Storage method and system for park energy data

Abstract

The embodiment of the invention provides a storage method and system for park energy data, and belongs to the technical field of data storage. The method comprises the steps of obtaining an access rate of park energy data in a historical unit time period; performing tagging processing on each data unit of the park energy data to obtain a tag set of each data unit; sorting each label according to the access rate and the label set so as to obtain a comprehensive sequence value of each label; obtaining a label of each to-be-stored data unit; determining an access rate sequence of each data unit according to the label and the comprehensive sequence value of each data unit; and determining the storage type of each data unit according to the access rate sequence. According to the method and system, the data storage efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data storage, and in particular to a method and system for storing park energy data. Background Art

[0002] In the current information wave, power energy data is gradually being informatized and digitized. Along with the digitization process, the amount of power energy data is growing at an exponential rate. These data play an indispensable role in optimizing energy management, improving energy efficiency, and formulating scientific energy policies. However, in the face of massive and complex energy data, existing data storage methods seem to be unable to cope with it.

[0003] Existing energy data storage usually adopts a relatively simple, random distributed storage method. In this mode, data is stored in one or more servers, and the choice of storage location often does not take into account the characteristics and usage of the data itself. This general storage strategy ignores the significant differences in the call frequency between different energy data. For example, some data such as real-time energy consumption monitoring data need to be frequently queried and processed, while data such as annual energy consumption summary may only need to be accessed in specific circumstances.

[0004] Due to the lack of targeted storage strategies, frequently called data may not receive effective priority processing, while infrequently called data may occupy valuable storage resources. This situation reduces the storage and access efficiency of the entire system to a certain extent. Summary of the invention

[0005] The purpose of the embodiments of the present invention is to provide a method and system for storing park energy data, which can improve the efficiency of data storage.

[0006] In order to achieve the above object, an embodiment of the present invention provides a method for storing park energy data, comprising:

[0007] Obtain the historical access rate of the park energy data within a unit time period;

[0008] Labeling each data unit of the park energy data to obtain a label set for each data unit;

[0009] sorting each of the tags according to the access rate and the tag set to obtain a comprehensive sequence value of each of the tags;

[0010] Obtaining a label for each of the data units to be stored;

[0011] Determine the access rate ranking of each of the data units according to the label and the comprehensive sequence value of each of the data units;

[0012] The storage type of each of the data units is determined according to the access rate ranking.

[0013] Optionally, obtaining the historical access rate of the park energy data within a unit time period includes:

[0014] Determine the number of access events of each data unit within the unit time period, the access time of each access event, and the access data volume;

[0015] Determine a valid access event according to the access time and access data volume;

[0016] The number of the effective access events is taken as the access rate.

[0017] Optionally, sorting each of the tags according to the access rate and the tag set to obtain a comprehensive sequence value of each of the tags includes:

[0018] Associating the access rate with each tag in the tag set to obtain an access rate sequence of each tag;

[0019] Removing the maximum value and the minimum value in the access rate sequence;

[0020] Calculating the variance of the access rate sequence;

[0021] Determining whether the variance is greater than or equal to a preset threshold;

[0022] When it is determined that the variance is less than the threshold, selecting the median in the access rate sequence as the characteristic access rate of the tag;

[0023] When it is determined that the variance is greater than or equal to the threshold, calculating the mean of the maximum value and the minimum value in the access rate sequence;

[0024] Counting the number of access rates greater than or equal to the mean and the number of access rates less than the mean respectively;

[0025] Selecting a minimum value of the larger of the number of the set of access rates greater than or equal to the mean and the set of access rates less than the mean as the characteristic access rate of the tag;

[0026] sorting the tags in descending order according to the feature access rates;

[0027] The sorting result is used as the comprehensive sequence value of the tag.

[0028] Optionally, determining the access rate ranking of each of the data units according to the label and the comprehensive sequence value of each of the data units includes:

[0029] Determining a sequence value of the data unit according to the access rate sorting;

[0030] Determine a comprehensive sequence value of the tag corresponding to each of the data units;

[0031] For each of the data units, arranging the labels in ascending order of the comprehensive sequence values;

[0032] Determining the number of tags included in the data unit that are within a preset minimum comprehensive sequence value interval;

[0033] The data units are sorted according to the number of tags to obtain an access rate sort corresponding to each of the data units.

