A high reliability data transmission method for smart microgrid system

Abstract

The present invention relates to the technical field of data transmission, and in particular to a highly reliable data transmission method for a smart microgrid system, the method comprising: obtaining load data of a target node for a smart microgrid system in a current time period and a historical time period; clustering and dividing the load data in the current time period, and adaptively merging all current load clusters; determining the frequency of data changes corresponding to each current load sub-segment; screening out similar load sub-segments corresponding to each current load sub-segment; determining the state influence factor corresponding to each current load sub-segment; screening out the historical load sequence that is the same as the corresponding stage of each current load sub-segment, and obtaining the state influence factor corresponding to the historical load sequence; transmitting the operating condition information collected in the current time period according to the determined target transmission level. The present invention performs data transmission based on a quantized transmission level, thereby improving the rationality of data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to a high-reliability data transmission method for a smart microgrid system. Background Art

[0002] With the rapid development of renewable energy technology, smart microgrids and their power transmission and distribution technologies have become an important development direction in the field of new energy. Smart microgrid is a highly integrated power system that includes multiple nodes such as bidirectional energy storage converters, energy storage devices, monitoring and protection equipment. These nodes can realize real-time control, scheduling and protection of power, thereby improving the flexibility and reliability of power supply. In order to monitor the operation of smart microgrids, it is often necessary to transmit the operating information that characterizes the operation of different nodes. At present, a method commonly used for data transmission is to transmit data without differentiating the priority. Among them, non-differentiated priority transmission means that during the transmission process, all data are treated indiscriminately, and no priority is distinguished according to the content or type of the data.

[0003] However, when the working condition information of different nodes is transmitted without different priorities, the following technical problems often occur:

[0004] Since the operating conditions of different nodes are often different, the urgency of analyzing the operating information of different nodes is often different. If the operating information of different nodes is transmitted without priority, the operating information of the abnormally operating nodes may not be transmitted in time, resulting in poor rationality of data transmission. Summary of the invention

[0005] In order to solve the technical problem of poor rationality of data transmission, the present invention proposes a high-reliability data transmission method for a smart microgrid system.

[0006] In a first aspect, the present invention provides a high-reliability smart microgrid system data transmission method, the method comprising:

[0007] Obtain load data of a target node for a smart microgrid system in a current time period and a historical time period;

[0008] Clustering the load data in the current time period to obtain the current load clusters, and adaptively merging all the current load clusters to obtain the current load sub-segments;

[0009] Determine the frequency of data changes corresponding to each current load sub-segment according to the fluctuation between the current load clusters in each current load sub-segment;

[0010] Based on the frequency of data changes corresponding to all current load sub-segments, similar load sub-segments corresponding to each current load sub-segment are screened out;

[0011] Determine a state influence factor corresponding to each current load subsegment according to the time intervals between all adjacent similar load subsegments corresponding to each current load subsegment;

[0012] Filter out the historical load sequence that is the same as the corresponding stage of each current load sub-segment from the load data in the historical time period, and obtain the state influencing factor corresponding to the historical load sequence;

[0013] Determining a target transmission level based on differences between state influencing factors corresponding to all current load sub-segments and their historical load sequences;

[0014] According to the target transmission level, the operating condition information collected during the current time period is transmitted.

[0015] In combination with the first aspect above, in a possible implementation manner, clustering the load data in the current time period to obtain the current load clusters includes:

[0016] Clustering the load data in the current time period to obtain a target cluster;

[0017] The continuous load data of the corresponding collection time in the same target cluster is clustered to form a small cluster;

[0018] Divide the current time period into equal parts to obtain sub-time periods;

[0019] The mean of the collection time corresponding to all load data in each small cluster is determined as the target representative time corresponding to each small cluster;

[0020] The corresponding target representative moments belong to the small clusters in the same sub-time period to form the current load cluster.

[0021] In combination with the first aspect, in a possible implementation manner, the adaptively merging all current load clusters to obtain the current load sub-segment includes:

[0022] Determine the average of the collection times corresponding to all load data in each current load cluster as the reference representative time corresponding to each current load cluster;

[0023] All current load clusters are sorted according to the reference representative time points corresponding to all current load clusters to obtain a current load cluster sequence;

[0024] Determining a load difference evaluation factor between each adjacent current load cluster in the current load cluster sequence according to the load difference between each adjacent current load cluster;

[0025] The current load clusters whose load difference evaluation factors in the current load cluster sequence are continuously greater than or equal to a preset load difference threshold constitute a current load sub-segment;

[0026] The current loads in the current load clustering sequence whose load difference evaluation factors are continuously smaller than a preset load difference threshold are clustered to form a current load sub-segment.

[0027] In combination with the first aspect, in a possible implementation manner, determining the load difference evaluation factor between each adjacent current load cluster in the current load cluster sequence according to the load difference between each adjacent current load cluster includes:

[0028] Determine the mean of all load data in each current load cluster as load representative data corresponding to each current load cluster;

[0029] The absolute value of the difference between the load representative data corresponding to each adjacent current load cluster is determined as the load difference between each adjacent current load cluster;

[0030] According to the load difference between each adjacent current load cluster, a load difference evaluation factor between each adjacent current load cluster is determined, wherein the load difference is positively correlated with the load difference evaluation factor.

