CN110689953A - Data storage method and device, data searching method and device, and electronic equipment - Google Patents
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Abstract
The invention relates to a data storage method and device, a data searching method and device, electronic equipment and a storage medium. The data storage method comprises the following steps: for each equipment attribute, sampling the attribute data of the equipment according to a preset sampling strategy to obtain sampling data; determining a time identifier corresponding to the sampling time of the sampling data; compressing the sampling data to obtain compressed data; and storing the compressed data and the time identification corresponding to the sampling moment of the compressed data in a server. The invention compresses and stores the sampled data, converts the sampling time of the sampled data into the time identifier and stores the time identifier, thereby reducing the occupation of the storage space. Therefore, when data query is carried out, the target data can be rapidly and accurately acquired.
Description
Technical Field
The invention relates to the technical field of big data management, in particular to a data storage method and device, a data searching method and device, electronic equipment and a storage medium.
Background
The large data technology for the working condition of the large medical equipment is an application technology which evaluates the reliability of equipment in service by periodically or continuously collecting and monitoring various attributes capable of reflecting the condition of the equipment and makes a scientific and reasonable maintenance strategy.
In the related art, a time stamp storage method is generally adopted as a common method for large data management of large medical equipment. The time stamp storage method is to collect the attribute values of each device at the device end through the network at regular intervals, and store the collected sampled values together with the corresponding time points (time fields) into the query database. The mode is full information storage, and the storage space occupies a large amount. When data is read, a large amount of time field data needs to be read, and the data reading speed is low.
Disclosure of Invention
In view of the above, the invention provides a data storage method and apparatus, a data search method and apparatus, an electronic device, and a storage medium.
Specifically, the invention is realized by the following technical scheme:
according to a first aspect of the present invention, there is provided a data storage method, comprising:
for each equipment attribute, sampling the attribute data of the equipment according to a preset sampling strategy to obtain sampling data;
determining a time identifier corresponding to the sampling time of the sampling data;
compressing the sampling data to obtain compressed data;
and storing the compressed data and the time identification corresponding to the sampling moment of the compressed data in a server.
According to a second aspect of the present invention, there is provided a data query method for a server; the server stores compressed data and time identification which are stored by using the data storage method of the first aspect;
the data query method comprises the following steps:
receiving a query request; the query request comprises a starting point time and an end point time;
acquiring a sampling strategy corresponding to the compressed data;
respectively determining time identifications corresponding to the starting point moment and the end point moment according to the sampling strategy;
and inquiring target data from the compressed data according to the time identification.
According to a third aspect of the present invention, there is provided a data storage apparatus comprising:
the sampling unit is used for sampling attribute data of the equipment according to a preset sampling strategy aiming at each equipment attribute to obtain sampling data;
the first calculation unit is used for determining a time identifier corresponding to the sampling moment of the sampling data according to the sampling strategy;
the data compression unit is used for compressing the sampling data to obtain compressed data;
and the storage unit is used for storing the compressed data and the time identification corresponding to the sampling moment of the compressed data into a server.
According to a fourth aspect of the present invention, there is provided a data query apparatus, wherein the data query apparatus stores the compressed data and the time identification stored by the data storage apparatus of the third aspect;
the data inquiry device comprises:
a receiving unit, configured to receive a query request; the query request comprises a starting point time and an end point time;
the acquisition unit is used for acquiring and storing a sampling strategy corresponding to the compressed data;
the second calculation unit is used for respectively determining time identifications corresponding to the starting point moment and the end point moment according to the sampling strategy;
and the query unit is used for querying the target data from the compressed data according to the time identification.
According to a fifth aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the data storage method of the first aspect when executing the computer program.
According to a sixth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the data storage method of the first aspect.
According to a seventh aspect of the present invention, there is provided an electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the data query method of the second aspect when executing the computer program.
According to an eighth aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the data query method of the second aspect.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
and the sampled data is compressed and then stored, so that the occupation of the data storage space is greatly saved. And the sampling time of the sampling data is converted into the time identifier, so that the storage space occupied by the time field is further reduced. When data is inquired, a large amount of time field data does not need to be read, and target data can be quickly and accurately acquired by using a sampling strategy and a time mark.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic diagram illustrating a medical device system according to an exemplary embodiment.
FIG. 2A is a flow chart illustrating a method of data storage according to an exemplary embodiment.
FIG. 2B is a flow chart illustrating another method of data storage according to an example embodiment.
FIG. 2C is a flow chart illustrating another method of data storage according to an exemplary embodiment.
FIG. 2D is a flow chart illustrating another method of data storage according to an example embodiment.
FIG. 2E is a flow chart illustrating another method of data storage according to an example embodiment.