[0034] Optionally, the storage types include high-performance storage servers, large-capacity storage servers, and archive storage servers;

[0035] Determining the storage type of each of the data units according to the access rate ranking includes:

[0036] The data units are divided into three groups according to the access rate ranking, wherein the access rates of the three groups of data units are ranked in increasing order, and are stored in the high-performance storage server, the large-capacity storage server and the archive storage server in turn.

[0037] On the other hand, the present invention further provides a storage system for park energy data, the system comprising a processor, the processor being used to:

[0038] Obtain the historical access rate of the park energy data within a unit time period;

[0039] Labeling each data unit of the park energy data to obtain a label set for each data unit;

[0040] sorting each of the tags according to the access rate and the tag set to obtain a comprehensive sequence value of each of the tags;

[0041] Obtaining a label for each of the data units to be stored;

[0042] Determine the access rate ranking of each of the data units according to the label and the comprehensive sequence value of each of the data units;

[0043] The storage type of each of the data units is determined according to the access rate ranking.

[0044] Optionally, the processor is configured to:

[0045] Determine the number of access events of each data unit within the unit time period, the access time of each access event, and the access data volume;

[0046] Determine a valid access event according to the access time and access data volume;

[0047] The number of the effective access events is taken as the access rate.

[0048] Optionally, the processor is configured to:

[0049] Associating the access rate with each tag in the tag set to obtain an access rate sequence of each tag;

[0050] Removing the maximum value and the minimum value in the access rate sequence;

[0051] Calculating the variance of the access rate sequence;

[0052] Determining whether the variance is greater than or equal to a preset threshold;

[0053] When it is determined that the variance is less than the threshold, selecting the median in the access rate sequence as the characteristic access rate of the tag;

[0054] When it is determined that the variance is greater than or equal to the threshold, calculating the mean of the maximum value and the minimum value in the access rate sequence;

[0055] Counting the number of access rates greater than or equal to the mean and the number of access rates less than the mean respectively;

[0056] Selecting a minimum value of the larger of the number of the set of access rates greater than or equal to the mean and the set of access rates less than the mean as the characteristic access rate of the tag;

[0057] sorting the tags in descending order according to the feature access rates;

[0058] The sorting result is used as the comprehensive sequence value of the tag.

[0059] Optionally, the processor is configured to:

[0060] Determining a sequence value of the data unit according to the access rate sorting;

[0061] Determine a comprehensive sequence value of the tag corresponding to each of the data units;

[0062] For each of the data units, arranging the labels in ascending order of the comprehensive sequence values;

[0063] Determining the number of tags included in the data unit that are within a preset minimum comprehensive sequence value interval;

[0064] The data units are sorted according to the number of tags to obtain an access rate sort corresponding to each of the data units.

[0065] Optionally, the storage types include high-performance storage servers, large-capacity storage servers, and archive storage servers;

[0066] The processor is used to:

[0067] The data units are divided into three groups according to the access rate ranking, wherein the access rates of the three groups of data units are ranked in increasing order, and are stored in the high-performance storage server, the large-capacity storage server and the archive storage server in turn.

[0068] Through the above technical solution, the embodiment of the present invention provides a method and system for storing park energy data. The method and system analyze the access rate of energy data and classify and store the energy data based on the analysis results, so that frequently accessed energy data can be called more efficiently, thereby improving the efficiency of energy data calling. Compared with the general storage method of the prior art, the method and system provided by the present invention improves the storage and calling efficiency of energy data by combining the access rate of the energy data itself and the storage characteristics of the storage server.

[0069] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0071] Figure 1 is a flow chart of a method for storing park energy data according to an embodiment of the present invention;

[0072] Figure 2 is a flow chart of a method for obtaining the energy data according to an embodiment of the present invention;

[0073] Figure 3 is a flowchart of the method of step S12 according to one embodiment of the present invention;

[0074] Figure 4 is a flowchart of a method for determining access rate ranking according to an embodiment of the present invention. DETAILED DESCRIPTION

[0075] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0076] like Figure 1 The figure is a flow chart of a method for storing park energy data according to one embodiment of the present invention. Figure 1 In the storage method, the storage method may include the following steps:

[0077] In step S10, the access rate of the historical park energy data within a unit time period is obtained;

[0078] In step S11, each data unit of the park energy data is labeled to obtain a label set for each data unit;

[0079] In step S12, each tag is sorted according to the access rate and the tag set to obtain a comprehensive sequence value of each tag;

[0080] In step S13, a label of each data unit to be stored is obtained;

[0081] In step S14, the access rate ranking of each data unit is determined according to the label and the comprehensive sequence value of each data unit;

[0082] In step S15, the storage type of each data unit is determined according to the access rate ranking.

