improved, and in addition, the consistency verification and data retransmission mechanism of the sliced data is provided, so that the consistency of data transmission is ensured.

Description

Technical Field

[0001] This invention relates to the field of smart meter communication technology, and more specifically, to a distributed remote power data collection system. Background Technology

[0002] A power data collection system refers to the transmission of power data collected by smart meters to the power company's data center via a network, thereby enabling remote reading of meter data. Existing power data collection systems typically employ a master-slave architecture, where the master node initiates meter reading commands, and the slave nodes respond and send power data. However, during power data transmission over the network, network fluctuations or failures may occur, making it impossible to guarantee the time consistency of power data across all slave nodes.

[0003] Existing technology involves the slave node synchronizing data after receiving a synchronization meter reading message from the master node. First, it checks whether the data transmission channel is occupied. If it is occupied, it randomly delays until the data transmission channel is free, and then sends a data transmission request to the master node. After the master node confirms, it sends an acknowledgment message. After receiving the acknowledgment message, the slave node starts sending data until all slave nodes have sent their data, thus completing one meter reading process.

[0004] While the above scheme can ensure the time consistency of power data across all slave nodes through message acknowledgment mechanisms, it may increase data transmission time when the transmitted power data is large, or the slave node may not receive the acknowledgment message from the master node due to network latency. In such cases, the slave node will remain in a waiting state, resulting in the inability to send data and low data transmission efficiency. Summary of the Invention

[0005] This invention provides a distributed remote power data collection system to solve the technical problems mentioned in the background.

[0006] This invention provides a distributed remote power data collection system, comprising:

[0007] The message synchronization module is used by the master node to send synchronization messages to the slave node, and the slave node queries and obtains the locally stored power data after receiving the synchronization message.

[0008] The master node corresponds to the power company's data center;

[0009] Each slave node corresponds to a smart meter;

[0010] The data slicing module is used to sort the power data of a slave nodes in descending order of time, and slice them at equal time intervals to obtain b slice data.

[0011] Each slice of data is assigned two globally unique codes, where the first code is used to identify the power data of the slave node, and the second code is used to identify the slice data; the first code and the second code are encoded using a hash function, and the first code is the same for each slice of data of the same slave node;

[0012] The data transmission scheme generation module is used to generate a data transmission scheme through the whale optimization algorithm, and to transmit b slices of data from a slave node to n transmission channels of the master node through the data transmission scheme; before executing the data transmission scheme, the size of each slice of data, the bandwidth and latency of the corresponding transmission channel of the slice of data are obtained;

[0013] The data consistency verification module is used by the master node to verify the consistency of power data after the data transmission scheme is executed.

[0014] Furthermore, the node queries the locally stored power data based on the start and end times of the synchronized power data in the synchronization message.

[0015] Furthermore, the types of transmission channels through which slice data is transmitted to the master node include wired communication, wireless communication, and power line communication.

[0016] Furthermore, generating a data transmission scheme using the whale optimization algorithm includes the following steps:

[0017] Step S201: Randomly generate an initial population consisting of J individuals that meet the constraints.

[0018] The constraints include: Constraint 1, each slice of data must be allocated one transmission channel; Constraint 2, the maximum number of slices allocated to a transmission channel is K; Constraint 3, a slice of data can only be allocated to one transmission channel at a time.

[0019] The vector encoding Q of the position of an individual in the initial population is represented as:

[0020]

[0021] in and These represent the real-number codes of the first, second, (n-1), and (n-1)th transmission channels, respectively, assigned to the first to the Kth slices of data. The real-number codes of the slice data range from 0 to c, where c = a * b. When the real-number code of a slice assigned to a transmission channel is 0, it means that the transmission channel has not assigned that slice data.

[0022] Step S202: Iterative convergence, update the vector encoding of the position of individuals in the initial population;

[0023] Step S203: Determine whether the iteration termination condition is met. If the iteration termination condition is met, select the individual with the largest fitness value in the initial population as the optimal individual, and decode the data transmission scheme according to the vector encoding of the optimal individual's position. Otherwise, return to step S202.

