Electricity meter data transmission method and device based on probabilistic load transfer and acquisition and transmission system

Abstract

The invention discloses an electricity meter data transmission method and device based on probabilistic load transfer and an acquisition and transmission system. The method comprises the following steps: acquiring an electric energy signal of each electricity meter in an electricity meter transmission network; calculating a transmission load rate corresponding to each ammeter based on the electric energy signal corresponding to each ammeter; based on the transmission load rate corresponding to each electricity meter, calculating the state transition probability of each electricity meter, the state transition probability representing the probability that the electricity meter transfers the load to other electricity meters in the network; according to the state transition probability of the electricity meter, it is determined that part of load of the electricity meter is transferred to other electricity meters for transmission; through combination of dynamic task allocation of probability load transfer and load rate calculation, dynamic task allocation and efficient load balancing are realized, the response speed and the processing capacity of the system can be effectively improved, and the stability and the reliability of data transmission are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric meter data transmission, and in particular to an electric meter data transmission method, device and acquisition and transmission system based on probabilistic load transfer. Background Art

[0002] With the rapid development of smart grids and the increasing requirements for the reliability and efficiency of power systems, the meter data transmission system plays a vital role in the automated management and optimization of power systems. However, the traditional meter data transmission method has many limitations, such as slow transmission speed, poor data synchronization, insufficient reliability, etc., which makes it difficult to meet the requirements of modern smart grids for real-time, accuracy and efficiency. And with the construction and development of smart grids, higher requirements are put forward for the real-time, accuracy and synchronization of meter data transmission. Therefore, the development of a high-performance and high-synchronization meter data transmission system has become an important demand of the power industry.

[0003] The meter data transmission system is a core component of the smart grid. It is not only responsible for collecting and transmitting electric energy data, but also an important basis for realizing the automation, intelligence and efficient operation of the power system. In the traditional power system, the collection and transmission of meter data mainly rely on manual meter reading or simple communication protocols such as DL / T 645. These methods are not only inefficient, but also easily affected by human factors, resulting in inaccurate and untimely data.

[0004] In modern smart grids, meter data transmission systems need to support multiple communication protocols, such as DL / T 645, IEC62055-41, etc., to meet the communication needs in different scenarios. At the same time, the system also needs to have high-speed data transmission capabilities to meet the real-time collection and transmission needs of large amounts of meter data. In addition, as the complexity of the power system increases, data synchronization and reliability have also become key issues. The synchronization of data transmission directly affects the scheduling and optimization of the power system, while reliability is related to the integrity and accuracy of the data.

[0005] Therefore, the meter data transmission system still faces some challenges in practical applications. First, the data transmission speed and synchronization are insufficient, resulting in data delay and deviation, which affects the real-time monitoring and dispatching of the power system. Second, the compatibility and scalability of traditional communication protocols are limited, making it difficult to adapt to the diverse needs of smart grids. In addition, the security and anti-interference capabilities of data transmission need to be improved to ensure the integrity and reliability of data.

[0006] In the related art, the patent application document with publication number CN116634306A proposes a cellular automation as the main design concept, which can achieve adaptability in the smart meter transmission system. However, the cellular decision algorithm used in this scheme is complex and needs to update the cellular state at each time step. In the process of electric energy data collection and transmission, the amount of electric energy data is large and the timing requirements are high. The use of this scheme will inevitably require high hardware computing resources, and it is difficult to achieve the requirements of high-speed data real-time transmission and real-time calculation; this scheme requires synchronous update of the cellular state, but no implementation plan is given for how to ensure synchronization. In addition, this scheme does not filter out interference based on the multi-band characteristics of electric energy signals. Summary of the invention

[0007] The technical problem to be solved by the present invention is how to solve the problem of poor stability and low reliability of data transmission.

[0008] The present invention solves the above technical problems by the following technical means:

[0009] On the one hand, a method for transmitting electric meter data based on probabilistic load transfer is proposed, the method comprising:

[0010] Collecting electric energy signals from each meter in the meter transmission network;

[0011] Based on the electric energy signals corresponding to the electric meters, the transmission load rates corresponding to the electric meters are calculated;

[0012] Based on the transmission load rate corresponding to each meter, the state transition probability of each meter is calculated, and the state transition probability represents the probability of the meter transferring load to other meters in the network;

[0013] According to the state transition probability of the electric meter, part of the load of the electric meter is transferred to other electric meters for transmission.

[0014] Furthermore, the collecting of electric energy signals of each electric meter in the electric meter transmission network includes:

[0015] The FPGA chip connected to each electric meter is used to collect the electric energy signal of the corresponding electric meter.

[0016] Furthermore, after collecting the electric energy signals of each electric meter in the electric meter transmission network, the method further includes:

[0017] A dynamic iterative filtering algorithm is used to filter the electric energy signal to obtain a filtered electric energy signal;

[0018] Accordingly, the transmission load rate corresponding to each electric meter is calculated based on the electric energy signal corresponding to each electric meter, specifically:

[0019] Based on the filtered electric energy signals corresponding to the electric meters, the transmission load rates corresponding to the electric meters are calculated.