FIG. 2F is a block schematic diagram illustrating a data storage device in accordance with an exemplary embodiment.
FIG. 2G is a block diagram illustrating another data storage device according to an example embodiment.
FIG. 2H is a block diagram illustrating another data storage device according to an example embodiment.
FIG. 3A is a flow diagram illustrating a method of data query in accordance with an exemplary embodiment.
FIG. 3B is a block diagram illustrating a data query device in accordance with an exemplary embodiment.
FIG. 4 is a schematic diagram illustrating the structure of an electrical device in accordance with an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present invention. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
The operational reliability of large medical equipment is directly related to the accuracy of disease diagnosis and treatment. Currently, for the operational reliability of the equipment, the judgment is performed according to the attribute data of the equipment. Therefore, efficient management of attribute data of large medical devices is of paramount importance, wherein efficient management of data includes: accurate and flexible data collection, efficient data storage and fast data reading.
Referring to fig. 1, a schematic structural diagram of a medical device system according to an example of the present invention is shown, where the system includes a device side and a server side. The device side includes a control unit and a device body (e.g., a CT device). The control unit is arranged on the same site of the equipment body and can control the equipment body to complete work tasks. The control unit may be a computer equipped with an operating system, or may be a control device of other hardware architectures such as ARM, or a control module integrated in the device body. The device side and the server side are generally located at different geographical locations, and data interaction between the two sides is generally carried out through a network.
The medical equipment body can generate attribute data when being started, and when data management is carried out, on the premise that no component is additionally arranged, the control unit can be adopted to realize the corresponding function of the data storage device, and the server is adopted to realize the corresponding function of the data inquiry device, namely, the control unit samples the data generated by the medical equipment body according to the sampling strategy sent by the server and sends the sampled data to the server database for centralized storage, and the server inquires target data according to the inquiry request. Wherein, for each device, the attribute data includes a plurality of operating parameters (attributes), which may include but are not limited to: network connection information, accelerator temperature, filament temperature, lumen humidity, fan speed, etc. It will be appreciated that the operating parameters that need to be monitored may vary from device to device. It should be noted that, for one apparatus body, a plurality of control units may be provided to sample different sampling attributes, or only one control unit may be used to sample different sampling attributes.
Due to the fact that the structure and the working principle of the large-scale medical equipment have certain complexity and the composition elements are diversified, the attribute data capable of reflecting the equipment condition at each moment can be divided according to different dimensions, so that the attribute data are uniformly divided according to the sampling attributes, namely the server independently stores the sampling data uploaded by the equipment terminal according to each sampling attribute.
The following describes a specific implementation process of the data storage method of the present invention in detail with respect to one device attribute of a large medical device.
Referring to fig. 2A, a flow chart of an embodiment of a data storage method according to the present invention is applied to a control unit, and includes the following steps:
When large medical equipment is used for carrying out medical actions on patients, much working data are quite different from ordinary times and even reach a peak value. And the occurrence time of the behavior is random, and the proportion on the total working time is random. Therefore, if accurate and comprehensive data collection is required, different sampling strategies need to be formulated for different equipment attributes. Multiple sampling strategies can also be set for one device attribute, for example, one sampling strategy is set for temperature sampling in an operating state, and another sampling strategy is set for temperature sampling in a non-operating state. The flexible data sampling mechanism can eliminate useless data (part of data in a non-working state) on one hand and reduce the occupation of storage space; on the other hand, the data which can truly reflect the running state of the equipment can be more effectively collected.
In this embodiment, the sampling policy includes: initial sampling time, sampling end time and sampling interval (time length between two adjacent samplings); or, the sampling strategy comprises: sampling trigger events, sampling intervals and ending sampling events; or, the sampling strategy comprises: sampling trigger events, sampling intervals, and sampling times.
That is, the initial sampling time may be set as a trigger condition for starting sampling, that is, when the preset sampling time is reached, the control unit starts sampling; it is also possible to use a sampling trigger event (characteristic event generated by the device) as a trigger condition for the start of sampling, for example, use the time when the CT device starts scanning (characteristic event) as an initial sampling time, and then the control unit starts sampling when the CT device starts scanning. The characteristic event here can be a mark generated by the device body and used for sampling start, and is not limited to a specific action of the device body, such as scanning start and scanning end; the device can also be an abnormal signal generated by the device body, such as receiving a bulb tube ignition fault code, giving a high-voltage alarm to a certain part and the like. Of course, the trigger condition for the start of sampling may be a combination of the above types.
Similar to the initial sampling time, a specific sampling end time can be set as a sampling end condition, namely a preset stop time is reached, and the control unit stops sampling; the end of sampling event may also be used as a condition for ending sampling, for example, when the CT apparatus ends scanning, the control unit stops sampling when the CT scanning ends. In addition, the sampling end condition may also be an upper limit of the sampling number, that is, when the sampling number of the control unit reaches a preset upper limit of the sampling number, the sampling is stopped. Of course, the setting of the sampling end time may be a combination of the above types.
