CN109828991B - Query ordering method, device, equipment and storage medium under multi-space-time condition - Google Patents
Query ordering method, device, equipment and storage medium under multi-space-time condition Download PDFInfo
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Abstract
The invention discloses a query ordering method under multi-space-time conditions, which is widely applied to query of various target characteristic values, such as terminal information, intelligent IC card information, ID (identity) card information and the like, integrates space similarity and space diversity, and is more accurate in query matching results.
Description
Technical Field
The invention relates to the field of time sequence data query sequencing, in particular to a query sequencing method, a query sequencing device, query sequencing equipment and a storage medium under a multi-space-time condition.
Background
At present, many time series data calculation models are available, including TSDB time series databases, etc., many monitoring systems employ TSDB as database system to store various index data which is massive, strictly time-increasing, and to a certain extent, the structure is very simple, the stored data structure is simple, a certain metric index has only one value at a certain time-space point, and there is no complex structure (nesting, hierarchy, etc.) and relationship (association, main foreign key, etc.), the existing target query based on time series databases is to query and sort the data of a single time-space, such as to search the time space where a specified Mac address appears, or sort the indexes, and there is no query related to uncertain target characteristic values in multi-time-space state, and the query process does not take the spatial similarity and spatial diversity into comprehensive consideration, so a comprehensive evaluation method needs to be proposed, sequencing the query results is realized, so that the target characteristic value data information of the uncertain target is obtained.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. Therefore, the invention aims to provide a query sorting method, a query sorting device, query sorting equipment and a storage medium under the multi-space-time condition by comprehensively considering spatial similarity and spatial diversity.
The technical scheme adopted by the invention is as follows:
in a first aspect, the present invention provides a query ordering method under multiple spatiotemporal conditions, comprising the steps of:
collecting at least one piece of space-time point information corresponding to the query target characteristic value;
performing corresponding space-time segment data expansion on each piece of space-time point information to obtain data to be inquired;
classifying data to be inquired according to different target characteristic values, and calculating a plurality of index quantity values corresponding to each target characteristic value in different inquiry dates under different preset evaluation indexes;
obtaining query values according to a comprehensive sorting rule, and sorting the query values;
the preset evaluation indexes comprise: a first evaluation index, a second evaluation index, and a third evaluation index;
the comprehensive sorting rule is that the same type of index values corresponding to different target characteristic values on different query dates are combined, and the combined index values of each target characteristic value are subjected to dimensionless operation and are averaged to obtain a query value corresponding to each target characteristic value;
and the combination and the addition are carried out on the same type of index magnitude values of different query dates.
Further, the first evaluation index is a self-information quantity index, and specifically includes:
wherein x represents a target characteristic value, p (x) represents the probability of the target characteristic value, cnt represents the number of times of the target characteristic value appearing in one day, N represents a reference number, ibAnd a self-information quantity index quantity value representing the target characteristic value.
Further, the second evaluation index is a jaccard similarity index, and specifically includes:
wherein N ismatchRepresenting the number of times of matching of the target characteristic value x in one day, NtotalRepresenting the total number of occurrences of the target characteristic value x, NqueryRepresenting all spatio-temporal points in the query's time period, sim (x) representing the similarity index magnitude of the target feature value x.
Further, the third evaluation index is a spatial diversity index, and specifically includes:
wherein S isiRepresenting the number of times that the target characteristic value x appears at the site i, S representing the total number of times that the target characteristic value x appears at all sites, and j (x) representing the spatial diversity index magnitude of the target characteristic value x.
Further, the merging specifically includes:
the non-dimensionalization is carried out by using a Z-score method, and specifically comprises the following steps:
where d represents the total number of query days, hi(x)mI-th index magnitude value, X, representing target feature value X of day miRepresenting the ith index magnitude, Mean, after the target characteristic values x are combinediMean, MSE, representing the magnitude of the i-th indexiMean square error, Z, of the magnitude of the i-th indexiA dimensionless index value representing the ith index value of the target feature value x;
the comprehensive sequencing rule is specifically as follows:
wherein n represents 3 preset evaluation indexes, ZscoreA query value representing a target feature value x.
Further, the target feature value includes: the terminal information, intelligent IC card and ID card ID, the terminal information includes: terminal Mac address and terminal imsi data.
Further, when the target characteristic value is a terminal Mac address, obtaining data to be queried and then data sampling, where the data sampling specifically includes: and removing data containing '048C' in the Mac address and/or removing data with signal strength of-1.
