CN113407575B - Case merging method and device based on multiple dimensions and storage medium - Google Patents
Case merging method and device based on multiple dimensions and storage medium Download PDFInfo
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Abstract
The invention discloses a case merging method, a device and a storage medium based on multiple dimensions, wherein the method and the device respectively calculate the first similarity of each first dimension and each second dimension which is the same as the first dimension in the selected candidate cases by acquiring an original point case and a candidate case set, and determine the second similarity and a data set of the original point case and the selected candidate cases according to the first similarity and a preset dimension weight, so that merged cases are obtained, and the merged cases are automatically determined based on the similarity of the original point case and the candidate cases, so that the analysis efficiency of associated cases is improved; and the merging result is obtained by iterating until reaching the preset iterating condition, so that the final merging result comprises a plurality of candidate cases related to the original point case, the automatic analysis efficiency can be realized under the condition of having a plurality of candidate cases, and the effectiveness of the analysis of the related cases is ensured to a certain extent.
Description
Technical Field
The invention relates to the field of data processing, in particular to a case merging method and device based on multiple dimensions and a storage medium.
Background
Nowadays, a case database is established in a police system after years of development, a large amount of police conditions and data of different cases are accumulated in the database, but in actual work, whether the different cases are related or not is deeply analyzed, whether the multiple cases are independent cases or series of cases often depend on experienced personnel to inquire through individual keywords, and then the inquired cases are analyzed and judged to judge whether to be combined or not based on the keywords and other possible related points.
Disclosure of Invention
In view of the above, the present invention aims to provide a case merging method, device and storage medium based on multiple dimensions, which improves analysis efficiency and ensures validity of associated case analysis.
The technical scheme adopted by the invention is as follows:
a case merging method based on multiple dimensions, comprising:
Acquiring an origin case and a candidate case set; the origin case comprises a first dimension set, the first dimension set comprises a plurality of first dimensions, the candidate case set comprises a plurality of candidate cases, and each candidate case comprises a plurality of second dimensions;
selecting one candidate case from the candidate case set;
respectively calculating first similarity of each first dimension and each second dimension which is the same as the first dimension in the selected candidate cases;
determining a second similarity between the origin case and the selected candidate case according to the first similarity and a preset dimension weight;
determining a data set according to the second similarity; the data set comprises first key class data or second key class data, and the importance of the second key class data is higher than that of the first key class data;
combining the second key class data with the original point case to obtain a combined case;
and taking the combined case as a new original point case, selecting a new candidate case from the candidate cases except the data set in the candidate case set, and returning to the step of respectively calculating the first similarity of each first dimension and each second dimension which is the same as the first dimension in the selected candidate cases until a preset iteration condition is reached, thereby obtaining a combined result.
Further, each first dimension includes at least one first element, each second dimension includes at least one second element, and the calculating a first similarity between each first dimension and each second dimension, which is the same as the first dimension, in the selected candidate case includes:
obtaining a first data depth score for the first element in each of the first dimensions and a second data depth score for the second element in each of the second dimensions;
and determining the first similarity of each first dimension and each second dimension identical to the first dimension in the selected candidate cases according to the first data depth score of the first element and the second data depth score of the second element identical to the first dimension corresponding to the first element.
Further, the first dimension includes what victim dimension, when dimension, where dimension, what suspect dimension, what tool dimension, what means dimension, what reason dimension, what behavior dimension, what result dimension, and what status dimension.
Further, the determining, according to the first similarity and a preset dimension weight, the second similarity between the origin case and the selected candidate case includes:
Weighting the first similarity and the preset dimension weight to obtain second similarity of the origin case and the selected candidate case; the weight in the preset dimension weight is sequentially from large to small, and the weight is a behavior dimension, a time dimension, a tool dimension, a means dimension, a place dimension, a reason dimension, a result dimension, a state dimension, a suspicion dimension and a victim dimension.
Further, the determining the data set according to the second similarity includes:
when the second similarity is larger than or equal to a first threshold value and smaller than a second threshold value, determining that the selected candidate case is the first key class data;
or alternatively, the process may be performed,
and when the second similarity is greater than or equal to a second threshold, determining that the selected candidate case is the second key class data.
Further, the reaching the preset iteration condition includes:
when the total number of key class data in all the data sets is larger than or equal to a third threshold value, the preset iteration condition is reached;
or alternatively, the process may be performed,
when the number of times of iteration is larger than a fourth threshold value, the preset iteration condition is reached;
or alternatively, the process may be performed,
and if the total number of the key class data in all the current data sets is equal to the total number of the key class data in the data set obtained by the last iteration, the preset iteration condition is reached.
