WO2022007169A1 - 智能电表数据存储方法及装置 - Google Patents

智能电表数据存储方法及装置 Download PDF

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Publication number
WO2022007169A1
WO2022007169A1 PCT/CN2020/113960 CN2020113960W WO2022007169A1 WO 2022007169 A1 WO2022007169 A1 WO 2022007169A1 CN 2020113960 W CN2020113960 W CN 2020113960W WO 2022007169 A1 WO2022007169 A1 WO 2022007169A1
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time
data
recording
time index
index
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PCT/CN2020/113960
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English (en)
French (fr)
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石理宁
李军
杨勇
李斌
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威胜集团有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
    • G06F3/0608—Saving storage space on storage systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/13—File access structures, e.g. distributed indices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/17—Details of further file system functions
    • G06F16/174—Redundancy elimination performed by the file system
    • G06F16/1744—Redundancy elimination performed by the file system using compression, e.g. sparse files
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0628—Interfaces specially adapted for storage systems making use of a particular technique
    • G06F3/0638—Organizing or formatting or addressing of data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0668—Interfaces specially adapted for storage systems adopting a particular infrastructure
    • G06F3/0671—In-line storage system
    • G06F3/0673—Single storage device
    • G06F3/0679—Non-volatile semiconductor memory device, e.g. flash memory, one time programmable memory [OTP]

Definitions

  • the present invention relates to the technical field of smart electricity meters, and in particular, to a method and device for storing data of a smart electricity meter.
  • the demand for smart meters is showing explosive growth, and the original non-smart meters will be gradually replaced by smart meters.
  • the advantages of smart meters are self-evident.
  • the most common ones are load curve records, maximum demand, etc. Based on the current situation of use and the trend of future demand for user data, the demand for user data will be greater in the future.
  • curve data is undoubtedly the top priority of user data.
  • These data often include user electricity consumption data, time data and information on the running status of the electricity meter.
  • the data storage space requirements of the electricity meter are also increasing, which will mean that the cost of smart meters will increase.
  • the traditional non-compressed storage solution has a large amount of data, which brings great difficulties to data storage and retrieval.
  • the main purpose of the present invention is to provide a smart meter data storage method and device, aiming at the technical problems that the smart meter data has a large demand for storage capacity and data retrieval is difficult.
  • the present invention provides a method for storing data of a smart meter, the method comprising:
  • the power consumption data and the time index are associated and saved.
  • the step of performing byte compression on the recording time node according to the preset reference time and the electricity meter recording period to obtain a time index specifically includes:
  • Byte compression is performed on the recording time node according to the time division data and the recording period of the electricity meter, so as to obtain a time index.
  • the step of performing byte compression on the recording time node according to the time-division data and the electricity meter recording period to obtain a time index includes:
  • byte compression is performed on the recording time node by the first calculation formula to obtain a time index
  • the first calculation formula is:
  • N is the time index
  • H is the time-division data
  • t is the recording period of the electricity meter
  • T is the preset reference period.
  • the step of associating the power consumption data with the time index and saving it specifically includes:
  • mapping relationship between the power consumption data and the time index is established, and the mapping relationship is saved.
  • the method further includes:
  • the target power consumption data corresponding to the query time index is searched in the mapping relationship, and the target power consumption data is sent to the user.
  • the present invention also provides a smart meter data storage device, the device comprising:
  • an acquisition module configured to acquire the electricity consumption data and the recording time node corresponding to the electricity consumption data
  • the reading module is used to read the preset reference time and the meter recording cycle
  • a compression module configured to perform byte compression on the recording time node according to the preset reference time and the electricity meter recording period to obtain a time index
  • a storage module configured to associate the power consumption data with the time index and save it.
  • the compression module includes:
  • a calculation submodule used for calculating the date-time difference between the preset reference time and the recording time node
  • a conversion submodule for converting the date-time difference into time-division data
  • a compression sub-module configured to perform byte compression on the recording time node according to the time-division data and the recording period of the electricity meter, so as to obtain a time index.
  • the compression submodule includes:
  • a compression unit configured to perform byte compression on the recording time node by using the first calculation formula according to the time division data and the recording period of the electricity meter to obtain a time index
  • the first calculation formula is:
  • N is the time index
  • H is the time-division data
  • t is the recording period of the electricity meter
  • T is the preset reference period.
  • the storage module is further configured to establish a mapping relationship between the power consumption data and the time index, and store the mapping relationship.
  • the device further comprises:
  • a receiving module configured to receive a data query instruction input by a user, and read the query time node included in the data query instruction
  • a compression module further configured to perform byte compression on the query time node according to the preset reference time and the electricity meter recording period to obtain a query time index
  • An output module configured to look up the target power consumption data corresponding to the query time index in the mapping relationship, and send the target power consumption data to the user.
  • the present invention obtains the electricity consumption data and the recording time node corresponding to the electricity consumption data; reads the preset reference time and the recording period of the electricity meter; Byte compression to obtain a time index; the power consumption data and the time index are associated and saved.
  • the present invention reduces the storage pressure of the curve data, and because the calculation method of the time index is simple, data storage and data query can be performed with a faster response speed.
  • FIG. 1 is a schematic flowchart of a first embodiment of a data storage method for a smart meter according to the present invention
  • FIG. 3 is a schematic diagram of curve data storage according to an embodiment of a smart meter data storage method according to the present invention.
  • FIG. 4 is a schematic flowchart of a second embodiment of a data storage method for a smart meter according to the present invention.
  • FIG. 5 is a schematic flowchart of a third embodiment of a data storage method for a smart meter according to the present invention.
  • FIG. 6 is a structural block diagram of the first embodiment of the smart meter data storage device according to the present invention.
