CN109857774B - Deformation monitoring data statistics method and device based on multi-sensor fusion - Google Patents
Deformation monitoring data statistics method and device based on multi-sensor fusion Download PDFInfo
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Abstract
The invention relates to the technical field of monitoring data analysis, and discloses a multi-sensor fusion deformation monitoring data statistics method, which comprises the following steps: s1: retrieving the statistical message at regular time; s2: acquiring data from a site database which is integrated by a plurality of sensors according to the statistical message; s3: confirming whether the acquired data are data in a target time range, if so, counting the data in the target time range, wherein the target time range is a preset time range taking the trigger time of the counted message as an end point; s4: and storing the counted data result. The invention also discloses a deformation monitoring data statistics device based on multi-sensor fusion. The method can solve the problem of large thread quantity when the multi-sensor fusion deformation monitoring data are counted.
Description
Technical Field
The invention relates to the technical field of monitoring data analysis, in particular to a multi-sensor fusion deformation monitoring data statistics method and device.
Background
Currently, sensors applied to deformation monitoring industry are more and more diversified, and feature values (average value/accumulation value/maximum value/minimum value) and deformation values (speed/acceleration) of monitoring data are calculated, wherein the data statistics of the average value/accumulation value and the speed/acceleration are mainly. Through unified, efficient and stable data monitoring (characteristic values and deformation values), reliable basis can be provided for prediction or early warning of a monitoring target in a certain period of time.
Some statistics mechanisms exist in the market, each sensor is used as an independent thread for statistics, when data of a plurality of sensors needs to be counted, resource waste caused by large thread volume is caused, and the more threads are, the higher the corresponding CPU occupancy rate is, so that the CPU operation is not facilitated, and therefore, a better multi-sensor fusion deformation monitoring data statistics method is needed.
Disclosure of Invention
In order to overcome the defects of the prior art, one of the purposes of the invention is to provide a multi-sensor fusion deformation monitoring data statistics method, which can solve the problem of large thread quantity when the multi-sensor fusion deformation monitoring data are counted.
The second object of the present invention is to provide a multi-sensor fusion deformation monitoring data statistics device, which can solve the problem of large thread volume when the multi-sensor fusion deformation monitoring data is counted.
One of the purposes of the invention is realized by adopting the following technical scheme:
A deformation monitoring data statistical method based on multi-sensor fusion comprises the following steps:
s1: retrieving the statistical message at regular time;
S2: acquiring data from a site database which is integrated by a plurality of sensors according to the statistical message;
S3: confirming whether the acquired data are data in a target time range, if so, counting the data in the target time range, wherein the target time range is a preset time range taking the trigger time of the counted message as an end point;
s4: and storing the counted data result.
Further, S5: if there are several statistical messages, after completing S2-S4 according to one statistical message, creating a new message, informing to execute S2-S4 according to another statistical message.
Further, the minimum unit of the preset time range is 1 hour
Further, the site database is a Redis database.
Further, the statistics are statistics of characteristic values and variation values of the data in the target time range.
Further, the S2 further includes S2a: and storing the acquired data in groups according to a certain time sequence as a data source.
Further, the step S4 further includes step S4a: the stored data results are grouped in time order of S2a and the data sources corresponding to the times are covered.
Further, S1a: and carrying out rechecking statistics on the data in the target time range.
Further, the S1a further includes S1b: and (4) covering the corresponding data result in the S4 with the statistical result after rechecking statistics and storing the data result.
The second purpose of the invention is realized by adopting the following technical scheme:
A deformation monitoring data statistics device based on multi-sensor fusion, comprising:
and a retrieval module: for retrieving statistics messages at regular intervals;
the acquisition module is used for: the method comprises the steps of acquiring data from a site database which is assembled by a plurality of sensors according to statistical information;
And a statistics module: the method comprises the steps of determining whether acquired data are data in a target time range, if so, counting the data in the target time range, wherein the target time range is a preset time range taking the trigger time of a counted message as an end point;
And a storage module: for storing the counted data results.
Compared with the prior art, the invention has the beneficial effects that: the multi-sensor fusion deformation monitoring data statistics method and device take stations as a set of a plurality of sensors, take each station as a thread, and perform statistics operation concurrently, so that the thread quantity is reduced, and the high-efficiency stable statistics of the data of the plurality of sensors is ensured; searching data in a preset time range, counting the data in the preset time range, setting preset time according to requirements by a user, reducing performance loss caused by frequent deletion of meaningless data, improving practicality and expanding the application range of the invention.
Drawings
FIG. 1 is a flow chart diagram of a multi-sensor fusion deformation monitoring data statistics method according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a multi-sensor fusion deformation monitoring data statistics device according to a second embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and detailed description, wherein, on the premise of no conflict, the following embodiments or technical features can be arbitrarily combined to form new embodiments:
Counting, wherein the counting mode and the counting item are adopted; the statistical mode is as follows: hour statistics, day statistics, month statistics, quarter statistics and year statistics; statistical terms, including eigenvalues (mean/sum), variance values (velocity, acceleration); statistical sequence: the hour statistics > day statistics > month statistics > quarter statistics > year statistics.