Further, the method further comprises:
displaying the origin case and each data set on a map, and displaying the association relation between the second dimension and the first dimension of each data set and the origin case;
or alternatively, the process may be performed,
displaying the origin case and each data set on a map, responding to a track determining operation, and displaying a space-time motion track of the merging result based on a time dimension and a place dimension; the first dimension and the data set each comprise a time dimension and a place dimension;
or alternatively, the process may be performed,
and displaying the origin case and each data set on a map, and responding to element input operation, and positioning the origin case or the data set corresponding to the input element on the map.
The invention also provides a case merging device based on multiple dimensions, which comprises:
the acquisition module is used for acquiring the original point cases and the candidate case sets; the origin case comprises a first dimension set, the first dimension set comprises a plurality of first dimensions, the candidate case set comprises a plurality of candidate cases, and each candidate case comprises a plurality of second dimensions;
The selection module is used for selecting one candidate case from the candidate case set;
the first calculation module is used for calculating first similarity of each first dimension and each second dimension which is the same as the first dimension in the selected candidate cases;
the first determining module is used for determining second similarity between the original point case and the selected candidate case according to the first similarity and a preset dimension weight;
a second determining module, configured to determine a data set according to the second similarity; the data set comprises at least one of first key class data and second key class data, and the importance of the second key class data is higher than that of the first key class data;
the merging module is used for merging the second key class data with the original point case to obtain a merged case;
and the iteration module is used for taking the combined case as a new original point case, selecting a new candidate case from candidate cases except the data set in the candidate case set, and returning to the step of respectively calculating the first similarity of each first dimension and each second dimension which is the same as the first dimension in the selected candidate cases, and iterating until a preset iteration condition is reached, thereby obtaining a combined result.
The invention also provides a case merging device based on multiple dimensions, which comprises a processor and a memory;
the memory stores a program;
the processor executes the program to implement the method.
The present invention also provides a computer-readable storage medium storing a program which, when executed by a processor, implements the method.
The beneficial effects of the invention are as follows: acquiring an origin case and a candidate case set, respectively calculating first similarity of each first dimension and each second dimension which is the same as the first dimension in the selected candidate cases, determining second similarity of the origin case and the selected candidate cases according to the first similarity and preset dimension weight, determining a data set according to the second similarity, merging the second important class data with the origin case to obtain a merged case, and automatically determining the merged case based on the similarity of the origin case and the candidate cases, thereby improving analysis efficiency of associated cases; and taking the combined case as a new original point case, selecting a new candidate case from the candidate cases except the data set in the candidate case set, returning to the step of respectively calculating the first similarity of each first dimension and each second dimension which is the same as the first dimension in the selected candidate cases, iterating until a preset iteration condition is reached, and obtaining a combined result, so that the final combined result comprises a plurality of candidate cases related to the original point case, the automatic analysis efficiency can be realized under the condition of having a plurality of candidate cases, and the effectiveness of the related case analysis is ensured to a certain extent.
Drawings
FIG. 1 is a flow chart of steps of a case merging method based on multiple dimensions according to the present invention;
FIG. 2 is a schematic diagram of a serial-parallel intelligent operation center according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a visual relationship map according to an embodiment of the present invention.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
The terms "first," "second," "third," and "fourth" and the like in the description and in the claims and drawings are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
As shown in fig. 1, the embodiment provides a case merging method based on multiple dimensions, which includes steps S100-S700:
s100, acquiring an origin case and a candidate case set.
Specifically, the origin case refers to a case in which whether other associated cases need to be determined, the origin case includes a first dimension set including a plurality of first dimensions. The candidate case set includes a plurality of candidate cases, each candidate case including a plurality of second dimensions. Optionally, the first dimension includes what victim dimension, when dimension, where dimension, what suspect dimension, what tool dimension, what means dimension, what reason dimension, what behavior dimension, what result dimension, and what status dimension, i.e., the first dimension set includes ten kinds of first dimensions; likewise, the second dimension also includes the ten dimensions described above.
