A data transmission method for power distribution system equipment

Abstract

The present invention relates to the technical field of data transmission, and provides a data transmission method for power distribution system equipment, including: obtaining power distribution data, the power distribution data including multi-dimensional power distribution data at several times in a day; analyzing the data fluctuations at each time according to the multi-dimensional power distribution data at several times in a day and dividing it into multiple time periods; obtaining the power load index of each time period based on the data fluctuations at all times in each time period; obtaining the compression priority of each time period according to the power load index of each time period; determining the data compression order of each time period within the day according to the compression priority of each time period, and compressing and transmitting according to the data compression order of each time period. This method can give priority to compressing data during peak power consumption periods, thereby reducing the required transmission bandwidth, improving data transmission speed and efficiency, and ensuring the timely transmission of key information.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to a data transmission method for equipment in a power transformation and distribution system. Background Art

[0002] The equipment in the power distribution system mainly includes various electrical equipment used to regulate, control and distribute electricity. The role of these devices is to ensure that electricity is transmitted from the power plant to the user through the transmission network, while maintaining the quality of power and the safety and stability of the system. The intelligent power distribution system of the commercial center is a highly automated and optimized power distribution and management system, which not only improves the efficiency and safety of energy use, but also provides strong support for the continuous operation of commercial buildings, and realizes more economical, more environmentally friendly and more intelligent energy management.

[0003] At present, the real-time data detected by the intelligent power distribution system of the commercial center is compressed and transmitted in the order of the corresponding time sequence. However, since the power demand of the commercial center usually shows obvious intraday fluctuations, there is usually a peak period in the afternoon or evening, and other time periods are relatively stable. If it is compressed and transmitted according to the time sequence, there will be a problem of low data transmission efficiency.

[0004] In order to improve the efficiency of data transmission, existing technologies have made predictions based on historical data and predicted the priority of data transmission according to the prediction results. However, this method is only applicable to larger regional areas and medium- and long-term time windows. The proportion of data noise generated by random factors of various types of power users in a larger regional area gradually decreases as the area increases (after the accumulation of multiple independently distributed normal random variables, the growth of the standard deviation is only proportional to the square root of the mean), so the predictability gradually increases. However, for scenarios such as a single commercial center, the data noise generated by random factors is difficult to offset each other, making predictions very difficult. In addition, the historical data prediction method is applicable to medium- and long-term time windows. Shorter time windows are more affected by unpredictable random factors and are more difficult to predict.

[0005] Another existing technology uses the technical means of differential compression algorithm to improve data transmission efficiency. One is to determine the encoding length of the data based on the maximum value in the data differential value. The disadvantage of this method is that a part of the data that can be compressed to a smaller length cannot achieve the ideal compression rate, and the transmission efficiency is not high, especially in the time window with large data fluctuations, there will be a large number of redundant fields. The second is to preset several encoding intervals and obtain the encoding length of each encoding interval, segment the differential data according to the encoding interval to which each data in the differential data belongs, obtain several data segments of each data and the encoding length of each data segment, and calculate the initial compression rate of the data segment according to the encoding length of the data segment, the amount of data contained in the data segment, and the original number of bits of a data. This method requires a large number of calculations on the user side, which not only increases the equipment cost but also reduces the stability of equipment operation. Once the calculation of different nodes is delayed, a large number of missing values ​​or abnormal values ​​will be caused. Summary of the invention

[0006] In order to solve the technical problem of low data transmission efficiency, the purpose of the present invention is to provide a data transmission method for power distribution system equipment, and the technical solution adopted is as follows:

[0007] A data transmission method for power transformation and distribution system equipment, comprising:

[0008] Acquire power distribution data, wherein the power distribution data includes multi-dimensional power distribution data at several times in a day;

[0009] According to the multi-dimensional power distribution data at several moments in the day, the data fluctuation at each moment is analyzed and divided into multiple time periods;

[0010] The power load index of each time period is obtained based on the data fluctuations at all times in each time period;

[0011] Obtaining the compression priority of each time period according to the power load index of each time period;

[0012] The data compression order of each time period within the day is determined according to the compression priority of each time period, and the data is compressed and transmitted according to the data compression order of each time period.