[0083] In this Figure 1 In the method shown, step S10 can be used to obtain the access rate of the historical energy data of the park within a unit time period. The access rate can be the click rate of the energy data within a unit time. The method for obtaining the access rate can be various forms known to those skilled in the art, including but not limited to directly performing flow analysis within a unit time. In one example of the present invention, the method for obtaining the energy data can include: Figure 2 The steps shown. Figure 2 The method for obtaining the energy data may include the following steps:

[0084] In step S20, the number of access events of each data unit in a unit time period, the access time of each access event, and the access data volume are determined;

[0085] In step S21, a valid access event is determined according to the access time and the access data volume;

[0086] In step S22, the number of effective access events is taken as the access rate.

[0087] In this Figure 2 In the method shown, step S20 can be used to determine the number of access events of each data unit within a unit time period, the access time of each access event, and the amount of access data. In this example, considering the staff's misoperation or invalid operation, it is also necessary to consider the access time (access duration) of each access event, that is, step S21 determines the valid access event according to the access time and the amount of access data. Specifically, considering that if the access time is very long but the amount of access data is very small, it can be considered that the staff did not exit after the misoperation. At this time, step S21 can be to determine whether the access time is greater than the preset first time maximum value and whether the access data amount is less than the first data amount minimum value. If so, it can be considered that the access event is not a valid access event. In another example of the present invention, considering that if the access time is very short and the access data amount is very large, it can be considered that the staff is performing a demonstrative operation, which is an event of random click access and also an invalid operation. Therefore, step S21 can also be to determine whether the access time is less than the preset first time minimum value and the access data amount is greater than the preset first data amount maximum value. If so, it can be considered that the access event is not a valid access event. Otherwise, it is a valid access event.

[0088] Step S11 performs labeling processing on each data unit of the park energy data to obtain a label set for each data unit, and step S12 is used to sort each label according to the access rate and the label set to obtain a comprehensive sequence value of each label. Specifically, in this embodiment, step S12 may further include: Figure 3 In the method shown in Figure 3 The step S12 may further include the following steps:

[0089] In step S30, the access rate is associated with each tag in the tag set to obtain an access rate sequence of each tag;

[0090] In step S31, the maximum value and the minimum value in the access rate sequence are removed;

[0091] In step S32, the variance of the access rate sequence is calculated;

[0092] In step S33, it is determined whether the variance is greater than or equal to a preset threshold;

[0093] In step S34, when it is determined that the variance is less than the threshold, the median in the access rate sequence is selected as the characteristic access rate of the tag;

[0094] In step S35, when it is determined that the variance is greater than or equal to the threshold, the mean of the maximum value and the minimum value in the access rate sequence is calculated;

[0095] In step S36, the number of access rates greater than or equal to the mean and the number of access rates less than the mean are counted respectively;

[0096] In step S37, the minimum value of the larger number of the set of access rates greater than or equal to the mean and the set of access rates less than the mean is selected as the characteristic access rate of the tag;

[0097] In step S38, the tags are sorted in descending order according to the feature access rate;

[0098] In step S39, the sorting result is used as the comprehensive sequence value of the tag.

[0099] In this Figure 3 In the method shown, step S30 is used to associate the access rate with each tag in the tag set to obtain the access rate sequence of each tag. In order to determine the characteristic access rate of each tag, it is necessary to consider the concentration of the access rate. Therefore, in this example, the maximum and minimum values ​​are first removed by step S31 to avoid interference caused by the two extreme values, and then the variance of the access rate sequence is calculated by step S32, and combined with step S33 to determine whether the access rate sequence corresponding to the tag is concentrated. If the numerical difference between each access rate in the access rate sequence is small, the mean of the maximum and minimum values ​​of the access rate sequence (not the original maximum and minimum values ​​here) can be directly used as the characteristic access rate.

[0100] If step S33 determines that the variance is greater than or equal to the threshold, it means that the data of the access rate sequence is relatively scattered and the mean cannot be directly used as the characteristic access rate. Therefore, in this example, steps S35 to S37 can be used to divide the access rates into two sets greater than or less than the mean, and then determine the size relationship of the number of access rates in the two sets, and finally select the minimum value of the larger one as the characteristic access rate of the tag.

[0101] Finally, through the sorting operation of step S38 and the numbering operation of step S39, the comprehensive sequence value is obtained.