  • FIG. 1 is a schematic flowchart of a first embodiment of a method for storing data of a smart electric meter according to the present invention.
  • FIG. 2 is a schematic diagram of curve data storage in the prior art.
  • the present embodiment provides a method for storing data of a smart meter, and the method for storing data of a smart meter includes the following steps:
  • Step S100 Acquire electricity consumption data and a recording time node corresponding to the electricity consumption data.
  • each record contains the electricity consumption data and the current time of multiple recording objects, and the recording time node is the current time, which corresponds to the current electricity consumption data of the multiple recording objects. time information. Recording time data directly will cause more storage space consumption, so it is necessary to process the acquired recording time nodes.
  • Step S200 Read the preset reference time and the recording period of the electricity meter.
  • the preset reference time is a preset value in the smart meter, and all the periodic, time-related data in the smart meter is based on the preset reference time. Since different smart meters have different settings, and the user can reset the smart meters during use, the preset reference time and the meter recording cycle should be read before recording and storage.
  • the preset reference time can be selected at 0:00 of the whole year, and the preset reference time can also be adjusted according to different recording requirements.
  • Data storage according to the preset reference time can reduce the storage pressure of the curve data, and the preset reference time can be appropriately adjusted according to the actual usage of the smart meter, so that the calculated time index value will not overflow, and at the same time It can also simplify the calculation process.
  • the recording cycle of the electric meter is recorded twice per hour, that is, once every half an hour.
  • the recording period of the electric meter is the time interval for each time the electric meter records the electricity consumption data.
  • Step S300 Perform byte compression on the recording time node according to the preset reference time and the electricity meter recording period to obtain a time index.
  • step S300 specifically includes: calculating the date-time difference between the preset reference time and the recording time node; converting the date-time difference into time-division data; according to the time-division data and the electricity meter The recording period performs byte compression on the recording time node to obtain a time index.
  • the preset reference time is 0:00 every year
  • the recording period of the electricity meter is half an hour. Taking January 1, 2020, 00:00 as an example, the current recording time node is February 2020.
  • the date and time difference is 18 hours and 30 minutes on the 35th (31 days + 4 days + 18 hours + 30 minutes).
  • the 35 days, 18 hours and 30 minutes are converted into time-division data, that is, 858.5 hours.
  • date and time difference can be obtained through a perpetual calendar algorithm.
  • the step of performing byte compression on the recording time node according to the time-division data and the electricity meter recording cycle to obtain a time index includes: according to the time-division data and the electricity meter recording cycle, through the first A calculation formula performs byte compression on the recording time node to obtain a time index; wherein, the first calculation formula is:
  • N is the time index
  • H is the time-division data
  • t is the recording period of the electricity meter
  • T is the preset reference period.
  • the time-division data is 858.5 hours
  • the recording period of the electricity meter is half an hour
  • the preset reference period is one hour in this embodiment, so the time index N is 1717.
  • FIG. 3 is a schematic diagram of curve data storage according to an embodiment of the smart meter data storage method of the present invention.
  • the time index occupies 4 bytes, which reduces the required storage space.
  • Step S400 Associate the power consumption data with the time index and save it.
  • time index obtained after the above calculation reduces the required storage space, and each time index corresponds to each time node, and there is no duplication. association, to replace the original time data preservation.
  • the calculation method of the time index is simple and unique, which greatly reduces the storage space occupied by the curve data, reduces the storage space requirement in the smart meter, and reduces the The cost of smart meters.
  • FIG. 4 is a schematic flowchart of the second embodiment of the smart meter data storage method of the present invention.
  • the step S400 specifically includes step S401: establishing a mapping relationship between the power consumption data and the time index, and saving the mapping relationship.
  • the electricity consumption data and the time index are stored by establishing a mapping.
  • the mapping of the electricity consumption data and the time index can be sent to a remote site. Management platform for saving or analysis.
  • the time index and time node also show a mapping relationship. When the reference time and recording period remain unchanged, the conversion can be directly performed according to the mapping to save the time required for byte compression and improve the response efficiency of the smart meter.
  • step S401 it also includes:
  • Step S501 Receive a data query instruction input by a user, and read a query time node included in the data query instruction.
  • the data query instruction can include multiple query time nodes, that is, the user can use the data query instruction to query the electricity consumption data at one moment, the electricity consumption data at multiple moments, or the electricity consumption in a period of time. data to query.
  • Step S502 Perform byte compression on the query time node according to the preset reference time and the electricity meter recording period to obtain a query time index.
  • the user has issued a data query instruction to query the electricity consumption data from 0:00 on February 5, 2020 to 0:00 on February 6, 2020.
  • the calculation method of the time index has been described in the first embodiment, and will not be repeated here. Through calculation, it can be known that the time indexes corresponding to the above two time nodes are 1680 and 1728 respectively, so the time index interval corresponding to the electricity consumption data to be queried by the user is 1680 to 1728.
  • Step S503 Find the target power consumption data corresponding to the query time index in the mapping relationship, and send the target power consumption data to the user.
  • the interval of the time index obtained is 1680 to 1728
  • the power consumption data corresponding to the time index 1680 to 1728 is obtained in the saved record
  • the power consumption data is the query power consumption data , and output the queried electricity consumption data to the user so that the user can obtain it.
  • the time index is calculated from the date and time through the above method, and the electricity consumption data is obtained by utilizing the uniqueness of the time index. Due to the numerical type check of the time index, the search algorithm is greatly optimized. There is no need to compare each data, as long as the time index corresponding to the time range is calculated first, it can be quickly searched.
  • FIG. 5 is a schematic flowchart of the third embodiment of the smart meter data storage method of the present invention.