Different statistical modes and corresponding statistical time lengths are different. For example, an hour statistic, its statistic time is 1 hour, so it takes data within one hour to perform a statistic calculation; for example, daily statistics, its statistical time is 1 day, so it takes data in one day to make statistical calculations. And so on for subsequent statistics: carrying out annual statistics, taking characteristic values of quarter statistics, and carrying out statistical calculation to obtain characteristic values of annual statistics; (using the characteristic value of the current statistics-the characteristic value of the last year statistics)/12 months to obtain the speed; (using the current statistical speed-the last year statistical speed)/12 months to obtain acceleration; quarter statistics, calculating characteristic values of month statistics, and performing logic same as the characteristic values; month statistics, calculating the characteristic value of the sun statistics, and the logic is the same as that of the month statistics; daily statistics, namely taking characteristic values of hour statistics to calculate, wherein the logic is the same as that of the characteristic values; and counting the hours, and taking the original statistical data to calculate the characteristic value, wherein the logic is the same as that of the characteristic value.
Example 1
The embodiment of the invention discloses a multi-sensor fusion deformation monitoring data statistics-based method, which can be executed by hardware or/and software, and is shown in fig. 1, and comprises the following steps:
S1: the statistical information is searched regularly (the statistical information can be one or a plurality of statistical information is arranged in the information queue, the statistical information can be searched from the information queue, and the timing time can be set according to the user requirement);
S2: acquiring data from a site database which takes a plurality of sensors as a set according to a statistical message (one site sets a plurality of sensors, the data of the plurality of sensors are all concentrated in the site database, the site database in the embodiment is a redis database, one site corresponds to one thread, and a plurality of threads are counted simultaneously);
S2a: the acquired data are stored in groups according to a certain time sequence (statistical time) and are used as data sources (specifically, the acquired data are classified and sorted according to the statistical time length corresponding to the site and the current statistical mode, and finally, the sites are grouped into sets, and each set is also grouped according to the statistical time (and is ordered from the early to the late) and is used as the data sources);
S3: confirming whether the acquired data is data in a target time range, if so, counting the data in the target time range (in the embodiment, performing statistics calculation on the data in the target time range by using site traversal asynchronization), wherein the target time range is a preset time range taking the trigger time of the statistical message as an end point (the minimum calculation unit of the preset time range is 1 hour, the trigger time of the statistical message is the time when the statistical message is received by S2, and the target time range takes the trigger time of the statistical message as the end point, namely, the target time range can be one hour or a plurality of hours before the time when the statistical message is received;
S4: storing the counted data results (the data results are arranged in a time axis from hours, days, months, quarters to years);
S4a: grouping the stored data results according to the time sequence (statistical time) of S2a, and covering a data source corresponding to the time, namely updating the existing data;
s5: if the number of the statistical messages is a plurality, after finishing S2-S4a according to a certain statistical message, creating a new message, and informing to execute S2-S4a according to another statistical message;
S1a: rechecking and counting the data in the target time range;
s1b: covering the corresponding data result in the S4 with the statistical result after the re-audit statistics and storing (after covering the original data result with the statistical result of the S1a re-audit, the data result will not exist any more, and the statistical result of the S1a audit is taken as the final statistical result);
In addition, S1a and S1b may process other data as required by the client by using these two steps, in addition to processing the data within the target time range.
In this embodiment, the data statistics calculation is performed only at the whole time, if the trigger time of the statistics message is 2018-10-25:11:00 and the preset time is 1 hour, the target time range is 2018-10-25:10:00-2018-10-25:59:59, the data in the target time range is performed with the hour statistics calculation by S3, the result of the hour data stored by S4 is 2018-10-25:10:00, and the other non-whole time is not performed with the hour statistics, and so on.
Example two
A second embodiment discloses a multi-sensor fusion deformation monitoring data statistics device corresponding to the above embodiment, please refer to fig. 2, which includes:
and a retrieval module: for retrieving statistics messages at regular intervals;
the acquisition module is used for: the method comprises the steps of acquiring data from a site database which is assembled by a plurality of sensors according to statistical information;
And a statistics module: the method comprises the steps of determining whether acquired data are data in a target time range, if so, counting the data in the target time range, wherein the target time range is a preset time range taking the trigger time of a counted message as an end point;
And a storage module: for storing the counted data results.