[0013] Preferably, the data fluctuation at each time is analyzed according to the multi-dimensional power distribution data at several times in the day and divided into multiple time periods, including:

[0014] Obtaining a data distribution coefficient at each moment according to the multi-dimensional power distribution data at the plurality of moments;

[0015] Calculating the possibility of each moment as a segment position based on the data distribution coefficient at each moment;

[0016] The segment position moments are determined and the time periods are divided according to the possibilities of the respective moments as segment positions.

[0017] Preferably, the data distribution coefficient of each moment obtained according to the multidimensional power distribution data at the said several moments is, the multidimensional data characteristic value of each moment is obtained according to the multidimensional power distribution data at the said several moments, and the data distribution coefficient is calculated based on the average difference of the data characteristic values ​​of two adjacent dimensions at each moment and the maximum difference between all dimensions.

[0018] Preferably, the possibility of each moment as a segmentation position is calculated based on the data distribution coefficient of each moment, and the possibility of the moment as a segmentation position is calculated based on the data distribution coefficient of the current moment, the difference between the data distribution coefficient of the current moment and the data distribution coefficients of all previous moments, and the difference between the data distribution coefficient of the current moment and the data distribution coefficients of all subsequent moments.

[0019] Preferably, the segment position moment is determined according to the possibility of each moment being a segment position, a threshold is preset, and if the possibility of the moment being a segment position is greater than or equal to the threshold, the moment is determined as the segment position.

[0020] Preferably, the power load index of each time period is calculated based on the distribution coefficient at all times in each time period.

[0021] Preferably, obtaining the compression priority of each time period according to the power load index of each time period includes:

[0022] The compression priority of the time period is calculated based on the power load index of the current time period, the data duration of the current time period, and the maximum power load index of all time periods of the day.

[0023] Preferably, obtaining the compression priority of each time period according to the power load index of each time period further includes:

[0024] The compression priority is modified according to the time period corresponding to the time period in a day.

[0025] A data transmission method for power transformation and distribution system equipment, comprising:

[0026] Acquire power distribution data for multiple days, wherein the power distribution data includes multi-dimensional power distribution data at several times in a day;

[0027] According to the multi-dimensional power distribution data at several moments in the day, the data fluctuation at each moment is analyzed and divided into multiple time periods;

[0028] The power load index of each time period is obtained based on the data fluctuations at all times in each time period;

[0029] Obtaining the compression priority of each time period according to the power load index of each time period, and determining the data compression order of each time period within the day according to the compression priority of each time period;

[0030] Obtain compression order indexes for different days according to the compression priorities of all time periods on different days, and determine data compression orders for different days according to the compression order indexes for different days;

[0031] The data are compressed and transmitted according to a data compression sequence, wherein the data compression sequence includes the data compression sequence of the different days and the data compression sequence of the various time periods.

[0032] Preferably, the compression order index of different days is obtained according to the compression priorities of all time periods in different days, including:

[0033] Determine a time period with a high compression priority according to the compression priority of each time period;

[0034] The compression order indexes of different days are obtained according to the duration of the time periods with high compression priority in different days.

[0035] The present invention has the following beneficial effects: the data transmission method for power distribution system equipment proposed in the present invention obtains multi-dimensional power distribution data, divides the data into multiple time periods according to the data fluctuation conditions at different times, and obtains the compression priority of different time periods, thereby determining the data compression order. In this way, data during peak power consumption periods can be compressed preferentially, thereby reducing the required transmission bandwidth, improving data transmission speed and efficiency, and ensuring the timely transmission of key information. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] 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.