[0031] In combination with the first aspect, in a possible implementation, determining the frequency of data changes corresponding to each current load subsegment according to fluctuations between current load clusters in each current load subsegment includes:

[0032] Determine the mean of all load data in each current load cluster as load representative data corresponding to each current load cluster;

[0033] Determine the fluctuation change factor corresponding to each current load subsegment according to the variance of the load representative data corresponding to all current load clusters in each current load subsegment;

[0034] The frequency of data changes corresponding to each current load subsegment is determined based on the fluctuation change factor corresponding to each current load subsegment, the number of current load clusters in each current load subsegment, and the load difference evaluation factor between the current load cluster in each current load subsegment and its adjacent current load clusters.

[0035] In combination with the first aspect above, in a possible implementation manner, a formula corresponding to the frequency of data changes corresponding to the current load sub-segment is:

[0036] ;

[0037] ;

[0038] ;in, It is The frequency of data changes corresponding to the current load sub-segment; is the sequence number of the current load sub-segment; is the normalization function; It is The fluctuation change factor corresponding to the current load sub-segment; It is The variance of the load representative data corresponding to all current load clusters in the current load sub-segment; It is The load corresponding to all current load clusters in the current load sub-segment represents the maximum value in the data; It is The load corresponding to all current load clusters in the current load sub-segment represents the minimum value in the data; is a pre-set factor greater than 0; It is The number of current load clusters within the current load subsegment; It is The sequence number of the current load cluster within the current load subsegment; It is The average value of the load difference evaluation factors between all current load clusters and their adjacent current load clusters in the current load sub-segment; It is In the current load sub-segment, The current load clusters and The load difference evaluation factor between the current load clusters.

[0039] In combination with the first aspect, in a possible implementation, the filtering out similar load sub-segments corresponding to each current load sub-segment based on the frequency of data changes corresponding to all current load sub-segments includes:

[0040] Determine any current load subsegment as a marked load subsegment, and determine each current load subsegment except the marked load subsegment as a reference load subsegment;

[0041] Determine the data variation range corresponding to the marked load subsegment by adding or subtracting a fixed number range of the frequency of data variation corresponding to the marked load subsegment as the data variation range corresponding to the marked load subsegment;

[0042] A reference load subsegment whose corresponding data change frequency falls within the data change range corresponding to the marked load subsegment is selected from all reference load subsegments as a similar load subsegment corresponding to the marked load subsegment.

[0043] In combination with the first aspect above, in a possible implementation manner, a formula corresponding to the state impact factor corresponding to the current load sub-segment is:

[0044] ;in, It is The state influencing factor corresponding to the current load sub-segment; is the sequence number of the current load sub-segment; It is The number of similar load sub-segments corresponding to the current load sub-segment; It is The sequence number of the similar load sub-segment corresponding to the current load sub-segment; is a natural exponential function; It is The current load sub-segment corresponds to the similar load sub-segments and The time interval between similar load sub-segments.

[0045] In combination with the first aspect, in a possible implementation manner, determining the target transmission level based on the difference between the state impact factors corresponding to all current load sub-segments and their historical load sequences includes:

[0046] Clustering and adaptively merging the load data within the historical time period to obtain historical load sub-segments;

[0047] The target transmission level is determined according to the quantity difference between the historical load sub-segment and the current load sub-segment, and the difference between the state influence factors corresponding to all the current load sub-segments and their historical load sequences.

[0048] In combination with the first aspect above, in a possible implementation manner, a formula corresponding to the target transmission level is:

[0049] ;

[0050] ;

[0051] ;in, is the target transmission level; is the normalization function; Represents the overall difference between all current load sub-segments and their historical load sequences; is the absolute value of the difference between the number of historical load sub-segments and the number of current load sub-segments; is the number of current load sub-segments; is the sequence number of the current load sub-segment; Indicates The difference between the current load sub-segment and its historical load sequence; It is the absolute value function; It is The state influencing factor corresponding to the current load sub-segment; It is The state influencing factor corresponding to the historical load sequence corresponding to the current load sub-segment; It is A load difference evaluation factor between a current load sub-segment and its corresponding historical load sequence; It is The number of load data in the current load subsegment; It is The number of load data in the longest historical load subsegment among the historical load subsegments to which all load data in the historical load sequence corresponding to the current load subsegment belong.

[0052] In a second aspect, the present invention provides a highly reliable smart microgrid system data transmission system, the system comprising:

[0053] A load data acquisition module, used to acquire load data of a target node for a smart microgrid system in a current time period and a historical time period;

[0054] The clustering division and merging module is used to cluster the load data in the current time period to obtain the current load clusters, and adaptively merge all the current load clusters to obtain the current load sub-segments;

[0055] A data change frequency determination module, used to determine the data change frequency corresponding to each current load sub-segment according to the fluctuation between the current load clusters in each current load sub-segment;

[0056] A similar load sub-segment screening module is used to screen out similar load sub-segments corresponding to each current load sub-segment based on the frequency of data changes corresponding to all current load sub-segments;

[0057] A state impact factor determination module, used to determine the state impact factor corresponding to each current load subsegment according to the time intervals between all adjacent similar load subsegments corresponding to each current load subsegment;

[0058] A screening and determination module is used to screen out the historical load sequence that is the same as the corresponding stage of each current load sub-segment from the load data in the historical time period, and obtain the state influencing factor corresponding to the historical load sequence;

[0059] a target transmission level determination module, configured to determine a target transmission level based on differences between state influencing factors corresponding to all current load sub-segments and their historical load sequences;

[0060] The information transmission module is used to transmit the operating condition information collected in the current time period according to the target transmission level.