In an embodiment of the present invention, each first dimension includes at least one first element, for example: in what victim dimension, the first element includes, but is not limited to, an identification number, a residence address, a cell phone number, a gender, an age group (teenager [ age < =17 ], young [18< =age < =45 ], middle-aged [46< =age < =69 ], elderly [70< =age ]), profession, through, cell phone, mailbox, etc.; in the time dimension, the first element includes, but is not limited to, a date of the event, a time period of the event (early morning [02:00-06:00], morning [06:00-12:00], noon [12:00-14:00], afternoon [14:00-17:00], evening [17:00-24:00], late night [24:00-03:00 ]), a weekday, a weekend, a holiday, and the like; in which dimension the first element includes, but is not limited to, a case address, a location type (garden cell, villa, multi-story building, high-rise building, urban villa, old house, dormitory, campus dormitory, factory dormitory, etc.), a affiliated zone, longitude and latitude coordinates (84 coordinate system), a peripheral base station, and information acquisition device information; in what suspicion dimension, the first element includes, but is not limited to, an element such as an identification number, a physical feature, a cell phone number, a person name, a nickname, a gender, an age group (teenager [ age < =17 ], young adult [18< =age < =45 ], middle-aged [46< =age < =69 ], elderly [70< =age ]); in which tool dimension the first element includes, but is not limited to, a bank account number, an opening card name, a QQ number, a micro-letter number, a payment instrument number, a related website, an acquisition phone number, a phone serial number SN number, a MAC address, a related vehicle information (license plate number, frame number, brand) element; in what measure dimension, the first element includes, but is not limited to, manufacturing conditions, preparation tools, intrusion from doors, entrance to holes, bruise, gun holding, handling, riot, hijacking, broken lock theft, vehicle theft, impersonation of identity, fraud, entrainment, concealment, other measures; in what cause dimension, the first element includes, but is not limited to, political motivations, financial motivations, return motivations, fear motivations, other motivations, and the like; in what behavioral dimension, the first element includes, but is not limited to, elements such as disputes, public security, criminal, theft (burglary, pickles, other theft), robbery, fraud (contact fraud, non-contact fraud), and case of a suspected toxicity; in what dimension of outcome, the first element includes, but is not limited to, a medical identification done, a light injury, a heavy injury, a mortality, an uninjured, an economic violation, and no confirmation; in what state dimension, the first element includes, but is not limited to, under-process, case-setting, audit, executing, case-breaking, case-setting. Similarly, each second dimension includes at least one second element, and the second element and the first element are the same in kind and will not be described in detail.
It should be noted that, before acquiring the origin case and the candidate case set, the case data may be collected and preprocessed, so as to obtain the origin case and the candidate case set. Optionally, preprocessing may include data cleansing, conversion (normalization), loading, and so forth. The data cleaning may clean corresponding elements in the candidate set according to the element blacklist.
S200, selecting one candidate case from the candidate case set.
Optionally, the candidate cases in the candidate case set are numbered or arranged in sequence, and one candidate case is selected from the candidate case set according to the serial number or the sequence of arrangement. For example, the origin case set y= { Y1, Y2, Y3...the term Yn, yn is the nth origin case, and assuming that one origin case Y1 is selected, the candidate case set is m= { M1, M2, M3..the term Mn, mn is the nth candidate case, and the candidate case M1 is selected from the candidate case set.
S300, respectively calculating the first similarity of each first dimension and each second dimension which is the same as the first dimension in the selected candidate cases.
Specifically, the second dimension identical to the first dimension refers to the dimension of the same type, for example, the first dimension includes ten dimensions, the second dimension includes ten dimensions, when the first dimension is a victim dimension, the first dimension of why the second dimension is identical to the first similarity of the second dimension is calculated, that is, the first similarity of what victim dimension of the origin case Y1 and what victim dimension of the candidate case M1 is calculated, it is understood that the first similarity of each first dimension and each corresponding second dimension identical to the first dimension can be determined through ten calculations, and ten first similarities are finally obtained.
Optionally, step S300 includes steps S310-S320:
s310, a first data depth score of a first element in each first dimension and a second data depth score of a second element in each second dimension are obtained.
Specifically, the first data depth score and the second data depth score are determined based on element depth, and since elements in the data dimension may include a relationship between upper and lower levels, for example, a situation where the dimension is a case where the element belongs to a cell, specifically, a surrounding cell, where the surrounding cell is data with a deeper depth than the cell, the data depth score of the surrounding cell may be higher than the data depth score of the cell, and since the same criminal frequently perpetrates on the same type of cell from the action rule of the criminal and the analysis of the contact circle, the data weighting depth is higher for the next level of the data node in the process of data processing. It should be noted that, the setting principle of the data depth score of other dimensions is similar, and may be determined according to actual needs, and the embodiment of the present invention is not limited specifically.
S320, determining the first similarity of each first dimension and each second dimension identical to the first dimension in the selected candidate cases according to the first data depth score of the first element and the second data depth score of the second element identical to the first dimension corresponding to the first element.
Specifically, the formula is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
for the first similarity, the more similar 1 means that the two elements of the dimension are, the more similar x means the first dimension, y means the same type of second dimension as the first dimension,/the more similar>
Represents the i first element in x, and (2)>
Represents the ith second element in y, n is the number of the first element or the second element, < ->
A first data depth score representing a first element, < ->
A second data depth score representing a second element. Optionally, ->
And->
A value may be preset to be substituted to obtain a rational number such as 1, 2, etc. It can be understood that, by the above formula, the first similarity between the first dimension and the second dimension identical to the first dimension can be calculated, and by multiple calculations, each first similarity between each first dimension and each second dimension identical to each first dimension can be obtained, that is, ten first similarities are obtained, and ten first similarities corresponding to ten dimensions between the origin case Y1 and the candidate case M1 are obtained. It should be noted that, in the embodiment of the present invention, since the first element and the second element have element widenings, each dimension relates to one element or multiple elements, and the case of multiple elements, such as a case-related object of a burglary case, includes a mobile phone and precious jewelry at the same time, the multiple elements need to be matched in the process of data processing, so as to increase the comprehensiveness and accuracy of similarity matching.
