CN116934556B - Target personnel accurate control method based on big data fusion - Google Patents
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Abstract
The application discloses a target personnel accurate management and control method based on big data fusion, which relates to data fusion, and comprises the steps of crawling multi-source heterogeneous data stream selection segments, comparing and transcribing data related to an anchoring library in the multi-source heterogeneous data; acquiring time stamps related to the transcription data, recording a plurality of target personnel related to a plurality of time stamp nodes, and recording the association relation of the target personnel according to the data corresponding to the time stamp nodes; classifying a plurality of time stamp nodes of the same target person, taking each target person as a minimum unit, and expanding the radial association relationship with different target persons according to a time sequence; constructing a plurality of local relation maps, and importing the local relation maps into a data stream to generate a relation map with data: importing data content marked by a time stamp into the association relation of the local relation map, crawling information of target personnel in the time stamp, and outputting the relation map with the data for auxiliary management and control. The application facilitates the staff to manage and control the work of the target staff.
Description
Technical Field
The application relates to the field of data fusion, in particular to a target personnel accurate management and control method based on big data fusion.
Background
For the travel of the involved person under the condition of unknowing, the situation is true, and multi-terminal danger is caused.
Therefore, there is a need for a precise management and control method for target personnel based on big data fusion, which takes the personnel involved as the target personnel in advance, namely, observes the target user, manages and controls the multi-source data information of the target user and realizes precise scheduling.
Disclosure of Invention
The target personnel accurate control method based on big data fusion solves the problems in the prior art; comprising the following steps:
crawling multi-source heterogeneous data stream segments, and comparing and transcribing data related to an anchoring library in the multi-source heterogeneous data, wherein the anchoring library is related to keywords for traveling of target personnel serving as a management and control target;
acquiring time stamps related to the transcription data, recording a plurality of target personnel related to a plurality of time stamp nodes, and recording the association relation of the target personnel according to the data corresponding to the time stamp nodes;
classifying a plurality of time stamp nodes of the same target person, taking each target person as a minimum unit, and expanding the radial association relationship with different target persons according to a time sequence;
constructing a plurality of local relationship maps, wherein each local relationship map is all the relationship maps of at least two target persons with the relationship;
generating a data-bearing relationship graph for the local relationship graph import data flow: importing the data content marked by the time stamp into the association relation of the local relation map, crawling the information of the target personnel in the time stamp, and outputting the relation map with the data for auxiliary management and control.
Preferably, the crawling multi-source heterogeneous data stream is selected, and data related to an anchoring library in the multi-source heterogeneous data are compared and transcribed, wherein the anchoring library is related to keywords for a target person serving as a management and control target, and the crawling multi-source heterogeneous data stream comprises:
and setting different section selecting periods for the structured data, the semi-structured data and the unstructured data, and extracting different multi-source heterogeneous data streams with different duty ratios.
Preferably, comparing the data related to the anchoring library comprises selecting different comparison modes to compare the similarity according to the data type, and transcribing and storing the multi-source heterogeneous data stream segment data with the comparison similarity higher than a threshold value.
Preferably, the target person travel related keywords comprise keywords of various data types, and specifically comprise different data types of the same keyword, and different contents of the same keyword under the same data type are realized.
Preferably, the acquiring the time stamp related to the transcription data, recording a plurality of target personnel related to a plurality of time stamp nodes, and recording the association relation of the target personnel according to the data corresponding to the time stamp nodes, including:
building a relation model about the transcription time stamp, the transcription data corresponding to the time stamp and the target personnel corresponding to the time stamp node;
the relation model is used for analyzing the historical data and positioning the historical data according to the searching content, and specifically, the relation model is used for generating data managed in a relation database table form according to the transcription time stamp, the transcription data corresponding to the time stamp and the target personnel corresponding to the time stamp node;
the relation model is used for analyzing historical data, including searching for a front data segment adjacent to a crawling data stream selection segment based on the type and form of multi-source heterogeneous data in the position adjacent to a time stamp, taking the related keywords in an anchoring base as objects, comparing and searching for a new unknown time stamp in the adjacent front data segment until the new unknown time stamp is not found or the data stream selection segment selected in the previous selection segment period is crawled, stopping, and transcribing the new unknown time stamp, the transcription data corresponding to the new unknown time stamp and the target personnel corresponding to the new unknown time stamp to the relation model.