[0061] In a third aspect, a server is provided, comprising a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes the method in the first aspect or any possible implementation of the first aspect.

[0062] In a fourth aspect, a computer program product is provided, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the method in the first aspect or any possible implementation of the first aspect.

[0063] In a fifth aspect, a computer-readable storage medium is provided, which stores a computer program code. When the computer program code runs on a computer, the computer executes the method in the above-mentioned first aspect or any possible implementation manner of the first aspect.

[0064] The present invention has the following beneficial effects:

[0065] A highly reliable data transmission method for a smart microgrid system of the present invention performs data transmission based on a quantized transmission level, solves the technical problem of poor rationality of data transmission, and improves the rationality of data transmission. Compared with the indiscriminate priority transmission of the working condition information of different nodes, the present invention comprehensively considers multiple characteristics related to the operating state of the target node in the current time period, such as the frequency of data changes and the state influencing factor, thereby quantifying the target transmission level that characterizes the degree of abnormal operation of the target node in the current time period. The larger the value, the more abnormal the operating state of the target node in the current time period may be, which often indicates that the working condition information characterizing the operating state of the target node in the current time period needs to be transmitted as soon as possible. Therefore, based on the target transmission level, the working condition information collected in the current time period is transmitted, which improves the rationality of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0067] Figure 1A flow chart of a high-reliability smart microgrid system data transmission method of the present invention;

[0068] Figure 2 A schematic diagram of the structure of a highly reliable smart microgrid system data transmission system of the present invention;

[0069] Figure 3 The figure is a schematic diagram of the structure of a computer device of the present invention. DETAILED DESCRIPTION

[0070] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the technical solutions proposed by the present invention are described in detail below in conjunction 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.

[0071] 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 invention belongs.

[0072] refer to Figure 1 , shows the process of some embodiments of a high-reliability smart microgrid system data transmission method according to the present invention. The high-reliability smart microgrid system data transmission method includes the following steps:

[0073] Step S1, obtaining load data of a target node for a smart microgrid system in a current time period and a historical time period.

[0074] Among them, the smart microgrid system can be a small power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, loads, monitoring and protection devices, etc. The target node can be a device used in the smart microgrid system. The duration corresponding to the current time period and the historical time period can be the same, and the historical time period is earlier than the current time period, and the end time of the historical time period can be the start time of the current time period. The end time of the current time period can be the current time. For example, the duration corresponding to the current time period and the historical time period can both be 1 hour. The load data can characterize the load condition of the device. For example, the load data can be a load rate.

[0075] As an example, the load rate of the target node may be collected every 1 second in the current time period and the historical time period as load data.

[0076] It should be noted that the device load can characterize the operation of the device to a certain extent. For example, the device load under high access is often different from the device load under low access. Generally speaking, when the device load is abnormal, its operation status is often abnormal. Therefore, obtaining the load data of the target node in the current time period and the historical time period can characterize the operation of the target node in the current time period for subsequent analysis.

[0077] Step S2: clustering the load data in the current time period to obtain current load clusters, and adaptively merging all current load clusters to obtain current load sub-segments.

[0078] As an example, this step may include the following steps:

[0079] The first step is to cluster the load data in the current time period to obtain the target cluster.

[0080] For example, the AP (Affinity Propagation) clustering algorithm may be used to cluster the load data in the current time period, and the cluster cluster obtained by clustering at this time is used as the target cluster cluster.

[0081] It should be noted that the load data within the target cluster is often relatively similar data, and the operating states represented by them are often relatively similar.

[0082] In the second step, the continuous load data at corresponding collection times within the same target cluster are clustered to form small clusters.

[0083] For example, if a target cluster includes first load data, second load data, third load data, fourth load data, fifth load data and sixth load data, and the collection times corresponding to these load data are: 10:06:01 on November 14, 2024, 10:06:02 on November 14, 2024, 10:06:03 on November 14, 2024, 10:08:00 on November 14, 2024, 10:11:07 on November 14, 2024, 10:11:08 on November 14, 2024, and the duration between adjacent collection times is 1 second, then the target cluster can be divided into 3 small clusters, and these 3 small clusters can be {first load data, second load data, third load data}, {fourth load data} and {fifth load data, sixth load data} respectively.

[0084] It should be noted that the small cluster is a further division of the target cluster, which can be composed of load data that is continuous in time and of similar size.

[0085] The third step is to divide the current time period into equal parts to obtain sub-time periods.

[0086] For example, the current time period may be equally divided into a preset number of time periods, and each time period thus equally divided is used as a sub-time period. The preset number may be the number of equally divided time periods set in advance. For example, the preset number may be 20.