It should be noted that, the first element and the second element in the embodiment of the present invention also have element ambiguity and element noise. The element ambiguity refers to a special symbol in case data, which may store blurring processing, and in most cases, part of information is not acquired very accurately, and only records omitted in the data can be used. Exemplary, e.g., suspects mailbox address for M1 case: the method is characterized in that 'XY 275 x 478@qq.com', mailbox address record of M2 case 'XY2758112478@qq.com', character string matching method is used in the data processing process, original elements are divided into arrays through x, the arrays are sequentially compared through matching rules, if matching can be achieved, the fact that M1 and M2 suspicious mailbox elements have identity is indicated, and overall data are weighted. And the element noise performance refers to that in the data preprocessing process, if the first element and the second element contain 'other', 'equal', 'uncertain' elements, the elements need to be removed in a preprocessing link because of having no serial-parallel meaning and possibly affecting the serial-parallel accuracy.
In the embodiment of the invention, aiming at the data analysis of a large number of forensic police cases, the cases of different criminal types are found, and the obtained clues of ten elements (namely the first dimension and the second dimension) are different. Specifically, the suspected person obtained by the fraud police has more abundant and detailed dimensions, including channel information such as fraud nicknames, remittance bank accounts, contact ways and the like. The relative theft police cases and suspects are basically difficult to acquire, and the relative theft police cases and suspects are mainly characterized in that the relative theft police cases and suspects are characterized in that information such as tools, mode means, fingerprints, shoe marks and the like for implementing theft is included, so that from the perspective of missing serial-parallel data, comprehensive calculation is needed from the aspect of ten element dimensions.
S400, determining second similarity of the original point case and the selected candidate case according to the first similarity and the preset dimension weight.
Optionally, step S400 includes step S410:
and S410, weighting the first similarity and the preset dimension weight to obtain the second similarity of the original point case and the selected candidate case.
In the embodiment of the invention, the preset dimension weights are sequentially from large to small, namely, what behavior dimension, when dimension, tool dimension, means dimension, place dimension, reason dimension, result dimension, state dimension, suspected person dimension and victim dimension: the preset dimension weight is based on what action dimension
)>When dimension (+)>
)>What tool dimension (+)>
)>What means dimension (++>
)>What dimension (+)>
)>What reason dimension (+)>
)>What result dimension (+)>
)>What state dimension (++>
)>What suspicion dimension (+)>
)>What victim dimension (+)>
) And->
，/>
>/>
>/>
>/>
>/>
>/>
>/>
>/>
>/>
,
+/>
+/>
+/>
+/>
+/>
+/>
+/>
+/>
=1。
It should be noted that the preset dimension weight may be set according to the actual requirement. In the embodiment of the invention, through mass data practice analysis, the serial-parallel value of the crime type and the behavior dimension is not great. Illustratively, if the theft case is different from the robbery case, the rest 9 dimensions are different, and the comprehensive similarity basically approaches to 0; when the dimension of the time space is expressed as the space-time mutual exclusion, the criminal suspects cannot implement multiple cases at the same time, but have the habit of doing cases in the same time period; the invention relates to a main idea that the same criminal or criminal group has little meaning on different affected groups, and deserialized victim information per se, thereby determining the preset dimension weight.
Specifically, the second similarity is calculated according to the formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
for the second similarity, ++>
For the mth first similarity, by the above
Is calculated by the formula of->
The weight corresponding to the m-th dimension. The second similarity represents the overall similarity between the origin case Y1 and the candidate case M1.
It should be noted that, as shown in fig. 2, the data processing in the embodiment of the present invention is implemented by a serial-parallel intelligent operation center, which provides operation management services, for example, services including obtaining an origin case and a candidate case set and calculating a first similarity and a second similarity, etc., and adopts a modeling serial-parallel operation basic distributed node (i.e. an algorithm operation slice E),
e= { E1, E2, E3, …, en }, en represents an nth node which performs data preprocessing on an algorithm operation service to which inner management belongs and required hardware computing resources, externally receives a processing task request of a task initiator, simultaneously broadcasts self service state information including data storage fragments, idle task numbers, load conditions of the hardware computing resources and the like to the whole network, loads the preprocessed data to the operation service node through an ETL data exchange tool, waits for the initiation of the operation task, and can be used for generating a visual relation map after the data processed by the algorithm operation fragments E are stored. Wherein, the sentinel represents the transmission of the sentinel data.
S500, determining a data set according to the second similarity.
Specifically, the data set includes first emphasis class data or second emphasis class data, and the importance of the second emphasis class data is higher than that of the first emphasis class data.
Optionally, step S500 includes step S510 or step 520:
s510, when the second similarity is greater than or equal to the first threshold and smaller than the second threshold, determining that the selected candidate case is the first key class data.