In a second aspect, the present invention further provides a query sorting apparatus under multiple spatiotemporal conditions, including:
a data acquisition module: the system is used for acquiring at least one piece of space-time point information corresponding to the query target characteristic value;
a data expansion module: the system is used for performing corresponding space-time segment data expansion on each piece of space-time point information to obtain data to be inquired;
the index quantity value obtaining module is used for classifying the data to be inquired according to different target characteristic values and calculating a plurality of index quantity values corresponding to each target characteristic value in different inquiry dates under different preset evaluation indexes;
a sorting module: and the query value is obtained according to the comprehensive sorting rule and is sorted.
In a third aspect, the present invention further provides a control device for query sorting under multiple spatiotemporal conditions, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
In a fourth aspect, the present invention also provides a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the method according to any one of the first aspect.
The invention has the beneficial effects that:
the invention comprehensively considers the space similarity and the space diversity, combines the self-information quantity index, the similarity index and the space diversity index, obtains the query value corresponding to each target characteristic value by carrying out dimensionless on the index quantity values of different target characteristic values by a Z-score evaluation method, realizes the query sequencing of the target characteristics in a multi-space-time state, namely, the target characteristic values meeting space-time query conditions are retrieved by sequencing, and the most matched query result is further determined after dimensionless is carried out on the index quantity values under different evaluation indexes.
Drawings
FIG. 1 is a flow diagram of a query ranking method under multiple spatiotemporal conditions in accordance with an embodiment of the present invention;
FIG. 2 is a block diagram of a query ranking device under multiple spatiotemporal conditions in accordance with an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
The application scenario of the method is mainly positioning terminal information, an intelligent IC card and an ID card under a plurality of space-time conditions, such as scenario one: the method comprises the steps of obtaining a photo of a specific person, needing to find out the mobile phone number of the target person, locking the target person through the video through prior information, and finding out a plurality of time-space information of the target from historical videos, namely the time of the target in some places, namely finding out the most matched mobile phone number through the query sorting method under the multi-space-time condition. Scene two: if a specific person is required to be found, for example, the card swiping information appears in a subway station, a rough space-time range is defined, query and sorting are performed in a collection library, the intelligent IC card number which is the best match can be found by the query and sorting method under the multi-space-time condition of the invention, and similarly, query and search can be performed in a database through the ID number information of the identification card.
The first embodiment is as follows:
this embodiment takes an example of a query sorting process of querying the terminal Mac information of the target.
As shown in fig. 1, a flowchart of a query sorting method under multiple spatiotemporal conditions in this embodiment includes the steps of:
s1: data acquisition: collecting at least one piece of space-time point information corresponding to the query target characteristic value, for example, determining information that the target appears in different places at different times, where the target characteristic value includes: the terminal information, intelligent IC card and ID card ID, the terminal information includes: the terminal Mac address and the terminal imsi data, etc.
S2: data expansion: and performing corresponding space-time segment data expansion on each piece of space-time point information to obtain data to be inquired, wherein the acquisition time cannot be accurate to a certain point, but the acquisition can be performed within a period of time (such as 10 minutes before and after) when a target appears, so that the space-time point is required to be expanded into a space-time segment, the data expansion is performed by taking minutes as a unit, and the Mac data within one minute needs to be deduplicated if the Mac information appearing per minute is acquired.
When the target characteristic value is a terminal Mac address, data sampling is needed after data to be inquired are obtained, the purpose of sampling is to remove pseudo Mac, and the data sampling specifically comprises the following steps: (1) removing data containing '048C' in the Mac address; (2) data with signal strength-1 are discarded.
S3: obtaining index quantity values, calculating a plurality of corresponding index quantity values of each item of data in the data to be queried under different preset evaluation indexes, and carrying out non-dimensionalization on each index quantity value, wherein the preset evaluation indexes comprise: a first evaluation index, a second evaluation index, and a third evaluation index.
S4: and (4) sequencing and outputting: and obtaining a query value of each item of data to be queried according to a comprehensive ranking rule, and ranking the query values, wherein the comprehensive ranking rule is used for averaging a plurality of dimensionless index quantity values corresponding to each item of data to be queried.
The following are specific different preset evaluation indexes.
Assume a scenario, for example, where three spatiotemporal point information are collected:
space-time point 1: date (first day): 2018-10-24 time: 14:20:00 sites: a;
space-time point 2: date (next day): 2018-09-19 time: 21:40:00 sites: b;
space-time point 3: date (third day): 2018-10-19 time: 11:55:00 sites: c;
since the target does not necessarily appear exactly at the three time-space points, and the target may appear in a period of time before and after the time-space points, the three time-space points are respectively extended to a period of time by 10 minutes before and after, for example, the time-space point 1 may be extended to: date: 2018-10-24 time: 14:10: 00-14: 30:00, location: and A and the like, inquiring all Mac information collected in the time range, wherein the Mac information generally comprises tens of thousands of Mac information or mobile phone number information, and 500 Mac information are assumed in the scene, wherein the occurrence frequency of a certain Mac is calculated by taking a day as a unit, such as 5 Mac information appears in the first day, 0 Mac information appears in the second day, 3 Mac information appears in the third day and the like.