And step 220, determining a time identifier corresponding to the sampling moment of the sampling data according to the sampling strategy.
The calculation formula of the time identifier is as follows:
wherein, Sampling time represents Sampling time of Sampling data; base Time represents the initial sampling Time; interval Time represents the sampling Interval; n denotes the time stamp, i.e. the nth sample.
If the sampling time of a piece of sampling data is 7:20:00, the sampling strategy for obtaining the piece of data comprises the following steps: the initial sampling time is 7:00:00, the sampling interval is 10 minutes, and the values are substituted into the formula (1) to obtain:
the time mark representing the sampling time of the sample data is 3, that is, the sample data is the data obtained by sampling the 3 rd time.
Thus, all time stamps corresponding to the sampling instants of the sampled data can be calculated according to equation (1). It should be noted that, in the case that there is no missing sampling, the time markers corresponding to all the sampled data under one sampling strategy are continuous numerical values.
And step 230, compressing the sampled data to obtain compressed data.
Under the non-working state, the numerical fluctuation range of each equipment attribute of the large-scale medical equipment is small, and the large fluctuation can be realized only when the large-scale medical equipment works or a certain specific action occurs. Such as the exposure seconds of the CT device (which changes only during scanning), the number of bulb strikes (which changes only when a scan is fired). Therefore, there is a large compression space for these data.
On the basis of fig. 2A, referring to fig. 2B, a possible implementation of step 230 is shown:
and step 230-1, judging whether the sampling data corresponding to two adjacent sampling moments are the same.
In step 230-1, if yes, go to step 230-2; if not, the sampled data is not compressed, and the process returns to step 210 to continue data sampling.
Step 230-2, determine whether the difference between the two sampling times is equal to the sampling interval.
In the step 230-2, if the judgment result is yes, the data obtained by the two previous sampling and the data obtained by the two previous sampling are the same, and no sampling missing situation exists, and the data can be compressed, the step 230-3 is executed; if not, the situation of missing sampling is described, and in order to ensure the accuracy of data recording when the sampling point is missing, the piece of sampling data is not compressed, that is, the piece of sampling data is not deleted, the step 210 is returned, and data sampling is continued.
And step 230-3, deleting the sampling data corresponding to the larger sampling time in the two sampling times.
And (5) repeatedly executing the steps 230-1-230-3 until the sampling end moment is reached, so that all the sampled data can be compressed, and the storage space is saved.
On the basis of fig. 2A, referring to fig. 2C, another possible implementation of step 230 is shown:
and step 230-1', judging whether the sampling data corresponding to two adjacent sampling moments are the same.
In the step 230-1 ', if yes, executing the step 230-2'; if not, the sampled data is not compressed, and the process returns to step 210 to continue data sampling.
Step 230-2', determine whether the time stamps of the two sampling instants are consecutive values.
In the step 230-2 ', if yes, it is stated that the sampled data obtained by the two previous and subsequent samplings are the same and no missing sampling exists, and the data can be compressed, then the step 230-3' is executed; if not, the situation of missing sampling is described, and in order to ensure the accuracy of data recording when the sampling point is missing, the piece of sampling data is not compressed, that is, the piece of sampling data is not deleted, the step 210 is returned, and data sampling is continued.
Step 230-3', deleting the sampling data corresponding to the time mark with the larger value in the two time marks.
And (5) repeating the steps 230-1 'to 230-3' until the sampling end time is reached, so that all the sampled data can be compressed, and the storage space is saved.
Table 1 shows a presentation of the results of compressing the sampled data, wherein the device attribute is temperature, and the method steps shown in fig. 2C are further explained in conjunction with table 1.
TABLE 1
| Rec ID | Start TimeID | End TimeID | Value |
| 1 | 1 | 2 | 36.5 |
| 2 | 3 | 4 | 37.65 |
| 3 | 6 | 47 | 37.65 |
| 4 | 48 | 70 | 37.85 |
In table 1, the field Rec ID represents the pipelining record ID of the data in the table; the field Value represents a specific Value of the device attribute; the Start TimeID and End TimeID represent the Start time identification and End time identification, respectively, of a data record.
Assuming that the temperature of a certain node of the equipment is sampled, the initial sampling Time in the sampling strategy is 2018-6-17:00:00, the sampling Interval is 10 minutes, at this Time, the Base Time is 2018-6-17:00:00, and the Interval Time is 10.