Specifically, the first threshold is
The second threshold is->
，/>
When->
And determining the selected candidate case M1 as the first key class data D1.
And S520, when the second similarity is greater than or equal to a second threshold value, determining that the selected candidate case is the second key class data.
In particular, when
And determining the selected candidate case M1 as the second key class data D2. When the candidate cases are the second key class data, the candidate cases are considered to have serial-parallel value, and the candidate cases may be serial-parallel cases in different criminal forms or the candidate cases may be the same group or the candidate cases may have the same criminal form, so that the cases may be combined.
And S600, merging the second key class data with the original point case to obtain a merged case.
Specifically, when the candidate case is the second key class data, the candidate case is combined with the origin case. For example: and when the candidate case M1 is the second key class data D2, merging the M1 and the D2 to obtain a merged case B1. It will be appreciated that merging corresponds to adding a second element in a second dimension of M1 to a corresponding first dimension that is the same as the second dimension, and that one can be preserved from repetition if the second element is the same as the first element.
For example, if a criminal in one case uses a QQ number and another case uses a bank card number, but it is determined that the case is the second key type data through the above similarity calculation, the QQ numbers and the bank card numbers of the two cases are all necessary to be used as the meaning of the next round of serial-parallel elements, that is, the bank card numbers and the QQ numbers are all combined into the dimension of one case.
S700, taking the combined case as a new original point case, selecting a new candidate case from candidate cases except the data set in the candidate case set, and returning to the step of respectively calculating the first similarity of each first dimension and each second dimension which is the same as the first dimension in the selected candidate cases for iteration until a preset iteration condition is reached, thereby obtaining a combined result.
Specifically, the merging case B1 is taken as a new origin case, a new candidate case is selected from candidate cases except for the data set in the candidate case set M, for example, a new candidate case is selected from M2-Mn, for example, M2, the step of respectively calculating the first similarity of each first dimension and each second dimension identical to the first dimension in the selected candidate cases is returned to iterate, that is, B1 is taken as Y1 in the step S300, M2 is taken as M1 in the step S300, the step S300 is re-executed, the new data set and the new merging case B2 are determined, and the result of one iteration is obtained until the preset iteration condition is reached, and a plurality of new data sets and new merging cases Bn are determined, thereby obtaining the final merging result. It should be noted that, the merging result is a result obtained by merging the non-origin case Y1 with all candidate cases determined as the second key class data in the candidate case set M.
The merging mode disclosed by the embodiment of the invention increases the longitudinal serial-parallel deep digging method, namely, on the basis of transversely calculating the similarity between other cases and the origin case Y1, threads possibly having serial-parallel value in other cases are longitudinally added into the origin data packet, and the serial-parallel deep digging is performed at the next round, so that the merging universality and inclusion are increased.
It can be understood that when the iteration is finished, a new origin case Yn (for example, Y2) may be extracted from the origin case set, and the new merging result is obtained with the candidate case set M by adopting the steps described above, and the iteration is repeated until all operation analysis of the Yn origin case is finished, which is not repeated. The merging result of each original point case is stored in the data memory so as to trace back the subsequent task type process, and a realistic analysis basis is provided for case serial-parallel clues.
Optionally, the reaching of the preset iteration condition in step S700 may include steps S710, S720 or S730:
s710, when the total number of the key class data in all the data sets is larger than or equal to a third threshold value, reaching a preset iteration condition.
Specifically, the total number of the key class data in all the data sets refers to the total number of the first key class data and the second key class data in the plurality of data sets determined through the iterative process, and the total number is used 
N denotes the number of iterations, +.>
Each refers to the total number of key class data in all data sets after the nth iteration, ++>
Is a third threshold value when
And (5) considering preset iteration conditions to obtain a final merging result.
S720, when the number of times of iteration is larger than a fourth threshold, reaching a preset iteration condition.
Specifically, when the number of iterations is greater than the fourth threshold
Reaching the preset iteration condition and reaching the preset iteration condition.
It should be noted that the number of the substrates,
、/>
can be set according to the actual situation, for example +.>
100%>
For 20 cases, that is, the merging result of the cases cannot exceed 100 cases, the serial-parallel number of the origin data packet (that is, the new merged case Bn) should not exceed 20 times, and the reason for setting the third threshold value and the fourth threshold value is that if the conditions of the third threshold value and the fourth threshold value are not met, the obtained final merging result may have abnormal data, the obtained serial-parallel result is meaningless, and an abnormal oversized analysis data packet may be generated, so that service overload operation is caused. For example, if a criminal suspects person uses a QQ number and uses a short message '10086' number to disguise as a case, and another criminal person uses a bank card number and also uses a disguise '10086' as a case, the result is that the origin data packet uses the QQ number and the bank card number which are erroneously associated with the origin data packet to perform deep mining and serial-parallel connection, so that the number and serial-parallel number of serial-parallel cases are greatly deviated, and elements causing data abnormality need to be added into a blacklist. In addition, in the data preprocessing, the blacklist of the data result required to be loaded with the data operation slice En can be filtered, abnormal data is removed, and for newly discovered elements causing data abnormality, the measure of fusing the third threshold and the fourth threshold is adopted in the operation process, and the newly discovered elements are added into the blacklist after being audited.