1) The first evaluation index is a self-information quantity index, the smaller the probability of the target characteristic value appearing is, the larger the information quantity is, that is, the effective information quantity of the target characteristic value appears is calculated, and if the more space-time points of the target characteristic value appearing on the same day are, the lower the information quantity is, specifically:
wherein x represents a target characteristic value, p (x) represents the probability of the target characteristic value, cnt represents the number of times of the target characteristic value appearing in one day, N represents a reference number, ibAnd a self-information quantity index quantity value representing the target characteristic value.
For example, the total number of occurrences of Mac1 on different dates is: x represents a target characteristic value Mac1, p (x) represents the probability of Mac1, cnt represents the frequency of Mac1 appearing in one day, the different dates correspond to 5 times, 0 times and 3 times respectively, N represents a reference number and takes the value of 60 x 24, ibRepresenting the self information content indicator magnitude of Mac 1.
2) The second evaluation index is a jaccard similarity index, the input query condition is regarded as a segment of space-time sequence, the evaluation standard of the query result can be regarded as measuring the similarity of two space-time sequences, and therefore a jaccard similarity coefficient is introduced for carrying out space-time similarity measurement, specifically:
wherein N ismatchRepresenting the number of times of matching of the target characteristic value x in one day, NtotalRepresenting the total number of occurrences of the target characteristic value x, NqueryRepresenting all spatiotemporal points in the query's time period, sim (x) representing the similarity index magnitude.
For example, if the Mac1 appears on different dates 5 times, 0 times and 3 times respectively, the query sequence after data expansion has 500 Mac information, N ismatchRepresenting the matching times of Mac1 in one day, wherein different dates correspond to 5 times, 0 times and 3 times respectively, and N istotalRepresents the total number of Mac1 occurrences, 5+0+ 3-8 times, NqueryAll the space-time points in the query time period are expressed in units of minutes, and the three time periods are respectively expanded into time periods in the front and back 10 minutes, namely NqueryDenotes 20 × 3 ═ 60 (minutes), sim (x) denotes the similarity index measure of Mac 1.
3) The third evaluation index is a spatial diversity index, namely, the spatial diversity is measured, namely, whether the query result contains enough sites or not is judged, and a shannon wiener uniformity index is introduced to evaluate the spatial diversity, and the third evaluation index specifically comprises the following steps:
wherein S isiRepresenting the number of times that the target characteristic value x appears at the site i, S representing the total number of times that the target characteristic value x appears at all sites, and j (x) representing the spatial diversity index magnitude of the target characteristic value x.
For example, Mac1 may appear 2 times in the query at point A, 3 times at point B, and 5 times at point C, i.e., S, among all queriesiRepresenting the number of times of appearance of Mac1 at site i, three sites A \ B \ C correspond to 2 times, 3 times and 5 times respectively, and S represents Mac1 at all sitesThe total number of times a point occurs, i.e., 3+3+5, 10 times, j (x) represents the spatial diversity index magnitude of Mac 1.
For example, of 5 matches, { a: 1 time, B: 2 times, C: the result of 2 times is better than { a: 3 times, B: 2 times }, the first case site distribution is more uniform.
In this embodiment, the three index quantity values corresponding to different target characteristic values and different query dates are combined, specifically:
where d represents the total number of query days, hi(x)mThe ith index value representing the target eigenvalue x of the mth day includes, for example, query conditions for 5 days, the target eigenvalue Mac1 has 3 index values each day, which are the self-information index value, the similarity index value and the spatial diversity index value of day1 to day5, the self-information index value of day1 to day5 is combined and added to obtain the self-information index value of Mac1, and similarly, the similarity index value and the spatial diversity index value of Mac1 are obtained.
And then, carrying out non-dimensionalization on the 3 index values of the Mac1 by using a Z-score method, wherein the non-dimensionalization specifically comprises the following steps:
where d represents the total number of query days, hi(x)mI-th index magnitude value, X, representing target feature value X of day miRepresenting the ith index magnitude, Mean, after the target characteristic values x are combinediMean, MSE, representing the magnitude of the i-th indexiMean square error, Z, of the magnitude of the i-th indexiAnd a dimensionless index value representing the ith index value of the target characteristic value x.