  • step S401 the method further includes:
  • Step S601 Receive a time query instruction input by a user, and read that the time query instruction includes query power consumption data.
  • the data query instruction may include multiple query power consumption data, that is, the user can use the time query instruction to search for a corresponding moment of one power consumption data, a moment corresponding to a plurality of power consumption data, or an interval. Query the time interval corresponding to the electricity consumption data.
  • Step S602 Search for the target time index corresponding to the query power consumption data in the mapping relationship.
  • the first power consumption data can be obtained.
  • a time index interval corresponding to the second power consumption data, and the time interval is derived according to the time index interval.
  • the time index corresponding to the first power consumption data is 1680
  • the time index corresponding to the second power consumption data is 1728. Therefore, the time index interval corresponding to the time interval to be queried by the user is 1680 to 1728.
  • Step S603 Perform byte decompression on the target time index according to the preset reference time and the electricity meter recording period to obtain a target time node, and send the target time node to the user.
  • the method in the embodiment of the present invention decompresses the time index into time data, and the method can ensure that both the date and time can be calculated from the time index, and the time index can be calculated from the date and time, which provides convenience for reading the meter data and saves money. It saves storage space and improves retrieval efficiency.
  • FIG. 6 is a structural block diagram of a first embodiment of a data storage device for a smart meter according to the present invention.
  • a smart meter data storage device includes:
  • the obtaining module 10 is configured to obtain the electricity consumption data and the recording time node corresponding to the electricity consumption data.
  • each record contains the electricity consumption data and the current time of multiple recording objects, and the recording time node is the current time, which corresponds to the current electricity consumption data of the multiple recording objects. time information. Recording time data directly will cause more storage space consumption, so it is necessary to process the acquired recording time nodes.
  • the reading module 20 is used for reading the preset reference time and the recording period of the electricity meter.
  • the preset reference time is a preset value in the smart meter, and all the periodic, time-related data in the smart meter is based on the preset reference time. Since different smart meters have different settings, and the user can reset the smart meters during use, the preset reference time and the meter recording cycle should be read before recording and storage.
  • the preset reference time can be selected at 0:00 of the whole year, and the preset reference time can also be adjusted according to different recording requirements.
  • Data storage according to the preset reference time can reduce the storage pressure of the curve data, and the preset reference time can be appropriately adjusted according to the actual usage of the smart meter, so that the calculated time index value will not overflow, and at the same time It can also simplify the calculation process.
  • the recording cycle of the electric meter is recorded twice per hour, that is, once every half an hour.
  • the recording period of the electric meter is the time interval for each time the electric meter records the electricity consumption data.
  • the compression module 30 is configured to perform byte compression on the recording time node according to the preset reference time and the electricity meter recording period to obtain a time index.
  • the compression module 30 includes: a calculation sub-module for calculating the date-time difference between the preset reference time and the recording time node; a conversion sub-module for converting the date-time difference Converting into time-division data; and a compression sub-module, configured to perform byte compression on the recording time node according to the time-division data and the electric meter recording cycle to obtain a time index.
  • the preset reference time is 0:00 every year, and the recording period of the electricity meter is half an hour. Taking January 1, 2020, 00:00 as an example, the current recording time node is February 2020. At 18:30 on the 5th of the month, the date and time difference is 18 hours and 30 minutes on the 35th (31 days + 4 days + 18 hours + 30 minutes). Convert the 35 days, 18 hours and 30 minutes into time-division data, that is, 858.5 hours.
  • date and time difference can be obtained through a perpetual calendar algorithm.
  • the compression sub-module includes: a compression unit, configured to perform byte compression on the recording time node according to the time-division data and the electricity meter recording period, so as to obtain a time index.
  • the step includes: according to the time-division data For the data and the recording period of the electricity meter, the recording time node is byte-compressed by a first calculation formula to obtain a time index; wherein, the first calculation formula is:
  • N is the time index
  • H is the time-division data
  • t is the recording period of the electricity meter
  • T is the preset reference period.
  • the time-division data is 858.5 hours
  • the recording period of the electricity meter is half an hour
  • the preset reference period is one hour in this embodiment, so the time index N is 1717.
  • FIG. 3 is a schematic diagram of curve data storage according to an embodiment of the smart meter data storage method of the present invention.
  • the time index occupies 4 bytes, which reduces the required storage space.
  • the storage module 40 is configured to associate the power consumption data with the time index and store it.
  • time index obtained after the above calculation reduces the required storage space, and each time index corresponds to each time node, and there is no duplication. association, to replace the original time data preservation.
  • the storage module 40 is specifically configured to establish a mapping relationship between the power consumption data and the time index, and store the mapping relationship.
  • the electricity consumption data and the time index are stored by establishing a mapping.
  • the mapping of the electricity consumption data and the time index can be sent to a remote site. Management platform for saving or analysis.
  • the time index and time node also show a mapping relationship. When the reference time and recording period remain unchanged, the conversion can be directly performed according to the mapping to save the time required for byte compression and improve the response efficiency of the smart meter.
  • the calculation method of the time index is simple and unique, which greatly reduces the storage space occupied by the curve data and reduces the storage space in the smart meter. demand, reducing the cost of smart meters.
  • the device further includes: a receiving module, configured to receive a data query instruction input by a user, and read a query time node included in the data query instruction.
  • a receiving module configured to receive a data query instruction input by a user, and read a query time node included in the data query instruction.
  • the data query instruction can include multiple query time nodes, that is, the user can use the data query instruction to query the electricity consumption data at one moment, the electricity consumption data at multiple moments, or the electricity consumption in a period of time. data to query.