And S730, if the total quantity of the key class data in all the current data sets is equal to the total quantity of the key class data in the data set obtained by the last iteration, reaching a preset iteration condition.
Also, specifically, the total number of the importance class data in all the data sets refers to the total number of the first importance class data and the second importance class data in the plurality of data sets determined by the iterative process, the total number being used
N denotes the number of iterations, +.>
The number refers to the total number of key class data in all data sets after the nth iteration, when
，/>
The total quantity of key class data in all data sets after the n-1 th iteration is considered as a preset iteration condition to obtain a final merging result.
The case merging method based on the multi-dimension of the embodiment of the invention can further comprise the steps of S810, S820 or S830, providing a visual map service of merging results, wherein the whole case serial-parallel (i.e. merging) process has high cross correlation, constructing a flexible user interface and exploratory mining experience in the process of mining from B1 data to Bn multi-dimension data, displaying a layer-by-layer serial-parallel process in a visual relation map mode, and constructing a multi-dimension deep-mining serial-parallel method in the serial-parallel model iterative serial-parallel process, so that a user performs operations such as drilling, screening, early warning value setting and the like in a delivery interface. In addition, for the display of track information, a GIS technology is adopted to display the occurrence place of the case and the motion track of the suspicious person on a map in a scattered point mode. The information of all the original point cases, the data sets, the combined cases and the like supports one-file EXCEL export of the final result, specifically, the original data are exported according to each dimension of each item of the sheet tab by using an EXCEL method through the serial-parallel result of each layer in the device, and a data base is provided for the follow-up detection of the cases.
Specifically:
and S810, displaying the original point case and each data set on the map, and displaying each data set and the association relation between the second dimension and the first dimension of the original point case.
Specifically, the combination result and the data set of each time are displayed in a layering way by taking the original point case as the center of a circle and are combined and displayed on a map,
as shown in fig. 3, the first layer and the second layer are provided, the origin (cases) Y1 and Mn cases are related by the similarity of ten elements (namely ten dimensions), so that the application value and accuracy of the related serial-parallel cases can be clearly determined, and the association relationship between each data set and the second dimension and the first dimension of the origin cases can be well shown.
Optionally, the association relation mined by each layer of serial-parallel (i.e. merging) can be displayed in a page display mode or in a process animation mode during display, and specifically, { is displayed in the first layer of serial-parallel
Cases and related ten elements and brief alert, ++>
For the original case, add->
For the data set obtained in the first iteration, the second serial-parallel (i.e. merging) result is displayed when the second drill-down is continued, including {>
Cases and related dimension elements, +. >
And (5) obtaining a data set for the second iteration. Similarly, the drill may be stepped down until the end of the serial-parallel operation. The serial-parallel results for different layers are differentThe deeper the drill-down level is, the more the serial-parallel data result tends to be geometrically multiplied, and the data accuracy will be reduced, so that the drill-down serial-parallel result of the corresponding level needs to be used in combination with the actual service condition.
S820, displaying the original point cases and each data set on a map, responding to the track determining operation, and displaying the space-time motion track of the merging result based on the time dimension and the place dimension.
Specifically, the first dimension and the data sets each include a time dimension and a place dimension, the origin case and each data set are displayed on the map, the system uses the place dimension and the time dimension in ten dimension elements of the case in response to the track determining operation of the user, for example, an 84 coordinate system GIS address of the case occurrence of the origin case and the data sets is obtained, and then according to the specific time of the time dimension occurrence, a space-time motion track of a serial-parallel case result (i.e. a merging result) can be displayed on the display page, so that a new technical means is provided for case detection, and the analysis efficiency is improved.
And S830, displaying the original point cases and each data set on the map, and responding to the element input operation, positioning the original point cases or the data sets corresponding to the input elements on the map.
Specifically, a search screening text box can be provided on the display page for the user to input elements, such as a first element or a second element in ten dimensions, and the system responds to the element input operation of the user to locate the position of an origin case or a data set corresponding to the input element on the map so as to quickly locate the case position, such as the position of the origin case or the data set, in the relation map on the map.
In summary, the embodiment of the invention calculates and classifies the origin case and the candidate case set M containing police cases, case information, stroke data and the like, obtains ten dimensional characteristics of the candidate cases in the origin case and the candidate case set M, carries out similarity calculation, obtains a data matrix under the candidate case set M, and judges that the data are effective merging results after the result meets the rule of the set threshold value; and carrying out association mining calculation on the similarity array matrix algorithm through a structural similarity comparison algorithm, and finally carrying out iterative deep mining to form a case clue relation chain map.