[0102] Step S14 is used to determine the access rate ranking of each data unit according to the tag and the comprehensive sequence value of each data unit. Since the comprehensive sequence value calculated in step S12 corresponds to each tag, and each data unit includes multiple tags, it is impossible to directly determine the access rate ranking of the data unit through the comprehensive sequence value. The specific method for determining the access rate ranking can be in various forms known to those skilled in the art. In one example of the present invention, the method for determining the access rate ranking may include: Figure 4 The steps shown. Figure 4In the method for determining the access rate ranking, the method may include the following steps:

[0103] In step S40, the sequence value of the data unit is determined according to the access rate sorting;

[0104] In step S41, the comprehensive sequence value of the tag corresponding to each data unit is determined;

[0105] In step S42, for each data unit, the labels are arranged in ascending order of the comprehensive sequence value;

[0106] In step S43, the number of tags included in the data unit that are within a preset minimum comprehensive sequence value interval is determined;

[0107] In step S44, the data units are sorted according to the number of tags to obtain the access rate sorting corresponding to each data unit.

[0108] Step S15 is used to determine the storage type of each data unit according to the access rate ranking. Among them, considering the current division according to call efficiency and storage form, the storage server generally includes a high-performance storage server, a large-capacity storage server, and an archive storage server. In an example of the present invention, step S15 can be to divide the data units into three groups according to the access rate ranking, wherein the access rates of the three groups of data units are ranked in increasing order, and are stored in the high-performance storage server, the large-capacity storage server, and the archive storage server in turn.

[0109] On the other hand, the present invention also provides a storage system for park energy data, the system includes a processor, and the processor can be used to execute the following method:

[0110] In step S10, the access rate of the historical park energy data within a unit time period is obtained;

[0111] In step S11, each data unit of the park energy data is labeled to obtain a label set for each data unit;

[0112] In step S12, each tag is sorted according to the access rate and the tag set to obtain a comprehensive sequence value of each tag;

[0113] In step S13, a label of each data unit to be stored is obtained;

[0114] In step S14, the access rate ranking of each data unit is determined according to the label and the comprehensive sequence value of each data unit;

[0115] In step S15, the storage type of each data unit is determined according to the access rate ranking.

[0116] In this Figure 1 In the method shown, step S10 can be used to obtain the access rate of the historical energy data of the park within a unit time period. The access rate can be the click rate of the energy data within a unit time. The method for obtaining the access rate can be various forms known to those skilled in the art, including but not limited to directly performing flow analysis within a unit time. In one example of the present invention, the method for obtaining the energy data can include: Figure 2 The steps shown. Figure 2 In the method for obtaining the energy data, the method may include the following steps:

[0117] In step S20, the number of access events of each data unit in a unit time period, the access time of each access event, and the access data volume are determined;

[0118] In step S21, a valid access event is determined according to the access time and the access data volume;

[0119] In step S22, the number of effective access events is taken as the access rate.

[0120] In this Figure 2 In the method shown, step S20 can be used to determine the number of access events of each data unit within a unit time period, the access time of each access event, and the amount of access data. In this example, considering the staff's misoperation or invalid operation, it is also necessary to consider the access time (access duration) of each access event, that is, step S21 determines the valid access event according to the access time and the amount of access data. Specifically, considering that if the access time is very long but the amount of access data is very small, it can be considered that the staff did not exit after the misoperation. At this time, step S21 can be to determine whether the access time is greater than the preset first time maximum value and whether the access data amount is less than the first data amount minimum value. If so, it can be considered that the access event is not a valid access event. In another example of the present invention, considering that if the access time is very short and the access data amount is very large, it can be considered that the staff is performing a demonstrative operation, which is an event of random click access and also an invalid operation. Therefore, step S21 can also be to determine whether the access time is less than the preset first time minimum value and the access data amount is greater than the preset first data amount maximum value. If so, it can be considered that the access event is not a valid access event. Otherwise, it is a valid access event.

[0121] Step S11 performs labeling processing on each data unit of the park energy data to obtain a label set for each data unit, and step S12 is used to sort each label according to the access rate and the label set to obtain a comprehensive sequence value of each label. Specifically, in this embodiment, step S12 may further include: Figure 3 In the method shown in Figure 3 The step S12 may further include the following steps:

[0122] In step S30, the access rate is associated with each tag in the tag set to obtain an access rate sequence of each tag;

[0123] In step S31, the maximum value and the minimum value in the access rate sequence are removed;

[0124] In step S32, the variance of the access rate sequence is calculated;

[0125] In step S33, it is determined whether the variance is greater than or equal to a preset threshold;

