CN111861830A - Information cloud platform - Google Patents
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Abstract
The application discloses an intelligence cloud platform, which comprises a data layer, an intermediate layer and a service layer, wherein the data layer is used for acquiring original data and carrying out data standardization processing on the original data to obtain standardized data; the intermediate layer is used for performing data dimension reduction processing on the standardized data to obtain a plurality of data sets; and the service layer is used for realizing a preset intelligence function according to the plurality of data sets. Through the information cloud platform, the information efficiency can be improved, and the information department can be helped to effectively respond to new challenges brought by new situation.
Description
Technical Field
The application relates to the technical field of big data construction and application, in particular to an information cloud platform.
Background
Since the construction of big intelligence data, the technology has been continuous for many years, and the overall resultant force in terms of data total amount, storage capacity, processing capacity and the like is mainly focused. However, the current big data construction mode of the information cannot be continuously optimized, the scale effect cannot be formed, the data value mining capability based on the information service is not formed, the value of the data is not really reflected, and the method is mainly represented by the following steps: the convergence and fusion of external data resources are insufficient, and the problems of undefined access target and poor implementability exist; the innovation of the information mode is insufficient, the decision-making scientificity, the management precision and the service high-efficiency degree need to be optimized; data centers of information departments in various regions are built in a centralized mode rather than in an intensive mode, for example, the information departments in various regions build data centers which are supported by a cloud computing technology, but actually, the information departments in various regions fight each other, the interoperability has a large problem, real unification is not realized in management, unified scheduling of resources cannot be realized, and the goals of intensive management and efficient application cannot be achieved; the phenomenon of 'information island' caused by data barriers still exists, for example, from district/county information, ground-level city and provincial-level information, uniform large data value mining around multiple information departments is not formed. Therefore, when the current large information data is applied, the efficiency of information management is extremely low.
Disclosure of Invention
The embodiment of the application provides an information cloud platform, which is transversely used for multiple types of information departments and longitudinally used for various levels of organizations, and data is subjected to standardized processing through a data layer so as to meet data requirements in different application scenes, thereby being beneficial to improving the information efficiency and helping the information departments to effectively deal with new challenges brought by new situations.
In a first aspect, an embodiment of the present application provides an intelligence cloud platform, including a data layer, an intermediate layer and a service layer,
the data layer is used for acquiring original data and carrying out data standardization processing on the original data to obtain standardized data;
the intermediate layer is used for performing data dimension reduction processing on the standardized data to obtain a plurality of data sets;
and the service layer is used for realizing a preset intelligence function according to the plurality of data sets.
Optionally, the intelligence cloud platform further comprises data connector middleware and communication agent middleware, the raw data comprises first raw data, second raw data and third raw data, and the data layer comprises:
an original library for acquiring the first original data through the data connector middleware and the second original data through the communication agent middleware, and storing the first original data and the second original data;
The private library is used for acquiring and storing the third original data, and the third original data is the original data transmitted to the data layer through the data connector middleware or the communication agent middleware;
the standard library is used for acquiring the first original data and the second original data from the original library, acquiring third original data from the private library, performing data standardization processing on the first original data, the second original data and the third original data to obtain standardized data, and storing the standardized data;
and the data interface is used for transmitting the standardized data stored in the standard library to the middle layer.
Optionally, the standard library comprises:
the data dictionary module is used for constructing a data dictionary standard so as to realize unified management of standardized data;
a data resource registration module, configured to register the first original data, the second original data, and the third original data to form a corresponding data table structure in the standard library, and establish a standardized data structure according to the data dictionary standard and the data table structure;
The data blood relationship module is used for establishing a data blood relationship according to the data dictionary standard and the standardized data structure so as to reduce data dimensionality;
the first storage module is configured to perform data normalization processing on the first raw data, the second raw data, and the third raw data according to the data dictionary standard, the normalized data structure, and the data blood relationship, to obtain normalized data, and store the normalized data.
Optionally, the data lineage relationship includes a first hierarchical relationship, a second hierarchical relationship, a third hierarchical relationship and a fourth hierarchical relationship, the first hierarchical relationship being a data owner, the second hierarchical relationship being a data warehouse, the third hierarchical relationship being a data table, and the fourth hierarchical relationship being a data field.