[0020] Furthermore, the adopting of a dynamic iterative filtering algorithm to filter the electric energy signal to obtain a filtered electric energy signal includes:

[0021] Inputting the electric energy signal x(n) into the filter so that the filter outputs the actual filtered electric energy signal, and calculating the error signal e(n) between the filtered electric energy target signal and the actual filtered electric energy signal;

[0022] The iteration length of the update filter is s(n+1)=z(e(n),s(n))=αs(n)+βe(n) 2 , where s(n+1) is the iteration length corresponding to time n+1, s(n) is the iteration length corresponding to time n, z() is the iteration length update function, α is the decreasing coefficient, and β is the iteration length adjustment coefficient;

[0023] According to the updated step size s(n+1) and the error signal e(n), the filter weight vector is updated to q(n+1)=q(n)+s(n+1)e(n)x(n)+α(n)+β(n), where q(n+1) is the weight vector corresponding to the time n+1, q(n) is the weight vector corresponding to the time n, x(n) is the collected original electric energy signal, and α(n) and β(n) are adaptive adjustment coefficients;

[0024] The next step size update is performed until the convergence condition is reached, so as to use the trained filter to filter out the noise of the input power signal.

[0025] Furthermore, the method further comprises:

[0026] The adaptive adjustment coefficients α(n) and β(n) are dynamically adjusted according to the error signal e(n), and the formula is expressed as:

[0027]

[0028] Where: η is the learning rate, α(n+1) and β(n+1) are the adaptive adjustment coefficients corresponding to time n+1, and α(n) and β(n) are the adaptive adjustment coefficients corresponding to time n.

[0029] Furthermore, based on the electric energy signal corresponding to each electric meter, the transmission load rate corresponding to each electric meter is calculated, and the formula is expressed as:

[0030]

[0031] Where: Y is the load rate, A total is the total actual used resources, and R is the bus rate.

[0032] Furthermore, the state transition probability of each electric meter is calculated based on the transmission load rate corresponding to each electric meter, and the state transition probability represents the probability of the electric meter transferring the load to other electric meters in the network, including:

[0033] Based on the transmission load rate corresponding to each electric meter, the transfer weight between each electric meter and other adjacent electric meters in the network is calculated;

[0034] Based on the transfer weights between each meter and other adjacent meters in the network, the normalized weight corresponding to each meter is calculated;

[0035] The state transition probability of the electric meter is calculated based on the normalized weight corresponding to the electric meter and the probability that the load of the transfer target electric meter is transferred to the rated load.

[0036] Furthermore, the transfer weight between each meter and other adjacent meters in the network is:

[0037]

[0038] Where: w(Y i (t),Y j (t)) represents the transfer weight between meter i and meter j, Y i (t),Y j (t) are the load rates of meter i and meter j respectively, and ∈ is a constant.

[0039] Furthermore, the formula for the state transition probability of the electric meter is expressed as:

[0040]

[0041] Where: P(Y i (t+1)) represents the probability of meter i transferring load to target meter j, Y i (t),Y j (t) are the load rates of meter i and meter j, P(Y j (t)→Y′) represents the probability that the load of the transfer target meter j is transferred to the rated load Y′, w norm (Y i (t),Y j (t)) is the normalized weight.

[0042] Furthermore, the calculation formula for the probability that the load of the transfer target electric meter j is transferred to the rated load Y′ is:

[0043]

[0044] Furthermore, the step of determining, according to the state transition probability of the electric meter, to transfer part of the load of the electric meter to other electric meters for transmission comprises:

[0045] The state transition probability P(Y i (t+1)) is compared with the set load transfer threshold η. If η is satisfied <P(Y i (t+1)), then at the next moment, part of the load of meter i is transferred to other adjacent meters j for transmission, and meter i maintains rated load operation at the next moment;

[0046] Otherwise, the electric energy signal is transmitted according to the current transmission load rate of the electric meter i.

[0047] Furthermore, the method further comprises:

[0048] The load transfer between the electric meters is realized by utilizing the communication between the FPGA chips connected to each electric meter, and each FPGA chip performs synchronous transmission of the electric energy signal based on the time signal provided by the GPS module.

[0049] In addition, the present invention also proposes an electric meter data transmission device based on probabilistic load transfer, comprising:

[0050] An electric energy signal acquisition module is used to collect electric energy signals from each electric meter in the electric meter transmission network;

[0051] A load rate calculation module, used to calculate the transmission load rate corresponding to each electric meter based on the electric energy signal corresponding to each electric meter;

[0052] A state transition probability calculation module, used to calculate the state transition probability of each electric meter based on the transmission load rate corresponding to each electric meter, and the state transition probability represents the probability of the electric meter transferring load to other electric meters in the network;

[0053] The load transfer module is used to determine, based on the state transfer probability of the electric meter, to transfer part of the load of the electric meter to other electric meters for transmission.

[0054] In addition, the present invention also proposes an electric meter data acquisition and transmission system, the system includes an FPGA chip connected to each electric meter data acquisition terminal, each FPGA chip is connected to a time synchronization module, and the output of each FPGA chip is connected to a server platform;

[0055] The electric meter data acquisition terminal is used to collect electric energy signals from the electric meter;

[0056] The FPGA chip calculates the transmission load rate corresponding to each meter based on the electric energy signal of the corresponding meter, and calculates the state transition probability of each meter based on the transmission load rate corresponding to each meter, and transfers part of the load of the meter to other meters for transmission. The state transition probability represents the probability of the meter transferring the load to other meters in the network;

[0057] The time synchronization module is used to control each FPGA chip to synchronously transmit the power signal to the server platform.

[0058] Furthermore, the system also includes a filter connected to the electric meter data acquisition terminal;

[0059] The filter is used to filter the electric energy signal using a dynamic iterative filtering algorithm to obtain a filtered electric energy signal;

[0060] Correspondingly, the FPGA chip calculates the transmission load rate corresponding to each electric meter based on the filtered electric energy signal of the corresponding electric meter.

