A remote upgrade method for household distributed photovoltaic inverters

Abstract

The present application relates to the technical field of inverter remote upgrade, and specifically to a method for remote upgrade of household distributed photovoltaic inverters, the method comprising: constructing a transmission stability weight of a node through the bit error rate of a test signal of each node, the discrete degree of signal-to-noise ratio data and the change trend of the data, and constructing an accuracy characteristic value of each node in combination with the state transition probability of the bit error rate and signal-to-noise ratio data; using a moving average model (MA) to predict the accuracy characteristic value of a receiving end, and constructing the overall transmission accuracy of the receiving end based on the difference between the accuracy characteristic value and the actual value and the overall change of the accuracy characteristic values ​​of all nodes; adaptively adjusting the redundancy of a forward error correction algorithm based on the overall transmission accuracy, thereby achieving the goal of reducing unnecessary bandwidth and storage resource consumption while ensuring the reliability of data transmission, improving the accuracy and efficiency of upgrade package transmission, and thereby improving the reliability of remote upgrade of the inverter.

Description

Technical Field

[0001] The present application relates to the technical field of inverter remote upgrade, and in particular to a method for remote upgrading a household distributed photovoltaic inverter. Background Art

[0002] The remote upgrade of household distributed photovoltaic inverters is a new service model that has emerged with the development of smart grid and Internet of Things technology. With the continuous advancement of photovoltaic technology, more and more families choose to install distributed photovoltaic power generation systems. However, traditional photovoltaic systems may face problems such as low efficiency and high maintenance costs during operation. Remote upgrade technology can effectively solve these problems and improve the stability and efficiency of system operation.

[0003] However, in the process of remote upgrading of photovoltaic inverters, due to distance problems, the transmitted upgrade package needs to pass through several transmission nodes to reach the receiving end. Between different transmission nodes, due to the cross-packeting and congestion of communication data and the interference of the harsh environment on site, the integrity and reliability of data transmission will be affected, resulting in packet loss of the transmitted data, which will have an adverse impact on the entire upgrade process. During transmission, it is necessary to ensure the integrity and accuracy of the data packet when it is transmitted through multiple nodes to avoid packet loss caused by communication interference between nodes, data congestion or environmental factors. The existing technology has failed to fully solve the problem of efficient and reliable data exchange in multi-node transmission, resulting in incomplete or erroneous data during the upgrade process, causing unstable system operation and even failure. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a method for remote upgrading of household distributed photovoltaic inverters to solve the existing problems.

[0005] A household distributed photovoltaic inverter remote upgrade method of the present application adopts the following technical solution:

[0006] An embodiment of the present application provides a method for remotely upgrading a household distributed photovoltaic inverter, the method comprising the following steps:

[0007] The inverter is used as the receiving end, and multiple test signals are sent from the transmitting end to collect the bit error rate and signal-to-noise ratio of the test signals received by each node and the receiving end during the signal transmission process;

[0008] For each node, the sequence of bit error rates and the sequence of signal-to-noise ratios of all test signals of the node are recorded as bit error rate sequence and signal-to-noise ratio sequence respectively; the transmission stability weight of the node is constructed based on the discrete degree of data in the bit error rate sequence and signal-to-noise ratio sequence and the data change trend; the elements are divided into states based on the element sizes in the bit error rate sequence and signal-to-noise ratio sequence respectively; based on the element state changes and combined with the node's transmission stability weight, the node's accuracy characteristic value is constructed;

[0009] Predicting the accuracy characteristic value of the receiving end based on the accuracy characteristic values ​​of all nodes; constructing the overall transmission accuracy of the receiving end based on the difference between the actual value and the predicted value of the accuracy characteristic value of the receiving end and the overall change of the accuracy characteristic values ​​of all nodes;

[0010] The initial redundancy of the forward error correction algorithm is adaptively adjusted based on the overall transmission accuracy. Based on the adjusted redundancy, the upgrade package is corrected in combination with the forward error correction algorithm to remotely upgrade the photovoltaic inverter.