For example, in the query condition, there are 5 different target characteristic values from Mac1 to Mac5, and each target characteristic value corresponds to 3 combined index quantitiesValue, Mean in dimensionless process for the self-information-content indicator value of Mac1iThe mean value, MSE, of the self-information-content index magnitudes of different target characteristic values day 1-day 5iThe mean square error of the self-information quantity index values of the target characteristic values of day 1-day 5 can be used to obtain two other dimensionless process parameters of the index values.
After the indicator quantity values are dimensionless, query values corresponding to different target characteristic values need to be obtained according to a comprehensive ordering rule, wherein the comprehensive ordering rule specifically comprises:
wherein n represents 3 preset evaluation indexes, ZscoreA query value representing a target feature value x.
For example, for the target characteristic value Mac1, the above process obtains 3 dimensionless index values corresponding to Mac1, and the sum and average of the index values can obtain the query value of the target characteristic value Mac 1.
And sorting the query values corresponding to different target characteristic values from large to small, and selecting the target characteristic value of the first bit as a target Mac value to be searched.
The results were analyzed as follows for a specific example.
Scene: knowing the space-time range of the appearance of a target person, the mobile phone MAC address of the person is sought to be found.
These spatio-temporal range points are collated as follows:
1): 12680070002018-09-1921: 50:00- > expanded to- >21:40: 00-22: 00
2): 12600190002018-10-1912: 00:00- > expanded to- >11:50: 00-12: 10:00
3): 12610290002018-08-0814: 40:00- > is expanded to- >14:30: 00-14: 50:00
4): 12680120002018-08-0221: 40:00- > expanded to- >21:30: 00-22: 00-
Four locations and time periods appear in total, the number of the subway station is at the top, the appearing time range is about 20 minutes, in these time periods, many different mobile phone numbers or MAC information, usually tens of thousands of MAC or mobile phone numbers, may not collect the data of the user, and simultaneously some interference information brought by mobile phones carried by people often appearing in these locations, so that it is difficult to find out the Mac of the target person from the equipment appearing in these time periods,
in this embodiment, the space-time ranges are used to search for the appearing MACs in the collection library, a total of 8636 MACs are collected at these times, the appearing mobile phone numbers are queried and sorted, the mobile phone number with the top sorting value is most likely to be the mobile phone number of the target person, and the MACs are respectively sorted according to the sorting method as shown in table 1 below.
Wherein: bits: representing a self-information quantity index magnitude; jaccard: representing a similarity index magnitude; shannon wiener: representing a spatial diversity index magnitude; zscore: indicating the rank value.
After sorting, the top three ranking values (Zscore) are:
Mac1：4040A7E5ABEA；Zscore：0.692217353835432；
Mac2：FC4203C1A4E8；Zscore：0.5710468790679801；
Mac3：5CF7E6BF8839；Zscore：0.5419621587618433。
and finally, verifying, inquiring the MAC track of the target person, and confirming that the MAC with the first rank is the MAC address of the mobile phone held by the target person.
Example two:
as shown in fig. 2, a block diagram of a query sorting apparatus under multiple spatiotemporal conditions in this embodiment includes: a data acquisition module: the system is used for acquiring at least one piece of space-time point information corresponding to the query target characteristic value; a data expansion module: the system is used for performing corresponding space-time segment data expansion on each piece of space-time point information to obtain data to be inquired; the index quantity value obtaining module is used for classifying the data to be inquired according to different target characteristic values and calculating a plurality of index quantity values corresponding to each target characteristic value in different inquiry dates under different preset evaluation indexes; a sorting module: and the query value is obtained according to the comprehensive sorting rule and is sorted.
In addition, the invention also provides a control device for query sequencing under the multi-space-time condition, which comprises: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to perform the method according to embodiment one.
In addition, the present invention also provides a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method according to the first embodiment.