[0061] Furthermore, the training process of the filter is:

[0062] Inputting the electric energy signal x(n) into the filter so that the filter outputs the actual filtered electric energy signal, and calculating the error signal e(n) between the filtered electric energy target signal and the actual filtered electric energy signal;

[0063] The iteration length of the update filter is s(n+1)=z(e(n),s(n))=αs(n)+βe(n) 2 , where s(n+1) is the iteration length corresponding to time n+1, s(n) is the iteration length corresponding to time n, z() is the iteration length update function, α is the decreasing coefficient, and β is the iteration length adjustment coefficient;

[0064] According to the updated step size s(n+1) and the error signal e(n), the filter weight vector is updated to q(n+1)=q(n)+s(n+1)e(n)x(n)+α(n)+β(n), where q(n+1) is the weight vector corresponding to the n+1 moment, q(n) is the weight vector corresponding to the n moment, x(n) is the collected original electric energy signal, α(n) and β(n) are adaptive adjustment coefficients, Where: η is the learning rate, α(n+1) and β(n+1) are the adaptive adjustment coefficients corresponding to time n+1, and α(n) and β(n) are the adaptive adjustment coefficients corresponding to time n;

[0065] When the next step size update is performed until the convergence condition is reached, a trained filter for filtering noise from the input power signal is obtained.

[0066] Furthermore, the state transition probability of the electric meter is expressed as:

[0067]

[0068] Where: P(Y i (t+1)) represents the probability of meter i transferring load to target meter j, Y i (t),Y j (t) are the load rates of meter i and meter j, P(Y j (t)→Y′) represents the probability that the load of the transfer target meter j is transferred to the rated load Y′, w norm (Y i (t),Y j (t)) is the normalized weight.

[0069] In addition, the present invention also proposes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the electric meter data transmission method based on probabilistic load transfer as described above is implemented.

[0070] The advantages of the present invention are:

[0071] (1) The present invention calculates the transmission load rate corresponding to the electric meter based on the electric energy signal of the electric meter, and calculates the probability of the electric meter transferring the load to other electric meters in the network based on the transmission load rate of the electric meter. When the electric meter is overloaded, part of the load of the electric meter is transferred to other electric meters for transmission, thereby realizing dynamic task allocation and load balancing through probabilistic load transfer, so as to optimize the efficiency of data transmission, avoid network congestion, avoid overload and waste of resources, improve the response speed and processing capacity of the system, thereby improving the stability and reliability of data transmission, and reducing the risk of data loss.

[0072] (2) By adopting a dynamic iterative filtering algorithm, the filter parameters can be adaptively adjusted to effectively filter out the noise in the electric energy signal and improve the purity of the signal, thereby enhancing the accuracy of the signal and providing a higher quality data source for subsequent data transmission and processing.

[0073] (3) By using the precise time synchronization signal and time information provided by GPS between FPGA chips, high-precision time synchronization and data communication can be achieved, ensuring the synchronization and accuracy of data transmission, and further improving the reliability and stability of the system.

[0074] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1It is a flow chart of a method for transmitting electric meter data based on probabilistic load transfer proposed in one embodiment of the present invention;

[0076] Figure 2 It is a schematic diagram of the data transmission process of a smart meter in one embodiment of the present invention;

[0077] Figure 3 is a schematic diagram of the edge meter transmission network structure in one embodiment of the present invention;

[0078] Figure 4 It is a structural schematic diagram of an electric meter data transmission system based on probabilistic load transfer proposed in one embodiment of the present invention. DETAILED DESCRIPTION

[0079] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0080] like Figure 1 As shown, the first embodiment of the present invention proposes a method for transmitting electric meter data based on probabilistic load transfer, and the method includes the following steps:

[0081] S10, collecting electric energy signals of each electric meter in the electric meter transmission network;

[0082] It should be noted that, since FPGA chips have advantages in high-speed data communication and data processing, this embodiment can connect the electric meter and the FPGA chip through the JESD204C protocol to realize the collection of electric energy data of the electric meter.

[0083] S20, calculating the transmission load rate corresponding to each electric meter based on the electric energy signal corresponding to each electric meter;

[0084] It should be noted that, in this embodiment, by calculating the load rate of data transmission at different electricity meter transmission ends at a certain moment based on the electric energy signal, the efficiency of data transmission can be optimized and network congestion can be avoided.

[0085] S30, calculating the state transition probability of each electric meter based on the transmission load rate corresponding to each electric meter, where the state transition probability represents the probability of the electric meter transferring the load to other electric meters in the network;

[0086] S40: Determine, based on the state transition probability of the electric meter, to transfer part of the load of the electric meter to other electric meters for transmission.

[0087] It should be noted that this embodiment calculates the probability of an electric meter transferring its load to other electric meters in the network based on the transmission load rate of the electric meter. When the electric meter is overloaded, part of the load of the electric meter is transferred to other electric meters for transmission, thereby realizing dynamic task allocation and load balancing through probabilistic load transfer, so as to optimize the efficiency of data transmission, avoid network congestion, avoid overload and waste of resources, improve the response speed and processing capacity of the system, thereby improving the stability and reliability of data transmission, and reducing the risk of data loss.

[0088] As a further preferred technical solution, the step S10: collecting electric energy signals of each electric meter in the electric meter transmission network includes:

[0089] The FPGA chip connected to each electric meter is used to collect the electric energy signal of the corresponding electric meter.

[0090] Specifically, this embodiment connects the electric meter with the FPGA chip to ensure that the JESD204C interface of the electric meter is correctly connected to the SerDes module of the FPGA chip; then connects the device clock and the SYSREF clock to ensure that their frequency and phase meet the requirements of the JESD204C protocol; then configures the SerDes module of the FPGA chip and initializes the JESD204C IP core of the FPGA chip.