[0024] The iteration termination conditions include: Iteration termination condition 1, the current iteration number is greater than or equal to the maximum iteration number F; Iteration termination condition 2, within L consecutive iterations, the difference between the maximum fitness values ​​of individuals is less than or equal to G.

[0025] Furthermore, J, K, F, L, and G are all user-defined parameters.

[0026] Furthermore, iterative convergence includes the following steps:

[0027] Step S301: Calculate the fitness value of each individual in the initial population using the fitness function;

[0028] Step S302: Randomly generate a first random number between 0 and 1. If the first random number is greater than or equal to the first threshold, perform a spiral attack; otherwise, proceed to step S303.

[0029] The first threshold is a user-defined parameter;

[0030] Step S303: Randomly generate a first vector and take its modulus to obtain a second random number. If the second random number is greater than or equal to a second threshold, then perform a random search; otherwise, surround the prey. The second threshold is a user-defined parameter.

[0031] Furthermore, the formula for calculating iterative convergence includes:

[0032] The formula for calculating random search is as follows:

[0033]

[0034]

[0035]

[0036] in and Let represent the vector codes for the positions of a random individual in the initial population at iteration number t+1 and iteration number t, respectively. The vector encoding representing the position of the i-th individual in the initial population at the t-th iteration number. Denotes the first vector. Represents the second vector. and Both represent random numbers between 0 and 1, t represents the t-th iteration number, and F represents the maximum number of iterations;

[0037] The formula for calculating the spiral attack is as follows:

[0038]

[0039] in and These are vector codes representing the positions of the i-th individual in the initial population at iteration number t+1 and iteration number t, respectively. The vector encoding represents the position of the individual with the largest fitness value in the t-th iteration, where e is the natural constant, P is a random number between -1 and 1, and cos represents the cosine function;

[0040] The formula for calculating the encirclement of prey is as follows:

[0041]

[0042] in and These are vector codes representing the positions of the i-th individual in the initial population at iteration number t+1 and iteration number t, respectively. The vector encoding representing the position of the individual with the largest fitness value at the t-th iteration. Denotes the first vector. This represents the second vector.

[0043] Furthermore, the formula for calculating the fitness function is as follows:

[0044] Where 1≤u≤a*b, 1≤v≤n, and Fit represents the fitness value of an individual. and The values ​​represent the size, bandwidth, and latency of the slice data transmitted from the u-th slice data to the v-th transmission channel, corresponding to the non-zero dimension values ​​in the vector encoding of an individual's position.

[0045] Furthermore, when the master node receives slice data from a slave node, it inputs the slice data into the same hash function and outputs a third code. If the third code is the same as the second code, it means that the consistency check of the slice data has passed. Otherwise, the master node sends a data retransmission message to the slave node so that the slave node can retransmit the slice data.

[0046] Furthermore, after the master node receives the slice data from all slave nodes, it categorizes the slice data belonging to the same first code to obtain the slice data set for each slave node. When the number of slice data of a slave node is equal to b, it means that the consistency check of the slice data has passed. Otherwise, the master node sends a data retransmission message to the slave node for the slave node to retransmit the slice data of the slave node.

[0047] The beneficial effects of this invention are as follows: This invention improves the efficiency of data transmission by slicing the power data of multiple smart meters and completing the multi-channel transmission of power data through the whale optimization algorithm. In addition, this invention provides a consistency verification and data retransmission mechanism for sliced ​​data, thereby ensuring the consistency of data transmission. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of a distributed remote power data collection system according to the present invention;

[0049] Figure 2 This is a flowchart of the data transmission scheme generated by the whale optimization algorithm of the present invention;

[0050] Figure 3 This is a flowchart of the iterative convergence process of the present invention;

[0051] Figure 4 This is a comparison chart of the data transmission schemes generated by the present invention using genetic algorithms, particle swarm optimization algorithms, and the whale optimization algorithm provided by the present invention.