The invention comprehensively considers the space similarity and the space diversity, combines the self-information quantity index, the similarity index and the space diversity index, obtains the query value corresponding to each target characteristic value by carrying out dimensionless on the index quantity values of different target characteristic values by a Z-score evaluation method, realizes the query sequencing of the target characteristics in a multi-space-time state, namely, the target characteristic values meeting space-time query conditions are retrieved by sequencing, and the most matched query result is further determined after dimensionless is carried out on the index quantity values under different evaluation indexes.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A query ordering method under a multi-space-time condition is characterized by comprising the following steps:
collecting at least one piece of space-time point information corresponding to the query target characteristic value;
performing corresponding space-time segment data expansion on each piece of space-time point information to obtain data to be inquired;
classifying data to be inquired according to different target characteristic values, and calculating a plurality of index quantity values corresponding to each target characteristic value in different inquiry dates under different preset evaluation indexes; the target feature value includes: the terminal information, intelligent IC card and ID card ID, the terminal information includes: a terminal Mac address and terminal imsi data;
obtaining query values according to a comprehensive sorting rule, and sorting the query values;
the preset evaluation indexes comprise: a first evaluation index, a second evaluation index, and a third evaluation index; the first evaluation index is a self-information content index, the second evaluation index is a jaccard similarity index, and the third evaluation index is a spatial diversity index;
the comprehensive sorting rule is that the same type of index values corresponding to the same target characteristic value on different query dates are combined, and the combined index values of each target characteristic value are subjected to dimensionless operation and are averaged to obtain a query value corresponding to each target characteristic value; and the combination and the addition are carried out on the same type of index magnitude values of different query dates.
2. The query ranking method under multiple spatiotemporal conditions according to claim 1, wherein the first evaluation index is a self-information quantity index, specifically:
wherein x represents a target characteristic value, p (x) represents the probability of the target characteristic value, cnt represents the number of times of the target characteristic value appearing in one day, N represents a reference number, ibAnd a self-information quantity index quantity value representing the target characteristic value.
3. The query ranking method under the multi-temporal-spatial condition according to claim 1, wherein the second evaluation index is a jaccard similarity index, specifically:
wherein N ismatchRepresenting the number of times of matching of the target characteristic value x in one day, NtotalRepresenting the total number of occurrences of the target characteristic value x, NqueryRepresenting all spatio-temporal points in the query's time period, sim (x) representing the similarity index magnitude of the target feature value x.
4. The query ranking method according to claim 1, wherein the third evaluation index is a spatial diversity index, specifically:
wherein S isiRepresenting the number of times that the target characteristic value x appears at the site i, S representing the total number of times that the target characteristic value x appears at all sites, and j (x) representing the spatial diversity index magnitude of the target characteristic value x.
5. The query ranking method under multiple spatiotemporal conditions according to claim 1, wherein the merging specifically is:
the non-dimensionalization is carried out by using a Z-score method, and specifically comprises the following steps:
where d represents the total number of query days, hi(x)mI-th index magnitude value, X, representing target feature value X of day miRepresenting the ith index magnitude, Mean, after the target characteristic values x are combinediMean, MSE, representing the magnitude of the i-th indexiMean square error, Z, of the magnitude of the i-th indexiA dimensionless index value representing the ith index value of the target feature value x;
the comprehensive sequencing rule is specifically as follows:
wherein n represents 3 preset evaluation indexes, ZscoreA query value representing a target feature value x.
6. The query sorting method under the multi-spatiotemporal condition according to claim 1, wherein when the target eigenvalue is a terminal Mac address, obtaining data to be queried further comprises data sampling, and the data sampling specifically comprises: and removing data containing '048C' in the Mac address and/or removing data with signal strength of-1.
7. A query ranking device under multiple spatiotemporal conditions, comprising:
a data acquisition module: the system is used for acquiring at least one piece of space-time point information corresponding to the query target characteristic value;
a data expansion module: the system is used for performing corresponding space-time segment data expansion on each piece of space-time point information to obtain data to be inquired;
an index magnitude acquisition module: the system comprises a data query module, a data query module and a data processing module, wherein the data query module is used for classifying data to be queried according to different target characteristic values and calculating a plurality of index quantity values corresponding to each target characteristic value in different query dates under different preset evaluation indexes; the target feature value includes: the terminal information, intelligent IC card and ID card ID, the terminal information includes: a terminal Mac address and terminal imsi data;
a sorting module: the query value is obtained according to the comprehensive ranking rule, and the query value is ranked;
a preset evaluation index module: the evaluation index comprises a first evaluation index, a second evaluation index and a third evaluation index; the first evaluation index is a self-information content index, the second evaluation index is a jaccard similarity index, and the third evaluation index is a spatial diversity index;
a comprehensive sequencing rule module: the system comprises a plurality of index values, a query value and a target characteristic value, wherein the index values are used for merging the same type of index values corresponding to the same target characteristic value on different query dates, and the plurality of index values after each target characteristic value is merged are subjected to non-dimensionalization and are averaged to obtain the query value corresponding to each target characteristic value; and the combination and the addition are carried out on the same type of index magnitude values of different query dates.
8. A control apparatus for query ranking under multiple spatiotemporal conditions, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.
9. A computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the method of any one of claims 1 to 6.
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