[0037] Figure 1 A schematic diagram of a flow chart of a data transmission method for power transformation and distribution system equipment provided in the first embodiment of the present invention;

[0038] Figure 2 A schematic diagram of a sub-flow chart of step S2 in the data transmission method for power transformation and distribution system equipment provided in the first embodiment of the present invention;

[0039] Figure 3 This is a flow chart of a data transmission method for power transformation and distribution system equipment provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the data transmission method of a power distribution system equipment proposed by the present invention, its specific implementation method, structure, features and effects, 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.

[0041] 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.

[0042] The specific scheme of the data transmission method for power distribution system equipment provided by the present invention is described in detail below with reference to the accompanying drawings.

[0043] Embodiment 1

[0044] See also Figure 1 , which shows a method flow chart of a method for data transmission of a power distribution system device provided by an embodiment of the present invention, including:

[0045] Step S1, obtaining power distribution data, wherein the power distribution data includes multi-dimensional power distribution data at several times in a day;

[0046] Step S2, analyzing the data fluctuation at each time according to the multi-dimensional power distribution data at several times in the day and dividing the data into multiple time periods;

[0047] Step S3, obtaining the power load index of each time period based on the data fluctuations at all times in each time period;

[0048] Step S4, obtaining the compression priority of each time period according to the power load index of each time period;

[0049] Step S5, determining the data compression order of each time period within the day according to the compression priority of each time period, and compressing and transmitting the data according to the data compression order of each time period.

[0050] The specific scenario targeted by this embodiment is: in the scenario of compressing and transmitting data of the intelligent power distribution system of a commercial center, since there are differences in power distribution data in different time periods, this embodiment determines the data compression priority of different time periods based on this difference to improve the compression transmission efficiency.

[0051] The data transmission method for power transformation and distribution system equipment provided in this embodiment obtains multi-dimensional power distribution data, divides the data into multiple time periods according to data fluctuations at different times, and obtains compression priorities for different time periods, thereby determining the data compression order. In this way, data during peak power consumption periods can be compressed preferentially, thereby reducing the required transmission bandwidth, improving data transmission speed and efficiency, and ensuring timely transmission of key information.

[0052] The following is a detailed description of each step:

[0053] In step S1, the source of power distribution data can be sensors installed on equipment related to the intelligent power distribution system, and multi-dimensional power distribution data includes multi-dimensional data such as voltage, current, frequency, power and temperature, which represent the operating status of this power distribution system. That is, by installing corresponding sensors on equipment related to the intelligent power distribution system of the commercial center, the real-time multi-dimensional data such as voltage, current, frequency, power and temperature are collected. Specifically, the data collection interval can be 15 minutes, and the real-time power distribution data of this system is collected at the same time, standardized and pre-processed, and then mapped to the sample space.

[0054] For the real-time power distribution data of a certain day, since the power demand of commercial centers usually shows obvious intraday fluctuations, the power demand of commercial centers during the daytime is generally low, while most people concentrate on shopping and entertainment activities in the afternoon or evening. Therefore, afternoon to evening is the main peak power consumption period of commercial centers, corresponding to a larger power load, and the possibility of problems is also greater. Therefore, this plan hopes to give priority to compressing the data during this peak period to ensure the timely transmission of key information.

[0055] Therefore, in the next step, the compression priority of the corresponding data is calculated by analyzing the daily fluctuation characteristics of the distribution system data and the time period. In order to give priority to compressing the data during the peak period of electricity consumption, different data segments are divided by analyzing the differences in the development trend of the distribution time series data. Since the data fluctuation characteristics of different data segments represent the operating status of the distribution system in the corresponding period, the fluctuation characteristics of different data segments are analyzed to calculate the system load of the corresponding period, and then the corresponding data compression priority is determined based on the load conditions corresponding to different data segments and the time period, so as to efficiently transmit after compression.

[0056] In step S2, since the load conditions of the commercial center power distribution system vary at different times of the day, in order to distinguish different load differences, this step calculates the power data distribution coefficient at the corresponding time by analyzing the distribution characteristics of the distribution data at different sampling times, and then compares the differences in data distribution coefficients at multiple sampling times to divide the real-time data of each day.