Preferably, the classifying the plurality of time stamp nodes of the same target person, taking each target person as a minimum unit, expanding the radiation type association relationship with different target persons according to a time sequence, includes:
judging whether target personnel corresponding to a plurality of time stamps are the same target personnel or not, classifying all relevant time stamps corresponding to the target personnel according to whether the unique identification classification of the target personnel belongs to the same unique identification or not, and connecting the incidence relation of the related target personnel according to at least two target personnel corresponding to each time stamp;
and assigning a characteristic strength degree to the association relation of the connecting lines according to the data frequency of the data related to the anchor base corresponding to the time stamp node.
Preferably, the constructing a plurality of local relationship maps, where each local relationship map is all relationship maps including at least two target persons with relationship, includes:
converting the assignment of the characterization intensity into the number of the same association relationship according to the existence of the association relationship and the association relationship intensity of other target personnel, calculating the number of paths of each target personnel reaching other target personnel, setting path weights, setting the longer the paths, setting the lower the weights of the paths, calculating the path weight sum of each target personnel reaching other target personnel, deleting the association relationship of two target personnel lower than the weight sum threshold, generating a local relationship map of a three-dimensional space, wherein the local relationship map comprises each target personnel, and removing the association relationship of the two target personnel lower than the weight sum threshold, thereby obtaining a set of radiation type association relationships of all relevant time stamps.
Preferably, the importing the local relationship map into the data stream generates a data-bearing relationship map: importing the data content marked by the time stamp into the association relation of the local relation map, crawling the information of the target personnel in the time stamp, and outputting the relation map with the data for auxiliary management and control, wherein the method comprises the following steps of:
in the local relationship graph, the relationship model multiline Cheng Luru data is invoked and a exposable local relationship graph is generated.
The principle of the application is as follows:
the multisource data fusion is used for controlling target personnel, the accurate control is realized through the technical scheme, the richness of keyword types is achieved, the target personnel accurately judges unique identification, the non-section selecting part of the crawling history timestamp is supplemented, the unitized time sequence radiation process is minimized, and the accurate control is realized through the technical means;
the realization logic and beneficial effects of the application are as follows:
the data of different structures, different data sources and different types have differences, the data are kept in the differences, namely, the original state of the data under machine conversion is kept, error interference of recognition and conversion algorithms is prevented, the original state multi-source heterogeneous data position and content with high data contrast of an anchoring library are quickly searched through the setting of a relation model, then the local relation map is formed through comparison, searching and screening, the local relation map is attached to the target personnel information and the data content, the time stamp is displayed, meanwhile, the data form, the type and the content of the original state are kept in the data content, and finally the local relation map serves for management and control.
According to the target personnel accurate management and control method based on big data fusion, the searching data, the data positioning and the data displaying are separated, the data content of the same target personnel is classified, and the complexity of presentation is greatly saved;
the utility model provides a plurality of local relation atlas through visual display, makes things convenient for the staff to manage and control target personnel work.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the present application and are incorporated in and constitute a part of this application, illustrate embodiments of the present application and together with the description serve to explain the principle of the present application. In the drawings:
fig. 1 is a flowchart of a target person precision management and control method based on big data fusion according to an exemplary embodiment of the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
For the travel of the involved person under the condition of unknowing, the situation is true, and multi-terminal danger is caused. Because of the cross-regional influence, the peripheral data acquisition device and the position data are all unfavorable, so that statistics and data investigation are very difficult, under the existing condition, manual investigation is mostly adopted, an information channel is received through a personnel uploading mode, trip hazards are prevented, because the intelligent equipment is high in popularization degree, but high in information redundancy degree, the timeliness requirement on data processing is high for tasks belonging to the task with high timeliness requirement, the data volume is reduced as much as possible, and personnel scheduling is adaptively arranged on the premise of limited resources, so that management and control of target personnel to be observed are realized.