[0011] In one embodiment, the process of acquiring the transmission stability weight of the node is:

[0012] The slope of the fitted straight line of all data in the bit error rate sequence is recorded as the first slope; the slope of the fitted straight line of all data in the signal-to-noise ratio sequence is recorded as the second slope; the degree of dispersion of all data in the bit error rate sequence is recorded as the first dispersion; the degree of dispersion of all data in the signal-to-noise ratio sequence is recorded as the second dispersion; the transmission stability weight of the node is negatively correlated with the first dispersion, the second dispersion, the first slope and the second slope respectively.

[0013] In one embodiment, the state classification of the elements is specifically as follows:

[0014] The state of the elements in the bit error rate sequence whose bit error rate is greater than the preset bit error rate threshold is set to a "bad state", and the state of the elements whose bit error rate is less than or equal to the preset bit error rate threshold is set to a "good state"; the state of the elements in the signal-to-noise ratio sequence whose bit error rate is greater than the preset signal-to-noise ratio threshold is set to a "good state", and the state of the elements whose bit error rate is less than or equal to the preset signal-to-noise ratio threshold is set to a "bad state".

[0015] In one embodiment, the process of obtaining the accuracy characteristic value of the node is:

[0016] The states of all elements in the bit error rate sequence are used as inputs of the Markov chain, and the state transition probabilities of the bit error rate changing from a "bad state" to a "good state" and the state transition probabilities of the bit error rate changing from a "good state" to a "bad state" are obtained from the output results, which are recorded as the first probability and the second probability respectively; the ratio of the first probability to the second probability is calculated, which is recorded as the first ratio;

[0017] Based on the states of all elements in the signal-to-noise ratio sequence, a second ratio of the signal-to-noise ratio sequence is obtained in the same manner as the first ratio;

[0018] The accuracy characteristic value of the node is positively correlated with the transmission stability weight of the node, the first ratio and the second ratio respectively.

[0019] In one embodiment, the predicting of the accuracy characteristic value of the receiving end is specifically:

[0020] The sequence composed of the accuracy characteristic values ​​of all nodes is recorded as the accuracy sequence; the accuracy sequence is used as the input of the prediction algorithm, and the output is the predicted value of the accuracy characteristic value of the receiving end.

[0021] In one embodiment, the process of obtaining the overall transmission accuracy of the receiving end is:

[0022] Combined with the differences and the overall changes in the accuracy characteristic values ​​of all nodes, the node impact factor of data transmission is constructed; combined with the accuracy characteristic value of the receiving end, the overall transmission accuracy of the receiving end is calculated.

[0023] In one embodiment, the process of obtaining the node influence factor of the data transmission is as follows:

[0024] The difference is recorded as the first difference; the mean of all data in the accuracy sequence is calculated and recorded as the first mean; the node influence factor of data transmission is recorded as c, and the calculation expression of c is: , where p is the first mean, q is the first difference, The default value is a positive number.

[0025] In one embodiment, the calculation expression of the overall transmission accuracy is: , where B is the overall transmission accuracy of the receiving end, is the actual value of the accuracy characteristic value at the receiving end, and c is the node influence factor of data transmission.

[0026] In one embodiment, the expression for adaptively adjusting the initial redundancy of the forward error correction algorithm is: , where is the adjusted redundancy, is the initial redundancy, is a preset positive number, B is the overall transmission accuracy of the receiving end, is an exponential function with the natural constant e as base, is the ceiling function.

[0027] In one embodiment, the photovoltaic inverter is remotely upgraded by correcting the upgrade package based on the adjusted redundancy and combining the forward error correction algorithm, specifically:

[0028] A forward error correction algorithm is set in the communication module between the transmitter and the inverter, and redundant data is generated using the adjusted redundancy. During the transmission process, the erroneous data in the upgrade package is corrected by the redundant data, and the receiver receives the upgrade package to complete the remote upgrade.