[0057] For multi-dimensional real-time power distribution data of a certain day, since the power demand during the day is relatively low and the power consumption is stable, the collected data is generally at a lower level, and the real-time fluctuation of the data is low. In the afternoon or evening, the power demand is large, and the corresponding data is at a higher level. Due to the large system load, the data fluctuation will be more severe. Therefore, the time series data can be divided according to different distribution load differences to facilitate data compression.

[0058] like Figure 2 As shown, preferably, step S2 includes:

[0059] Step S21, obtaining a data distribution coefficient at each moment according to the multi-dimensional power distribution data at the plurality of moments;

[0060] Step S22, calculating the possibility of each moment being a segment position based on the data distribution coefficient at each moment;

[0061] Step S23, determining the segmented position moments and dividing the time periods according to the possibility of each moment being a segmented position.

[0062] In step S21, preferably, the multidimensional data characteristic value at each moment is obtained according to the multidimensional power distribution data at the several moments, and the data distribution coefficient is calculated based on the average difference of the data characteristic values ​​of two adjacent dimensions at each moment and the maximum difference between all dimensions.

[0063] Specifically, for the descending sequence of multidimensional data at any sampling time within a day ( is the number of dimensions of the sampled data), the calculation formula of the data distribution coefficient is:

[0064]

[0065] In the formula is the sampling time within a certain day The corresponding data distribution coefficient is, and They are Time series Middle The first The characteristic value of the dimensional data, is the number of dimensions of the sampled data, and For sequence Middle dimensions and the eigenvalues ​​of the first dimension data.

[0066] therefore, Represents The average difference of the characteristic values ​​of two-dimensional data of any size at any time. If this difference is larger, it means that the longitudinal fluctuation of the multi-dimensional power distribution data at this time is greater, then this time is more likely to be a time of high power load, and the corresponding data distribution coefficient is larger. At the same time, combined with the sampling time The maximum fluctuation range of all dimension data The larger the value, the greater the fluctuation amplitude of the data at this moment, that is, the greater the power load at this moment, the greater the data distribution coefficient.

[0067] In step S22, it can be understood that since the data distribution coefficients corresponding to similar power load moments are similar, and the data distribution coefficients corresponding to different power loads are different, the differences in distribution coefficients at different sampling moments can be compared to divide the time periods, and the data at all moments in different time periods form data segments.

[0068] Preferably, the possibility of the moment being a segment position is calculated based on the data distribution coefficient at the current moment, the difference between the data distribution coefficient at the current moment and the data distribution coefficients at all previous moments, and the difference between the data distribution coefficient at the current moment and the data distribution coefficients at all subsequent moments.

[0069] Specifically, any sampling time The probability of it being a segment position is calculated as:

[0070]

[0071] In the formula, For the day The possibility of using the sampling moments as segment positions, represents the normalization function, For the The data distribution coefficient corresponding to the sampling time is For the The distribution coefficient corresponding to the time, Represents the last sampling moment of the day. Represents the parameter adjustment factor. To prevent the denominator from being 0, a parameter adjustment factor greater than 0 is added to the denominator, which can be, for example, 0.01.

[0072] therefore, For the The average difference in distribution coefficient between a sampling moment and all previous sampling moments is For the The average difference in distribution coefficient between a sampling time and all subsequent sampling times is greater if the two average differences are larger. The larger the sampling time The greater the difference in power load between the previous and next periods, the The greater the possibility that the time corresponding to the moment is the optimal segmentation position. Represents the normalization function, mapping the result to within the range.

[0073] In step S23, preferably, the segment position moment is determined according to the possibility of each moment being a segment position, and a preset threshold is set. If the possibility of the moment being a segment position is greater than or equal to the threshold, the moment is determined as the segment position. Specifically, the threshold is 0.9, that is, All corresponding sampling moments are used as segment positions to divide the time series data.