  • the compression module 30 is further configured to perform byte compression on the query time node according to the preset reference time and the electricity meter recording period to obtain a query time index.
  • the user has issued a data query instruction to query the electricity consumption data from 0:00 on February 5, 2020 to 0:00 on February 6, 2020.
  • the calculation method of the time index has been described in the first embodiment, and will not be repeated here. Through calculation, it can be known that the time indexes corresponding to the above two time nodes are 1680 and 1728 respectively, so the time index interval corresponding to the electricity consumption data to be queried by the user is 1680 to 1728.
  • An output module configured to look up the target power consumption data corresponding to the query time index in the mapping relationship, and send the target power consumption data to the user.
  • the interval of the time index obtained is 1680 to 1728
  • the power consumption data corresponding to the time index 1680 to 1728 is obtained in the saved record
  • the power consumption data is the query power consumption data , and output the queried electricity consumption data to the user so that the user can obtain it.
  • the device calculates the time index from the date and time, and obtains the electricity consumption data by using the uniqueness of the time index. Due to the numerical type check of the time index, the search algorithm is greatly optimized. There is no need to compare each data, as long as the time index corresponding to the time range is calculated first, it can be quickly searched.
  • the receiving module is configured to receive a time query instruction input by a user, and read that the time query instruction includes query power consumption data.
  • the data query instruction may include multiple query power consumption data, that is, the user can use the time query instruction to search for a corresponding moment of one power consumption data, a moment corresponding to a plurality of power consumption data, or an interval. Query the time interval corresponding to the electricity consumption data.
  • the obtaining module 10 is further configured to search the target time index corresponding to the queried electricity consumption data in the mapping relationship.
  • the first power consumption data can be obtained.
  • a time index interval corresponding to the second power consumption data, and the time interval is derived according to the time index interval.
  • the time index corresponding to the first power consumption data is 1680
  • the time index corresponding to the second power consumption data is 1728. Therefore, the time index interval corresponding to the time interval to be queried by the user is 1680 to 1728.
  • a decompression module configured to perform byte decompression on the target time index according to the preset reference time and the electricity meter recording period to obtain a target time node, and send the target time node to the user.
