CN118018882B - Internet of things data acquisition and storage method - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The invention relates to the technical field of data processing of the Internet of things, in particular to a method for collecting and storing data of the Internet of things, which comprises the following steps: dividing a single day period into a plurality of acquisition periods in a preset dividing mode according to the temperature distribution of each acquisition node; determining the characterization degree of the acquisition data according to the duration of the acquisition period corresponding to the acquisition data of the acquisition node and the acquisition frequency; setting the demand level of the collected data according to the characterization level of the collected data and the calling times of the collected data of the collected node in a history period; comparing the demand degree of the acquired data with a preset interval to determine a storage mode of the acquired data; by dividing the acquisition time period and dynamically evaluating the demand degree of the acquired data, the differential acquisition and storage modes based on the data characteristics are set, so that the overall storage space and the data processing burden can be effectively reduced, and the storage pressure of the storage device is effectively reduced while the data characterization is ensured.
Description
Technical Field
The invention relates to the technical field of data processing of the Internet of things, in particular to a method for collecting and storing data of the Internet of things.
Background
The internet of things technology is increasingly applied to facility agriculture along with the rapid development of information technology. The existing data acquisition and storage method of the Internet of things is as follows.
Chinese patent application publication No.: CN116340413a discloses a method for collecting and storing data at the edge of the internet of things, which comprises the following steps: s1, constructing an Internet of things management platform and setting a plurality of equipment access modes; s2, based on functions and definitions of equipment, an object model of the equipment is built on the Internet of things management platform, an access mode matched with the equipment is selected, the equipment is dynamically modeled by adopting the object model, the equipment is accessed into the Internet of things management platform, and the equipment is subjected to full life cycle on-line management; s3, constructing a visual rule engine, acquiring equipment information, and performing data conversion on the equipment information based on the visual rule engine to acquire equipment data; s4, storing the converted equipment data into different application databases in a classified mode.
Therefore, the method can collect and store multiple types of data, but in the application of agricultural greenhouse temperature collection, the continuous working period of the sensor is longer, and the data volume collected and stored by using the method is overlarge, so that the storage pressure and the processing pressure for collecting the data are larger.
Disclosure of Invention
Therefore, the invention provides a method for acquiring and storing data of the Internet of things, which is used for solving the problems of large storage pressure and processing pressure on acquired data caused by overlarge data quantity acquired and stored in the prior art.
In order to achieve the above object, the present invention provides a method for collecting and storing data of internet of things, comprising:
collecting temperature data at each collecting node in the greenhouse;
Dividing a single day period into a plurality of acquisition periods in a preset dividing mode according to the temperature distribution of each acquisition node;
Determining the characterization degree of the acquisition data according to the duration of the acquisition period corresponding to the acquisition data of the acquisition node and the acquisition frequency;
Setting the demand level of the collected data according to the characterization level of the collected data and the calling times of the collected data of the collected node in a history period;
comparing the demand degree of the acquired data with a preset interval to determine a storage mode of the acquired data;
The preset dividing mode meets the requirement that the temperature distribution of each acquisition period in the history period is different, and the storage mode comprises temporary storage, cloud platform storage and/or hard disk storage;
and for the temporarily stored acquired data, deleting the temporarily stored acquired data when the acquired data are stored for a preset time period and are not called.
Further, the preset dividing mode specifically comprises,
Determining the average temperature of each acquisition node in the history period at each moment in a single day;
determining a plurality of temperature points which are uniformly distributed in the average temperature of each moment in a single day, wherein each temperature point comprises the maximum temperature and the minimum temperature in the average temperature of each moment in the single day;
Determining a period which is different from each temperature point by a preset temperature value and corresponds to the acquisition time to be an acquisition period;
The remaining periods that do not match each of the temperature points are also determined as acquisition periods, respectively.
Further, the characterization degree of the acquired data is inversely proportional to the duration and the acquisition frequency of the acquisition period corresponding to the acquisition node.
Further, the demand level of the collected data is proportional to the characterization level and the calling number.
Further, the cloud platform storage and the hard disk storage are carried out on the collected data with the demand degree larger than the maximum value of the preset interval.
Further, the cloud platform stores the acquired data with the demand degree within a preset interval.
Further, the hard disk is stored for the collected data with the demand degree smaller than the minimum value of the preset interval and the calling times of the collected data corresponding to the collected nodes in the history period not being 0.