The Value of the sampled data obtained for the first time is 36.5, the Sampling time of the sampled data is 7:00:00, that is, Sampling time is 7:00:00, and N is 1, that is, Start TimeID is 1, obtained according to the calculation formula of the time identifier, then the Start TimeID is 1, EndTimeID is 1, and Value is 36.5, recorded in the line where Rec ID is 1 in the table. It should be noted that, when the sample data is obtained for the first time, the End TimeID value defaults to 1, and the End TimeID value is updated according to the sample data obtained later, and a specific updating manner is described below.
The numerical value of the sampled data obtained for the second time is 36.5, the sampling time of the sampled data is 7:10:00, and N is obtained to be 2 according to a time identification calculation formula; because the value obtained for the second time is the same as the value obtained for the first time, the data sampled for the second time is not recorded independently, and only the End TimeID with the Rec ID of 1 is updated to 2;
the value of the sampling data obtained for the third time is 37.65, the sampling time of the sampling data is 7:20:00, and N is 3 according to a time identification calculation formula; because the Value of the third sample is different from the Value of the second sample, the data of the third sample is recorded separately, and Start time ID is 3, End time ID is 3, and Value is 37.65 are recorded in the row with Rec ID of 2;
the value of the sampling data obtained for the fourth time is 37.65, the sampling time of the sampling data is 7:30:00, and N is obtained to be 4 according to a time identification calculation formula; since the value obtained for the fourth time is the same as the value obtained for the third time, the data sampled for the fourth time is not separately recorded, and only the End TimeID with the Rec ID of 2 is updated to 4;
the value of the sampling data obtained in the fifth time is 37.65, the sampling time of the sampling data is 7:50:00, and N is 6 according to a time identifier calculation formula; it should be particularly noted that although the Value obtained fifth time is the same as the Value obtained fourth time, the N Value is not continuous, that is, the difference between the sampling time of the sampling data obtained fifth time and the sampling time of the sampling data obtained fourth time is greater than the sampling interval, which indicates that there is a sampling missing situation, at this time, the sampling data obtained fifth time needs to be separately recorded, and a Start time ID of 6, an End time ID of 6, and a Value of 37.65 are recorded in the row with a Rec ID of 3;
the value of the sampling data obtained at the sixth time is 37.65, the sampling time of the sampling data is 8:00:00, and N is 7 according to a time identification calculation formula; since the value obtained at the sixth time is the same as the value obtained at the fifth time, the data obtained at the sixth time is not separately recorded, and only the End TimeID in which the Rec ID is 3 is updated to 7; by analogy, the End TimeID is updated every time sampling data is obtained until the value of the obtained sampling data is not 37.65 or N obtained by calculation according to the sampling time of the sampling data obtained twice in the adjacent process is discontinuous.
In table 1, the record with Rec ID of 1 indicates that the values of the sample data of the 1 st sample and the 2 nd sample are both 36.5; the record with Rec ID of 2 indicates that the values of the sampled data of the 3 rd sample and the 4 th sample are 37.65; the record with Rec ID of 3 indicates that the values of the sampled data of the 6 th sample and the 47 th sample are 37.65; the record with Rec ID 4 indicates that the values of the sampled data of the 48 th sample and the 70 th sample are both 37.85; wherein the data obtained by the 5 th sampling is not obtained.
And 240, sending the compressed data and the time identifier corresponding to the compressed data to a server for storage.
The results of the data stored in step 240 to the server are shown in table 1.
It should be noted that, in this embodiment, the server separately stores the compressed sample data uploaded by the device side according to each device attribute, that is, sets one data table for each device attribute, so as to store the sample data of the device attribute.
In the embodiment, the sampled data is compressed and then stored, so that the occupation of the data storage space is greatly saved. And the sampling time of the sampling data is converted into the time identifier, so that the storage space occupied by the time field is further reduced. By using the sampling strategy and the time identification, the target data can be quickly and accurately acquired during data searching.
Referring to fig. 2D, a flowchart of another embodiment of the data storage method of the present invention is shown, the flow steps of this embodiment are substantially the same as those of fig. 2A, as shown in fig. 2D, except that in this embodiment, before step 240, the method further includes:
If the determination result is no, which indicates that the network connection state between the control unit and the server is not good at this time and data transmission cannot be realized, step 232 is executed.
If the answer is yes, it indicates that the network connection status between the control unit and the server is better at this time, step 240 is executed.
In this embodiment, the control unit provides a cache table for caching when the compressed data cannot be uploaded to the server in time. The cache table includes information elements of the server to be uploaded, and the representation form is shown in table 1, and in addition, the cache table may further include a Property identifier (Property) for characterizing the Property of the device. And when the control unit establishes communication connection with the server, transmitting the data in the cache table to the server.
In this embodiment, a cache mechanism is provided, which ensures validity of data storage, and data is not lost even in the case of a poor network.