Optionally, the architecture of the data warehouse is a star-connected network that uses a fact table as a main key and uses an identity dimension table, a archive dimension table, a relationship dimension table, a trajectory dimension table, a monitoring entity list dimension table, an economic dimension table, an organization dimension table and an address dimension table as outer keys, the fact table includes indexes composed of identities, archives, relationships, trajectories, monitoring entity lists, economies, organizations and addresses, the trajectory dimension table includes a personnel trajectory dimension and an article trajectory dimension, the address dimension table includes an IP address library dimension and an address library dimension, and the archive dimension includes a personnel archive dimension, a case archive dimension, an article archive dimension, a site archive dimension, a group archive dimension and an event archive dimension.
Optionally, the data owner includes people, cases, places, events and items, and the first storage module includes:
the human dimension data dimension pre-reducing unit is used for performing data dimension pre-reduction on the first original data, the second original data and the third original data according to human dimensions to obtain a first target data set;
the case dimension data dimension pre-reducing unit is used for performing data dimension pre-reduction on the first original data, the second original data and the third original data according to case dimensions to obtain a second target data set;
a place dimension data dimension pre-reducing unit, configured to perform data dimension pre-reduction on the first original data, the second original data, and the third original data according to a place dimension to obtain a third target data set;
the event dimension data dimension pre-reducing unit is used for performing data dimension pre-reduction on the first original data, the second original data and the third original data according to an event dimension to obtain a fourth target data set;
and the article dimension data dimension pre-reducing unit is used for performing data dimension pre-reduction on the first original data, the second original data and the third original data according to article dimensions to obtain a fifth target data set.
Optionally, the first storage module further includes:
the first data standardization unit is used for carrying out data standardization processing on the data in the first target data set to obtain first standardized data;
the second data standardization unit is used for carrying out data standardization processing on the data in the second target data set to obtain second standardized data;
the third data standardization unit is used for carrying out data standardization processing on the data in the third target data set to obtain third standardized data;
the fourth data standardization unit is used for carrying out data standardization processing on the data in the fourth target data set to obtain fourth standardized data;
and the fifth data standardization unit is used for carrying out data standardization processing on the data in the fifth target data set to obtain fifth standardized data.
Optionally, the performing data normalization processing on the data in the first target data set to obtain first normalized data includes:
acquiring data associated with a preset data field in the first target data set according to the preset data field;
and carrying out data standardization processing on the data associated with the preset data field according to a preset data standard to obtain standardized data of the preset data field.
Optionally, the first storage module further includes:
a first storage unit for storing the first normalized data according to the data dictionary standard, the normalized data structure, and the data consanguinity relationship;
a second storage unit for storing the second normalized data according to the data dictionary standard, the normalized data structure, and the data consanguinity relationship;
a third storage unit for storing the third normalized data according to the data dictionary standard, the normalized data structure, and the data consanguinity relationship;
a fourth storage unit for storing the fourth normalized data according to the data dictionary standard, the normalized data structure, and the data consanguinity relationship;
a fifth storage unit for storing the fifth normalized data according to the data dictionary standard, the normalized data structure, and the data consanguinity relationship.
Optionally, the standard library further comprises:
and the view library is used for acquiring target video data and target image data from the first original data, the second original data and the third original data, carrying out data standardization processing on the target video data and the target image data to obtain standardized video data and standardized image data, and storing the standardized video data and the standardized image data.
In a second aspect, an embodiment of the present application provides a client, where the client includes the service layer of the intelligence cloud platform in the first aspect.
In a third aspect, an embodiment of the present application provides a server, where the server includes the data layer and the middle layer of the intelligence cloud platform described in the first aspect.
The information cloud platform comprises a data layer, an intermediate layer and a service layer, wherein the data layer is used for acquiring original data and carrying out data standardization processing on the original data to obtain standardized data; the intermediate layer is used for performing data dimension reduction processing on the standardized data to obtain a plurality of data sets; and the service layer is used for realizing a preset intelligence function according to the plurality of data sets. Therefore, the information cloud platform provided by the application can be used for transversely serving multiple types of information departments and longitudinally serving each level of organization, and data is subjected to standardized processing through the data layer so as to meet the data requirements in different application scenes, so that the information efficiency can be improved, and the information can be helped to effectively meet new challenges brought by new situation.