Further, the temporary storage is carried out on the collected data, the demand degree of which is smaller than the minimum value of the preset interval and the calling times of the collected data of the corresponding collection node in the history period are 0.
Further, the method further comprises integrating a plurality of acquired data with the same temperature corresponding to continuous moments in a single acquisition period into one piece of acquired data.
Further, the preset duration is the same as the duration of the history period.
Compared with the prior art, the method has the beneficial effects that the single day period is divided into a plurality of acquisition periods according to the temperature distribution characteristics, and the characterization degree of the data is determined according to the data characteristics of each acquisition period, namely the time length and the acquisition frequency, so that the demand degree of the acquired data is dynamically evaluated, the overall storage space and the data processing burden can be effectively reduced through the differentiated acquisition and storage modes based on the data characteristics, and the storage pressure of the storage device is effectively reduced while the characterization of the data is ensured.
Furthermore, the invention divides the single-day acquisition of the acquisition node into a plurality of acquisition time periods, each acquisition time period corresponds to different temperature distribution states, the characterizations of the acquired data in the different temperature distribution states are different, effective support can be provided for the subsequent judgment of the storage mode through the division of the acquisition time periods, and the storage pressure of the storage equipment is further reduced while the characterizations of the data are ensured.
Furthermore, the characterization degree can effectively reflect the capability of the collected data to characterize the temperature in the greenhouse, if the collection period corresponding to the collected data is longer, the collected data are large in quantity and are all at similar temperature, wherein the capability of single data to characterize the temperature in the greenhouse is lower, the collection frequency is the same, the determination of the characterization degree can provide effective support for the determination of the subsequent storage mode, and the storage pressure of the storage device is further reduced while the characterization of the data is ensured.
Further, the method and the device can effectively reflect the degree of the acquired data required during data analysis, the more the calling times of the acquisition nodes are, the higher the degree of the acquired data required is, the higher the characterizability is, the closer the result obtained during data analysis is to the actual result, the effective support can be provided for the judgment of the subsequent storage mode by determining the degree of the acquired data, and the storage pressure of the storage device is further reduced while the characterizability of the data is ensured.
Furthermore, the method stores the acquired data with different demands in different storage modes, and ensures the safety and integrity of key data by adopting a cloud platform and hard disk dual storage mode for the data with high demands. And for data with medium demand, adopting a cloud platform for storage, and meeting the daily monitoring analysis demand. And for data with low demand and historical call, adopting hard disk storage to release the storage space of the cloud platform. For data with low demand but not called historic, temporary storage is adopted, and the data is deleted when the data exceeds a preset storage period and is not called, so that storage resources are further optimized, and the storage pressure of the storage device is further reduced while the data characterization is ensured.
Furthermore, the invention integrates a plurality of pieces of acquisition data with the same temperature corresponding to continuous moments in a single acquisition period into one piece of acquisition data, avoids invalid storage of repeated acquisition data, and further reduces the storage pressure of the storage device while guaranteeing the data characterization.
Drawings
FIG. 1 is a flow chart of a method for collecting and storing data of the Internet of things according to the present invention;
FIG. 2 is a flow chart of a preset division mode according to an embodiment of the invention
FIG. 3 is a flow chart of a storage mode determination according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating data integration according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, but do not indicate or imply that the apparatus or element must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, a flowchart of a method for collecting and storing internet of things data according to the present invention is shown, and the method for collecting and storing internet of things data includes:
step S1, collecting temperature data at each collecting node in a greenhouse;
step S2, dividing a single day period into a plurality of acquisition periods in a preset dividing mode according to the temperature distribution of each acquisition node;
Step S3, determining the characterization degree of the acquired data according to the duration of the acquired data of the acquired node corresponding to the acquired period and the acquired frequency;
step S4, setting the demand level of the collected data according to the characterization level of the collected data and the calling times of the collected data of the collected node in a history period;
S5, comparing the demand degree of the acquired data with a preset interval to determine a storage mode of the acquired data;
the method comprises the steps that a preset dividing mode meets the condition that temperature distribution of each acquisition period in a history period is different, wherein the storage mode comprises temporary storage, cloud platform storage and/or hard disk storage;
For temporarily stored acquisition data, the acquisition data is deleted when the acquisition data is stored for a preset time period and is not called.