[0074] In step S3, after the segmentation is completed, since the corresponding power distribution data load conditions in different data segments are different, and the power distribution data in the high-load period needs to be compressed first, this step needs to calculate the power load index of each time period. Since the data distribution coefficient corresponding to different sampling moments represents the fluctuation degree of the power distribution data at the corresponding moment, the greater the data fluctuation degree, the greater the power load at the corresponding moment. Therefore, preferably, the power load index of each time period is calculated according to the distribution coefficient of all moments in each time period.

[0075] Since each data segment contains data from multiple sampling moments, and the distribution coefficients corresponding to each sampling moment are similar, the calculation formula for the power load index corresponding to each time period is:

[0076]

[0077] In the formula, is the power load index corresponding to a certain time period, represents the sigmoid function, For this period of time The data distribution coefficient corresponding to the sampling time is The number of sampling moments contained in this data segment.

[0078] That is, the average value of the distribution coefficient of all sampling moments in the time period is calculated to reflect the system load situation in this time period. If the power load index is larger, it means that the time corresponding to this time period is more likely to be a high-load moment, and vice versa. So far, the load situation corresponding to different data segments in a certain day is obtained, and then the corresponding compression priority is determined according to the power load situation corresponding to different data segments.

[0079] In step S4, since the high power load period corresponds to the peak power consumption period of the commercial center, the larger the load index corresponding to a data segment, the higher the data compression priority of the corresponding data segment. Preferably, the compression priority of each time period is obtained according to the power load index of each time period, including: the compression priority of the time period is calculated based on the power load index of the current time period, the data duration of the current time period and the maximum power load index of all time periods of the day.

[0080] Since the peak period of electricity consumption in commercial centers is generally in the afternoon or evening, the compression priority can be determined in combination with the time point of the data segment. If the time of the data segment is closer to the afternoon or evening and the duration is longer, the corresponding compression priority is higher. Therefore, in some preferred implementations, the compression priority of each time period is obtained according to the power load index of each time period, and the compression priority is modified according to the time period corresponding to the time period in a day.

[0081] Specifically, the calculation formula for compression priority in any time period of a day is:

[0082]

[0083] In the formula, Any time period during a day The corresponding compression priority, represents the normalization function, For time period The corresponding power load index, The maximum value of the power load index corresponding to all time periods of this day. and The time periods The start sampling time and the end sampling time, For time period Middle The time corresponding to the sampling moment.

[0084] therefore, Represents the time period The relative power load level, because the greater the power load level, the higher the corresponding data compression priority, so The larger the value, the higher the compression priority for this time period. Represents the time period The data duration, Represents the time period The time period of the day at which the corresponding moment is located. If the time period The closer the corresponding data is to the afternoon or evening during the day, the The larger the value, the longer the duration. The larger the value of , the more likely the corresponding data is that the power system of this commercial center is in a high-load operation state, so the corresponding data should have a higher compression priority.

[0085] In step S5, the compression priorities of each time period are arranged in descending order, which is the data compression order of each time period within the day, and then compressed and transmitted in order. Specifically, the data is compressed according to the order with the help of relevant lossless compression methods (such as Huffman coding, etc.) and uploaded to the corresponding server, thereby improving the data transmission speed and efficiency, ensuring the timely transmission of key information, and the power grid operator can more quickly identify load fluctuations and potential power supply problems, so as to adjust or optimize the power grid operation in time.

[0086] This embodiment analyzes the fluctuation characteristics of different data and calculates the corresponding compression priority, thereby giving priority to compressing the data during the peak period of electricity consumption, thereby improving the efficiency of compression transmission and ensuring the timely transmission of key information. In particular, the corresponding compression priority is obtained for the data in a certain day, and the transmission order of the data within a certain day is determined. This overcomes the problem of poor compression efficiency caused by the inability to give priority to compressing the data during the peak period of electricity consumption due to the intra-day fluctuation difference of electricity demand when compressing in sequence at present.