These and other aspects of the present application will be more readily apparent from the following description of the embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of a software architecture of an intelligence cloud platform according to an embodiment of the present disclosure.
Fig. 2 is a schematic diagram of a hierarchy of data relationship provided in an embodiment of the present application.
Fig. 3 is a schematic structural diagram of a data warehouse according to an embodiment of the present application.
Fig. 4 is a schematic diagram of a software architecture of another intelligence cloud platform according to an embodiment of the present disclosure.
Fig. 5 is a schematic diagram of a hardware structure of an intelligence cloud platform according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase 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. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Referring to fig. 1, fig. 1 is a schematic diagram of a software architecture of an intelligence cloud platform according to an embodiment of the present disclosure. As shown in fig. 1, the intelligence cloud platform includes: the data layer is used for acquiring original data and carrying out data standardization processing on the original data to obtain standardized data; the intermediate layer is used for performing data dimension reduction processing on the standardized data to obtain a plurality of data sets; and the service layer is used for realizing a preset intelligence function according to the plurality of data sets.
For example, the data layer may obtain original data, and construct a data dictionary standard through a data dictionary to realize unified management of standardized data; through data resource registration, each data resource represents a table structure stored in a database, and a standardized data structure is established by using a data dictionary; the data standardization and the data standardization are realized in a data layer, the data consanguinity relation is established through a data dictionary, the dimensionality of big data is reduced, and then a standard library of data storage is established through the data dictionary, the data resource registration and the data consanguinity relation, so that the data of a client is standardized, the purposes of data cleaning and conversion are achieved, and standardized services are provided for upper-layer services.
The middle layer performs dimensionality reduction processing on data such as multiple sources based on the understanding of intelligence services, so that data modeling of dimensionalities such as 'human and land things' is achieved, namely, the data with smaller data volume is obtained by classifying all data in the standard library according to service requirement types.
The service layer realizes a preset intelligence function according to the data provided by the data layer and the middle layer, namely realizes value application to a target client based on the data encapsulated by the data layer and the middle layer.
In addition, it should be noted that the service layer is disposed in the client device, and the data layer and the middle layer are disposed in the server.
Therefore, the information cloud platform provided by the application can be used for transversely serving multiple types of information departments and longitudinally serving each level of organization, and data is subjected to standardized processing through the data layer so as to meet the data requirements in different application scenes, thereby being beneficial to improving the information efficiency and helping the information departments to effectively deal with new challenges brought by new situation.
In one possible example, the intelligence cloud platform further comprises data connector middleware and communication agent middleware, the raw data comprising first raw data, second raw data, and third raw data, the data layer comprising:
an original library for acquiring the first original data through the data connector middleware and the second original data through the communication agent middleware, and storing the first original data and the second original data;
the private library is used for acquiring and storing the third original data, and the third original data is the original data transmitted to the data layer through the data connector middleware or the communication agent middleware;
The standard library is used for acquiring the first original data and the second original data from the original library, acquiring third original data from the private library, performing data standardization processing on the first original data, the second original data and the third original data to obtain standardized data, and storing the standardized data;
and the data interface is used for transmitting the standardized data stored in the standard library to the middle layer.
The middleware of the data connector is a data access product facing multiple data sources (a third-party platform), can realize access, cleaning and conversion of multi-source data, and enables the data to enter a data layer. Specifically, the data layer comprises an original library and a standard library, and the data connector middleware accesses data into the original library of the data layer to construct the standard library; the third-party platform comprises an internet bar system, a railway (high-speed rail) system, a hotel system, a gas-water electric system, a bank system, an internet platform and the like.
The communication agent middleware is an access software product for collecting data facing the sensing equipment, and realizes the functions of configuration and management of the sensing equipment and data receiving, storage and distribution; the communication agent middleware may also transmit data in reconnaissance, micro-activity, social security, Mobile intelligence Platform (PMP), etc. to the data layer.