Referring to fig. 2E, a flowchart of another embodiment of the data storage method of the present invention is shown, the flow steps of this embodiment are substantially the same as those of fig. 2D, as shown in fig. 2E, except that in this embodiment, before step 210, the method further includes:
Wherein the sampling command comprises a sampling strategy. The sampling policy in the sampling command sent by the server to the device side is sent to the control unit of the device side in the form of a configuration table, where the configuration table includes device attributes to be collected, and the sampling policy for each device attribute may include, for example: the device comprises a device attribute identifier, an initial sampling time, a sampling interval and a sampling end time. Other sampling strategies may also be adopted according to the sampling requirement, which is not described herein in detail.
After receiving the configuration table, the device side control unit firstly stores the configuration table as basic information, and then generates a time table of the next sampling task of each device attribute, wherein the time table is divided by time points to account for the initial sampling time of each device attribute so as to realize the on-time execution of the data sampling task. When the sampling is finished by a certain device attribute and the server retransmits a new configuration table, the control unit needs to update the time table.
In this embodiment, the server sends the sampling task to the device side, but the control unit of the device side performs data sampling instead of directly performing data sampling by the server, and since the execution unit and the device body are on the same site, the punctuality of data sampling is ensured not to be affected by external factors such as a network, and the occurrence of data sampling leakage caused by poor network states of the server and the device side is greatly reduced.
Corresponding to the foregoing data storage method embodiments, the present invention also provides embodiments of a data storage device.
Referring to fig. 2F, a schematic diagram of an embodiment of the data storage apparatus according to the present invention, the data storage apparatus is used in a device side, the data storage apparatus can be implemented by, but is not limited to, the control unit in fig. 1, and the device side further includes a device body; the device body runs to generate attribute data, and the attribute data comprises a plurality of device attributes. As shown in fig. 2F, the data storage device includes: a sampling unit 21, a first calculation unit 22, a data compression unit 23, and a storage unit 24.
The sampling unit 21 is configured to sample attribute data of the device according to a preset sampling policy for each device attribute, so as to obtain sampled data.
In one implementation manner of this embodiment, the sampling policy includes: an initial sampling instant, a sampling interval, and a sampling end instant. The sampling unit 21 is specifically configured to: and triggering the sampling to start according to the initial sampling time, sampling the attribute data according to the sampling interval, and triggering the sampling to end according to the sampling end time.
In another implementation manner of this embodiment, the sampling policy includes: sampling trigger events, sampling intervals and ending sampling events; the sampling unit 21 is specifically configured to: and triggering the sampling to start according to the sampling trigger event, sampling the attribute data according to the sampling interval, and triggering the sampling to end according to the sampling end event.
In another implementation manner of this embodiment, the sampling policy includes: sampling trigger events, sampling intervals, and sampling times. The sampling unit 21 is specifically configured to: and triggering the sampling to start according to the sampling trigger event, sampling the attribute data according to the sampling interval, and triggering the sampling to end according to the sampling times.
The first calculation unit 22 is configured to determine a time stamp corresponding to a sampling instant of the sampled data according to the sampling strategy.
In one embodiment, the first computing unit 22 is specifically configured to:
and calculating the time identification according to the sampling time, the sampling interval and the time for triggering the sampling to start.
The data compression unit 23 is configured to compress the sample data to obtain compressed data.
In one embodiment, the data compression unit 23 is specifically configured to:
when the sampling data corresponding to two adjacent sampling moments are judged to be the same, further judging whether the difference between the two sampling moments is equal to the sampling interval;
and when the difference between the two sampling moments is judged to be equal to the sampling interval, deleting the sampling data corresponding to the larger sampling moment in the two sampling moments.
In another embodiment, the data compression unit 23 is specifically configured to:
when the sampling data corresponding to two adjacent sampling moments are judged to be the same, further judging whether the time marks of the two sampling moments are continuous numerical values;
and when the time marks at the two sampling moments are judged to be continuous numerical values, deleting the sampling data corresponding to the time mark with the larger value in the two time marks.
The storage unit 24 is configured to store the compressed data and the time identifier corresponding to the sampling time of the compressed data in the data query device.
The sampling strategy and the time identification are used for acquiring target data during data searching.
Referring to fig. 2G, a schematic diagram of another embodiment of the data storage device of the present invention is shown, the data storage device of this embodiment is substantially the same as the data storage device shown in fig. 2F, except that the data storage device of this embodiment further includes: a communication unit 25 and a caching unit 26. The communication unit 25 is configured to invoke the storage unit 24 to store the compressed data and the time identifier in the data query device when establishing a communication connection with the data query device. The communication unit 25 is further configured to invoke the cache unit 26 to cache the compressed data and the time identifier locally when a communication connection with the data query device is not established.