[0029] This application has at least the following beneficial effects:

[0030] The present application proposes a method for remote upgrading of household distributed photovoltaic inverters. For the problem of evaluating the quality of communication links, the transmission stability weight of the node is constructed by using the bit error rate of the test signal of each node, the discrete degree of the signal-to-noise ratio data and the change trend of the data. The accuracy characteristic value of each node is constructed by combining the state transition probability of the bit error rate and the signal-to-noise ratio data, which reflects the transmission stability and accuracy of each node during the test signal period and solves the problem of quantifying the transmission quality of the node. For the problem of evaluating the overall transmission accuracy of the receiving end, the moving average model (MA) is used to predict the accuracy characteristic value of the receiving end. The overall transmission accuracy of the receiving end is constructed based on the difference between the accuracy characteristic value and the actual value and the overall change of the accuracy characteristic value of all nodes, which reflects the probability that the signal in the entire transmission link remains accurate and complete, and eliminates the influence of the transmission quality of a single node on the overall transmission accuracy. The redundancy of the forward error correction algorithm is adaptively adjusted based on the overall transmission accuracy, so as to achieve the goal of reducing unnecessary bandwidth and storage resource consumption while ensuring the reliability of data transmission. The system can be flexibly adjusted according to the actual transmission conditions, so that the remote upgrade of the inverter can maintain stable operation under different transmission conditions, improve the accuracy and efficiency of the upgrade package transmission, and thus improve the reliability of the remote upgrade of the inverter. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 A flowchart of a remote upgrade method for household distributed photovoltaic inverters provided in this application;

[0033] Figure 2 This is a schematic diagram of the prediction results of the moving average model;

[0034] Figure 3Schematic diagram of the relationship between transmission accuracy and transmission rate;

[0035] Figure 4 The relationship between transmission accuracy and bit error rate. DETAILED DESCRIPTION

[0036] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a household distributed photovoltaic inverter remote upgrade method proposed in the present application in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0037] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0038] The specific scheme of the remote upgrade method of a household distributed photovoltaic inverter provided by the present application is described in detail below with reference to the accompanying drawings.

[0039] An embodiment of the present application provides a method for remotely upgrading a household distributed photovoltaic inverter.

[0040] Specifically, the following method for remote upgrading of household distributed photovoltaic inverters is provided. Figure 1 , the method comprises the following steps:

[0041] Step S1, before sending the upgrade package, multiple test signals are sent from the sending end, and the bit error rate and signal-to-noise ratio of the test signals received by each node and the receiving end during the signal transmission process are measured by a communication tester.

[0042] Before upgrading the user's PV inverter, first determine the necessity of the upgrade, customize the upgrade content according to the inverter model, software version and user feedback, and design and develop a new software version according to the needs, including fixing known defects, optimizing system performance, adding new features, etc. And identify the specific models and configurations of different user devices to ensure that the distributed upgrade package is compatible with the user's device, and then integrate the software files, configuration files and upgrade instructions into a data package to ensure the accuracy and integrity of all data.

[0043] Before sending the upgrade package, a known test signal is continuously sent n times from the transmitter to the inverter to be upgraded. The inverter to be upgraded is the receiving end. During the data transmission process from the transmitter to the receiving end, multiple nodes are passed through. The communication tester is used to analyze each node and each test signal received by the receiving end, including measuring the bit error rate and signal-to-noise ratio of the test signal.

[0044] Preferably, in the embodiment of the present application, the value of n is set to 50. In other embodiments of the present application, the value of n can be set by the implementer according to the actual situation. In the embodiment of the present application, the test signal can be a pseudo-random sequence. It should be noted that in other embodiments of the present application, the test signal can be set by the implementer, and the present application does not make any specific restrictions.

[0045] Step S2, constructing the transmission stability weight of the selected node based on the bit error rate and signal-to-noise ratio data changes of the test signal at each node; dividing the bit error rate and signal-to-noise ratio of each test signal into states based on the size of the collected bit error rate and signal-to-noise ratio data; calculating the accuracy characteristic value of each node based on the state transition probability of the bit error rate and signal-to-noise ratio of the test signal combined with the transmission stability weight.

[0046] For each stage of signal transmission, in addition to the sending end, the success rate of long-distance signal transmission between the intermediate nodes and the receiving end may be affected by many factors, such as abnormal power failure of the terminal, packet congestion of communication data, and interference from harsh on-site environments. These problems may affect the integrity and reliability of data transmission, thereby adversely affecting the entire upgrade process.