It is to be noted that the private repository is used for storing structured data and unstructured data, i.e. data other than the first user data and the second user information, such as user data provided by citizens. With respect to private libraries, data is not entered through data connectors nor through communication agents, but rather is imported by the intelligence personnel themselves. Specifically, the intelligence cloud platform has a preset function, namely, a user uploads a data table, and then the data is stored in a private library. Private libraries and public libraries (standard libraries) are parallel, so called private libraries, because the vast amount of data gathered by folks is unverified, which can be confusing if you add to a public library without permission.
It can be seen that, in this example, the data layer stores the acquired data in the original library and the private library respectively according to different data sources, then performs data standardization on the acquired data, stores the standardized data in the standard library, and transmits the standardized data in the standard library to the intermediate layer through the data interface, so that the acquired data can meet data requirements in different application scenarios after being subjected to standardized processing, thereby facilitating improvement of information effectiveness and helping the information department to effectively cope with new challenges brought by new situations.
In one possible example, the criteria library includes:
the data dictionary module is used for constructing a data dictionary standard so as to realize unified management of standardized data;
a data resource registration module, configured to register the first original data, the second original data, and the third original data to form a corresponding data table structure in the standard library, and establish a standardized data structure according to the data dictionary standard and the data table structure;
the data blood relationship module is used for establishing a data blood relationship according to the data dictionary standard and the standardized data structure so as to reduce data dimensionality;
the first storage module is configured to perform data normalization processing on the first raw data, the second raw data, and the third raw data according to the data dictionary standard, the normalized data structure, and the data blood relationship, to obtain normalized data, and store the normalized data.
As can be seen, in this example, the data layer is specifically configured to perform data normalization processing on the raw data through a data dictionary, data resource registration, and data relationship to obtain the normalized data, and then store the normalized data in a standard library.
In one possible example, referring collectively to fig. 2, the data lineage relationships include a first hierarchical relationship that is a data owner, a second hierarchical relationship that is a data warehouse, a third hierarchical relationship that is a data table, and a fourth hierarchical relationship that is a data field.
The data blooding relationship can be used for representing the generation, processing fusion, circulation and final extinction of the data, so that references are provided for data traceability, data value evaluation, data quality evaluation, data archiving and destruction.
In one possible example, referring to fig. 3 together, the data warehouse is configured as a star-connected network with a fact table as a main key and an identity dimension table, a archive dimension table, a relationship dimension table, a trajectory dimension table, a monitoring entity list dimension table, an economic dimension table, an organizational dimension table, and an address dimension table as outer keys, the fact table includes indexes composed of identities, archives, relationships, trajectories, monitoring entity lists, economies, organizations, and addresses, the trajectory dimension table includes a personnel trajectory dimension and an item trajectory dimension, the address dimension table includes an IP address pool dimension and an address pool dimension, and the archive dimension includes a personnel archive dimension, a case archive dimension, an item archive dimension, a site archive dimension, a group archive dimension, and an event archive dimension.
Therefore, in this example, the data warehouse after data standardization is a relational database with a star structure, which is beneficial to searching the data warehouse and improving the efficiency of accessing standardized data.
In one possible example, the data owner includes a person, a case, a place, an event, and an item, the first storage module includes:
the human dimension data dimension pre-reducing unit is used for performing data dimension pre-reduction on the first original data, the second original data and the third original data according to human dimensions to obtain a first target data set;
the case dimension data dimension pre-reducing unit is used for performing data dimension pre-reduction on the first original data, the second original data and the third original data according to case dimensions to obtain a second target data set;
a place dimension data dimension pre-reducing unit, configured to perform data dimension pre-reduction on the first original data, the second original data, and the third original data according to a place dimension to obtain a third target data set;
the event dimension data dimension pre-reducing unit is used for performing data dimension pre-reduction on the first original data, the second original data and the third original data according to an event dimension to obtain a fourth target data set;
And the article dimension data dimension pre-reducing unit is used for performing data dimension pre-reduction on the first original data, the second original data and the third original data according to article dimensions to obtain a fifth target data set.
For example, if a trajectory is generated by the activity of a person, then the dimension of the data is reduced around the data generated by the "person". Therefore, even if the total amount of data is 1000 hundred million pieces per day, after the data is subjected to dimensionality reduction, for 3000 thousands of people in a city, each person has several hundred pieces of data on average. Therefore, dimension reduction can be performed on the total data amount, and reasonable aggregation of 3000 ten thousand people can be realized through an algorithm.