[0126] In step S34, when it is determined that the variance is less than the threshold, the median in the access rate sequence is selected as the characteristic access rate of the tag;

[0127] In step S35, when it is determined that the variance is greater than or equal to the threshold, the mean of the maximum value and the minimum value in the access rate sequence is calculated;

[0128] In step S36, the number of access rates greater than or equal to the mean and the number of access rates less than the mean are counted respectively;

[0129] In step S37, the minimum value of the larger number of the set of access rates greater than or equal to the mean and the set of access rates less than the mean is selected as the characteristic access rate of the tag;

[0130] In step S38, the tags are sorted in descending order according to the feature access rate;

[0131] In step S39, the sorting result is used as the comprehensive sequence value of the tag.

[0132] In this Figure 3 In the method shown, step S30 is used to associate the access rate with each tag in the tag set to obtain the access rate sequence of each tag. In order to determine the characteristic access rate of each tag, it is necessary to consider the concentration of the access rate. Therefore, in this example, the maximum and minimum values ​​are first removed by step S31 to avoid interference caused by the two extreme values, and then the variance of the access rate sequence is calculated by step S32, and combined with step S33 to determine whether the access rate sequence corresponding to the tag is concentrated. If the numerical difference between each access rate in the access rate sequence is small, the mean of the maximum and minimum values ​​of the access rate sequence (not the original maximum and minimum values ​​here) can be directly used as the characteristic access rate.

[0133] If step S33 determines that the variance is greater than or equal to the threshold, it means that the data of the access rate sequence is relatively scattered and the mean cannot be directly used as the characteristic access rate. Therefore, in this example, steps S35 to S37 can be used to divide the access rates into two sets greater than or less than the mean, and then determine the size relationship of the number of access rates in the two sets, and finally select the minimum value of the larger one as the characteristic access rate of the tag.

[0134] Finally, through the sorting operation of step S38 and the numbering operation of step S39, the comprehensive sequence value is obtained.

[0135] Step S14 is used to determine the access rate ranking of each data unit according to the tag and the comprehensive sequence value of each data unit. Since the comprehensive sequence value calculated in step S12 corresponds to each tag, and each data unit includes multiple tags, it is impossible to directly determine the access rate ranking of the data unit through the comprehensive sequence value. The specific method for determining the access rate ranking can be in various forms known to those skilled in the art. In one example of the present invention, the method for determining the access rate ranking may include: Figure 4 The steps shown. Figure 4 In the method for determining the access rate ranking, the method may include the following steps:

[0136] In step S40, the sequence value of the data unit is determined according to the access rate sorting;

[0137] In step S41, the comprehensive sequence value of the tag corresponding to each data unit is determined;

[0138] In step S42, for each data unit, the labels are arranged in ascending order of the comprehensive sequence value;

[0139] In step S43, the number of tags included in the data unit that are within a preset minimum comprehensive sequence value interval is determined;

[0140] In step S44, the data units are sorted according to the number of tags to obtain the access rate sorting corresponding to each data unit.

[0141] Step S15 is used to determine the storage type of each data unit according to the access rate ranking. Among them, considering the current division according to call efficiency and storage form, the storage server generally includes a high-performance storage server, a large-capacity storage server, and an archive storage server. In an example of the present invention, step S15 can be to divide the data units into three groups according to the access rate ranking, wherein the access rates of the three groups of data units are ranked in increasing order, and are stored in the high-performance storage server, the large-capacity storage server, and the archive storage server in turn.

[0142] Through the above technical solution, the embodiment of the present invention provides a method and system for storing park energy data. The method and system analyze the access rate of energy data and classify and store the energy data based on the analysis results, so that frequently accessed energy data can be called more efficiently, thereby improving the efficiency of energy data calling. Compared with the general storage method of the prior art, the method and system provided by the present invention improves the storage and calling efficiency of energy data by combining the access rate of the energy data itself and the storage characteristics of the storage server.

[0143] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0144] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0145] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0146] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0147] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0148] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0149] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0150] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0151] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for storing park energy data, characterized in that: The storage method comprises: Obtain the historical access rate of the park energy data within a unit time period; Labeling each data unit of the park energy data to obtain a label set for each data unit; sorting each of the tags according to the access rate and the tag set to obtain a comprehensive sequence value of each of the tags; Obtaining a label for each of the data units to be stored; Determine the access rate ranking of each of the data units according to the label and the comprehensive sequence value of each of the data units; The storage type of each of the data units is determined according to the access rate ranking.