In the above embodiment, the single day period is divided into the plurality of acquisition periods according to the temperature distribution characteristics, and the characterization degree of the data is determined according to the data characteristics, namely the time length and the acquisition frequency, of each acquisition period, so that the demand degree of the acquired data is dynamically evaluated, and the overall storage space and the data processing burden can be effectively reduced through the differentiated acquisition and storage mode based on the data characteristics, so that the storage pressure of the storage device is effectively reduced while the data characterization is ensured.
Referring to fig. 2, the preset dividing modes specifically include,
Step S21, determining the average temperature of each acquisition node in a history period at each moment in a single day;
step S22, determining a plurality of temperature points which are uniformly distributed in the average temperature at each time in a single day, wherein each temperature point comprises the maximum temperature and the minimum temperature in the average temperature at each time in the single day;
Step S23, determining a period which is different from each temperature point by a preset temperature value and corresponds to the acquisition time to be continuous as an acquisition period;
in step S24, the remaining periods that do not match the respective temperature points are also determined as acquisition periods, respectively.
Of course, it can be understood that the preset temperature value is related to the number of temperature points, and the preset temperature value should meet that the preset temperature value is not intersected after the temperature points rise or fall, and can be correspondingly set according to the number and the numerical value of the temperature points, which is not described herein.
In the above embodiment, the single-day acquisition of the acquisition node is divided into a plurality of acquisition time periods, each acquisition time period corresponds to different temperature distribution states, the characterizations of the acquired data in the different temperature distribution states are different, effective support can be provided for the subsequent judgment of the storage mode through the division of the acquisition time periods, and the storage pressure of the storage device is further reduced while the characterizations of the data are ensured.
Specifically, the characterization degree of the acquired data is inversely proportional to the duration t and the acquisition frequency f of the acquisition period corresponding to the acquisition node.
Alternatively, the degree of characterizationWherein T is the working time of the acquisition node corresponding to the acquired data in a single day, and F is the average acquisition frequency of the acquisition node corresponding to the acquired data in a single day.
In the above embodiment, the characterization degree can effectively reflect the capability of the collected data to characterize the temperature in the greenhouse, if the collection period corresponding to the collected data is longer, the collected data are large in quantity and are all at similar temperature, wherein the capability of single data to characterize the temperature in the greenhouse is lower, the collection frequency is the same, the determination of the characterization degree can provide effective support for the determination of the subsequent storage mode, and the storage pressure of the storage device is further reduced while the characterization of the data is ensured.
Specifically, the desirability a of collecting data is proportional to the characterizations k and the number of calls n.
Optionally, the degree of demandWherein N is the total calling times of the collected data of each collecting node in the history period.
In the above embodiment, the degree of demand can effectively reflect the degree of demand of the collected data when data analysis is performed, the more the call times of the collection node are, the higher the degree of demand of the corresponding collected data is, the closer the result obtained when the data analysis is performed is to the actual result, the higher the characterizability is, the effective support can be provided for the subsequent judgment of the storage mode by determining the degree of demand, and the storage pressure of the storage device is further reduced while the characterizability of the data is ensured.
Referring to fig. 3, the cloud platform storage and the hard disk storage are performed on the collected data with the demand degree greater than the maximum value of the preset interval.
Specifically, cloud platform storage is performed on collected data with the demand degree in a preset interval.
Specifically, the collected data with the demand degree smaller than the minimum value of the preset interval and the calling times of the collected data corresponding to the collected node in the history period not being 0 is stored in a hard disk.
Specifically, the collected data with the demand degree smaller than the minimum value of the preset interval and the calling number of times of 0 in the history period is temporarily stored.
In the embodiment, the collected data with different demand degrees are stored in different storage modes, and for the data with high demand degrees, a cloud platform and hard disk dual storage mode is adopted to ensure the safety and integrity of key data. And for data with medium demand, adopting a cloud platform for storage, and meeting the daily monitoring analysis demand. And for data with low demand and historical call, adopting hard disk storage to release the storage space of the cloud platform. For data with low demand but no history call, temporary storage is adopted, and the data is deleted when the data exceeds a preset storage period and is not called, so that storage resources are further optimized, and the storage pressure of the storage device is further reduced while the data characterization is ensured.
Specifically, the setting of the preset interval is determined according to the memory of the hard disk, the larger the memory of the hard disk is, the larger the range of the preset interval is, the endpoint value of the preset interval can be set to other values according to the actual working condition and the scene, or can be set to a plurality of changeable determined values, and the determination requirement can be met, which is not repeated here.