Therefore, in this example, the dimension of the original data can be reduced on the total data amount by the data pre-dimension reduction around the human dimension, the case dimension, the place dimension, the event dimension and the article dimension, so that the complexity of the original data is reduced, and the efficiency of data processing is improved.
In one possible example, the first storage module further comprises:
the first data standardization unit is used for carrying out data standardization processing on the data in the first target data set to obtain first standardized data;
The second data standardization unit is used for carrying out data standardization processing on the data in the second target data set to obtain second standardized data;
the third data standardization unit is used for carrying out data standardization processing on the data in the third target data set to obtain third standardized data;
the fourth data standardization unit is used for carrying out data standardization processing on the data in the fourth target data set to obtain fourth standardized data;
and the fifth data standardization unit is used for carrying out data standardization processing on the data in the fifth target data set to obtain fifth standardized data.
As can be seen, in this example, data normalization processing may be performed on data after dimensionality reduction of different data, so as to obtain normalized data of different data dimensions.
In one possible example, the performing data normalization on the data in the first target data set to obtain first normalized data includes: acquiring data associated with a preset data field in the first target data set according to the preset data field; and carrying out data standardization processing on the data associated with the preset data field according to a preset data standard to obtain standardized data of the preset data field.
The preset data field may be a plurality of preset data fields corresponding to a plurality of preset data standards, and the plurality of preset data fields correspond to the plurality of preset data standards one to one.
For example, hotel data is of many types, five-star hotel data is relatively standardized, and three-star hotel data is relatively disorderly. Then, for hotel data as well, unified management is required for the intelligence department, so that data standardization is required. Specifically, for example, a field of an identity card (preset data field), a field of an identity card in the five-star hotel data is named as ID1, and a field of an identity card in the three-star hotel data is named as ID2, but for the intelligence department, the fields need to be processed, which are collectively called as an ID (preset data standard), and this is data standardization processing; for another example, the gender field (preset data field), 1 in the five-star hotel data represents a male, 2 represents a female, and 3 represents the other, a, B, and C in the three-star hotel data represent a male, a female, and the other, and for the intelligence department, the data must be "known" uniformly, for example, 11 represents a male, 12 represents a female, and 19 represents the other (preset data standard).
It should be noted that, for the specific step of performing data normalization processing on the data in the second target data set, the third target data set, the fourth target data set, and the fifth target data set, reference may be made to the step of performing data normalization processing on the data in the first target data set, and details are not repeated here.
Therefore, in this example, the original data of different associated data standards is obtained through the data field and unified into one data standard, so that data standardization is realized, the degree of confusion of data is reduced, and the efficiency of data use is improved.
In one possible example, the first storage module further comprises:
a first storage unit for storing the first normalized data according to the data dictionary standard, the normalized data structure, and the data consanguinity relationship;
a second storage unit for storing the second normalized data according to the data dictionary standard, the normalized data structure, and the data consanguinity relationship;
a third storage unit for storing the third normalized data according to the data dictionary standard, the normalized data structure, and the data consanguinity relationship;
A fourth storage unit for storing the fourth normalized data according to the data dictionary standard, the normalized data structure, and the data consanguinity relationship;
a fifth storage unit for storing the fifth normalized data according to the data dictionary standard, the normalized data structure, and the data consanguinity relationship.
Therefore, in this example, standardized data obtained by dimensionality reduction and re-standardization of data with different dimensions can be respectively stored, which is beneficial to improving the efficiency of using standardized data.
In one possible example, the criteria library further comprises: and the view library is used for acquiring target video data and target image data from the first original data, the second original data and the third original data, carrying out data standardization processing on the target video data and the target image data to obtain standardized video data and standardized image data, and storing the standardized video data and the standardized image data.
In one possible example, the view library includes:
the video/image data standardization module is used for acquiring target video data and target image data from the first original data, the second original data and the third original data, and carrying out data standardization processing on the target video data and the target image data to obtain standardized video data and standardized image data;
And the video/image standardized data storage module is used for storing the standardized video data and the standardized image data.
Therefore, in this example, the data layer separately performs data standardization on the acquired video and image data, and then separately stores the acquired standardized video data and standardized video data, which is beneficial to improving the application efficiency of the intelligence cloud platform on the video and image data.