Therefore, there is a need for a precise management and control method for target personnel based on big data fusion, which takes the personnel involved as the target personnel in advance, namely, observes the target user, manages and controls the multi-source data information of the target user and realizes precise scheduling.
The application scene is in various target personnel observation systems.
The application provides a target personnel accurate management and control method based on big data fusion, which aims at solving the technical problems in the prior art.
The following describes the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Example 1:
the application provides a target personnel accurate management and control method based on big data fusion, as shown in fig. 1, comprising the following steps:
s1, crawling multi-source heterogeneous data stream segments, and comparing and transcribing data related to an anchoring library in the multi-source heterogeneous data, wherein the anchoring library is related to keywords for traveling of target personnel serving as a control target; setting different segment selection periods for structured data, semi-structured data and unstructured data, extracting different multi-source heterogeneous data streams with different duty ratios, comparing data related to an anchor library, selecting different comparison modes according to data types to compare similarity, and copying and storing the multi-source heterogeneous data stream segment selection data with the comparison similarity higher than a threshold value. The target person travel related keywords comprise keywords of various data types, specifically comprise different data types of the same keywords, and different contents of the same keywords under the same data types are realized. For example, keyword data types including text, voice, and the like; meanwhile, as a similarity comparison mode, characters are directly compared, and whether the characters are in a to-be-travelled state or not can be confirmed due to the fact that the number of the characters is large, a plurality of groups of threshold values for the comparison of the characters are set, the threshold values further comprise the overall accuracy of similarity of sound wave comparison, and if the overall accuracy exceeds the threshold values, fidelity can be achieved; because the display forms of the data of different structure types are different, for the data structure type with stronger relevance, for example, the sampling period of the unstructured data is smaller, because the unstructured data has larger compared systematic errors, the unstructured data is subjected to multi-sampling to fade, the structured data or the semi-structured data is acquired by adopting a larger sampling period, and because the data flow is overlarge and the flow speed is high, only periodic data flow sections are crawled in a communication frequency band, and the section length can be adaptively adjusted according to the two types of the data involved in the data flow. Because the data comparison similarity can cause different system errors caused by the data types, all the compared data are recorded, namely the compared data are transcribed for subsequent assistance in manual analysis.
S2, acquiring time stamps related to the transcription data, recording a plurality of target persons related to a plurality of time stamp nodes, and recording the association relation of the target persons according to the corresponding data of the time stamp nodes; building a relation model about the transcription time stamp, the transcription data corresponding to the time stamp and the target personnel corresponding to the time stamp node; the relation model is used for analyzing the historical data and positioning the historical data according to the searching content, and specifically, the relation model is used for generating data managed in a relation database table form according to the transcription time stamp, the transcription data corresponding to the time stamp and the target personnel corresponding to the time stamp node; the relation model is used for analyzing historical data, including searching for a front data segment adjacent to a crawling data stream selection segment based on the type and form of multi-source heterogeneous data in the position adjacent to a time stamp, taking the related keywords in an anchoring base as objects, comparing and searching for a new unknown time stamp in the adjacent front data segment until the new unknown time stamp is not found or the data stream selection segment selected in the previous selection segment period is crawled, stopping, and transcribing the new unknown time stamp, the transcription data corresponding to the new unknown time stamp and the target personnel corresponding to the new unknown time stamp to the relation model. The method comprises the steps of analyzing the data under the record, analyzing the time stamps, and arranging the association relation of the target personnel where the time stamps are located, namely, at least data communication between more than two target personnel is carried out on the data under each time stamp, when the data corresponding to the time stamps are searched, the data is obtained by crawling forward by adopting a relation model, keywords in an anchor library can not be found directly, the keywords are directly found by the operation, the data integrity of the time stamps acquired in the section is supplemented, and when the time stamps are recorded only, the occupied space for the data transfer is reduced, and meanwhile, the influence of the data stream on the original data history caused by the opposite impact is reduced.