  • the time interval corresponding to the time index interval 1680 to 1728 can be obtained through decompression, which is from 0:00 on February 5, 2020 to 2020. February 6th at 0:00.
  • the device in the embodiment of the present invention can ensure that both the date and time can be calculated from the time index, and the time index can be calculated from the date and time, which provides convenience for reading the meter data and saves money It saves storage space and improves retrieval efficiency.

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Abstract

本发明涉及智能电表技术领域，尤其涉及一种智能电表数据存储方法及装置。所述方法包括：获取用电数据和所述用电数据对应的记录时间节点；读取预设基准时间及电表记录周期；根据所述预设基准时间及所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引；将所述用电数据和所述时间索引进行关联后保存。本发明通过上述方法，使得曲线数据的储存压力降低，由于时间索引的计算方式简洁，能以较快的响应速度进行数据存储及数据查询。

Description

智能电表数据存储方法及装置 技术领域
本发明涉及智能电表技术领域，尤其涉及一种智能电表数据存储方法及装置。
背景技术
当前，智能电表需求呈现爆炸式增长，原有的非智能电表将逐步为智能电表更新取代，智能电表的优势不言而喻。在智能电表的用户数据中最常见的例如负荷曲线记录、最大需量等等，基于使用现状以及未来对用户数据需求的趋势判断，将来对用户数据的需求将会更大，通过大量的历史数据来分析能源分配，解决能源分配问题。特别是曲线数据，无疑是用户数据重中之重。这些数据中往往包含用户用电数据、时间数据及电表运行状态信息。当历史数据不断增加，则电表的数据存储空间需求也不断增大，这将意味着智能电表的成本将会增加。这种情况下，传统的非压缩存储方案数据量大，给数据存储和检索带来比较大的困难。
上述内容仅用于辅助理解本发明的技术方案，并不代表承认上述内容是现有技术。
发明内容
本发明的主要目的在于提供一种智能电表数据存储方法及装置，旨在智能电表数据对存储容量需求大及数据检索难的技术问题。
为实现上述目的，本发明提供了一种智能电表数据存储方法，所述方法包括：
获取用电数据和所述用电数据对应的记录时间节点；
读取预设基准时间及电表记录周期；
根据所述预设基准时间及所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引；
将所述用电数据和所述时间索引进行关联后保存。
优选地，所述根据所述预设基准时间及所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引的步骤，具体包括：
计算所述预设基准时间与所述记录时间节点的日期时间差值；
将所述日期时间差值转换为时分数据；
根据所述时分数据和所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引。
优选地，所述根据所述时分数据和所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引的步骤，包括：
根据所述时分数据以及所述电表记录周期，通过第一计算公式对所述记录时间节点进行字节压缩，以获得时间索引；
其中，所述第一计算公式为：
N＝(T/t)*H
其中，N为时间索引，H为时分数据，t为电表记录周期，T为预设基准周期。
所述将所述用电数据和所述时间索引进行关联后保存的步骤，具体包括：
建立所述用电数据和所述时间索引之间的映射关系，并对所述映射关系进行保存。
优选地，所述将所述用电数据和所述时间索引进行关联后保存步骤之后，所述方法还包括：
接收用户输入的数据查询指令，读取所述数据查询指令中包含的查询时间节点；
根据所述预设基准时间及所述电表记录周期对所述查询时间节点进行字节压缩，以获得查询时间索引；
在所述映射关系中查找所述查询时间索引对应的目标用电数据，并将所述目标用电数据发送至所述用户。
此外，为实现上述目的，本发明还提出一种智能电表数据存储装置，所述装置包括：
获取模块，用于获取用电数据和所述用电数据对应的记录时间节点；
读取模块，用于读取预设基准时间及电表记录周期；
压缩模块，用于根据所述预设基准时间及所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引；
存储模块，用于将所述用电数据和所述时间索引进行关联后保存。
优选地，所述压缩模块包括：
计算子模块，用于计算所述预设基准时间与所述记录时间节点的日期时间差值；
转换子模块，用于将所述日期时间差值转换为时分数据；
压缩子模块，用于根据所述时分数据和所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引。
优选地，所述压缩子模块包括：
压缩单元，用于根据所述时分数据以及所述电表记录周期，通过第一计算公式对所述记录时间节点进行字节压缩，以获得时间索引；
其中，所述第一计算公式为：
N＝(T/t)*H
其中，N为时间索引，H为时分数据，t为电表记录周期，T为预设基准周期。
优选地，所述存储模块，还用于建立所述用电数据和所述时间索引之间的映射关系，并对所述映射关系进行保存。
优选地，所述装置还包括：
接收模块，用于接收用户输入的数据查询指令，读取所述数据查询指令中包含的查询时间节点；
压缩模块，还用于根据所述预设基准时间及所述电表记录周期对所述查询时间节点进行字节压缩，以获得查询时间索引；
输出模块，用于在所述映射关系中查找所述查询时间索引对应的目标用电数据，并将所述目标用电数据发送至所述用户。
本发明通过获取用电数据和所述用电数据对应的记录时间节点；读取预设基准时间及电表记录周期；根据所述预设基准时间及所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引；将所述用电数据和所述时间索引进行关联后保存。本发明通过上述方法，使得曲线数据的储存压力 降低，由于时间索引的计算方式简洁，能以较快的响应速度进行数据存储及数据查询。
附图说明
图1为本发明智能电表数据存储方法第一实施例的流程示意图；
图2为现有技术的曲线数据存储示意图；
图3为本发明智能电表数据存储方法一实施例的曲线数据存储示意图；
图4为本发明智能电表数据存储方法第二实施例的流程示意图；
图5为本发明智能电表数据存储方法第三实施例的流程示意图；
图6为本发明智能电表数据存储装置第一实施例的结构框图。