Referring to fig. 2H, a schematic diagram of another embodiment of the data storage device of the present invention is shown, the data storage device of this embodiment is substantially the same as the data storage device shown in fig. 2G, except that the data storage device of this embodiment further includes: a first receiving unit 27. The first receiving unit 27 is used for receiving the sampling command sent by the data query device. Wherein the sampling command comprises: and (4) sampling strategy.
Referring to fig. 3A, a flowchart of an embodiment of a data query method according to the present invention is shown, where the method is applied to a data query device, and includes the following steps:
Wherein the query request includes a start time and an end time.
Formula (1) is also used for calculating the time markers of the starting point time and the ending point time.
TABLE 2
| Base Time | Interval Time | Rec ID |
| 2018-6-1 7:00:00 | 10min | 1 |
The sampling strategies in the data query device are stored in a table form, and table 2 shows that a sampling strategy is configured for a certain equipment attribute, the initial sampling time of the sampling strategy is 2018-6-17:00:00, and the sampling interval is 10 minutes; if a user wants to query the data of 2018-6-18: 10: 00-2018-6-111: 50:00, the starting time is 8:10:00, the ending time is 11:50:00, and the two times are regarded as the sampling time of the sampling data.
Thus, substituting the starting point time into equation (1) yields:
substituting the endpoint time into equation (1) yields:
Nstarting pointTime mark 8 is the starting time 8:10:00, NTerminal pointThe time identifier of the end point time 11:50:00 is 30.
It should be noted that sometimes the starting point time and the ending point time requested by the user cannot directly fall on a specific sampling time point, that is, the result obtained by the calculation of the formula (1) is not an integer, and at this time, the calculation result needs to be rounded, the rounding rule is that the starting point time needs to be rounded up when calculating, and the ending point time needs to be rounded up when calculating, and the specific formula is as follows:
the following specific example is used to illustrate the rounding rule when calculating the time stamp, and for example, if the starting time is 8:10:00 and the ending time is 11:50:00, if the initial sampling time of the sampling strategy is 7:00:00, the sampling interval is 30 minutes:
the time stamp of the starting moment is calculated using equation (1),
is decimal, and needs to be rounded up and N'Starting point＝Ceil(3.3)＝4；
The time stamp of the end point moment is calculated using equation (1),is decimal, and needs to be rounded up and N'Terminal point＝Floor(10.7)＝10。
NStarting point' 4 is the time mark of the starting time 8:10:00, NTerminal pointThe time identifier of the end point time 11:50:00 is 10.
TABLE 3
| Base Time | Interval Time | Rec ID |
| 2018-6-1 7:00:00 | 10min | 1 |
| 2018-6-1 8:00:00 | 6min | 39 |
| 2018-6-2 6:05:00 | 2min | 67 |
Table 3 shows that, for a certain device attribute, multiple sampling strategies are configured, where the sampling end time of each sampling strategy is the initial sampling time of the next sampling strategy. For a plurality of sampling strategies, the time identification calculation of the starting point time and the end point time is similar to that of one sampling strategy, if the query time range falls into two or more sampling strategies, the query time range needs to be split, and the time identification is calculated for each sampling strategy. Wherein, whether the query time range falls into two or more sampling strategies may be determined by comparing the start time and the end time of the query time range with the initial sampling time of the sampling strategies.
Or for example, the query time range is 2018-6-17: 30: 00-2018-6-111: 50:00, the starting time 2018-6-17: 30:00 of the time range is later than the first sampling strategy, the ending time 2018-6-111:50:00 is later than the second sampling strategy, and is earlier than the third sampling strategy, the time range is described to fall into 2 sampling strategies, the time range needs to be split, the range 1 is 2018-6-17: 30: 00-2018-18: 00:00, the range 2 is 2018-6-18: 00-2018-111: 50:00, the time identifier is calculated for the first sampling strategy in the sampling table 3 in the range 1, the time identifier is calculated for the 2 sampling strategy in the sampling table 3 in the range 2, and the specific calculation process is similar to that described above, and will not be described in detail herein.
And step 303, inquiring target data from the compressed data according to the time identification.
Wherein the compressed data is obtained by using the data storage method shown in any of the above embodiments.
If in step 302, according to the sampling strategy that the initial sampling time is 7:00:00 and the sampling interval is 10 minutes, the time identifier corresponding to the query time range of 7:30: 00-8: 30:00 is calculated to be 4-10, and the query result is obtained from two records with RecID of 2 and 3 by combining table 1. In order to facilitate the user to view, the target data displayed to the user needs to convert the time identifier into the sampling time of the sampling data according to the sampling strategy, and the compressed data is restored, and the query result is shown in table 4.