[0052] In the diagram: message synchronization module 101, data slicing module 102, data transmission scheme generation module 103, and data consistency verification module 104. Detailed Implementation

[0053] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0054] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of the present invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in one or more embodiments of the present invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0055] like Figures 1-4 As shown, a distributed remote power data collection system includes:

[0056] The message synchronization module 101 is used by the master node to send synchronization messages to the slave node, and the slave node queries and obtains the locally stored power data after receiving the synchronization message.

[0057] The master node corresponds to the power company's data center;

[0058] Each slave node corresponds to a smart meter;

[0059] The data slicing module 102 is used to sort the power data of a slave nodes in descending order of time, and slice them at equal time intervals to obtain b slice data.

[0060] Each slice of data is assigned two globally unique codes, where the first code is used to identify the power data of the slave node, and the second code is used to identify the slice data; the first code and the second code are encoded using a hash function, and the first code is the same for each slice of data of the same slave node;

[0061] The data transmission scheme generation module 103 is used to generate a data transmission scheme through the whale optimization algorithm, and to transmit b slices of a slave node to n transmission channels of the master node through the data transmission scheme.

[0062] Before executing the data transmission scheme, obtain the size of each data slice, the bandwidth of the corresponding transmission channel, and the latency.

[0063] The data consistency verification module 104 is used to verify the consistency of power data by the master node after the data transmission scheme is executed.

[0064] It should be noted that 'a' represents the total value of the slave nodes, 'b' represents the total value of the slice data of a slave node, and 'n' represents the total value of the transmission channels. 'n' is set according to the actual number of transmission channels between all slave nodes and the master node. Slicing the power data at equal time intervals is to compress the power data size, reduce transmission latency, and improve the utilization efficiency of network resources. In addition, since the size of each power data is different, the size of the slice data obtained by slicing at equal time intervals is also different.

[0065] In one embodiment of the present invention, the slave node queries the locally stored power data based on the start and end times of the synchronization power data in the synchronization message.

[0066] In one embodiment of the present invention, the synchronization message further includes: standard communication protocol (TCP / IP), IP addresses of the master node and slave node, physical addresses (MAC addresses) of the node and slave node, etc.

[0067] Furthermore, hash collisions can be reduced through double hashing, where a collision occurs when the encoding obtained through the first hash function, and a second hash function is used for re-encoding.

[0068] It should be noted that each timestamp in the power data of a smart meter corresponds to a voltage value, current value, power factor, and power value. The power factor refers to the ratio of active power to apparent power in the circuit. The unit of voltage value is volt, the unit of current value is ampere, and the unit of power value is watt.

[0069] In one embodiment of the present invention, the types of transmission channels for slice data to the master node include: wired communication, wireless communication, and power line communication; among which, wired communication mainly adopts RS-485 bus communication, that is, communication is carried out in a point-to-point manner through serial port; wireless communication includes: GPRS (data transmission technology based on GSM network), NB-IoT (low power wide area Internet of Things technology), and LoRa (low power, long distance wireless communication technology); power line communication transmits data through existing power lines, without the need for additional communication lines, and can directly realize data transmission on the power infrastructure.

[0070] In one embodiment of the present invention, such as Figure 2 As shown, generating a data transmission scheme using the whale optimization algorithm includes the following steps:

[0071] Step S201: Randomly generate an initial population consisting of J individuals that meet the constraints.

[0072] The constraints include: Constraint 1, each slice of data must be allocated one transmission channel; Constraint 2, the maximum number of slices allocated to a transmission channel is K; Constraint 3, a slice of data can only be allocated to one transmission channel at a time.

[0073] The vector encoding Q of the position of an individual in the initial population is represented as:

[0074]

[0075] in and These represent the real-number codes of the first, second, (n-1), and (n-1)th transmission channels, respectively, assigned to the first to the Kth slices of data. The real-number codes of the slice data range from 0 to c, where c = a * b. When the real-number code of a slice assigned to a transmission channel is 0, it means that the transmission channel has not assigned that slice data.