本发明目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅用以解释本发明，并不用于限定本发明。
本发明实施例提供了一种智能电表数据存储方法，参照图1，图1为本发明一种智能电表数据存储方法第一实施例的流程示意图。
需要说明的是，现有技术中，对存储曲线数据未作任何处理，存储的全部为原始数据，所消耗的存储空间无疑是巨大的。按照传统方式计算，例如：每条记录包含4个数据对象(每个数据对象为4字节)和一个时间对象(12字节)，那么一条记录需要28个字节。如果每15分钟进行一次记录存储，每小时进行4次存储，则每天进行48次存储。若存储空间为200K字节，以上述存储方式，能够存储约152天的数据。参考图2，图2为现有技术的曲线数据存储示意图。
应当理解的是，随着记录存储的数据对象增加，那么能够容纳的总条数就会减少。总条数减少，意味着记录的天数也就减少。实际上负荷曲线记录的数据对象可能比举例中更多，智能电表内的负荷曲线存在多种类型，多种负荷曲线综合使用，若存储空间有较高的要求，会增加智能电表中装置的复 杂度，和智能电表的实际成本。
为解决上述问题，本实施例提出一种智能电表数据存储方法，所述智能电表数据存储方法包括以下步骤：
步骤S100：获取用电数据和所述用电数据对应的记录时间节点。
易于理解的是，智能电表每次进行记录时，每个记录包含多个记录对象的用电数据和当前时刻，所述记录时间节点为所述当前时刻，是多个记录对象当前用电数据对应的时刻信息。直接记录时间数据，会造成较多的存储空间消耗，因此需要把获取到的记录时间节点进行处理。
步骤S200：读取预设基准时间及电表记录周期。
应当理解的是，所述预设基准时间为智能电表中的预设值，智能电表中所有的周期性的、与时间相关的数据都以预设基准时间为参考。由于不同的智能电表会存在不同的设置，且智能电表使用过程中用户可以进行重新设置，进行记录存储前应对预设基准时间及电表记录周期进行读取。可以选用整年的0点为预设基准时间，根据不同的记录需求也可对所述预设基准时间进行调整。根据预设基准时间进行数据存储可以使得曲线数据的存储压力降低，所述预设基准时间可以依照智能电表的实际使用情况进行适当调整，使得计算后的时间索引值不会出现溢出的情况，同时又可以简化计算过程。
易于理解的是，所述电表记录周期，在本实施例中以每小时记录两次，即，每半小时一次进行说明。所述电表记录周期，为电表每次记录用电数据的时间间隔。
步骤S300：根据所述预设基准时间及所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引。
需要说明的是，步骤S300具体包括：计算所述预设基准时间与所述记录时间节点的日期时间差值；将所述日期时间差值转换为时分数据；根据所述时分数据和所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引。
在具体实施中，例如：所述预设基准时间为每一年的0时，电表记录周期为半小时，以2020年1月1日00：00为例，当前的记录时间节点为2020年2月5日，18：30，则所述日期时间差值为35日18小时30分(31天+4天+18小时+30分)。将所述35日18小时30分转换为时分数据，即，858.5 小时。
应当理解的是，所述日期时间差值可以通过万年历算法进行获取。
进一步地，所述根据所述时分数据和所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引的步骤，包括：根据所述时分数据以及所述电表记录周期，通过第一计算公式对所述记录时间节点进行字节压缩，以获得时间索引；其中，所述第一计算公式为：
N＝(T/t)*H
其中，N为时间索引，H为时分数据，t为电表记录周期，T为预设基准周期。
在具体实施中，所述时分数据为858.5小时，电表记录周期为半小时，预设基准周期在本实施例中为一小时，因此所述时间索引N为1717。
需要说明的是，参考图3，图3为本发明智能电表数据存储方法一实施例的曲线数据存储示意图。将所述时间数据转换为时间索引存储时，所述时间索引占用4个字节，缩减了所需的存储空间。
应当理解的是，在电表记录周期固定时，每天的存储条数即是固定，保持了计算过程的一致性，可提升存储的响应速度。
步骤S400：将所述用电数据和所述时间索引进行关联后保存。
易于理解的是，通过上述计算后获得的时间索引减少了所需的存储空间，且每个时间索引对应每个时间节点，不存在重复，因此可以通过将所述时间索引与所述用电数据关联，以替代原时间数据保存。
本实施例通过将时间数据转换为时间索引，所述时间索引的计算方式简单，具有唯一性，极大地缩减曲线数据的存储空间的占用量，降低了对智能电表中存储空间的需求，降低了智能电表的成本。
基于本发明智能电表数据存储方法第一实施例，提出本发明智能电表数据存储方法第二实施例，参照图4，图4为本发明一种智能电表数据存储方法第二实施例的流程示意图。
所述步骤S400具体包括步骤S401：建立所述用电数据和所述时间索引之间的映射关系，并对所述映射关系进行保存。
易于理解的是，通过建立映射将所述用电数据和所述时间索引进行保存， 在具体实施中，若所述智能电表具有物联网功能，可以将用电数据和时间索引的映射发送至远程管理平台进行保存或分析。时间索引和时间节点也呈现映射关系，在基准时间和记录周期不变的情况下，可以直接根据映射进行转换，以节省字节压缩所需的时间，提升智能电表的响应效率。
步骤S401之后，还包括：
步骤S501：接收用户输入的数据查询指令，读取所述数据查询指令中包含的查询时间节点。
易于理解的是，所述数据查询指令可以包含多个查询时间节点，即，用户可以通过所述数据查询指令对一个时刻的用电数据、多个时刻的用电数据或者一个时间段的用电数据进行查询。
步骤S502：根据所述预设基准时间及所述电表记录周期对所述查询时间节点进行字节压缩，以获得查询时间索引。
易于理解的是，基于本发明第一实施例，例如：用户下达了要查询2020年2月5日0时至2020年2月6日0时的用电数据的数据查询指令。时间索引的计算方法在第一实施例中已经进行说明，此处不再一一赘述。通过计算，可以得知上述两个时间节点分别对应的时间索引是1680和1728，因此用户要查询的用电数据对应的时间索引区间为1680至1728。
步骤S503：在所述映射关系中查找所述查询时间索引对应的目标用电数据，并将所述目标用电数据发送至所述用户。
在具体实施中，例如：通过上述步骤，获取到了时间索引的区间为1680至1728，在保存的记录中获取时间索引1680至1728对应的用电数据，所述用电数据即为查询用电数据，将所述查询用电数据输出给用户，以使用户获取到。
本实施例通过上述方法，从日期时间计算出时间索引，利用时间索引的唯一性，获取到了用电数据。由于时间索引数值类型检查，查找算法得到很大的优化，不用将每个数据比较，只要先计算出时间范围对应的时间索引即可快速查找。
基于本发明智能电表数据存储方法第一实施例，提出本发明智能电表数据存储方法第三实施例，参照图5，图5为本发明一种智能电表数据存储方法 第三实施例的流程示意图。
步骤S401之后，所述方法还包括：
步骤S601：接收用户输入的时间查询指令，读取所述时间查询指令中包含查询用电数据。