[0087] In the fourth step, the mean of the collection time corresponding to all load data in each small cluster is determined as the target representative time corresponding to each small cluster.

[0088] In the fifth step, the corresponding target representative moments belong to the same sub-time period, forming the current load cluster.

[0089] The current load clustering may be composed of small clusters whose corresponding target representative moments belong to the same sub-time period.

[0090] It should be noted that the current load clustering can be composed of small clusters with similar time.

[0091] In the sixth step, the average of the collection times corresponding to all load data in each current load cluster is determined as the reference representative time corresponding to each current load cluster.

[0092] In the seventh step, all current load clusters are sorted according to the reference representative time points corresponding to all current load clusters to obtain a current load cluster sequence.

[0093] The current load clustering sequence may be a time series.

[0094] In step 8, determining the load difference evaluation factor between each adjacent current load cluster in the current load cluster sequence according to the load difference between each adjacent current load cluster may include the following sub-steps:

[0095] In the first sub-step, the average value of all load data in each current load cluster is determined as load representative data corresponding to each current load cluster.

[0096] In the second sub-step, the absolute value of the difference between the load representative data corresponding to each adjacent current load cluster is determined as the load difference between each adjacent current load cluster.

[0097] The third sub-step is to determine a load difference evaluation factor between each adjacent current load cluster according to the load difference between each adjacent current load cluster.

[0098] Among them, the load difference can be positively correlated with the load difference evaluation factor.

[0099] For example, the formula corresponding to determining the load difference evaluation factor between adjacent current load clusters may be:

[0100] ;in, It is The current load cluster and The load difference evaluation factor between the current load clusters. is the sequence number of the current load cluster in the current load cluster sequence. It is the absolute value function. It is The load corresponding to each current load cluster represents data. It is The load corresponding to each current load cluster represents data. It is The current load cluster and The load difference between the current load clusters. It is The current load cluster and The maximum value of all load data in the union of the current load clusters. It is The current load cluster and The minimum value of all load data in the union of the current load clusters. It is a pre-set factor greater than 0, mainly used to prevent the denominator from being 0, such as, It can be 0.0001.

[0101] It should be noted that when The larger the The current load cluster and The larger the load fluctuation between the current load clusters, the more likely the target node is in the The time period corresponding to the current load cluster and The more likely it is that the operating states in the time periods corresponding to the current load clusters are different.

[0102] In the ninth step, the current loads in the current load clustering sequence whose load difference evaluation factors are continuously greater than or equal to a preset load difference threshold are clustered to form a current load sub-segment.

[0103] The preset load difference threshold may be a threshold preset manually, for example, the preset load difference threshold may be 0.5.

[0104] In the tenth step, the current loads whose load difference evaluation factors in the current load clustering sequence are continuously smaller than a preset load difference threshold are clustered to form a current load sub-segment.

[0105] It should be noted that if the load difference evaluation factor between a current load cluster and the adjacent current load clusters on both sides is greater than or equal to the preset load difference threshold, and the other is less than the preset load difference threshold, the current load cluster is divided into the side where the load difference evaluation factor is less than the preset load difference threshold.

[0106] For example, if the current load clustering sequence is {first current load clustering, second current load clustering, third current load clustering, fourth current load clustering, fifth current load clustering, sixth current load clustering}, the preset load difference threshold is 0.5, and the load difference evaluation factors between adjacent current load clusters in the current load clustering sequence are: 0.5, 0.3, 0.2, 0.7 and 0.6 respectively, then 3 current load sub-segments can be obtained, and these 3 current load sub-segments can be {first current load clustering}, {second current load clustering, third current load clustering, fourth current load clustering} and {fifth current load clustering, sixth current load clustering} respectively.

[0107] It should be noted that, in the current load sub-segment formed by the current load clusters whose load difference evaluation factors in the current load cluster sequence are continuously greater than or equal to the preset load difference threshold, the load fluctuation degree between the current load clusters is often large. In the current load sub-segment formed by the current load clusters whose load difference evaluation factors in the current load cluster sequence are continuously less than the preset load difference threshold, the load fluctuation degree between the current load clusters is often small.

[0108] Step S3, determining the frequency of data changes corresponding to each current load sub-segment according to the fluctuation between the current load clusters in each current load sub-segment.

[0109] As an example, this step may include the following steps:

[0110] In the first step, the mean of all load data in each current load cluster is determined as the load representative data corresponding to each current load cluster.

[0111] In the second step, according to the variance of the load representative data corresponding to all the current load clusters in each current load sub-segment, the fluctuation change factor corresponding to each current load sub-segment is determined.

[0112] The third step is to determine the frequency of data changes corresponding to each current load subsegment based on the fluctuation change factor corresponding to each current load subsegment, the number of current load clusters in each current load subsegment, and the load difference evaluation factor between the current load cluster in each current load subsegment and its adjacent current load clusters.