[0076] For example A value of 0 indicates that the first slice of data allocated to the nth transmission channel is empty. When it equals c, it means that the Kth slice data allocated to the nth transmission channel is c, that is, the Kth slice data allocated to the nth transmission channel is the bth slice data of the ath slave node;

[0077] Step S202: Iterative convergence, update the vector encoding of the position of individuals in the initial population;

[0078] Step S203: Determine whether the iteration termination condition is met. If the iteration termination condition is met, select the individual with the largest fitness value in the initial population as the optimal individual, and decode the data transmission scheme according to the vector encoding of the optimal individual's position. Otherwise, return to step S202.

[0079] The iteration termination conditions include: Iteration termination condition 1, the current iteration number is greater than or equal to the maximum iteration number F; Iteration termination condition 2, within L consecutive iterations, the difference between the maximum fitness values ​​of individuals is less than or equal to G.

[0080] In one embodiment of the present invention, J, K, F, L and G are all custom parameters. Preferably, J is set to 100, K is set to the floor value of a*b / 2, F is set to 500, L is set to 5 and G is set to 0.1.

[0081] In one embodiment of the present invention, such as Figure 3 As shown, iterative convergence includes the following steps:

[0082] Step S301: Calculate the fitness value of each individual in the initial population using the fitness function;

[0083] Step S302: Randomly generate a first random number between 0 and 1. If the first random number is greater than or equal to the first threshold, perform a spiral attack; otherwise, proceed to step S303.

[0084] The first threshold is a custom parameter; preferably, the first threshold is set to 0.5.

[0085] Step S303: Randomly generate a first vector and take its modulus to obtain a second random number. If the second random number is greater than or equal to the second threshold, then perform a random search; otherwise, surround the prey.

[0086] The second threshold is a custom parameter; preferably, the second threshold is set to 0.7.

[0087] In one embodiment of the present invention, in order to balance the global search and local search capabilities of the whale optimization algorithm, the first threshold and the second threshold can also be obtained by the following calculation formula;

[0088] The formula for calculating the first threshold, Threshold1, is as follows:

[0089] Threshold1 = 0.7, t < 0.5 * F;

[0090] The formula for calculating the second threshold, Threshold2, is as follows:

[0091] Threshold2=0.4, t≥0.5*F;

[0092] Definition: t represents the t-th iteration number, and F represents the maximum number of iterations.

[0093] In one embodiment of the present invention, the calculation formula for iterative convergence includes:

[0094] The formula for calculating random search is as follows:

[0095]

[0096]

[0097]

[0098] in and Let represent the vector codes for the positions of a random individual in the initial population at iteration number t+1 and iteration number t, respectively. The vector encoding representing the position of the i-th individual in the initial population at the t-th iteration number. Denotes the first vector. Represents the second vector. and Both represent random numbers between 0 and 1, t represents the t-th iteration number, and F represents the maximum number of iterations;

[0099] The formula for calculating the spiral attack is as follows:

[0100]

[0101] in and These are vector codes representing the positions of the i-th individual in the initial population at iteration number t+1 and iteration number t, respectively. The vector encoding represents the position of the individual with the largest fitness value in the t-th iteration, where e is the natural constant, P is a random number between -1 and 1, and cos represents the cosine function;

[0102] The formula for calculating the encirclement of prey is as follows:

[0103]

[0104] in and These are vector codes representing the positions of the i-th individual in the initial population at iteration number t+1 and iteration number t, respectively. The vector encoding representing the position of the individual with the largest fitness value at the t-th iteration. Denotes the first vector. This represents the second vector.

[0105] In one embodiment of the present invention, the fitness function is calculated as follows:

[0106] Where 1≤u≤a*b, 1≤v≤n, and Fit represents the fitness value of an individual. and The values ​​represent the size, bandwidth, and latency of the slice data transmitted from the u-th slice data to the v-th transmission channel, corresponding to the non-zero dimension values ​​in the vector encoding of an individual's position.