The embodiment of the invention also provides a case merging device based on multiple dimensions, which comprises:
the acquisition module is used for acquiring the original point cases and the candidate case sets; the origin case comprises a first dimension set, the first dimension set comprises a plurality of first dimensions, the candidate case set comprises a plurality of candidate cases, and each candidate case comprises a plurality of second dimensions;
the selection module is used for selecting one candidate case from the candidate case set;
the first calculation module is used for calculating the first similarity of each first dimension and each second dimension which is the same as the first dimension in the selected candidate cases respectively;
the first determining module is used for determining the second similarity of the original point case and the selected candidate case according to the first similarity and the preset dimension weight;
the second determining module is used for determining a data set according to the second similarity; the data set comprises at least one of first key class data and second key class data, and the importance of the second key class data is higher than that of the first key class data;
the merging module is used for merging the second key class data with the original point case to obtain a merged case;
the iteration module is used for taking the combined case as a new original point case, selecting a new candidate case from candidate cases except the data set in the candidate case set, and returning to the step of respectively calculating the first similarity of each first dimension and each second dimension which is the same as the first dimension in the selected candidate cases for iteration until a preset iteration condition is reached, so that a combined result is obtained.
The content in the method embodiment is applicable to the system embodiment, the functions specifically realized by the system embodiment are the same as those of the method embodiment, and the achieved beneficial effects are the same as those of the method embodiment.
The embodiment of the invention also provides a case merging device based on multiple dimensions, which comprises a processor and a memory;
the memory is used for storing programs;
the processor is used for executing a program to realize the case merging method based on multiple dimensions. The device provided by the embodiment of the invention can realize the function of case merging based on multiple dimensions. The device can be any intelligent terminal including a mobile phone, a tablet personal computer, a personal digital assistant (Personal Digital Assistant, PDA for short), a Point of Sales (POS for short), a vehicle-mounted computer and the like.
The content in the method embodiment is applicable to the embodiment of the device, and the functions specifically realized by the embodiment of the device are the same as those of the method embodiment, and the obtained beneficial effects are the same as those of the method embodiment.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a program, and the program is executed by a processor to complete the case merging method based on multiple dimensions according to the embodiment of the invention.
The embodiments of the present invention also provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform the multi-dimensional case merging method of the embodiments of the present invention described above.
The terms "first," "second," "third," "fourth," and the like in the description of the present application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in this application, "at least one" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form. The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including multiple instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing a program.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (9)
1. A case merging method based on multiple dimensions, comprising:
acquiring an origin case and a candidate case set; the origin case comprises a first dimension set, the first dimension set comprises a plurality of first dimensions, the candidate case set comprises a plurality of candidate cases, and each candidate case comprises a plurality of second dimensions;
selecting one candidate case from the candidate case set;
respectively calculating first similarity of each first dimension and each second dimension which is the same as the first dimension in the selected candidate cases; wherein each first dimension includes at least one first element, each second dimension includes at least one second element, and the calculating a first similarity between each first dimension and each second dimension that is the same as the first dimension in the selected candidate case includes: obtaining a first data depth score for the first element in each of the first dimensions and a second data depth score for the second element in each of the second dimensions; determining a first similarity of each first dimension and each second dimension identical to the first dimension in the selected candidate cases according to a first data depth score of the first element and a second data depth score of a second element identical to the first dimension corresponding to the first element; according to the upper-lower relationship included between the elements of the data dimension, the higher the data weighted depth of the next stage of the data node in the data processing process is, the higher the data weighted depth is, the higher the data depth score is, and the specific calculation formula of the first similarity is as follows:
Wherein ssim (x, y) represents a first degree of similarity, x represents the first dimension, y represents the second dimension of the same type as the first dimension, x i The ith said first element, y in the representation i Representing the ith second element in y, wherein the value of n is the number of the first element or the second element, L x The first data depth score, L, representing the first element y The second data depth score representing the second element;
determining a second similarity between the origin case and the selected candidate case according to the first similarity and a preset dimension weight;
determining a data set according to the second similarity; the data set comprises first key class data or second key class data, and the importance of the second key class data is higher than that of the first key class data;
combining the second key class data with the original point case to obtain a combined case; if the second element in the second dimension of the candidate case corresponding to the second key class data is different from the first element in the first dimension identical to the second dimension, the merging is used for indicating that the second element in the second dimension of the candidate case corresponding to the second key class data is added in the corresponding first dimension identical to the second dimension;
And taking the combined case as a new original point case, selecting a new candidate case from the candidate cases except the data set in the candidate case set, and returning to the step of respectively calculating the first similarity of each first dimension and each second dimension which is the same as the first dimension in the selected candidate cases until a preset iteration condition is reached, thereby obtaining a combined result.
2. The case merging method based on multiple dimensions according to claim 1, wherein: the first dimension includes what victim dimension, when dimension, where dimension, what suspect dimension, what tool dimension, what means dimension, what cause dimension, what behavior dimension, what result dimension, and what status dimension.