[0047] Among all the nodes in the data transmission process, a node is randomly selected. Taking this node as an example, this node is called the selected node, and various types of data of the test signal of the selected node are analyzed:

[0048] (1) The bit error rates of all the test signals received by the selected node are arranged in ascending order according to the transmission time of the test signals, and the sequence obtained is recorded as the bit error rate sequence; the signal-to-noise ratios of all the test signals received by the selected node are arranged in ascending order according to the transmission time of the test signals, and the sequence obtained is recorded as the signal-to-noise ratio sequence;

[0049] (2) All data in the bit error rate sequence are linearly fitted by the least squares method to obtain a fitting straight line of the bit error rate sequence. The least squares fitting method is a well-known technology, and the specific process is not repeated here. It should be noted that for the linear fitting of all data in the bit error rate sequence, this application only provides a linear fitting method. There are many existing linear fitting methods. The implementer can also use other linear fitting algorithms to perform linear fitting on all data in the bit error rate sequence. This application does not make specific restrictions.

[0050] Based on the signal-to-noise ratio sequence, the fitting straight line of the signal-to-noise ratio sequence is obtained in the same manner as that of the fitting straight line of the bit error rate sequence.

[0051] (3) Based on the discrete degree of the data in the bit error rate sequence and the signal-to-noise ratio sequence, as well as the changing trend of the data in these two sequences, the transmission stability weight of the selected node is constructed:

[0052] The degree of discreteness of all data in the bit error rate sequence is recorded as the first discreteness; the degree of discreteness of all data in the signal-to-noise ratio sequence is recorded as the second discreteness; preferably, in one embodiment of the present application, the degree of discreteness may be the coefficient of variation of all data in the sequence. In other embodiments of the present application, the degree of discreteness may be the variance of all data in the sequence. The slope of the fitted straight line of the bit error rate sequence is calculated and recorded as the first slope; the slope of the fitted straight line of the signal-to-noise ratio sequence is calculated and recorded as the second slope. The transmission stability weight of the selected node is negatively correlated with the first discreteness, the second discreteness, the first slope and the second slope, respectively.

[0053] It should be noted that the negative correlation described in this application means that the independent variable decreases as the dependent variable increases, and the independent variable increases as the dependent variable decreases. The specific negative correlation can be determined according to actual conditions during application, and this application does not impose any special restrictions.

[0054] Preferably, in one embodiment of the present application, the calculation expression of the transmission stability weight of the selected node can be: , where a is the transmission stability weight of the selected node, m is the mean of the first discreteness and the second discreteness of the selected node, and n is the mean of the first slope and the second slope of the selected node. It is an exponential function with the natural constant e as its base.

[0055] In other embodiments of the application, the calculation expression of the transmission stability weight of the selected node can be: , where is the normalization function.

[0056] The dispersion reflects the degree of discreteness of the data in the sequence, and the slope of the fitted line reflects the trend of the sequence changing over time. The greater the dispersion and the slope of the fitted line, the more unstable the data changes in the sequence and the smaller the value of a.

[0057] (4) The elements in the bit error rate sequence and the signal-to-noise ratio sequence are divided into states based on the size of the elements in the bit error rate sequence and the signal-to-noise ratio sequence respectively:

[0058] The bit error rate threshold and the signal-to-noise ratio threshold are set. In the embodiment of the present application, the bit error rate threshold is set to the average of the maximum and minimum values ​​of all elements in the bit error rate sequence, and the signal-to-noise ratio threshold is set to the average of the maximum and minimum values ​​of all elements in the signal-to-noise ratio sequence. In other embodiments of the present application, the implementer can set the bit error rate threshold and the signal-to-noise ratio threshold according to actual conditions, and the present application does not impose specific restrictions. The state of the elements in the bit error rate sequence that are greater than the bit error rate threshold is set to "bad state", and the state of the elements that are less than or equal to the bit error rate threshold is set to "good state"; on the contrary, the state of the elements in the signal-to-noise ratio sequence that are greater than the signal-to-noise ratio threshold is set to "good state", and the state of the elements that are less than or equal to the signal-to-noise ratio threshold is set to "bad state". Thereby, the elements in the bit error rate sequence and the signal-to-noise ratio sequence are divided into two categories, respectively.