易于理解的是，所述数据查询指令可以包含多个查询用电数据，即，用户可以通过所述时间查询指令对一个用电数据的对应的时刻、多个用电数据对应的时刻或者一个区间的用电数据对应的时刻区间进行查询。
步骤S602：在所述映射关系中查找所述查询用电数据对应的目标时间索引。
易于理解的是，基于本发明第一实施例，例如：用户下达了要查询第一用电数据至第二用电数据之间的用电数据区间对应的时刻区间，可以获取第一用电数据和第二用电数据对应的时间索引区间，根据所述时间索引区间推出所述时刻区间。例如：第一用电数据对应的时间索引为1680，第二用电数据对应的时间索引为1728。因此用户要查询的时刻区间对应的时间索引区间为1680至1728。
步骤S603：根据所述预设基准时间及所述电表记录周期对所述目标时间索引进行字节解压缩，以获得目标时间节点，并将所述目标时间节点发送至所述用户。
易于理解的是，基于第一实施例中的计算方法，此处不再一一赘述，可以通过解压缩而获取到所述时间索引区间1680至1728对应的时刻区间为2020年2月5日0时至2020年2月6日0时。
本发明实施例方法，将时间索引解压缩至时间数据，本方法能够保证既能从时间索引计算出日期时间，又能从日期时间计算出时间索引，为电表数据的读取提供了便利，节省了存储空间又提高了检索效率。
参照图6，图6为本发明智能电表数据存储装置第一实施例的结构框图。
如图6所示，本发明实施例一种智能电表数据存储装置，所述装置包括：
获取模块10，用于获取用电数据和所述用电数据对应的记录时间节点。
易于理解的是，智能电表每次进行记录时，每个记录包含多个记录对象的用电数据和当前时刻，所述记录时间节点为所述当前时刻，是多个记录对 象当前用电数据对应的时刻信息。直接记录时间数据，会造成较多的存储空间消耗，因此需要把获取到的记录时间节点进行处理。
读取模块20，用于读取预设基准时间及电表记录周期。
应当理解的是，所述预设基准时间为智能电表中的预设值，智能电表中所有的周期性的、与时间相关的数据都以预设基准时间为参考。由于不同的智能电表会存在不同的设置，且智能电表使用过程中用户可以进行重新设置，进行记录存储前应对预设基准时间及电表记录周期进行读取。可以选用整年的0点为预设基准时间，根据不同的记录需求也可对所述预设基准时间进行调整。根据预设基准时间进行数据存储可以使得曲线数据的存储压力降低，所述预设基准时间可以依照智能电表的实际使用情况进行适当调整，使得计算后的时间索引值不会出现溢出的情况，同时又可以简化计算过程。
易于理解的是，所述电表记录周期，在本实施例中以每小时记录两次，即，每半小时一次进行说明。所述电表记录周期，为电表每次记录用电数据的时间间隔。
压缩模块30，用于根据所述预设基准时间及所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引。
需要说明的是，所述压缩模块30包括：计算子模块，用于计算所述预设基准时间与所述记录时间节点的日期时间差值；转换子模块，用于将所述日期时间差值转换为时分数据；压缩子模块，用于根据所述时分数据和所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引。
在具体实施中，例如：所述预设基准时间为每一年的0时，电表记录周期为半小时，以2020年1月1日00：00为例，当前的记录时间节点为2020年2月5日，18：30，则所述日期时间差值为35日18小时30分(31天+4天+18小时+30分)。将所述35日18小时30分转换为时分数据，即，858.5小时。
应当理解的是，所述日期时间差值可以通过万年历算法进行获取。
进一步地，所述压缩子模块包括：压缩单元，用于根据所述时分数据和所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引的步骤，包括：根据所述时分数据以及所述电表记录周期，通过第一计算公式对所述记录时间节点进行字节压缩，以获得时间索引；其中，所述第一计算公 式为：
N＝(T/t)*H
其中，N为时间索引，H为时分数据，t为电表记录周期，T为预设基准周期。
在具体实施中，所述时分数据为858.5小时，电表记录周期为半小时，预设基准周期在本实施例中为一小时，因此所述时间索引N为1717。
需要说明的是，参考图3，图3为本发明智能电表数据存储方法一实施例的曲线数据存储示意图。将所述时间数据转换为时间索引存储时，所述时间索引占用4个字节，缩减了所需的存储空间。
应当理解的是，在电表记录周期固定时，每天的存储条数即是固定，保持了计算过程的一致性，可提升存储的响应速度。
存储模块40，用于将所述用电数据和所述时间索引进行关联后保存。
易于理解的是，通过上述计算后获得的时间索引减少了所需的存储空间，且每个时间索引对应每个时间节点，不存在重复，因此可以通过将所述时间索引与所述用电数据关联，以替代原时间数据保存。
所述存储模块40具体用于建立所述用电数据和所述时间索引之间的映射关系，并对所述映射关系进行保存。
易于理解的是，通过建立映射将所述用电数据和所述时间索引进行保存，在具体实施中，若所述智能电表具有物联网功能，可以将用电数据和时间索引的映射发送至远程管理平台进行保存或分析。时间索引和时间节点也呈现映射关系，在基准时间和记录周期不变的情况下，可以直接根据映射进行转换，以节省字节压缩所需的时间，提升智能电表的响应效率。
应当理解的是，本装置通过将时间数据转换为时间索引，所述时间索引的计算方式简单，具有唯一性，极大地缩减曲线数据的存储空间的占用量，降低了对智能电表中存储空间的需求，降低了智能电表的成本。
需要说明的是，所述装置还包括：接收模块，用于接收用户输入的数据查询指令，读取所述数据查询指令中包含的查询时间节点。
易于理解的是，所述数据查询指令可以包含多个查询时间节点，即，用户可以通过所述数据查询指令对一个时刻的用电数据、多个时刻的用电数据或者一个时间段的用电数据进行查询。
压缩模块30，还用于根据所述预设基准时间及所述电表记录周期对所述查询时间节点进行字节压缩，以获得查询时间索引。
易于理解的是，基于本发明第一实施例，例如：用户下达了要查询2020年2月5日0时至2020年2月6日0时的用电数据的数据查询指令。时间索引的计算方法在第一实施例中已经进行说明，此处不再一一赘述。通过计算，可以得知上述两个时间节点分别对应的时间索引是1680和1728，因此用户要查询的用电数据对应的时间索引区间为1680至1728。
输出模块，用于在所述映射关系中查找所述查询时间索引对应的目标用电数据，并将所述目标用电数据发送至所述用户。
在具体实施中，例如：通过上述步骤，获取到了时间索引的区间为1680至1728，在保存的记录中获取时间索引1680至1728对应的用电数据，所述用电数据即为查询用电数据，将所述查询用电数据输出给用户，以使用户获取到。
应当理解的是，本装置从日期时间计算出时间索引，利用时间索引的唯一性，获取到了用电数据。由于时间索引数值类型检查，查找算法得到很大的优化，不用将每个数据比较，只要先计算出时间范围对应的时间索引即可快速查找。
需要说明的是，所述接收模块，用于接收用户输入的时间查询指令，读取所述时间查询指令中包含查询用电数据。
易于理解的是，所述数据查询指令可以包含多个查询用电数据，即，用户可以通过所述时间查询指令对一个用电数据的对应的时刻、多个用电数据对应的时刻或者一个区间的用电数据对应的时刻区间进行查询。