And a notification module: if the number of the statistical messages is a plurality, after the acquisition module, the statistical module and the storage module finish the work according to a certain statistical message, a new message is created, and the acquisition module is informed to execute the work according to another statistical message;
an auditing module: and the data processing module is used for carrying out rechecking statistics on the data in the target time range, and covering the corresponding data result counted by the original statistical module stored in the storage module with the statistical result after rechecking statistics. (the auditing module is provided with a manually triggered inlet, and can be manually triggered when the statistical data result is checked to be correct, and in addition, the auditing module can audit other data according to the requirement of a user, namely, the auditing module can be independently a logic relative to other modules);
And a data complement module: the method is used for firstly storing the acquired data in groups according to a certain time sequence (statistical time) as a data source (specifically, the acquired data is classified and sorted according to the statistical time length corresponding to the site and the current statistical mode, and finally, the acquired data are grouped into sets by sites, and each set is grouped by the statistical time (and sequenced from the early to the late) and is used as the data source); and grouping the stored data results according to the time sequence (statistical time) of the S2a, and covering a data source corresponding to the time, namely updating the existing data.
The multi-sensor fusion deformation monitoring data statistics method and device take stations as a set of a plurality of sensors, take each station as a thread, and perform statistics operation concurrently, so that the thread quantity is reduced, and the high-efficiency stable statistics of the data of the plurality of sensors is ensured; searching data in a preset time range, counting the data in the preset time range, setting preset time according to requirements by a user, reducing performance loss caused by frequent deletion of meaningless data, improving practicality and expanding the application range of the invention.
The multi-sensor fusion deformation monitoring data statistics device is mainly based on a retrieval module, an acquisition module, a statistics module, a storage module and a notification module, wherein an auditing module is auxiliary, the auditing module is another independent process in the device, the problem of time conflict is avoided, the auditing module can count the data in the acquisition module again, and if the statistics module is omitted or has errors in the statistics of the data originally, the checking module can check the missing leakage detection.
The second embodiment of the invention discloses a multi-sensor fusion deformation monitoring data statistics device, which can be applied to software or/and hardware such as electronic equipment or a computer readable storage medium, and the like, and is not described herein.
It should be noted that, in the embodiment of the multi-sensor fusion deformation monitoring data statistics device, each unit and module included in the multi-sensor fusion deformation monitoring data statistics device are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
It will be apparent to those skilled in the art from this disclosure that various other changes and modifications can be made which are within the scope of the invention as defined in the appended claims.
Claims (9)
1. A deformation monitoring data statistical method based on multi-sensor fusion is characterized in that: the method comprises the following steps:
s1: retrieving the statistical message at regular time;
s2: acquiring data from a site database which is integrated by a plurality of sensors according to the statistical message; wherein, a site gathers a plurality of sensors, and the data of the plurality of sensors are concentrated in a site database; one site corresponds to one thread, and a plurality of threads are counted simultaneously;
The S2 further includes S2a: the acquired data are stored in groups according to a certain time sequence and are used as a data source; the method comprises the following steps: classifying and sorting the acquired data according to the statistical time length corresponding to the site and the current statistical mode, and finally grouping the data into sets by site, grouping the data into each set by statistical time, and sequencing the data from the early to the late as a data source;
S3: confirming whether the acquired data are data in a target time range, if so, counting the data in the target time range, wherein the target time range is a preset time range taking the trigger time of the counted message as an end point;
s4: and storing the counted data result.
2. The multi-sensor fusion deformation monitoring data statistics method as claimed in claim 1, wherein: further comprising S5: if there are several statistical messages, after completing S2-S4 according to one statistical message, creating a new message, informing to execute S2-S4 according to another statistical message.
3. The multi-sensor fusion deformation monitoring data statistics method as claimed in claim 1, wherein: the minimum unit of the preset time range is 1 hour.
4. The multi-sensor fusion deformation monitoring data statistics method as claimed in claim 1, wherein: the site database is a Redis database.
5. The multi-sensor fusion deformation monitoring data statistics method as claimed in claim 1, wherein: and the statistics are statistics of characteristic values and variation values of the data in the target time range.
6. The multi-sensor fusion deformation monitoring data statistics method as claimed in claim 1, wherein: the S4 further includes S4a: the stored data results are grouped in time order of S2a and the data sources corresponding to the times are covered.
7. The multi-sensor fusion deformation monitoring data statistics method as claimed in claim 1, wherein: also included is S1a: and carrying out rechecking statistics on the data in the target time range.
8. The multi-sensor fusion deformation monitoring data statistics method as claimed in claim 7, wherein: the S1a further includes S1b: and (4) covering the corresponding data result in the S4 with the statistical result after rechecking statistics and storing the data result.
9. The device based on the multi-sensor fusion deformation monitoring data statistical method as claimed in claim 1, wherein: comprising the following steps:
and a retrieval module: for retrieving statistics messages at regular intervals;
the acquisition module is used for: the method comprises the steps of acquiring data from a site database which is assembled by a plurality of sensors according to statistical information;
And a statistics module: the method comprises the steps of determining whether acquired data are data in a target time range, if so, counting the data in the target time range, wherein the target time range is a preset time range taking the trigger time of a counted message as an end point;
And a storage module: for storing the counted data results.
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