[0059] (5) To predict the change of the bit error rate and the change of the signal-to-noise ratio when the next test signal is transmitted to the selected node, the states of all elements in the bit error rate sequence are used as the input of the Markov chain, and the output is a transition probability matrix. The state transition probability of the bit error rate of the next test signal changing from a "bad state" to a "good state" and the state transition probability of the bit error rate of the next test signal changing from a "good state" to a "bad state" are obtained from the transition probability matrix, which are recorded as the first probability and the second probability respectively; the ratio of the first probability to the second probability of the bit error rate sequence is calculated, which is recorded as the first ratio;

[0060] Based on the states of all elements in the signal-to-noise ratio sequence, the first probability and the second probability of the signal-to-noise ratio sequence are obtained in the same manner as the first probability and the second probability of the bit error rate sequence; and the ratio of the first probability to the second probability of the signal-to-noise ratio sequence is calculated, which is recorded as the second ratio.

[0061] (6) Based on the state changes of the elements in the bit error rate sequence and signal-to-noise ratio sequence of the selected node, combined with the transmission stability weight of the selected node, the accuracy characteristic value of the selected node is constructed:

[0062] The accuracy characteristic value of the selected node is positively correlated with the transmission stability weight of the selected node, the first ratio and the second ratio respectively.

[0063] It should be noted that the positive correlation described in this application means that the independent variable increases as the dependent variable increases, and the independent variable decreases as the dependent variable decreases. The specific positive correlation can be determined according to actual conditions during application, and this application does not impose any special restrictions.

[0064] Preferably, in one embodiment of the present application, the accuracy characteristic value calculation expression of the selected node is: , where A is the accuracy characteristic value of the selected node, a is the transmission stability weight of the selected node, and b is the average of the first ratio and the second ratio of the selected node.

[0065] The larger the value of a is, the better the transmission stability and the higher the transmission accuracy, and A increases accordingly; b reflects the probability of the signal maintaining high-quality transmission at the selected node during signal transmission. When b is less than 1, it means that the probability of "bad state" is higher and the transmission quality is less reliable. When b is greater than 1, the transmission quality is more reliable, and A increases with the increase of b.

[0066] The A value describes the accuracy and reliability of the upgrade package transmission at the selected node. A higher A value means that at the selected node, the transmission of the upgrade package not only shows higher stability during the test, but also has higher transmission quality at the predicted upgrade moment. This provides a higher confidence in the successful transmission of the upgrade package. The same method is used to calculate the accuracy feature values ​​of all nodes and the receiving end.

[0067] The accuracy characteristic value A on each node is a key indicator for evaluating the signal transmission quality of the node within a specific time window. The value of A directly affects the accuracy of the upgrade package transmission at the next moment. When the A value is high, it means that the node has higher transmission stability and lower transmission unreliability confidence, so the transmission of the upgrade package at this node is more secure, reducing the possibility of upgrade failure due to signal quality problems. On the contrary, if the A value is low, it means that the signal transmission quality of the node is unstable or unreliable, and the transmission of the upgrade package at this node may face greater risks, such as data loss or damage, resulting in upgrade failure or system instability.

[0068] Step S3, calculating the accuracy characteristic value of each node and the receiving end; predicting the accuracy characteristic value of the receiving end based on the accuracy characteristic values ​​of all nodes; calculating the overall transmission accuracy of the receiving end based on the difference between the actual value and the predicted value of the accuracy characteristic value of the receiving end and the overall change of the accuracy characteristic values ​​of all nodes;

[0069] (1) Based on each node and all test signals received by the receiving end during the signal transmission process, the accuracy characteristic value of each node and the receiving end is calculated by the above-mentioned method of obtaining the accuracy characteristic value of the selected node.

[0070] (2) The accuracy feature values ​​of all nodes are sorted according to the order in which the signal passes through the nodes, and the resulting sequence is recorded as the accuracy sequence; the accuracy sequence is used as the input of the moving average model (MA), and the output is the predicted value of the accuracy feature value at the receiving end. The prediction result diagram of the moving average model is shown in the figure below: Figure 2 It should be noted that, for the prediction of the accuracy characteristic value of the receiving end, this application only provides a prediction method. There are many existing prediction methods. The implementer can also use other prediction algorithms to predict the accuracy characteristic value of the receiving end. This application does not make specific restrictions.