TABLE 4
| TimeID | Sampling time | Value |
| 4 | 7:30:00 | 37.65 |
| 6 | 7:50:00 | 37.65 |
| 7 | 8:00:00 | 37.65 |
| 8 | 8:10:00 | 37.65 |
| 9 | 8:20:00 | 37.65 |
| 10 | 8:30:00 | 37.65 |
In the embodiment, the sampling time of the sampling data is converted into the time identifier and then stored in the data query device, so that on one hand, the storage space occupied by the time field is reduced, on the other hand, a large amount of time field data does not need to be read during data query, the energy consumption of data reading is reduced, and the data reading speed is improved.
Corresponding to the foregoing data query method embodiment, the present invention also provides an embodiment of a data query apparatus.
Referring to fig. 3B, which is a schematic diagram of an embodiment of a data query apparatus according to the present invention, the data query apparatus may be implemented by, but is not limited to, the server in fig. 1, and the data query apparatus stores compressed data and a time identifier, which are stored by using the data storage apparatus shown in the above embodiment, and the data query apparatus includes: a second receiving unit 31, a second calculating unit 32, an inquiring unit 33 and an obtaining unit 34.
The second receiving unit 31 is configured to receive a query request.
Wherein the query request includes a query time range; the query time range comprises a starting point time and an ending point time;
the obtaining unit 34 is configured to obtain a sampling policy corresponding to the stored compressed data.
The second calculation unit 32 is configured to determine time identifiers corresponding to the start time and the end time according to a sampling policy.
The second calculation unit 32 has a function for:
and respectively determining time identifications corresponding to the starting point time and the end point time according to the sampling interval and the initial sampling time.
The query unit 33 is configured to query the target data from the compressed data according to the time identification.
Referring to FIG. 4, a block diagram of an exemplary electronic device 40 suitable for use in implementing embodiments of the present invention is shown, as a schematic structural diagram of one embodiment of the electronic device of the present invention. The electronic device 40 shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 4, the electronic device 40 may be embodied in the form of a general purpose computing device, which may be, for example, a data query apparatus device. The components of electronic device 40 may include, but are not limited to: the at least one processor 41, the at least one memory 42, and a bus 43 connecting the various system components (including the memory 42 and the processor 41).
The bus 43 includes a data bus, an address bus, and a control bus.
The memory 42 may include volatile memory, such as Random Access Memory (RAM)421 and/or cache memory 422, and may further include Read Only Memory (ROM) 423.
Memory 42 may also include a program tool 425 (or utility tool) having a set (at least one) of program modules 424, such program modules 424 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The processor 41 executes various functional applications and data processing, such as the data query method provided in the above-described embodiment, by executing the computer program stored in the memory 42.
The electronic device 40 may also communicate with one or more external devices 44 (e.g., keyboard, pointing device, etc.). Such communication may be through an input/output (I/O) interface 45. Also, the model-generated electronic device 40 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via a network adapter 46. As shown, the network adapter 46 communicates with the other modules of the model-generated electronic device 40 over a bus 43. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the model-generating electronic device 40, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the data query method provided in the above embodiment.
More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
An embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where a specific structure of the electronic device in this embodiment is similar to that in fig. 4, and is not described herein again, but a difference is that the processor implements the data query method provided in the foregoing embodiment when executing the computer program.
The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the data query method provided by the above embodiment is implemented.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (16)
1. A data storage method, characterized in that the data storage method comprises:
for each equipment attribute, sampling the attribute data of the equipment according to a preset sampling strategy to obtain sampling data;
determining a time identifier corresponding to the sampling time of the sampling data;
compressing the sampling data to obtain compressed data;
and storing the compressed data and the time identification corresponding to the sampling moment of the compressed data in a server.
2. The data storage method of claim 1, wherein the sampling strategy comprises: initial sampling time and sampling interval;
sampling attribute data of equipment according to a preset sampling strategy, wherein the sampling strategy comprises the following steps:
triggering the start of sampling according to the initial sampling moment, and sampling the attribute data according to the sampling interval;
or, the sampling strategy comprises: sampling a trigger event, a sampling interval and an end sampling event;
sampling attribute data of equipment according to a preset sampling strategy, wherein the sampling strategy comprises the following steps:
triggering the sampling start according to the sampling trigger event, sampling the attribute data according to the sampling interval, and triggering the sampling end according to the sampling end event;
or, the sampling strategy comprises: sampling trigger events, sampling intervals and sampling times;
sampling attribute data of equipment according to a preset sampling strategy, wherein the sampling strategy comprises the following steps:
and triggering the sampling to start according to the sampling trigger event, sampling the attribute data according to the sampling interval, and triggering the sampling to end according to the sampling times.