[0113] For example, the formula for determining the frequency of data changes corresponding to the current load sub-segment may be:

[0114] ;

[0115] ;

[0116] ;in, It is The frequency of data changes corresponding to the current load sub-segment. is the sequence number of the current load sub-segment. is a normalization function. It is The fluctuation change factor corresponding to the current load sub-segment. It is The loads corresponding to all current load clusters in the current load subsegment represent the variance of the data. It is The loads corresponding to all current load clusters in the current load sub-segment represent the maximum value in the data. It is The loads corresponding to all current load clusters in the current load subsegment represent the minimum value in the data. It is a pre-set factor greater than 0, mainly used to prevent the denominator from being 0, such as, It can be 0.0001. It is The number of current load clusters within the current load subsegment. It is The sequence number of the current load cluster within the current load subsegment. It is The average value of the load difference evaluation factors between all current load clusters and their adjacent current load clusters in the current load sub-segment. It is In the current load sub-segment, The current load clusters and The load difference evaluation factor between the current load clusters.

[0117] It should be noted that when The larger the The more discrete the load distribution between different current load clusters in a current load sub-segment, the more likely it is that The more frequent the load changes in the current load sub-segment, the more frequent the load changes in the The more likely it is that an abnormal situation that needs to be analyzed will occur during the time period corresponding to the current load sub-segment. The larger the The more current load clusters there are in a current load subsegment, the more There are relatively more load changes within the current load sub-segment. The larger the The greater the load fluctuation between different current load clusters within a current load subsegment, the greater the load fluctuation between different current load clusters. The larger the The greater the load variation within the current load sub-segment, the more likely the target node is in the The greater the degree of change in the motion state in the time period corresponding to the current load sub-segment, the more likely the target node is in the The more unstable the time period corresponding to the current load sub-segment is, the more likely it is that the target node is in the The operating condition information of the operating conditions in the time period corresponding to the current load sub-segment should be transmitted first.

[0118] Step S4: based on the frequency of data changes corresponding to all current load sub-segments, similar load sub-segments corresponding to each current load sub-segment are screened out.

[0119] As an example, this step may include the following steps:

[0120] In the first step, any current load sub-segment is determined as a marked load sub-segment, and each current load sub-segment except the marked load sub-segment is determined as a reference load sub-segment.

[0121] In the second step, the range of plus or minus fixed numbers of the frequency of data changes corresponding to the above-mentioned marked load sub-segment is determined as the data change range corresponding to the above-mentioned marked load sub-segment.

[0122] The range of addition and subtraction fixed numbers is the range formed by adding and subtracting a fixed value from the data. For example, the fixed value may be 0.2.

[0123] For example, if the fixed value is 0.2, then The range of addition and subtraction of fixed numbers can be [ , ], and The data variation range corresponding to the current load sub-segment can be [ , ].in, It is The frequency of data changes corresponding to the current load sub-segment.

[0124] In the third step, a reference load sub-segment whose corresponding data change frequency falls within the data change range corresponding to the marked load sub-segment is selected from all reference load sub-segments as a similar load sub-segment corresponding to the marked load sub-segment.

[0125] Step S5: determining the state influence factor corresponding to each current load subsegment according to the time intervals between all adjacent similar load subsegments corresponding to each current load subsegment.

[0126] The adjacent similar load sub-segments may be similar load sub-segments with adjacent acquisition times.

[0127] As an example, the formula for determining the state impact factor corresponding to the current load sub-segment may be:

[0128] ;in, It is The state impact factor corresponding to the current load sub-segment. is the sequence number of the current load sub-segment. It is The number of similar load sub-segments corresponding to the current load sub-segment. It is The sequence number of the similar load sub-segment corresponding to the current load sub-segment may be a sequence number obtained by sorting all similar load sub-segments corresponding to the current load sub-segment based on the collection time corresponding to the similar load sub-segments. is a natural exponential function. It is The current load sub-segment corresponds to the similar load sub-segments and The time interval between similar load sub-segments. similar load sub-segments and The method for obtaining the time interval between similar load sub-segments may be: taking the latest collection time among the collection times corresponding to all load data in each similar load sub-segment as the end representative time corresponding to each similar load sub-segment; ... The end representative time corresponding to the similar load sub-segment and the The duration between the end representative moments corresponding to the similar load sub-segments is determined as similar load sub-segments and The time interval between similar load sub-segments.

[0129] It should be noted that in actual situations, if a load change occurs frequently and in clusters, it often means that the load change has a greater impact on the target node. The larger the The number of times similar to the current load sub-segment occurs is large, and it is similar to the The shorter the time interval between the occurrences of similar situations in the current load sub-segments, the shorter the time interval between the occurrences of similar situations in the current load sub-segments. The more similar the current load sub-segments are, the more likely they are to appear in clusters. The greater the impact of the load change corresponding to the current load sub-segment on the target node.

[0130] Step S6, filtering out the historical load sequence that is the same as the corresponding stage of each current load sub-segment from the load data in the historical time period, and obtaining the state influencing factor corresponding to the historical load sequence.

[0131] The historical load sequence that is the same as the corresponding stage of the current load sub-segment may include: load data at the same position as the current load sub-segment in the historical time period. The historical load sequence may be a time series. For example, if a current load sub-segment is the load data between the 6th minute and the 10th minute in the current time period, the historical load sequence that is the same as the corresponding stage of the current load sub-segment may include: load data between the 6th minute and the 10th minute in the historical time period.