[0091] It should be noted that the JESD204C protocol supports high-speed data transmission and is suitable for fast acquisition of power signals. Through the configuration of the SerDes module and the JESD204C IP core of the FPGA, high synchronization and accuracy of data transmission can be ensured, the speed and quality of data acquisition can be improved, and delays and errors in the data transmission process can be reduced.

[0092] As a further preferred technical solution, after the step S10: collecting the electric energy signals of each electric meter in the electric meter transmission network, the method further includes the following steps:

[0093] S10', filtering the electric energy signal using a dynamic iterative filtering algorithm to obtain a filtered electric energy signal;

[0094] Accordingly, the step S20: based on the electric energy signal corresponding to each electric meter, the transmission load rate corresponding to each electric meter is calculated, specifically:

[0095] S20', calculating the transmission load rate corresponding to each electric meter based on the filtered electric energy signal corresponding to each electric meter.

[0096] It should be noted that since the collected electric energy signals usually contain multiple frequency components, such as fundamental waves (50Hz or 60Hz) and higher harmonics, it is necessary to dynamically adjust the filter parameters to adapt to different frequency components, effectively filter out the noise in the electric energy signals, enhance the accuracy of the signals, and provide a higher quality data source for subsequent data transmission and processing.

[0097] As a further preferred technical solution, Figure 2 As shown, the step S10': using a dynamic iterative filtering algorithm to filter the electric energy signal to obtain a filtered electric energy signal specifically includes the following steps:

[0098] S11', inputting the electric energy signal x(n) into the filter so that the filter outputs the actual filtered electric energy signal, and calculating the error signal e(n) between the filtered electric energy target signal and the actual filtered electric energy signal;

[0099] It should be noted that the filter configuration vector q(0) and the iteration length s(0) are first initialized: q(0) is initialized to a zero vector or a small random value, where the initialization step size s(0) is a small positive number, such as 0.001; the adaptive adjustment coefficients α(0) and β(0) are initialized to small positive numbers, such as 0.005 and 0.001.

[0100] At time n, the historical electric energy signal input to the filter is x(n), the expected electric energy signal is c(n), and the actual filtered electric energy signal output by the filter is y(n) = q(n) T x(n), the filter output y(n) is the inner product of the current filter configuration vector q(n) and the input signal x(n), and the power signal x(n) is the original signal expected signal received from the FPGA chip;

[0101] The error signal e(n)=c(n)-y(n) is calculated. The error signal e(n) represents the difference between the desired electric energy signal c(n) and the actual filter output signal y(n). c(n) is the electric energy target signal after filtering, which can usually be obtained through experiments or simulation data.

[0102] S12', update the iteration length of the filter to s(n+1)=z(e(n),s(n))=αs(n)+βe(n) 2 , where s(n+1) is the iteration length corresponding to time n+1, s(n) is the iteration length corresponding to time n, z() is the iteration length update function, α is the decreasing coefficient, and β is the iteration length adjustment coefficient;

[0103] Specifically, α is a decreasing coefficient between 0 and 1, and β is a positive iteration length adjustment coefficient.

[0104] S13', according to the updated step size s(n+1) and the error signal e(n), the filter weight vector is updated to q(n+1)=q(n)+s(n+1)e(n)x(n)+α(n)+β(n), wherein q(n+1) is the weight vector corresponding to the time n+1, q(n) is the weight vector corresponding to the time n, x(n) is the collected original electric energy signal, α(n) and β(n) are adaptive adjustment coefficients;

[0105] It should be noted that this embodiment introduces adaptive adjustment coefficients α(n) and β(n), which can be dynamically adjusted according to the size of the error signal, thereby dynamically adjusting the filter parameters to adapt to different frequency components of the electric energy signal.

[0106] Specifically, the adaptive adjustment coefficients α(n) and β(n) are dynamically adjusted according to the error signal e(n), and the formula is expressed as:

[0107]

[0108] Where: η is the learning rate, α(n+1) and β(n+1) are the adaptive adjustment coefficients corresponding to time n+1, and α(n) and β(n) are the adaptive adjustment coefficients corresponding to time n.

[0109] S14', perform the next step size update until the convergence condition is reached, and the trained filter is obtained to filter out noise from the input electric energy signal.

[0110] As a further preferred technical solution, the step S20: based on the electric energy signal corresponding to each electric meter, the transmission load rate corresponding to each electric meter is calculated, and the formula is expressed as:

[0111]

[0112] Where: Y is the load rate, A total is the total actual used resources, and R is the bus rate.

[0113] Specifically, define L i : The length of the standard frame of the power signal (bit); I: The length of the power signal frame interval (bit); f i : The sending frequency of the i-th data frame (times / second); n: The number of data frame types; R: Bus rate (bits / second);

[0114] For each electric energy data frame i, the resource occupancy is:

[0115] A i =(L i +I)×f i ;

[0116] The total actual resource usage is:

[0117]

[0118] It should be noted that in this embodiment, the FPGA calculation module calculates the load rate of data transmission at different transmission ends at a certain moment after filtering. By accurately calculating the transmission period and load rate of the data frame, the efficiency of data transmission can be optimized, network congestion can be avoided, the stability and reliability of data transmission can be improved, and the risk of data loss can be reduced.

[0119] As a further preferred technical solution, the step S30: calculating the state transition probability of each electric meter based on the transmission load rate corresponding to each electric meter, where the state transition probability represents the probability of the electric meter transferring the load to other electric meters in the network, specifically includes the following steps:

[0120] S31, based on the transmission load rate corresponding to each electric meter, calculating the transfer weight between each electric meter and other adjacent electric meters in the network;

[0121] Specifically, the load rate Y can be defined as the load rates of meter nodes i and j at time t, respectively. i (t), Y j (t), define a weight function w(Y i (t),Y j (t)), represents the transfer weight between meter node i and meter node j:

[0122]

[0123] Where ∈ is a small constant to prevent the denominator from being zero.