[0107] In one embodiment of the present invention, the fitness function can also be constructed based on a multilayer perceptron. The input feature vector is connected to a classifier, and the label of the classifier represents the fitness value. The feature vector is obtained by concatenating the size of the slice data, the bandwidth of the corresponding transmission channel of the slice data, and the delay. The sample labels of the training samples for training the multilayer perceptron can be calibrated by relevant technical experts. The sample labels of the training samples of the multilayer perceptron can be discrete values ​​between 1 and 10. The training of the multilayer perceptron is a conventional technique and will not be described in detail here.

[0108] In one embodiment of the present invention, when the master node receives slice data from a slave node, it inputs the slice data into the same hash function and outputs a third code. When the third code is the same as the second code, it indicates that the consistency check of the slice data has passed. Otherwise, the master node sends a data retransmission message to the slave node for the slave node to retransmit the slice data.

[0109] In one embodiment of the present invention, after the master node receives the slice data of all slave nodes, it classifies the slice data belonging to the same first code to obtain the slice data set of each slave node. When the number of slice data of the slave node is equal to b, it indicates that the consistency check of the slice data has passed. Otherwise, the master node sends a data retransmission message to the slave node for the slave node to retransmit the slice data of the slave node.

[0110] like Figure 4 As shown, a data transmission scheme is generated using a genetic algorithm, a particle swarm optimization algorithm, and the whale optimization algorithm provided in this invention. According to the data transmission scheme, the power data from 1000 slave nodes is divided into 10 slices, and transmitted to 20 transmission channels of the master node. The initial population size is uniformly set to 100 individuals, and the maximum number of iterations is 1000. Figure 4 The horizontal axis in the graph represents the number of iterations, and the vertical axis represents the fitness value of the best individual in each iteration. Observation shows that the genetic algorithm gradually converges at around 850 iterations, the particle swarm optimization algorithm gradually converges at around 620 iterations, and the whale optimization algorithm provided in this invention gradually converges at around 360 iterations.

[0111] The embodiments of this example have been described above. However, this example is not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of this example, and all of them are within the protection scope of this example.

Claims

1. A distributed remote power data collection system, characterized in that, include: The message synchronization module is used by the master node to send synchronization messages to the slave node, and the slave node queries and obtains the locally stored power data after receiving the synchronization message. The master node corresponds to the power company's data center; Each slave node corresponds to a smart meter; The data slicing module is used to sort the power data of a slave nodes in descending order of time, and slice them at equal time intervals to obtain b slice data. Each slice of data is assigned two globally unique codes, where the first code is used to identify the power data of the slave node, and the second code is used to identify the slice data; the first code and the second code are encoded using a hash function, and the first code is the same for each slice of data of the same slave node; The data transmission scheme generation module is used to generate a data transmission scheme through the whale optimization algorithm, and to transmit b slices of data from a slave node to n transmission channels of the master node through the data transmission scheme; before executing the data transmission scheme, the size of each slice of data, the bandwidth and latency of the corresponding transmission channel of the slice of data are obtained; The data consistency verification module is used by the master node to verify the consistency of power data after the data transmission scheme is executed.

2. The distributed remote power data collection system according to claim 1, characterized in that, The node queries the locally stored power data based on the start and end times of the synchronized power data in the synchronization message.

3. The distributed remote power data collection system according to claim 1, characterized in that, The types of transmission channels for slice data to the master node include: wired communication, wireless communication, and power line communication.