[0132] As an example, this step may include the following steps:

[0133] In the first step, any current load sub-segment is determined as a marked load sub-segment.

[0134] In the second step, a time period that is in the same position as the time period corresponding to the marked load sub-segment in the current time period is selected from the historical time period as a reference time period corresponding to the marked load sub-segment.

[0135] For example, if the time period corresponding to the marked load subsegment is the time period between the 16th minute and the 18th minute in the current time period, the reference time period corresponding to the marked load subsegment may be the time period between the 16th minute and the 18th minute in the historical time period.

[0136] In the third step, the load data collected during the reference time period corresponding to the marked load sub-segment is used to form a historical load sequence corresponding to the marked load sub-segment.

[0137] In the fourth step, the method for obtaining the state influence factor corresponding to the historical load sequence can be the same as the method for obtaining the state influence factor corresponding to the current load sub-segment, which will not be repeated here.

[0138] Step S7: determining a target transmission level based on the difference between the state impact factors corresponding to all current load sub-segments and their historical load sequences.

[0139] As an example, this step may include the following steps:

[0140] In the first step, the load data within the historical time period is clustered and adaptively merged to obtain historical load sub-segments.

[0141] It should be noted that the method for obtaining the historical load sub-segment may be the same as the method for obtaining the current load sub-segment, which will not be described in detail herein.

[0142] In the second step, the target transmission level is determined according to the quantity difference between the historical load sub-segments and the current load sub-segments, and the difference between the state influence factors corresponding to all the current load sub-segments and their historical load sequences.

[0143] For example, the formula for determining the target transmission level may be:

[0144] ;

[0145] ;

[0146] ;in, is the target transmission level. is a normalization function. Represents the overall difference between all current load subsegments and their historical load sequences. It is the absolute value of the difference between the number of historical load sub-segments and the number of current load sub-segments. is the number of current load subsegments. is the sequence number of the current load sub-segment. Indicates The difference between the current load sub-segment and its historical load sequence. It is the absolute value function. It is The state impact factor corresponding to the current load sub-segment. It is The state influencing factor corresponding to the historical load sequence corresponding to the current load sub-segment. It is The method for obtaining the load difference evaluation factor between a current load sub-segment and its corresponding historical load sequence may be the same as the method for obtaining the load difference evaluation factor between adjacent current load clusters, which will not be described in detail here. It is The number of payload data in the current payload subsegment. It is The number of load data in the longest historical load subsegment among the historical load subsegments to which all load data in the historical load sequence corresponding to the current load subsegment belong.

[0147] It should be noted that in actual situations, during normal operation of the equipment, its load changes are often relatively stable and often do not change suddenly. When a large load change occurs, it is often necessary to transmit data quickly in order to analyze the abnormal situation of the equipment later. The larger the The more different the impact of the current load sub-segment and the historical load sequence of the same stage on the target node is, the more likely it is that the The more different the operating status is between the current load sub-segment and the historical load sequence of the same stage. The larger the The greater the load fluctuation between the current load sub-segment and the historical load sequence of the same stage, the greater the load fluctuation between the current load sub-segment and the historical load sequence of the same stage. The larger the The load changes between the current load sub-segment and the historical load sequence of the same stage are less similar. When the value is larger, it often indicates that the load distribution in the current time period is more different from the load distribution in the historical time period, which often indicates that the target node is more likely to be abnormal in the current time period, and the working condition information of the target node in the current time period should be transmitted as soon as possible. The larger the value, the more different the load distribution in the current time period is from the load changes in the historical time period. The larger the value is, the more different the load distribution in the current time period is from the load change distribution in the historical time period. This often indicates that the target node is more likely to be abnormal in the current time period, and the operating condition information of the target node in the current time period should be transmitted as soon as possible.

[0148] Step S8: transmitting the operating condition information collected in the current time period according to the target transmission level.

[0149] The operating condition information may represent the operation status of the device. For example, the operating condition information of the target node at a certain moment may include but is not limited to: the load rate, timestamp, transmission data volume, voltage and current of the target node at that moment.

[0150] As an example, this step may include the following steps:

[0151] In the first step, during the current time period, the load rate, timestamp, amount of data transmitted, voltage and current of the target node are collected every 1 second to form the working condition information.

[0152] In the second step, if the target transmission level is greater than or equal to the preset change threshold, it can be determined that there are abnormal changes in the target node within the current time period. At this time, it is often necessary to transmit the operating condition information of the target node within the current time period as quickly as possible so that the staff can analyze the abnormal situation of the target node within the current time period.

[0153] The preset change threshold may be a preset threshold, for example, 0.5.

[0154] It should be noted that the larger the target transmission level is, the more likely it is that the load condition of the target node in the current time period is abnormal, and the operating information of the target node collected in the current time period needs to be analyzed as soon as possible. Therefore, in the case of insufficient space, the operating information of the target node collected in the current time period should be transmitted first.

[0155] In the third step, if the target transmission level is less than the preset change threshold, it can be determined that there is no abnormal change in the target node within the current time period. At this time, the staff often does not need to analyze the abnormal situation. The operating information of the target node within the current time period can be directly backed up without priority transmission.