[0087] Embodiment 2

[0088] For power distribution data of multiple days (such as a month or a year), the differences between different days need to be considered. For example, if a day is a major holiday or a product promotion day, then the business center may have more time periods with high power loads. In this case, all data of this day needs to be given a higher compression priority to ensure the timely delivery of key information.

[0089] The difference between this embodiment and the first embodiment is that the power distribution data of multiple days is obtained, and after obtaining the data sequence of each time period of each day, the data compression sequence of different days needs to be determined. Figure 3 As shown, a data transmission method for power distribution system equipment provided in this embodiment includes:

[0090] Step T1, obtaining power distribution data for multiple days, wherein the power distribution data includes multi-dimensional power distribution data at several times in a day;

[0091] Step T2, analyzing the data fluctuation at each time according to the multi-dimensional power distribution data at several times in the day and dividing the data into multiple time periods;

[0092] Step T3, obtaining the power load index of each time period based on the data fluctuations at all times in each time period;

[0093] Step T4, obtaining the compression priority of each time period according to the power load index of each time period, and determining the data compression order of each time period within the day according to the compression priority of each time period;

[0094] Step T5, obtaining compression order indexes of different days according to the compression priorities of all time periods in different days, and determining the data compression order of different days according to the compression order indexes of different days;

[0095] Step T6, compressing and transmitting according to the data compression sequence, wherein the data compression sequence includes the data compression sequence of the different days and the data compression sequence of the various time periods.

[0096] It can be understood that steps T1-T4 can refer to the description in the first embodiment and will not be repeated here.

[0097] In step T5, preferably, the compression order indexes of different days are obtained according to the compression priorities of all time periods in different days, including: determining the time period with high compression priority according to the compression priority of each time period; and obtaining the compression order indexes of different days according to the duration of the time period with high compression priority in different days.

[0098] The time period with high compression priority is determined according to the compression priority of each time period, and a preset threshold, such as 0.8, is set. If the compression priority of a time period is greater than or equal to the threshold 0.8, the time period is a time period with high compression priority.

[0099] Since large festivals or commodity promotion days have a great impact on the electricity consumption of commercial centers, the time corresponding to high power load on these special days may account for a larger proportion. Therefore, the compression order index of different days is obtained according to the duration of the time period with high compression priority in different days. Preferably, the compression order index of the day is determined by the ratio of the total duration of all high compression priority time periods in a day to the system working time. Specifically, the calculation formula of the compression order index is:

[0100]

[0101] In the formula, is the compression order index of the power distribution data corresponding to a certain day, For this reason, the total working time of the power distribution system of the commercial center on that day is For this day The duration of a high compression priority data segment. The larger the value is, the earlier all power distribution data of the day will be compressed and transmitted.

[0102] In step T6, it can be understood that, specifically, the data is first compressed and transmitted according to the data compression order of different days, and then according to the data compression order of different time periods in different days.

[0103] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. 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.

[0104] The various embodiments in this specification 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.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the contents of the invention; various other forms of modification, replacement or transformation of the contents of the present invention made according to the technical knowledge and customary means known to those skilled in the art all fall within the scope of protection of the present invention.

Claims

1. A data transmission method for power transformation and distribution system equipment, It is characterized in that include: Acquire power distribution data, wherein the power distribution data includes multi-dimensional power distribution data at several times in a day; According to the multi-dimensional power distribution data at several moments in the day, the data fluctuation at each moment is analyzed and divided into multiple time periods; The power load index of each time period is obtained based on the data fluctuations at all times in each time period; Obtaining the compression priority of each time period according to the power load index of each time period; Determine the data compression order of each time period within the day according to the compression priority of each time period, and compress and transmit the data according to the data compression order of each time period; According to the multi-dimensional power distribution data at several times in the day, the data fluctuation at each time is analyzed and divided into multiple time periods, including: Obtaining a data distribution coefficient at each moment according to the multi-dimensional power distribution data at the plurality of moments; Calculating the possibility of each moment as a segment position based on the data distribution coefficient at each moment; Determine the segmented position time and divide the time period according to the possibility of each time being a segmented position; According to the multi-dimensional power distribution data at the above mentioned times, the data distribution coefficient at each time is obtained as follows: According to the multidimensional power distribution data at the plurality of moments, a multidimensional data characteristic value at each moment is obtained, and the data distribution coefficient is calculated based on an average difference between the data characteristic values ​​of two adjacent dimensions at each moment and a maximum difference between all dimensions; The possibility of calculating each moment as a segment position based on the data distribution coefficient at each moment is: Calculate the possibility of the moment being a segment position based on the data distribution coefficient at the current moment, the difference between the data distribution coefficient at the current moment and the data distribution coefficients at all previous moments, and the difference between the data distribution coefficient at the current moment and the data distribution coefficients at all subsequent moments; A preset threshold value of the possibility of each moment being a segment position is determined, and if the possibility of the moment being a segment position is determined to be greater than or equal to the threshold value, the moment is determined to be a segment position.