S3, classifying a plurality of time stamp nodes of the same target person, taking each target person as a minimum unit, and expanding the radiation type association relation with different target persons according to a time sequence; judging whether target personnel corresponding to a plurality of time stamps are the same target personnel or not, classifying all relevant time stamps corresponding to the target personnel according to whether the unique identification classification of the target personnel belongs to the same unique identification or not, and connecting the incidence relation of the related target personnel according to at least two target personnel corresponding to each time stamp; and assigning a characteristic strength degree to the association relation of the connecting lines according to the data frequency of the data related to the anchor base corresponding to the time stamp node. The method comprises the steps that a target person externally comprises a radiation type association relation, a plurality of pieces of transcription data which are arranged in a time sequence are internally related, the association relation of the external radiation type has an association relation with the internal transcription data, and the final display of the person identification is unified because the person identification modes are different in different multi-source heterogeneous data in a data stream, when the communication record relates to the transcription data, the corresponding time stamp is generated, and the plurality of time stamps possibly correspond to the association relation of the same external radiation type, namely, the weight of the association relation is increased at the moment.
S4, constructing a plurality of local relation maps, wherein each local relation map is all relation maps of at least two target persons with association relations; converting the assignment of the characterization intensity into the number of the same association relationship according to the existence of the association relationship and the association relationship intensity of other target personnel, calculating the number of paths of each target personnel reaching other target personnel, setting path weights, setting the longer the paths, setting the lower the weights of the paths, calculating the path weight sum of each target personnel reaching other target personnel, deleting the association relationship of two target personnel lower than the weight sum threshold, generating a local relationship map of a three-dimensional space, wherein the local relationship map comprises each target personnel, and removing the association relationship of the two target personnel lower than the weight sum threshold, thereby obtaining a set of radiation type association relationships of all relevant time stamps. The local relation map is displayed in a mode, the unique problem is that a complex association relation exists, external association is generated between two adjacent local relation maps, and the s4 step is implemented to disconnect the association relation with low influence on the whole control method, so that the association relation of a core which can be used for analysis is reserved, and interference is reduced.
s5, generating a data-carrying relationship map for the local relationship map importing data flow: importing the data content marked by the time stamp into the association relation of the local relation map, crawling the information of the target personnel in the time stamp, and outputting the relation map with the data for auxiliary management and control. In the local relationship graph, the relationship model multiline Cheng Luru data is invoked and a exposable local relationship graph is generated. The method is realized through steps s1-s5, different structures, different data sources and different types of data are different, the difference is kept, namely, the original state of the data under machine conversion is kept, error interference of recognition and conversion algorithms is prevented, the original state multi-source heterogeneous data position and content with high data contrast of an anchoring library are quickly searched through the setting of a relation model, then the local relation map is formed through comparison, searching and screening, the information and the data content of a target person are attached, the time stamp is displayed, meanwhile, the data form, the type and the content of the original state are kept, and finally the local relation map serves for management and control.