3. The data storage method of claim 2, wherein determining a time stamp corresponding to a sampling instant of the sampled data according to the sampling policy comprises:
and determining the time identifier according to the sampling time, the sampling interval and the time for triggering the sampling to start.
4. The data storage method of claim 2, wherein compressing the sampled data comprises:
judging whether the sampling data corresponding to two adjacent sampling moments are the same;
under the condition that the sampling data corresponding to two adjacent sampling moments are the same, judging whether the time marks of the two sampling moments are continuous numerical values;
under the condition that the time marks of the two sampling moments are continuous numerical values, deleting the sampling data corresponding to the time mark with the larger value in the two time marks;
or, compressing the sample data, including:
judging whether the sampling data corresponding to two adjacent sampling moments are the same;
under the condition that the sampling data corresponding to two adjacent sampling moments are the same, judging whether the difference between the two sampling moments is equal to a sampling interval;
and deleting the sampling data corresponding to the larger sampling time in the two sampling times under the condition that the difference between the two sampling times is equal to the sampling interval.
5. The data storage method of claim 1, wherein prior to the step of storing the compressed data and the time stamp corresponding to the sampling instant of the compressed data in a server, comprising:
and caching the compressed data and the time identification.
6. A data query method is characterized in that the data query method is used for a server; the server stores compressed data and time identification stored by using the data storage method of any one of claims 1 to 5;
the data query method comprises the following steps:
receiving a query request; the query request comprises a starting point time and an end point time;
acquiring a sampling strategy corresponding to the compressed data;
respectively determining time identifications corresponding to the starting point moment and the end point moment according to the sampling strategy;
and inquiring target data from the compressed data according to the time identification.
7. A data storage device, characterized in that the data storage device comprises:
the sampling unit is used for sampling attribute data of the equipment according to a preset sampling strategy aiming at each equipment attribute to obtain sampling data;
the first calculation unit is used for determining a time identifier corresponding to the sampling moment of the sampling data according to the sampling strategy;
the data compression unit is used for compressing the sampling data to obtain compressed data;
and the storage unit is used for storing the compressed data and the time identification corresponding to the sampling moment of the compressed data into a server.
8. The data storage device of claim 7, wherein the sampling strategy comprises: an initial sampling time, a sampling interval and a sampling end time;
the sampling unit is specifically configured to:
triggering the sampling to start according to the initial sampling time, sampling the attribute data according to the sampling interval, and triggering the sampling to end according to the sampling end time;
or, the sampling strategy comprises: sampling trigger events, sampling intervals and ending sampling events;
the sampling unit is specifically configured to:
triggering the sampling start according to the sampling trigger event, sampling the attribute data according to the sampling interval, and triggering the sampling end according to the sampling end event;
or, the sampling strategy comprises: sampling trigger events, sampling intervals and sampling times;
the sampling unit is specifically configured to:
and triggering the sampling to start according to the sampling trigger event, sampling the attribute data according to the sampling interval, and triggering the sampling to end according to the sampling times.
9. The data storage device of claim 8, wherein the first computing unit is specifically configured to:
and determining the time identifier according to the sampling time, the sampling interval and the time for triggering the sampling to start.
10. The data storage device of claim 8, wherein the data compression unit is specifically to:
when the sampling data corresponding to two adjacent sampling moments are judged to be the same, further judging whether the time marks of the two sampling moments are continuous numerical values;
when the time marks of the two sampling moments are judged to be continuous numerical values, deleting the sampling data corresponding to the time mark with the larger value in the two time marks;
or, the data compression unit is specifically configured to:
judging whether the sampling data corresponding to two adjacent sampling moments are the same;
under the condition that the sampling data corresponding to two adjacent sampling moments are the same, judging whether the difference between the two sampling moments is equal to a sampling interval;
and deleting the sampling data corresponding to the larger sampling time in the two sampling times under the condition that the difference between the two sampling times is equal to the sampling interval.
11. The data storage device of claim 7, wherein the data storage device further comprises: a buffer unit;
the cache unit is used for caching the compressed data and the time identification.
12. A data query device, wherein the data query device stores compressed data and time identification stored by the data storage device according to any one of claims 7 to 11;
the data inquiry device comprises:
a receiving unit, configured to receive a query request; the query request comprises a starting point time and an end point time;
the acquisition unit is used for acquiring and storing a sampling strategy corresponding to the compressed data;
the second calculation unit is used for respectively determining time identifications corresponding to the starting point moment and the end point moment according to the sampling strategy;
and the query unit is used for querying the target data from the compressed data according to the time identification.
13. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the data storage method of any one of claims 1 to 5 when executing the computer program.
14. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the data storage method of any one of claims 1 to 5.
15. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the data query method of claim 6 when executing the computer program.
16. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the data query method of claim 6.
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