[0156] refer to Figure 2 Based on the same inventive concept as the above method embodiment, the present invention provides a high-reliability smart microgrid system data transmission system, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the above computer program is executed by the processor, the steps of implementing a high-reliability smart microgrid system data transmission method may specifically include:

[0157] A load data acquisition module 201 is used to acquire load data of a target node for a smart microgrid system in a current time period and a historical time period;

[0158] The clustering division and merging module 202 is used to cluster the load data in the current time period to obtain the current load clusters, and adaptively merge all the current load clusters to obtain the current load sub-segments;

[0159] The data change frequency determination module 203 is used to determine the data change frequency corresponding to each current load sub-segment according to the fluctuation between the current load clusters in each current load sub-segment;

[0160] A similar load sub-segment screening module 204 is used to screen out similar load sub-segments corresponding to each current load sub-segment based on the frequency of data changes corresponding to all current load sub-segments;

[0161] The state impact factor determination module 205 is used to determine the state impact factor corresponding to each current load subsegment according to the time intervals between all adjacent similar load subsegments corresponding to each current load subsegment;

[0162] A screening and determination module 206 is used to screen out the historical load sequence that is the same as the corresponding stage of each current load sub-segment from the load data in the historical time period, and obtain the state influencing factor corresponding to the historical load sequence;

[0163] A target transmission level determination module 207, configured to determine a target transmission level based on the difference between the state impact factors corresponding to all current load sub-segments and their historical load sequences;

[0164] The information transmission module 208 is used to transmit the operating condition information collected in the current time period according to the target transmission level.

[0165] Figure 3 is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Figure 3 As shown, the computer device 300 includes: a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302, wherein when the processor 302 executes the computer program 303, the computer device can execute any one of the high-reliability smart microgrid system data transmission methods introduced above.

[0166] Based on the same inventive concept as the above method embodiment, the present invention provides a server, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes any one of the above high-reliability smart microgrid system data transmission methods.

[0167] Based on the same inventive concept as the above-mentioned method embodiment, the present invention provides a computer program product, which includes: computer program code, when the computer program code runs on a computer, enables the computer to execute any one of the above-mentioned high-reliability smart microgrid system data transmission methods.

[0168] Based on the same inventive concept as the above-mentioned method embodiment, the present invention provides a computer-readable storage medium, which stores a computer program code. When the computer program code runs on a computer, the computer executes any one of the above-mentioned high-reliability smart microgrid system data transmission methods.

[0169] In summary, compared with the indiscriminate priority transmission of operating condition information of different nodes, the present invention comprehensively considers multiple characteristics related to the operating status of the target node in the current time period, such as the frequency of data changes and the status influencing factor, thereby quantifying the target transmission level that characterizes the degree of abnormal operation of the target node in the current time period. The larger the value, the more abnormal the operating status of the target node in the current time period may be, which often indicates that the operating condition information characterizing the operating status of the target node in the current time period needs to be transmitted as soon as possible. Therefore, based on the target transmission level, the operating condition information collected in the current time period is transmitted, which improves the rationality of data transmission.

[0170] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A highly reliable data transmission method for a smart microgrid system, characterized in that: The following steps are involved: Obtain load data of a target node for a smart microgrid system in a current time period and a historical time period; Clustering the load data in the current time period to obtain the current load clusters, and adaptively merging all the current load clusters to obtain the current load sub-segments; Determine the frequency of data changes corresponding to each current load sub-segment according to the fluctuation between the current load clusters in each current load sub-segment; Based on the frequency of data changes corresponding to all current load sub-segments, similar load sub-segments corresponding to each current load sub-segment are screened out; Determine the state impact factor corresponding to each current load subsegment according to the time intervals between all adjacent similar load subsegments corresponding to each current load subsegment, wherein the adjacent similar load subsegments are similar load subsegments with adjacent acquisition times; Filter out the historical load sequence that is the same as the corresponding stage of each current load sub-segment from the load data in the historical time period, and obtain the state influencing factor corresponding to the historical load sequence; Determining a target transmission level based on differences between state influencing factors corresponding to all current load sub-segments and their historical load sequences; According to the target transmission level, the working condition information collected in the current time period is transmitted; The step of determining the frequency of data changes corresponding to each current load sub-segment according to the fluctuation between the current load clusters in each current load sub-segment includes: Determine the mean of all load data in each current load cluster as load representative data corresponding to each current load cluster; Determine the fluctuation change factor corresponding to each current load subsegment according to the variance of the load representative data corresponding to all current load clusters in each current load subsegment; Determine the frequency of data changes corresponding to each current load subsegment according to the fluctuation change factor corresponding to each current load subsegment, the number of current load clusters in each current load subsegment, and the load difference evaluation factor between the current load cluster in each current load subsegment and its adjacent current load clusters; The formula corresponding to the state impact factor corresponding to the current load sub-segment is: ;in, It is The state influencing factor corresponding to the current load sub-segment; is the sequence number of the current load sub-segment; It is The number of similar load sub-segments corresponding to the current load sub-segment; It is The sequence number of the similar load sub-segment corresponding to the current load sub-segment; is a natural exponential function; It is The current load sub-segment corresponds to the similar load sub-segments and The time interval between similar load sub-segments.