In the several embodiments provided in this application, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in hardware plus software functional modules.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
It will be appreciated by those skilled in the art that embodiments of the invention may be provided as methods or systems. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (5)
1. The target personnel accurate control method based on big data fusion is characterized by comprising the following steps of:
crawling multi-source heterogeneous data stream segments, and comparing and transcribing data related to an anchoring library in the multi-source heterogeneous data, wherein the anchoring library is related to keywords for traveling of target personnel serving as a management and control target;
acquiring time stamps related to the transcription data, recording a plurality of target personnel related to a plurality of time stamp nodes, and recording the association relation of the target personnel according to the data corresponding to the time stamp nodes; building a relation model about the transcription time stamp, the transcription data corresponding to the time stamp and the target personnel corresponding to the time stamp node; the relation model is used for analyzing the historical data and positioning the historical data according to the searching content, and specifically, the relation model is used for generating data managed in a relation database table form according to the transcription time stamp, the transcription data corresponding to the time stamp and the target personnel corresponding to the time stamp node; the relation model is used for analyzing historical data, including searching a front data segment adjacent to a crawling data stream selection segment based on the type and form of multi-source heterogeneous data in the position adjacent to a time stamp, taking a keyword in an anchoring base as an object, comparing and searching a new unknown time stamp in the adjacent front data segment until the new unknown time stamp is not found or the data stream selection segment selected in the previous selection segment period is crawled, stopping, and transcribing the new unknown time stamp, the transcription data corresponding to the new unknown time stamp and a target person corresponding to the new unknown time stamp to the relation model;
classifying a plurality of time stamp nodes of the same target person, taking each target person as a minimum unit, and expanding the radial association relationship with different target persons according to a time sequence; judging whether target personnel corresponding to a plurality of time stamps are the same target personnel or not, classifying all relevant time stamps corresponding to the target personnel according to whether the unique identification classification of the target personnel belongs to the same unique identification or not, and connecting the incidence relation of the related target personnel according to at least two target personnel corresponding to each time stamp;
the method further comprises the steps of assigning a value representing the strength degree to the association relation of the connecting lines according to the data frequency of the data related to the anchor base corresponding to the time stamp node;
constructing a plurality of local relationship maps, wherein each local relationship map is all the relationship maps of at least two target persons with the relationship; converting the assignment of the characterization intensity into the number of the same association according to the existence of the association and the association intensity of other target personnel, calculating the number of paths of each target personnel reaching other target personnel, setting path weights, setting the longer the paths, setting the lower the weights of the paths, calculating the path weight sum of each target personnel reaching other target personnel, deleting the association of two target personnel lower than the weight sum threshold, generating a local relationship map of a three-dimensional space, wherein the local relationship map comprises each target personnel, and removing the association of the two target personnel lower than the weight sum threshold and the set of the radial association of all relevant timestamps owned by the target personnel;
generating a data-bearing relationship graph for the local relationship graph import data flow: importing the data content of the time stamp nodes into the association relation of the local relation map, crawling the information of the target personnel in the time stamp, and outputting the relation map with data for auxiliary management and control.
2. The method for accurately managing and controlling target personnel based on big data fusion according to claim 1, wherein the crawling multi-source heterogeneous data stream is selected, data related to an anchor library in the multi-source heterogeneous data are compared and transcribed, the anchor library is related to keywords for the travel of the target personnel serving as a management and control target, and the method comprises the following steps:
and setting different section selecting periods for the structured data, the semi-structured data and the unstructured data, and extracting different multi-source heterogeneous data streams with different duty ratios.
3. The method for accurately managing and controlling the target personnel based on big data fusion according to claim 2, wherein comparing the data related to the anchoring library comprises selecting different comparison modes according to data types to compare similarity, and transcribing and storing multi-source heterogeneous data stream segment data with the comparison similarity higher than a threshold value.
4. The method for accurately managing and controlling the target personnel based on big data fusion according to any one of claims 1 to 3, wherein the target personnel travel related keywords comprise keywords of various data types, specifically different data types comprising the same keyword, and different contents of the same keyword under the same data type are realized.
5. The method for accurately controlling the target personnel based on big data fusion according to claim 1, wherein the importing of the local relation map into the data stream generates a data relation map: importing the data content marked by the time stamp into the association relation of the local relation map, crawling the information of the target personnel in the time stamp, and outputting the relation map with the data for auxiliary management and control, wherein the method comprises the following steps of:
in the local relationship graph, the relationship model multiline Cheng Luru data is invoked and a exposable local relationship graph is generated.
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