[0124] S32, calculating the normalized weight corresponding to each electric meter based on the transfer weight between each electric meter and other adjacent electric meters in the network;

[0125] Specifically, this embodiment normalizes the transfer weights so that the sum of the weights of all neighbors is 1, ensuring that the sum of the transfer probabilities of each adjacent point is 1. The normalization calculation formula is:

[0126]

[0127] in, Represents the sum of the weights of all the meter nodes adjacent to meter node i.

[0128] S33. Calculate the state transition probability of the electric meter based on the normalized weight corresponding to the electric meter and the probability that the load of the transfer target electric meter is transferred to the rated load.

[0129] Specifically, assuming that the node corresponding to meter i transfers load to meter j, the probability that the load of the node corresponding to meter j is transferred to the rated load Y′ is defined as:

[0130]

[0131] It should be noted that this is a Sigmoid function, which ensures that when Y j When (t) is close to Y′, the transition probability is high, Y j (t) is the load of meter j at the current time t.

[0132] Then the formula for the state transition probability of meter i is expressed as:

[0133]

[0134] Where: P(Y i (t+1)) represents the probability of meter i transferring load to target meter j, Y i (t),Y j (t) are the load rates of meter i and meter j, P(Y j (t)→Y′) represents the probability that the load of the transfer target meter j is transferred to the rated load Y′.

[0135] As a further preferred technical solution, the step S40: determining, according to the state transition probability of the electric meter, to transfer part of the load of the electric meter to other electric meters for transmission, specifically includes:

[0136] The state transition probability P(Y i (t+1)) is compared with the set load transfer threshold η. If η is satisfied <P(Y i (t+1)), then at the next moment, part of the load of meter i is transferred to other adjacent meters j for transmission, and meter i maintains rated load operation at the next moment;

[0137] Otherwise, the electric energy signal is transmitted according to the current transmission load rate of the electric meter i.

[0138] Specifically, this embodiment sets the meter load transfer threshold η and η ≈ (0, 1). If η is satisfied <P(Y i (t+1)), part of the load is transferred from meter i to other meters, and the load state Y of meter i is updated according to the calculated state transition probability. i (t+1):

[0139]

[0140] The above formula shows that when the load on the meter is too large, part of the load will be transferred from meter i to other meters at the next moment, and the rated load will be maintained. When the load on the meter is appropriate, the node will not be transferred, and the original load will remain unchanged at the next moment.

[0141] As a further preferred technical solution, Figures 2 to 3 As shown, each electric meter in the edge meter network is connected to a corresponding FPGA chip, and the load transfer between the electric meters is realized by utilizing the communication between the FPGA chips connected to each electric meter, and each FPGA chip performs synchronous transmission of the electric energy signal based on the time signal provided by the GPS module.

[0142] Specifically, the AXI communication protocol stipulates that the sending end is the master end and the receiving end is the slave end. The basic code for AXIInterface to communicate between two Xilinx FPGAs is as follows:

[0143]

[0144]

[0145] It should be noted that this embodiment uses the GPS module to realize FPGA synchronous communication: in the process of load transfer, in order to ensure that the data of meter i at time t is sent synchronously with the data of meter j at time t, the GPS module is introduced to realize synchronous communication between FPGA chips. The core is to provide accurate time synchronization signal PPS and time information NMEA protocol through the GPS module, and process these signals through FPGA to realize high-precision time synchronization and data communication. The specific implementation process is:

[0146] (1) Select a GPS module that supports PPS (pulse per second) and NMEA protocols: GT-100.

[0147] (2) Connect the GPS module to the FPGA, specifically:

[0148] Connect the PPS signal of the GPS module to the GPIO pin of the FPGA, connect the NMEA signal (usually TTL level) of the GPS module to the UART interface of the FPGA, and ensure that the power and ground wires of the GPS module are correctly connected.

[0149] (3) GPS antenna installation: Install the GPS antenna in an open area to ensure that it can receive satellite signals and use a low-noise amplifier (LNA) to enhance the signal.

[0150] This embodiment first sends the data collected by the smart meter to the FPGA, calculates the data transmission load after filtering in the FPGA, and uses probabilistic load transfer to achieve dynamic task allocation and load balancing to determine which FPGA modules will transfer data and which FPGA modules will carry additional data. Different FPGA modules are synchronized through GPS. This embodiment simplifies the complexity of the algorithm based on the data transmission characteristics of the meter, reduces the performance requirements for hardware facilities, and uses the GPS synchronization method to ensure that each meter runs synchronously. At the same time, the iterative algorithm can also meet the data filtering requirements of multi-band power grid data.

[0151] By comprehensively applying multiple advanced technologies such as JESD204C protocol, dynamic iterative algorithm, probabilistic load transfer and GPS synchronous communication to the electric energy data transmission system, the combination of dynamic task allocation and load rate calculation of probabilistic load transfer realizes dynamic task allocation and efficient load balancing, which can effectively improve the response speed and processing capability of the system. The GPS module is used to achieve high-precision time synchronization, ensuring the synchronization and accuracy of data transmission.

[0152] In addition, if Figure 4 As shown, the second embodiment of the present invention further proposes an electric meter data transmission system based on probabilistic load transfer, the system comprising:

[0153] The electric energy signal acquisition module 10 is used to collect the electric energy signals of each electric meter in the electric meter transmission network;

[0154] The load rate calculation module 20 is used to calculate the transmission load rate corresponding to each electric meter based on the electric energy signal corresponding to each electric meter;

[0155] A state transition probability calculation module 30, for calculating the state transition probability of each electric meter based on the transmission load rate corresponding to each electric meter, the state transition probability representing the probability of the electric meter transferring load to other electric meters in the network;

[0156] The load transfer module 40 is used to determine, according to the state transfer probability of the electric meter, to transfer part of the load of the electric meter to other electric meters for transmission.