3. The case merging method based on multiple dimensions according to claim 2, wherein: the determining the second similarity between the origin case and the selected candidate case according to the first similarity and a preset dimension weight comprises the following steps:
weighting the first similarity and the preset dimension weight to obtain second similarity of the origin case and the selected candidate case; the weight in the preset dimension weight is sequentially from large to small, and the weight is a behavior dimension, a time dimension, a tool dimension, a means dimension, a place dimension, a reason dimension, a result dimension, a state dimension, a suspicion dimension and a victim dimension.
4. The case merging method based on multiple dimensions according to claim 1, wherein: said determining a data set according to said second similarity, comprising:
when the second similarity is larger than or equal to a first threshold value and smaller than a second threshold value, determining that the selected candidate case is the first key class data;
or alternatively, the process may be performed,
and when the second similarity is greater than or equal to a second threshold, determining that the selected candidate case is the second key class data.
5. The case merging method based on multiple dimensions according to claim 1, wherein: the reaching of the preset iteration condition includes:
when the total number of key class data in all the data sets is larger than or equal to a third threshold value, the preset iteration condition is reached;
or alternatively, the process may be performed,
when the number of times of iteration is larger than a fourth threshold value, the preset iteration condition is reached;
or alternatively, the process may be performed,
and if the total number of the key class data in all the current data sets is equal to the total number of the key class data in the data set obtained by the last iteration, the preset iteration condition is reached.
6. The multi-dimensional based case merging method according to any one of claims 1-5, wherein: the method further comprises the steps of:
Displaying the origin case and each data set on a map, and displaying the association relation between the second dimension and the first dimension of each data set and the origin case;
or alternatively, the process may be performed,
displaying the origin case and each data set on a map, responding to a track determining operation, and displaying a space-time motion track of the merging result based on a time dimension and a place dimension; the first dimension and the data set each comprise a time dimension and a place dimension;
or alternatively, the process may be performed,
and displaying the origin case and each data set on a map, and responding to element input operation, and positioning the origin case or the data set corresponding to the input element on the map.
7. A case merging device based on multiple dimensions, comprising:
the acquisition module is used for acquiring the original point cases and the candidate case sets; the origin case comprises a first dimension set, the first dimension set comprises a plurality of first dimensions, the candidate case set comprises a plurality of candidate cases, and each candidate case comprises a plurality of second dimensions;
the selection module is used for selecting one candidate case from the candidate case set;
The first calculation module is used for calculating first similarity of each first dimension and each second dimension which is the same as the first dimension in the selected candidate cases; wherein each first dimension includes at least one first element, each second dimension includes at least one second element, and the calculating a first similarity between each first dimension and each second dimension that is the same as the first dimension in the selected candidate case includes: obtaining a first data depth score for the first element in each of the first dimensions and a second data depth score for the second element in each of the second dimensions; determining a first similarity of each first dimension and each second dimension identical to the first dimension in the selected candidate cases according to a first data depth score of the first element and a second data depth score of a second element identical to the first dimension corresponding to the first element; according to the upper-lower relationship included between the elements of the data dimension, the higher the data weighted depth of the next stage of the data node in the data processing process is, the higher the data weighted depth is, the higher the data depth score is, and the specific calculation formula of the first similarity is as follows:
Wherein ssim (x, y) represents a first degree of similarity, x represents the first dimension, y represents the second dimension of the same type as the first dimension, x i The ith said first element, y in the representation i Representing the ith second element in y, wherein the value of n is the number of the first element or the second element, L x The first data depth score, L, representing the first element y The second data depth score representing the second element;
the first determining module is used for determining second similarity between the original point case and the selected candidate case according to the first similarity and a preset dimension weight;
a second determining module, configured to determine a data set according to the second similarity; the data set comprises at least one of first key class data and second key class data, and the importance of the second key class data is higher than that of the first key class data;
the merging module is used for merging the second key class data with the original point case to obtain a merged case; if the second element in the second dimension of the candidate case corresponding to the second key class data is different from the first element in the first dimension identical to the second dimension, the merging is used for indicating that the second element in the second dimension of the candidate case corresponding to the second key class data is added in the corresponding first dimension identical to the second dimension;
And the iteration module is used for taking the combined case as a new original point case, selecting a new candidate case from candidate cases except the data set in the candidate case set, and returning to the step of respectively calculating the first similarity of each first dimension and each second dimension which is the same as the first dimension in the selected candidate cases, and iterating until a preset iteration condition is reached, thereby obtaining a combined result.
8. The case merging device based on multiple dimensions is characterized by comprising a processor and a memory;
the memory stores a program;
the processor executes the program to implement the method of any one of claims 1-6.
9. A computer readable storage medium, characterized in that the storage medium stores a program which, when executed by a processor, implements the method according to any of claims 1-6.
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