2. A highly reliable smart microgrid system data transmission method according to claim 1, characterized in that: The clustering of the load data in the current time period to obtain the current load clusters includes: Clustering the load data in the current time period to obtain a target cluster; The continuous load data of the corresponding collection time in the same target cluster is clustered to form a small cluster; Divide the current time period into equal parts to obtain sub-time periods; The mean of the collection time corresponding to all load data in each small cluster is determined as the target representative time corresponding to each small cluster; The corresponding target representative moments belong to the small clusters in the same sub-time period to form the current load cluster.

3. A highly reliable smart microgrid system data transmission method according to claim 1, characterized in that: The adaptive merging of all current load clusters to obtain current load sub-segments includes: Determine the average of the collection times corresponding to all load data in each current load cluster as the reference representative time corresponding to each current load cluster; All current load clusters are sorted according to the reference representative time points corresponding to all current load clusters to obtain a current load cluster sequence; Determining a load difference evaluation factor between each adjacent current load cluster in the current load cluster sequence according to the load difference between each adjacent current load cluster; The current load clusters whose load difference evaluation factors in the current load cluster sequence are continuously greater than or equal to a preset load difference threshold constitute a current load sub-segment; The current loads in the current load clustering sequence whose load difference evaluation factors are continuously smaller than a preset load difference threshold are clustered to form a current load sub-segment.

4. A highly reliable smart microgrid system data transmission method according to claim 3, characterized in that: The step of determining the load difference evaluation factor between each adjacent current load cluster in the current load cluster sequence according to the load difference between each adjacent current load cluster includes: Determine the mean of all load data in each current load cluster as load representative data corresponding to each current load cluster; The absolute value of the difference between the load representative data corresponding to each adjacent current load cluster is determined as the load difference between each adjacent current load cluster; According to the load difference between each adjacent current load cluster, a load difference evaluation factor between each adjacent current load cluster is determined, wherein the load difference is positively correlated with the load difference evaluation factor.

5. A highly reliable smart microgrid system data transmission method according to claim 1, characterized in that: The formula corresponding to the frequency of data changes corresponding to the current load sub-segment is: ； ； ;in, It is The frequency of data changes corresponding to the current load sub-segment; is the sequence number of the current load sub-segment; is the normalization function; It is The fluctuation change factor corresponding to the current load sub-segment; It is The variance of the load representative data corresponding to all current load clusters in the current load sub-segment; It is The load corresponding to all current load clusters in the current load sub-segment represents the maximum value in the data; It is The load corresponding to all current load clusters in the current load sub-segment represents the minimum value in the data; is a pre-set factor greater than 0; It is The number of current load clusters within the current load subsegment; It is The sequence number of the current load cluster within the current load subsegment; It is The average value of the load difference evaluation factors between all current load clusters and their adjacent current load clusters in the current load sub-segment; It is In the current load sub-segment, The current load clusters and The load difference evaluation factor between the current load clusters.

6. A highly reliable smart microgrid system data transmission method according to claim 1, characterized in that: The filtering out similar load sub-segments corresponding to each current load sub-segment based on the frequency of data changes corresponding to all current load sub-segments includes: Determine any current load subsegment as a marked load subsegment, and determine each current load subsegment except the marked load subsegment as a reference load subsegment; Determine the data variation range corresponding to the marked load subsegment by adding or subtracting a fixed number range of the frequency of data variation corresponding to the marked load subsegment as the data variation range corresponding to the marked load subsegment; A reference load subsegment whose corresponding data change frequency falls within the data change range corresponding to the marked load subsegment is selected from all reference load subsegments as a similar load subsegment corresponding to the marked load subsegment.

7. A highly reliable smart microgrid system data transmission method according to claim 1, characterized in that: The determining of the target transmission level based on the difference between the state impact factors corresponding to all current load sub-segments and their historical load sequences includes: Clustering and adaptively merging the load data within the historical time period to obtain historical load sub-segments; The target transmission level is determined according to the quantity difference between the historical load sub-segment and the current load sub-segment, and the difference between the state influence factors corresponding to all the current load sub-segments and their historical load sequences.

8. A highly reliable smart microgrid system data transmission method according to claim 7, characterized in that: The formula corresponding to the target transmission level is: ； ； ;in, is the target transmission level; is the normalization function; Represents the overall difference between all current load sub-segments and their historical load sequences; is the absolute value of the difference between the number of historical load sub-segments and the number of current load sub-segments; is the number of current load sub-segments; is the sequence number of the current load sub-segment; Indicates The difference between the current load sub-segment and its historical load sequence; It is the absolute value function; It is The state influencing factor corresponding to the current load sub-segment; It is The state influencing factor corresponding to the historical load sequence corresponding to the current load sub-segment; It is A load difference evaluation factor between a current load sub-segment and its corresponding historical load sequence; It is The number of load data in the current load subsegment; It is The number of load data in the longest historical load subsegment among the historical load subsegments to which all load data in the historical load sequence corresponding to the current load subsegment belong.

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