Referring to fig. 4, the method for collecting and storing data of the internet of things further includes integrating a plurality of pieces of collected data with the same temperature corresponding to continuous moments in a single collection period into one piece of collected data, wherein the collected data of 12:10-12:50 are all 20.0 ℃, and only one piece of collected data is used for representing the data during storage.
In the embodiment, a plurality of pieces of acquisition data with the same temperature corresponding to continuous moments in a single acquisition period are integrated into one piece of acquisition data, so that invalid storage of repeated acquisition data is avoided, and the storage pressure of the storage device is further reduced while the data characterization is ensured.
Specifically, the preset duration is the same as the duration of the history period.
It will be appreciated that the historical period is the minimum time that can exhibit the statistical characteristics of the temperature data, alternatively, the historical period is 1 month.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing is merely a preferred embodiment of the present invention and is not intended to limit the present invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The method for acquiring and storing the data of the Internet of things comprises the steps of acquiring temperature data at each acquisition node in a greenhouse, and is characterized in that:
Dividing a single day period into a plurality of acquisition periods in a preset dividing mode according to the temperature distribution of each acquisition node;
Determining the characterization degree of the acquisition data according to the duration of the acquisition period corresponding to the acquisition data of the acquisition node and the acquisition frequency;
Setting the demand level of the collected data according to the characterization level of the collected data and the calling times of the collected data of the collected node in a history period;
comparing the demand degree of the acquired data with a preset interval to determine a storage mode of the acquired data;
The preset dividing mode meets the requirement that the temperature distribution of each acquisition period in the history period is different, and the storage mode comprises temporary storage, cloud platform storage and/or hard disk storage;
for the temporarily stored collected data, deleting the temporarily stored collected data when the collected data is stored for a preset time period and is not called;
the preset dividing mode specifically comprises the following steps of,
Determining the average temperature of each acquisition node in the history period at each moment in a single day;
determining a plurality of temperature points which are uniformly distributed in the average temperature of each moment in a single day, wherein each temperature point comprises the maximum temperature and the minimum temperature in the average temperature of each moment in the single day;
Determining a period which is different from each temperature point by a preset temperature value and corresponds to the acquisition time to be an acquisition period;
respectively determining the rest time periods which are not matched with the temperature points as acquisition time periods;
Characterization degree Wherein T is the working time of the acquisition node corresponding to the acquired data in a single day, F is the average acquisition frequency of the acquisition node corresponding to the acquired data in a single day, T is the time of the acquisition node corresponding to the acquisition period, and F is the acquisition frequency of the acquisition node corresponding to the acquisition period;
demand level Wherein, N is the total calling times of the collected data of each collecting node in the history period, and N represents the calling times.
2. The method for collecting and storing data of the internet of things according to claim 1, wherein the characterization degree of the collected data is inversely proportional to the duration and the collection frequency of the collection period corresponding to the collection node.
3. The method for collecting and storing data of the internet of things according to claim 2, wherein the demand level of the collected data is proportional to the characterization level and the call number.
4. The method for collecting and storing data of the internet of things according to claim 3, wherein the cloud platform storage and the hard disk storage are performed simultaneously on collected data with a demand level greater than a maximum value of a preset interval.
5. The method for collecting and storing data of the internet of things according to claim 4, wherein the cloud platform stores the collected data with the demand level within a preset interval.
6. The method for collecting and storing data of internet of things according to claim 5, wherein the hard disk is used for storing the collected data which has a demand level smaller than a minimum value of a preset interval and has a calling number of the collected data of the corresponding collection node in a history period not being 0.
7. The method for collecting and storing data of the internet of things according to claim 6, wherein the temporary storage is performed on the collected data with the demand level smaller than the minimum value of the preset interval and the call number of the collected data of the corresponding collection node in the history period being 0.
8. The method for collecting and storing data of internet of things according to claim 7, further comprising integrating a plurality of pieces of collected data with the same temperature corresponding to successive moments in a single collection period into one piece of collected data.
9. The method for collecting and storing data of the internet of things according to claim 8, wherein the preset duration is the same as the duration of the history period.
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| CN117118808B (en) * | 2023-10-19 | 2024-02-13 | 深圳市先行电气技术有限公司 | Multi-source ammeter data acquisition and analysis method, system and storage medium based on Internet of things |
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| CN116166691A (en) * | 2023-04-21 | 2023-05-26 | 中国科学院合肥物质科学研究院 | Data archiving system, method, device and equipment based on data division |
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