[0071] (3) Based on the difference between the actual value and the predicted value of the accuracy characteristic value at the receiving end and the overall change of the accuracy characteristic value of all nodes, the node influence factor of data transmission is constructed:

[0072] The difference between the actual value and the predicted value of the accuracy characteristic value of the receiving end is calculated and recorded as the first difference. Preferably, in one embodiment of the present application, the first difference can be the absolute value of the difference between the actual value and the predicted value of the accuracy characteristic value of the receiving end. In other embodiments of the present application, the first difference can be the square of the difference between the actual value and the predicted value of the accuracy characteristic value of the receiving end.

[0073] Calculate the mean of all data in the accuracy sequence and record it as the first mean.

[0074] The calculation expression of the node impact factor of data transmission can be: , where c is the node influencing factor of data transmission, p is the first mean, and q is the first difference, is a positive number that is artificially preset to prevent the denominator from being 0. The value of is set to 0.01. In other embodiments of the present application, The implementer can set the value according to the actual situation.

[0075] The node impact factor reflects the degree of influence of the transmission accuracy of the node on the quality of the signal received by the receiving end during the transmission process, and quantifies the change in data transmission accuracy caused by the node characteristics during the signal transmission process. The greater the difference between the predicted value and the actual value of the accuracy characteristic value of the receiving end, the less relevant the transmission accuracy between the node and the receiving end, and the smaller the impact on the receiving end. The mean of the accuracy sequence represents the degree of influence of the transmission node on the transmission accuracy of the receiving end.

[0076] (4) Based on the node influence factor of data transmission and the accuracy characteristic value of the receiving end, the overall transmission accuracy of the receiving end is calculated, and the expression is: , where B is the overall transmission accuracy of the receiving end, is the actual value of the accuracy characteristic value at the receiving end, and c is the node influence factor of data transmission.

[0077] The larger the c value, the greater the influence of the node on the transmission accuracy of the receiving end, and the smaller B is; the accuracy characteristic value of the receiving end It represents the accuracy of the signal quality at the receiving end during the actual transmission process. The larger the value, the greater the accuracy of the signal quality at the receiving end during the actual transmission process, and B increases accordingly.

[0078] B is a comprehensive measure of the signal transmission quality at the receiving end, taking into account the impact of all nodes in the transmission process and the performance of the receiving end. It describes the probability that the signal remains accurate and complete during the entire transmission process of the upgrade package from the sender to the receiver. The larger the value, the better the signal quality and integrity are maintained throughout the transmission link, thereby increasing the possibility of successful transmission of the upgrade package.

[0079] Step S4, adjusting the redundancy based on the overall transmission accuracy, improving the forward error correction algorithm based on the adjusted redundancy, correcting the erroneous data during the transmission of the upgrade package based on the improved forward error correction algorithm, and realizing remote upgrade of the inverter.

[0080] During the remote upgrade of the inverter, the success rate of long-distance data transmission may be affected by many factors, such as packet congestion, blockage, and interference from harsh on-site environments. These problems may affect the integrity and reliability of data transmission, resulting in packet loss of the transmitted data, which will adversely affect the entire upgrade process.

[0081] A forward error correction algorithm is set in the communication module of the transmitter and the inverter to reduce the transmission errors in the presence of interference or poor channel conditions. Before the transmitter sends the upgrade package, the forward error correction algorithm generates redundant data for the upgrade package through convolutional coding or Turbo coding, and the initial redundancy of the algorithm (i.e., the ratio of the number of redundant packets to the number of original data packets) is set to 5, that is, 5 redundant packets are generated to achieve error correction and occupy less bandwidth. In the forward error correction algorithm, the amount of redundant data determines the error correction capability of the transmitted data, and the redundant data will occupy bandwidth, resulting in reduced transmission efficiency. In this regard, based on the transmission quality of the test signal received by the receiving end, the redundancy of the forward error correction algorithm is adaptively adjusted, and the expression is:

[0082] , where is the adjusted redundancy, is the initial redundancy, is a positive number preset artificially to adjust the size of the calculation result. B is the overall transmission accuracy of the receiving end. is an exponential function with the natural constant e as base, is a rounding function. Preferably, in the embodiment of the present application, The value of is set to 1.5. In other embodiments of the present application, the implementer can set it according to the actual situation. The value of .