2. The method according to claim 1, characterized in that Obtain the historical access rate of campus energy data per unit time period, including: Determine the number of access events of each data unit within the unit time period, the access time of each access event, and the access data volume; Determine a valid access event according to the access time and access data volume; The number of the effective access events is taken as the access rate.

3. The method according to claim 1, characterized in that Sorting each of the tags according to the access rate and the tag set to obtain a comprehensive sequence value of each of the tags includes: Associating the access rate with each tag in the tag set to obtain an access rate sequence of each tag; Removing the maximum value and the minimum value in the access rate sequence; Calculating the variance of the access rate sequence; Determining whether the variance is greater than or equal to a preset threshold; When it is determined that the variance is less than the threshold, selecting the median in the access rate sequence as the characteristic access rate of the tag; When it is determined that the variance is greater than or equal to the threshold, calculating the mean of the maximum value and the minimum value in the access rate sequence; Counting the number of access rates greater than or equal to the mean and the number of access rates less than the mean respectively; Selecting a minimum value of the larger of the number of the set of access rates greater than or equal to the mean and the set of access rates less than the mean as the characteristic access rate of the tag; sorting the tags in descending order according to the feature access rates; The sorting result is used as the comprehensive sequence value of the tag.

4. The method according to claim 1, characterized in that: Determining the access rate ranking of each of the data units according to the label and the comprehensive sequence value of each of the data units includes: Determining a sequence value of the data unit according to the access rate sorting; Determine a comprehensive sequence value of the tag corresponding to each of the data units; For each of the data units, arranging the labels in ascending order of the comprehensive sequence values; Determining the number of tags included in the data unit that are within a preset minimum comprehensive sequence value interval; The data units are sorted according to the number of tags to obtain an access rate sort corresponding to each of the data units.

5. The method according to claim 1, characterized in that The storage types include high-performance storage servers, large-capacity storage servers, and archival storage servers; Determining the storage type of each of the data units according to the access rate ranking includes: The data units are divided into three groups according to the access rate ranking, wherein the access rates of the three groups of data units are ranked in increasing order, and are stored in the high-performance storage server, the large-capacity storage server and the archive storage server in turn.

6. A storage system for park energy data, characterized in that: The system includes a processor configured to: Obtain the historical access rate of the park energy data within a unit time period; Labeling each data unit of the park energy data to obtain a label set for each data unit; sorting each of the tags according to the access rate and the tag set to obtain a comprehensive sequence value of each of the tags; Obtaining a label for each of the data units to be stored; Determine the access rate ranking of each of the data units according to the label and the comprehensive sequence value of each of the data units; The storage type of each of the data units is determined according to the access rate ranking.

7. The system according to claim 6, characterized in that The processor is used to: Determine the number of access events of each data unit within the unit time period, the access time of each access event, and the access data volume; Determine a valid access event according to the access time and access data volume; The number of the effective access events is taken as the access rate.

8. The system according to claim 6, characterized in that The processor is used to: Associating the access rate with each tag in the tag set to obtain an access rate sequence of each tag; Removing the maximum value and the minimum value in the access rate sequence; Calculating the variance of the access rate sequence; Determining whether the variance is greater than or equal to a preset threshold; When it is determined that the variance is less than the threshold, selecting the median in the access rate sequence as the characteristic access rate of the tag; When it is determined that the variance is greater than or equal to the threshold, calculating the mean of the maximum value and the minimum value in the access rate sequence; Counting the number of access rates greater than or equal to the mean and the number of access rates less than the mean respectively; Selecting a minimum value of the larger of the number of the set of access rates greater than or equal to the mean and the set of access rates less than the mean as the characteristic access rate of the tag; sorting the tags in descending order according to the feature access rates; The sorting result is used as the comprehensive sequence value of the tag.

9. The system according to claim 6, characterized in that The processor is used to: Determining a sequence value of the data unit according to the access rate sorting; Determine a comprehensive sequence value of the tag corresponding to each of the data units; For each of the data units, arranging the labels in ascending order of the comprehensive sequence values; Determining the number of tags included in the data unit that are within a preset minimum comprehensive sequence value interval; The data units are sorted according to the number of tags to obtain an access rate sort corresponding to each of the data units.

10. The system according to claim 6, characterized in that The storage types include high-performance storage servers, large-capacity storage servers, and archival storage servers; The processor is used to: The data units are divided into three groups according to the access rate ranking, wherein the access rates of the three groups of data units are ranked in increasing order, and are stored in the high-performance storage server, the large-capacity storage server and the archive storage server in turn.

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