所述获取模块10，还用于在所述映射关系中查找所述查询用电数据对应的目标时间索引。
易于理解的是，基于本发明第一实施例，例如：用户下达了要查询第一用电数据至第二用电数据之间的用电数据区间对应的时刻区间，可以获取第一用电数据和第二用电数据对应的时间索引区间，根据所述时间索引区间推出所述时刻区间。例如：第一用电数据对应的时间索引为1680，第二用电数据对应的时间索引为1728。因此用户要查询的时刻区间对应的时间索引区间为1680至1728。
解压缩模块，用于根据所述预设基准时间及所述电表记录周期对所述目标时间索引进行字节解压缩，以获得目标时间节点，并将所述目标时间节点发送至所述用户。
易于理解的是，基于上述的计算方法，此处不再一一赘述，可以通过解压缩而获取到所述时间索引区间1680至1728对应的时刻区间为2020年2月5日0时至2020年2月6日0时。
本发明实施例装置通过将时间索引解压缩至时间数据，本方法能够保证既能从时间索引计算出日期时间，又能从日期时间计算出时间索引，为电表数据的读取提供了便利，节省了存储空间又提高了检索效率。
应当理解的是，以上仅为举例说明，对本发明的技术方案并不构成任何限定，在具体应用中，本领域的技术人员可以根据需要进行设置，本发明对此不做限制。
需要说明的是，以上所描述的工作流程仅仅是示意性的，并不对本发明的保护范围构成限定，在实际应用中，本领域的技术人员可以根据实际的需要选择其中的部分或者全部来实现本实施例方案的目的，此处不做限制。
另外，未在本实施例中详尽描述的技术细节，可参见本发明任意实施例所提供的智能电表数据存储方法，此处不再赘述。
此外，需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者系统不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者系统所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者系统中还存在另外的相同要素。
上述本发明实施例序号仅仅为了描述，不代表实施例的优劣。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如只读存储器(Read Only Memory，ROM)/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端 智能电表(可以是手机，计算机，服务器，或者网络智能电表等)执行本发明各个实施例所述的方法。
以上仅为本发明的优选实施例，并非因此限制本发明的专利范围，凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本发明的专利保护范围内。

Claims (10)

  1. 一种智能电表数据存储方法，其特征在于，所述方法包括：
    获取用电数据和所述用电数据对应的记录时间节点；
    读取预设基准时间及电表记录周期；
    根据所述预设基准时间及所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引；
    将所述用电数据和所述时间索引进行关联后保存。
  2. 如权利要求1所述的智能电表数据存储方法，其特征在于，所述根据所述预设基准时间及所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引的步骤，具体包括：
    计算所述预设基准时间与所述记录时间节点的日期时间差值；
    将所述日期时间差值转换为时分数据；
    根据所述时分数据和所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引。
  3. 如权利要求2所述的智能电表数据存储方法，其特征在于，所述根据所述时分数据和所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引的步骤，包括：
    根据所述时分数据以及所述电表记录周期，通过第一计算公式对所述记录时间节点进行字节压缩，以获得时间索引；
    其中，所述第一计算公式为：
    N＝(T/t)*H
    其中，N为时间索引，H为时分数据，t为电表记录周期，T为预设基准周期。
  4. 如权利要求1所述的智能电表数据存储方法，其特征在于，所述将所述用电数据和所述时间索引进行关联后保存的步骤，具体包括：
    建立所述用电数据和所述时间索引之间的映射关系，并对所述映射关系 进行保存。
  5. 如权利要求4所述的智能电表数据存储方法，其特征在于，所述建立所述用电数据和所述时间索引之间的映射关系，并对所述映射关系进行保存的步骤之后，还包括：
    接收用户输入的数据查询指令，读取所述数据查询指令中包含的查询时间节点；
    根据所述预设基准时间及所述电表记录周期对所述查询时间节点进行字节压缩，以获得查询时间索引；
    在所述映射关系中查找所述查询时间索引对应的目标用电数据，并将所述目标用电数据发送至所述用户。
  6. 一种智能电表数据存储装置，其特征在于，所述装置包括：
    获取模块，用于获取用电数据和所述用电数据对应的记录时间节点；
    读取模块，用于读取预设基准时间及电表记录周期；
    压缩模块，用于根据所述预设基准时间及所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引；
    存储模块，用于将所述用电数据和所述时间索引进行关联后保存。
  7. 如权利要求6所述的智能电表数据存储装置，其特征在于，所述压缩模块包括：
    计算子模块，用于计算所述预设基准时间与所述记录时间节点的日期时间差值；
    转换子模块，用于将所述日期时间差值转换为时分数据；
    压缩子模块，用于根据所述时分数据和所述电表记录周期对所述记录时间节点进行字节压缩，以获得时间索引。
  8. 如权利要求7所述的智能电表数据存储装置，其特征在于，所述压缩子模块包括：
    压缩单元，用于根据所述时分数据以及所述电表记录周期，通过第一计 算公式对所述记录时间节点进行字节压缩，以获得时间索引；
    其中，所述第一计算公式为：
    N＝(T/t)*H
    其中，N为时间索引，H为时分数据，t为电表记录周期，T为预设基准周期。
  9. 如权利要求6所述的智能电表数据存储装置，其特征在于，所述存储模块，还用于建立所述用电数据和所述时间索引之间的映射关系，并对所述映射关系进行保存。
  10. 如权利要求9所述的智能电表数据存储装置，其特征在于，所述装置还包括：
    接收模块，用于接收用户输入的数据查询指令，读取所述数据查询指令中包含的查询时间节点；
    压缩模块，还用于根据所述预设基准时间及所述电表记录周期对所述查询时间节点进行字节压缩，以获得查询时间索引；
    输出模块，用于在所述映射关系中查找所述查询时间索引对应的目标用电数据，并将所述目标用电数据发送至所述用户。
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