[0083] When the transmission accuracy B of the receiving end is high, it means that the signal transmission quality is good, so the redundancy can be appropriately reduced to improve the transmission efficiency. On the contrary, if B is low, it means that the transmission quality is poor, and the redundancy needs to be increased to improve the error correction capability. By adaptively adjusting the redundancy, it is possible to reduce unnecessary bandwidth and storage resource consumption while ensuring the reliability of data transmission. When the signal transmission quality is good, the generation of redundant data can be reduced, which can speed up the data transmission speed and improve the overall transmission efficiency. The adaptive adjustment of redundancy enables the system to be flexibly adjusted according to the actual transmission conditions, so that the remote upgrade of the inverter can maintain stable operation under different upgrade package transmission conditions, reduce system failures caused by transmission errors, and optimize resource usage. The receiving end receives the upgrade package to complete the remote upgrade of the photovoltaic inverter. Among them, the forward error correction algorithm and the remote upgrade of the photovoltaic inverter are well-known technologies, and the specific process will not be repeated.

[0084] The relationship between the transmission accuracy of the test signal sent at different times and the transmission rate and bit error rate is shown in the figure below. Figure 3 , Figure 4 shown.

[0085] In summary, the embodiment of the present application constructs the transmission stability weight of the node through the bit error rate of the test signal of each node, the discrete degree of the signal-to-noise ratio data and the change trend of the data, and constructs the accuracy characteristic value of each node in combination with the state transition probability of the bit error rate and the signal-to-noise ratio data, which reflects the transmission stability and accuracy of each node during the test signal, and solves the problem of quantifying the transmission quality of the node; for the overall transmission accuracy evaluation problem of the receiving end, the moving average model (MA) is used to predict the accuracy characteristic value of the receiving end, and the overall transmission accuracy of the receiving end is constructed based on the difference between it and the actual value and the overall change of the accuracy characteristic value of all nodes, which reflects the probability that the signal in the entire transmission link remains accurate and complete, and eliminates the influence of the transmission quality of a single node on the overall transmission accuracy; based on the overall transmission accuracy, the redundancy of the forward error correction algorithm is adaptively adjusted, so as to achieve the reduction of unnecessary bandwidth and storage resource consumption while ensuring the reliability of data transmission. The system can be flexibly adjusted according to the actual transmission conditions, so that the remote upgrade of the inverter can maintain stable operation under different transmission conditions, improve the accuracy and efficiency of the upgrade package transmission, and thus improve the reliability of the remote upgrade of the inverter.

[0086] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of the present application are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0088] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for remote upgrading of household distributed photovoltaic inverters, characterized in that: The method comprises the following steps: The inverter is used as the receiving end, and multiple test signals are sent from the transmitting end to collect the bit error rate and signal-to-noise ratio of the test signals received by each node and the receiving end during the signal transmission process; For each node, the sequence of bit error rates and the sequence of signal-to-noise ratios of all test signals of the node are recorded as bit error rate sequence and signal-to-noise ratio sequence respectively; the transmission stability weight of the node is constructed based on the discrete degree of data in the bit error rate sequence and signal-to-noise ratio sequence and the data change trend; the elements are divided into states based on the element sizes in the bit error rate sequence and signal-to-noise ratio sequence respectively; based on the element state changes and combined with the node's transmission stability weight, the node's accuracy characteristic value is constructed; Predicting the accuracy characteristic value of the receiving end based on the accuracy characteristic values ​​of all nodes; constructing the overall transmission accuracy of the receiving end based on the difference between the actual value and the predicted value of the accuracy characteristic value of the receiving end and the overall change of the accuracy characteristic values ​​of all nodes; The initial redundancy of the forward error correction algorithm is adaptively adjusted based on the overall transmission accuracy. Based on the adjusted redundancy, the upgrade package is corrected in combination with the forward error correction algorithm to remotely upgrade the photovoltaic inverter.