[0157] As a further preferred technical solution, the electric energy signal acquisition module 10 is specifically used to utilize the FPGA chip connected to each electric meter to acquire the electric energy signal of the corresponding electric meter.

[0158] As a further preferred technical solution, the system further includes a dynamic iterative filtering module, which is specifically used to: filter the electric energy signal using a dynamic iterative filtering algorithm to obtain a filtered electric energy signal;

[0159] Correspondingly, the load rate calculation module 20 is used to calculate the transmission load rate corresponding to each electric meter based on the filtered electric energy signal corresponding to each electric meter.

[0160] As a further preferred technical solution, the dynamic iterative filtering module includes:

[0161] A filtering unit, used for inputting the electric energy signal x(n) into the filter so that the filter outputs the actual filtered electric energy signal, and calculating the error signal e(n) between the filtered electric energy target signal and the actual filtered electric energy signal;

[0162] The length update unit is used to update the iteration length of the filter to s(n+1)=z(e(n),s(n))=αs(n)+βe(n) 2 , where s(n+1) is the iteration length corresponding to time n+1, s(n) is the iteration length corresponding to time n, z() is the iteration length update function, α is the decreasing coefficient, and β is the iteration length adjustment coefficient;

[0163] A weight vector updating unit, used for updating the filter weight vector to q(n+1)=q(n)+s(n+1)e(n)x(n)+α(n)+β(n) according to the updated step size s(n+1) and the error signal e(n), wherein q(n+1) is the weight vector corresponding to the n+1 moment, q(n) is the weight vector corresponding to the n moment, x(n) is the collected original electric energy signal, and α(n) and β(n) are adaptive adjustment coefficients;

[0164] The iterative training unit is used to update the next step size until the convergence condition is reached, and the trained filter is used to filter out noise from the input power signal.

[0165] As a further preferred technical solution, the system further includes a parameter adjustment module for dynamically adjusting the adaptive adjustment coefficients α(n) and β(n) according to the error signal e(n), and the formula is expressed as:

[0166]

[0167] Where: η is the learning rate, α(n+1) and β(n+1) are the adaptive adjustment coefficients corresponding to time n+1, and α(n) and β(n) are the adaptive adjustment coefficients corresponding to time n.

[0168] As a further preferred technical solution, the load rate calculation module 20 is used to calculate the transmission load rate corresponding to each electric meter based on the electric energy signal corresponding to each electric meter, and the formula is expressed as:

[0169]

[0170] Where: Y is the load rate, A total is the total actual used resources, and R is the bus rate.

[0171] As a further preferred technical solution, the state transition probability calculation module 30 includes:

[0172] a transfer weight calculation unit, used to calculate the transfer weight between each electric meter and other adjacent electric meters in the network based on the transmission load rate corresponding to each electric meter;

[0173] A normalization unit, used for calculating a normalized weight corresponding to each electric meter based on the transfer weight between each electric meter and other adjacent electric meters in the network;

[0174] The state transfer probability calculation unit is used to calculate the state transfer probability of the electric meter based on the normalized weight corresponding to the electric meter and the probability that the load of the transfer target electric meter is transferred to the rated load.

[0175] As a further preferred technical solution, the load transfer module 40 is specifically used for:

[0176] The state transition probability P(Y i (t+1)) is compared with the set load transfer threshold η. If η is satisfied <P(Y i (t+1)), then at the next moment, part of the load of meter i is transferred to other adjacent meters j for transmission, and meter i maintains rated load operation at the next moment;

[0177] Otherwise, the electric energy signal is transmitted according to the current transmission load rate of the electric meter i.

[0178] As a further preferred technical solution, the system also includes a synchronous transmission module, which is specifically used to: utilize the communication between the FPGA chips connected to each meter to realize load transfer between meters, and each FPGA chip synchronously transmits the electric energy signal based on the time signal provided by the GPS module.

[0179] It should be noted that other embodiments or specific implementation methods of the electric meter data transmission system based on probabilistic load transfer described in the present invention can refer to the above-mentioned method embodiments, which will not be repeated here.

[0180] In addition, an embodiment of the present invention further proposes an electric meter data acquisition and transmission system, the system comprising an FPGA chip connected to each electric meter data acquisition terminal, each FPGA chip is connected to a time synchronization module, and the output of each FPGA chip is connected to a server platform;

[0181] The electric meter data acquisition terminal is used to collect electric energy signals from the electric meter;

[0182] The FPGA chip calculates the transmission load rate corresponding to each meter based on the electric energy signal of the corresponding meter, and calculates the state transition probability of each meter based on the transmission load rate corresponding to each meter, and transfers part of the load of the meter to other meters for transmission. The state transition probability represents the probability of the meter transferring the load to other meters in the network;

[0183] The time synchronization module is used to control each FPGA chip to synchronously transmit the power signal to the server platform.

[0184] As a further preferred technical solution, the system further includes a filter connected to the electric meter data acquisition terminal;

[0185] The filter is used to filter the electric energy signal using a dynamic iterative filtering algorithm to obtain a filtered electric energy signal;

[0186] Correspondingly, the FPGA chip calculates the transmission load rate corresponding to each electric meter based on the filtered electric energy signal of the corresponding electric meter.