4. A distributed remote power data collection system according to claim 1, characterized in that, Generating a data transmission scheme using the whale optimization algorithm includes the following steps: Step S201: Randomly generate an initial population consisting of J individuals that meet the constraints. The constraints include: Constraint 1, each slice of data must be allocated one transmission channel; Constraint 2, the maximum number of slices allocated to a transmission channel is K; Constraint 3, a slice of data can only be allocated to one transmission channel at a time. The vector encoding Q of the position of an individual in the initial population is represented as: in and These represent the real-number codes of the first, second, (n-1), and (n-1)th transmission channels, respectively, assigned to the first to the Kth slices of data. The real-number codes of the slice data range from 0 to c, where c = a * b. When the real-number code of a slice assigned to a transmission channel is 0, it means that the transmission channel has not assigned that slice data. Step S202: Iterative convergence, update the vector encoding of the position of individuals in the initial population; Step S203: Determine whether the iteration termination condition is met. If the iteration termination condition is met, select the individual with the largest fitness value in the initial population as the optimal individual, and decode the data transmission scheme according to the vector encoding of the optimal individual's position. Otherwise, return to step S202. The iteration termination conditions include: Iteration termination condition 1, the current iteration number is greater than or equal to the maximum iteration number F; Iteration termination condition 2, within L consecutive iterations, the difference between the maximum fitness values ​​of individuals is less than or equal to G.

5. A distributed remote power data collection system according to claim 4, characterized in that, J, K, F, L, and G are all user-defined parameters.

6. A distributed remote power data collection system according to claim 4, characterized in that, Iterative convergence includes the following steps: Step S301: Calculate the fitness value of each individual in the initial population using the fitness function; Step S302: Randomly generate a first random number between 0 and 1. If the first random number is greater than or equal to the first threshold, perform a spiral attack; otherwise, proceed to step S303. The first threshold is a user-defined parameter; Step S303: Randomly generate a first vector and take its modulus to obtain a second random number. If the second random number is greater than or equal to a second threshold, then perform a random search; otherwise, surround the prey. The second threshold is a user-defined parameter.

7. A distributed remote power data collection system according to claim 6, characterized in that, The formulas for calculating iterative convergence include: The formula for calculating random search is as follows: in and Let represent the vector codes for the positions of a random individual in the initial population at iteration number t+1 and iteration number t, respectively. The vector encoding representing the position of the i-th individual in the initial population at the t-th iteration number. Denotes the first vector. Represents the second vector. and Both represent random numbers between 0 and 1, t represents the t-th iteration number, and F represents the maximum number of iterations; The formula for calculating the spiral attack is as follows: in and These are vector codes representing the positions of the i-th individual in the initial population at iteration number t+1 and iteration number t, respectively. The vector encoding represents the position of the individual with the largest fitness value in the t-th iteration, where e is the natural constant, P is a random number between -1 and 1, and cos represents the cosine function; The formula for calculating the encirclement of prey is as follows: in and These are vector codes representing the positions of the i-th individual in the initial population at iteration number t+1 and iteration number t, respectively. The vector encoding representing the position of the individual with the largest fitness value at the t-th iteration. Denotes the first vector. This represents the second vector.

8. A distributed remote power data collection system according to claim 6, characterized in that, The formula for calculating the fitness function is as follows: Where 1≤u≤a*b, 1≤v≤n, and Fit represents the fitness value of an individual. and The values ​​represent the size, bandwidth, and latency of the slice data transmitted from the u-th slice data to the v-th transmission channel, corresponding to the non-zero dimension values ​​in the vector encoding of an individual's position.

9. A distributed remote power data collection system according to claim 1, characterized in that, When the master node receives slice data from a slave node, it inputs the slice data into the same hash function and outputs a third code. If the third code is the same as the second code, it means that the consistency check of the slice data has passed. Otherwise, the master node sends a data retransmission message to the slave node so that the slave node can resend the slice data.

10. A distributed remote power data collection system according to claim 1, characterized in that, After the master node receives the slice data from all slave nodes, it categorizes the slice data belonging to the same first code to obtain the slice data set for each slave node. When the number of slice data of a slave node is equal to b, it means that the consistency check of the slice data has passed. Otherwise, the master node sends a data retransmission message to the slave node, so that the slave node can resend the slice data of the slave node.

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