2. A household distributed photovoltaic inverter remote upgrade method as claimed in claim 1, characterized in that: The process of obtaining the transmission stability weight of the node is as follows: The slope of the fitted straight line of all data in the bit error rate sequence is recorded as the first slope; the slope of the fitted straight line of all data in the signal-to-noise ratio sequence is recorded as the second slope; the degree of dispersion of all data in the bit error rate sequence is recorded as the first dispersion; the degree of dispersion of all data in the signal-to-noise ratio sequence is recorded as the second dispersion; the transmission stability weight of the node is negatively correlated with the first dispersion, the second dispersion, the first slope and the second slope respectively.

3. A household distributed photovoltaic inverter remote upgrade method as claimed in claim 1, characterized in that: The state division of the elements is specifically as follows: The state of the elements in the bit error rate sequence whose bit error rate is greater than the preset bit error rate threshold is set to a "bad state", and the state of the elements whose bit error rate is less than or equal to the preset bit error rate threshold is set to a "good state"; the state of the elements in the signal-to-noise ratio sequence whose bit error rate is greater than the preset signal-to-noise ratio threshold is set to a "good state", and the state of the elements whose bit error rate is less than or equal to the preset signal-to-noise ratio threshold is set to a "bad state".

4. A household distributed photovoltaic inverter remote upgrade method as claimed in claim 1, characterized in that: The process of obtaining the accuracy characteristic value of the node is as follows: The states of all elements in the bit error rate sequence are used as the input of the Markov chain, and the state transition probability of the bit error rate changing from a "bad state" to a "good state" and the state transition probability of the bit error rate changing from a "good state" to a "bad state" are obtained from the output result, which are recorded as the first probability and the second probability respectively; the ratio of the first probability to the second probability is calculated, which is recorded as the first ratio; Based on the states of all elements in the signal-to-noise ratio sequence, a second ratio of the signal-to-noise ratio sequence is obtained in the same manner as the first ratio; The accuracy characteristic value of the node is positively correlated with the transmission stability weight of the node, the first ratio and the second ratio respectively.

5. A household distributed photovoltaic inverter remote upgrade method as claimed in claim 1, characterized in that: The prediction of the accuracy characteristic value of the receiving end is specifically as follows: The sequence composed of the accuracy characteristic values ​​of all nodes is recorded as the accuracy sequence; the accuracy sequence is used as the input of the prediction algorithm, and the output is the predicted value of the accuracy characteristic value of the receiving end.

6. A household distributed photovoltaic inverter remote upgrade method as claimed in claim 5, characterized in that: The process of obtaining the overall transmission accuracy of the receiving end is as follows: In combination with the differences and the overall changes in the accuracy characteristic values ​​of all nodes, a node influencing factor for data transmission is constructed; in combination with the actual value of the accuracy characteristic value of the receiving end, the overall transmission accuracy of the receiving end is calculated.

7. A household distributed photovoltaic inverter remote upgrade method as claimed in claim 6, characterized in that: The process of obtaining the node influence factor of the data transmission is as follows: The difference is recorded as the first difference; the mean of all data in the accuracy sequence is calculated and recorded as the first mean; the node influence factor of data transmission is recorded as c, and the calculation expression of c is: , where p is the first mean, q is the first difference, Default is a positive number.

8. A method for remote upgrading of household distributed photovoltaic inverters as claimed in claim 6, characterized in that: The calculation expression of the overall transmission accuracy is: , where B is the overall transmission accuracy of the receiving end, is the actual value of the accuracy characteristic value at the receiving end, and c is the node influence factor of data transmission.

9. A method for remote upgrading of household distributed photovoltaic inverters as claimed in claim 1, characterized in that: The expression for adaptively adjusting the initial redundancy of the forward error correction algorithm is: , where is the adjusted redundancy, is the initial redundancy, is a preset positive number, B is the overall transmission accuracy of the receiving end, is an exponential function with the natural constant e as base, is the ceiling function.

10. A household distributed photovoltaic inverter remote upgrade method as claimed in claim 1, characterized in that: The above method is based on the adjusted redundancy and combines the forward error correction algorithm to correct the upgrade package and remotely upgrade the photovoltaic inverter, specifically: A forward error correction algorithm is set in the communication module between the transmitter and the inverter, and redundant data is generated using the adjusted redundancy. During the transmission process, the erroneous data in the upgrade package is corrected by the redundant data, and the receiver receives the upgrade package to complete the remote upgrade.

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