[0187] As a further preferred technical solution, the training process of the filter is:

[0188] Inputting the electric energy signal x(n) into the filter so that the filter outputs the actual filtered electric energy signal, and calculating the error signal e(n) between the filtered electric energy target signal and the actual filtered electric energy signal;

[0189] The iteration length of the update filter is s(n+1)=z(e(n),s(n))=αs(n)+βe(n) 2 , where s(n+1) is the iteration length corresponding to time n+1, s(n) is the iteration length corresponding to time n, z() is the iteration length update function, α is the decreasing coefficient, and β is the iteration length adjustment coefficient;

[0190] According to the updated step size s(n+1) and the error signal e(n), the filter weight vector is updated to q(n+1)=q(n)+s(n+1)e(n)x(n)+α(n)+β(n), where q(n+1) is the weight vector corresponding to the n+1 moment, q(n) is the weight vector corresponding to the n moment, x(n) is the collected original electric energy signal, α(n) and β(n) are adaptive adjustment coefficients, Where: η is the learning rate, α(n+1) and β(n+1) are the adaptive adjustment coefficients corresponding to time n+1, and α(n) and β(n) are the adaptive adjustment coefficients corresponding to time n;

[0191] When the next step size update is performed until the convergence condition is reached, a trained filter for filtering noise from the input power signal is obtained.

[0192] As a further preferred technical solution, the formula for the state transition probability of the electric meter is expressed as:

[0193]

[0194] Where: P(Y i (t+1)) represents the probability of meter i transferring load to target meter j, Y i (t),Y j (t) are the load rates of meter i and meter j, P(Y j (t)→Y′) represents the probability that the load of the transfer target meter j is transferred to the rated load Y′, w norm (Y i (t),Y j (t)) is the normalized weight.

[0195] It should be noted that the method implementation process of the FPGA chip in the electric meter data acquisition and transmission system of the present invention can refer to the other embodiments mentioned above, or the specific implementation method can refer to the above method embodiments, which will not be repeated here.

[0196] In addition, the third embodiment of the present invention proposes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the electric meter data transmission method based on probabilistic load transfer as described in the first embodiment above is implemented.

[0197] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0198] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0199] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0200] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0201] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for transmitting electric meter data based on probabilistic load transfer, characterized in that: include: Collecting electric energy signals from each meter in the meter transmission network; Based on the electric energy signals corresponding to the electric meters, the transmission load rates corresponding to the electric meters are calculated; Based on the transmission load rate corresponding to each meter, the state transition probability of each meter is calculated, and the state transition probability represents the probability of the meter transferring load to other meters in the network; According to the state transition probability of the electric meter, part of the load of the electric meter is transferred to other electric meters for transmission.

2. The method for transmitting electric meter data based on probabilistic load transfer according to claim 1, characterized in that: The collecting of electric energy signals of each electric meter in the electric meter transmission network comprises: The FPGA chip connected to each electric meter is used to collect the electric energy signal of the corresponding electric meter.

3. The method for transmitting electric meter data based on probabilistic load transfer according to claim 1, characterized in that: After collecting the electric energy signals of each electric meter in the electric meter transmission network, the method further includes: A dynamic iterative filtering algorithm is used to filter the electric energy signal to obtain a filtered electric energy signal; Accordingly, the transmission load rate corresponding to each electric meter is calculated based on the electric energy signal corresponding to each electric meter, specifically: Based on the filtered electric energy signals corresponding to the electric meters, the transmission load rates corresponding to the electric meters are calculated.

4. The method for transmitting electric meter data based on probabilistic load transfer according to claim 3, characterized in that: The method of filtering the electric energy signal using a dynamic iterative filtering algorithm to obtain a filtered electric energy signal includes: Inputting the electric energy signal x(n) into the filter so that the filter outputs the actual filtered electric energy signal, and calculating the error signal e(n) between the filtered electric energy target signal and the actual filtered electric energy signal; The iteration length of the update filter is s(n+1)=z(e(n),s(n))=αs(n)+βe(n) 2 , where s(n+1) is the iteration length corresponding to time n+1, s(n) is the iteration length corresponding to time n, z() is the iteration length update function, α is the decreasing coefficient, and β is the iteration length adjustment coefficient; According to the updated step size s(n+1) and the error signal e(n), the filter weight vector is updated to q(n+1)=q(n)+s(n+1)e(n)x(n)+α(n)+β(n), where q(n+1) is the weight vector corresponding to the time n+1, q(n) is the weight vector corresponding to the time n, x(n) is the collected original electric energy signal, and α(n) and β(n) are adaptive adjustment coefficients; When the next step size update is performed until the convergence condition is reached, the trained filter is used to filter out noise from the input power signal.

5. The method for transmitting electric meter data based on probabilistic load transfer according to claim 4, characterized in that: The method further comprises: The adaptive adjustment coefficients α(n) and β(n) are dynamically adjusted according to the error signal e(n), and the formula is expressed as: Where: η is the learning rate, α(n+1) and β(n+1) are the adaptive adjustment coefficients corresponding to time n+1, and α(n) and β(n) are the adaptive adjustment coefficients corresponding to time n.

6. The method for transmitting electric meter data based on probabilistic load transfer according to claim 1, characterized in that: Based on the electric energy signal corresponding to each electric meter, the transmission load rate corresponding to each electric meter is calculated, and the formula is expressed as: Where: Y is the load rate, A total is the total actual used resources, and R is the bus rate.

7. The method for transmitting electric meter data based on probabilistic load transfer according to claim 1, characterized in that: The state transition probability of each electric meter is calculated based on the transmission load rate corresponding to each electric meter, and the state transition probability represents the probability of the electric meter transferring the load to other electric meters in the network, including: Based on the transmission load rate corresponding to each electric meter, the transfer weight between each electric meter and other adjacent electric meters in the network is calculated; Based on the transfer weights between each meter and other adjacent meters in the network, the normalized weight corresponding to each meter is calculated; The state transition probability of the electric meter is calculated based on the normalized weight corresponding to the electric meter and the probability that the load of the transfer target electric meter is transferred to the rated load.