2. The data transmission method for power distribution system equipment according to claim 1, It is characterized in that The power load index of each time period is calculated based on the distribution coefficient at all times in each time period.

3. The data transmission method for power distribution system equipment according to any one of claims 1 to 2, It is characterized in that Obtaining the compression priority of each time period according to the power load index of each time period includes: The compression priority of the time period is calculated based on the power load index of the current time period, the data duration of the current time period, and the maximum power load index of all time periods of the day.

4. The data transmission method for power distribution system equipment according to claim 3, It is characterized in that Obtaining the compression priority of each time period according to the power load index of each time period, further comprising: The compression priority is modified according to the time period corresponding to the time period in a day.

5. A data transmission method for power distribution system equipment, It is characterized in that include: Acquire power distribution data for multiple days, wherein the power distribution data includes multi-dimensional power distribution data at several times in a day; According to the multi-dimensional power distribution data at several moments in the day, the data fluctuation at each moment is analyzed and divided into multiple time periods; The power load index of each time period is obtained based on the data fluctuations at all times in each time period; Obtaining the compression priority of each time period according to the power load index of each time period, and determining the data compression order of each time period within the day according to the compression priority of each time period; Obtain compression order indexes for different days according to the compression priorities of all time periods on different days, and determine data compression orders for different days according to the compression order indexes for different days; compressing and transmitting the data in accordance with a data compression sequence, wherein the data compression sequence includes a data compression sequence for different days and a data compression sequence for each time period; According to the multi-dimensional power distribution data at several times in the day, the data fluctuation at each time is analyzed and divided into multiple time periods, including: Obtaining a data distribution coefficient at each moment according to the multi-dimensional power distribution data at the plurality of moments; Calculating the possibility of each moment as a segment position based on the data distribution coefficient at each moment; Determine the segmented position time and divide the time period according to the possibility of each time being a segmented position; According to the multi-dimensional power distribution data at the above mentioned times, the data distribution coefficient at each time is obtained as follows: According to the multidimensional power distribution data at the plurality of moments, a multidimensional data characteristic value at each moment is obtained, and the data distribution coefficient is calculated based on an average difference between the data characteristic values ​​of two adjacent dimensions at each moment and a maximum difference between all dimensions; The possibility of calculating each moment as a segment position based on the data distribution coefficient at each moment is: Calculate the possibility of the moment being a segment position based on the data distribution coefficient at the current moment, the difference between the data distribution coefficient at the current moment and the data distribution coefficients at all previous moments, and the difference between the data distribution coefficient at the current moment and the data distribution coefficients at all subsequent moments; A preset threshold value of the possibility of each moment being a segment position is determined, and if the possibility of the moment being a segment position is determined to be greater than or equal to the threshold value, the moment is determined to be a segment position.

6. The data transmission method for power distribution system equipment according to claim 5, It is characterized in that The compression order index of different days is obtained according to the compression priority of all time periods in different days, including: Determine a time period with a high compression priority according to the compression priority of each time period; The compression order indexes of different days are obtained according to the duration of the time periods with high compression priority in different days.

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