8. The method for transmitting electric meter data based on probabilistic load transfer according to claim 7, characterized in that: The transfer weight between each meter and other adjacent meters in the network is: Where: w(Y i (t),Y j (t)) represents the transfer weight between meter i and meter j, Y i (t),Y j (t) are the load rates of meter i and meter j respectively, and ∈ is a constant.

9. The method for transmitting electric meter data based on probabilistic load transfer according to claim 7, characterized in that: The formula for the state transition probability of the electric meter is expressed as: Where: P(Y i (t+1)) represents the probability of meter i transferring load to target meter j, Y i (t),Y j (t) are the load rates of meter i and meter j, P(Y j (t)→Y′) represents the probability that the load of the transfer target meter j is transferred to the rated load Y′, w norm (Y i (t),Y j (t)) is the normalized weight.

10. The method for transmitting electric meter data based on probabilistic load transfer according to claim 9, characterized in that: The calculation formula for the probability that the load of the transfer target meter j is transferred to the rated load Y′ is:

11. The method for transmitting electric meter data based on probabilistic load transfer according to claim 1, characterized in that: The method of determining, according to the state transition probability of the electric meter, to transfer part of the load of the electric meter to other electric meters for transmission comprises: The state transition probability P(Y i (t+1)) is compared with the set load transfer threshold η. If η is satisfied <P(Y i (t+1)), then at the next moment, part of the load of meter i is transferred to other adjacent meters j for transmission, and meter i maintains rated load operation at the next moment; Otherwise, the electric energy signal is transmitted according to the current transmission load rate of the electric meter i.

12. The method for transmitting electric meter data based on probabilistic load transfer according to any one of claims 1 to 11, characterized in that: The method further comprises: The load transfer between the electric meters is realized by utilizing the communication between the FPGA chips connected to each electric meter, and each FPGA chip performs synchronous transmission of the electric energy signal based on the time signal provided by the GPS module.

13. An electric meter data transmission device based on probabilistic load transfer, characterized in that: include: An electric energy signal acquisition module is used to collect electric energy signals from each electric meter in the electric meter transmission network; A load rate calculation module, used to calculate the transmission load rate corresponding to each electric meter based on the electric energy signal corresponding to each electric meter; A state transition probability calculation module, used to calculate the state transition probability of each electric meter based on the transmission load rate corresponding to each electric meter, and the state transition probability represents the probability of the electric meter transferring load to other electric meters in the network; The load transfer module is used to determine, based on the state transfer probability of the electric meter, to transfer part of the load of the electric meter to other electric meters for transmission.

14. An electric meter data collection and transmission system, characterized in that: The system includes an FPGA chip connected to each meter data acquisition terminal, each FPGA chip is connected to a time synchronization module, and the output of each FPGA chip is connected to a server platform; The electric meter data acquisition terminal is used to collect electric energy signals from the electric meter; The FPGA chip calculates the transmission load rate corresponding to each meter based on the electric energy signal of the corresponding meter, and calculates the state transition probability of each meter based on the transmission load rate corresponding to each meter, and transfers part of the load of the meter to other meters for transmission. The state transition probability represents the probability of the meter transferring the load to other meters in the network; The time synchronization module is used to control each FPGA chip to synchronously transmit the power signal to the server platform.

15. The electric meter data collection and transmission system according to claim 14, characterized in that: The system also includes a filter connected to the meter data acquisition terminal; The filter is used to filter the electric energy signal using a dynamic iterative filtering algorithm to obtain a filtered electric energy signal; Correspondingly, the FPGA chip calculates the transmission load rate corresponding to each electric meter based on the filtered electric energy signal of the corresponding electric meter.

16. The electric meter data collection and transmission system according to claim 15, characterized in that: The training process of the filter is: Inputting the electric energy signal x(n) into the filter so that the filter outputs the actual filtered electric energy signal, and calculating the error signal e(n) between the filtered electric energy target signal and the actual filtered electric energy signal; The iteration length of the update filter is s(n+1)=z(e(n),s(n))=αs(n)+βe(n) 2 , where s(n+1) is the iteration length corresponding to time n+1, s(n) is the iteration length corresponding to time n, z() is the iteration length update function, α is the decreasing coefficient, and β is the iteration length adjustment coefficient; According to the updated step size s(n+1) and the error signal e(n), the filter weight vector is updated to q(n+1)=q(n)+s(n+1)e(n)x(n)+α(n)+β(n), where q(n+1) is the weight vector corresponding to the n+1 moment, q(n) is the weight vector corresponding to the n moment, x(n) is the collected original electric energy signal, α(n) and β(n) are adaptive adjustment coefficients, Where: η is the learning rate, α(n+1) and β(n+1) are the adaptive adjustment coefficients corresponding to time n+1, and α(n) and β(n) are the adaptive adjustment coefficients corresponding to time n; When the next step size update is performed until the convergence condition is reached, a trained filter for filtering noise from the input power signal is obtained.

17. The electric meter data collection and transmission system according to claim 14, characterized in that: The formula for the state transition probability of the electric meter is expressed as: Where: P(Y i (t+1)) represents the probability of meter i transferring load to target meter j, Y i (t),Y j (t) are the load rates of meter i and meter j, P(Y j (t)→Y′) represents the probability that the load of the transfer target meter j is transferred to the rated load Y′, w norm (Y i (t),Y h (t)) is the normalized weight.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the electric meter data transmission method based on probabilistic load transfer according to any one of claims 1 to 12 is implemented.

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