Referring to fig. 4, fig. 4 is a schematic diagram of a software architecture of another intelligence cloud platform according to an embodiment of the present disclosure. The intelligence cloud platform includes: the data layer is used for acquiring original data and carrying out data standardization processing on the original data to obtain standardized data; the intermediate layer is used for performing data dimension reduction processing on the standardized data to obtain a plurality of data sets; and the service layer is used for realizing a preset intelligence function according to the plurality of data sets.
The intelligence cloud platform shown in fig. 4 is an improvement on the intelligence cloud platform shown in fig. 1, and the structure in fig. 4 that is the same as that in fig. 1 refers to the description shown in fig. 1.
In one possible example, the original library comprises:
The first data integration module is used for acquiring data from an internet bar system, a hotel system, a high-speed rail system, a gas-water electric system, a bank system and the internet through the data connector middleware to perform data integration to obtain first original data, and acquiring data from sensing equipment, a mobile information platform, a reconnaissance platform, a micro-activity platform and a social health agency ring platform through the communication agent middleware to perform data integration to obtain second original data;
and the second storage module is used for storing the first original data and the second original data.
It should be noted that, the first data integration module may further obtain data from other third party platform systems except for an internet bar system, a hotel system, a high-speed rail system, a gas-water electric system, a bank system, and the internet through a data connector middleware, and the first data integration module may further obtain data from other information platforms except for a sensing device, a mobile information platform, a reconnaissance platform, a micro-activity platform, and a social health agency platform through a communication agent middleware, which is only an exemplary illustration, and this application is not limited thereto.
In one possible example, the first data consolidation module comprises:
the acquisition unit is used for acquiring data from an internet bar system, a hotel system, a high-speed rail system, a gas-water power system, a bank system and the internet through the data connector middleware and acquiring data from sensing equipment, a mobile information platform, a reconnaissance platform, a micro-activity platform and a social health and social withdrawal platform through the communication agent middleware;
the storage unit is used for classifying and storing the acquired data according to the data source;
the extraction unit is used for respectively extracting the characteristics of the data acquired from different data sources to correspondingly obtain a plurality of groups of data characteristics;
the comparison unit is used for comparing the characteristics of the multiple groups of data characteristics and determining the similarity between the data of different data sources;
and the integration unit is used for performing data integration on the acquired data according to the similarity between the feature comparison result and the data of different data sources.
The data characteristics comprise data type, data size, data content, key data and the like, and the Similarity can be calculated through calculation formulas such as Cosine Similarity (Cosine Similarity), Minkowski Distance (Minkowski Distance), Manhattan Distance (Manhattan Distance), euclidean Distance (euclidean Distance) and the like.
In one possible example, the integration unit is specifically configured to: if the number of the same data features in the multiple groups of data features is larger than a preset number, extracting data of one data source from the data of the different data sources for integration; if the number of the same data features in the multiple groups of data features is not greater than a preset number, judging whether the similarity between the data of different data sources is greater than a preset threshold value; if the similarity between the data of the different data sources is greater than a preset threshold value, extracting the data of one data source from the data of the different data sources for integration; and if the similarity between the data of the different data sources is not greater than a preset threshold, extracting all the data of the different data sources for integration.
In this example, the first integration module in the original library may collect, sort, clean, and convert data from different data sources and load the data into a new data source for storage, and may provide data in a data integration manner with a unified data view for data standardization in the standard library.
In one possible example, the private library comprises:
The second data integration module is used for acquiring structured data and unstructured data and performing data integration on the structured data and the unstructured data to obtain third original data;
and the third storage module is used for storing the third original data.
In this example, the second integration module in the private library may collect, sort, clean, convert, and load the structured data and the unstructured data into a new data source for storage, and may provide data of a data integration manner of a unified data view for data standardization in the standard library.
The intermediate layer is specifically configured to perform data dimension reduction processing according to at least one of the following dimensions to obtain a plurality of data sets: unifying identity, personnel files, case files, item files, location files, relationship files, personnel tracks, item tracks, monitoring entity lists, economic files, organizations, group files, IP address repositories, event files, address repositories.
Specifically, the unified identity includes: real identity information, virtual identity information, electronic identity information;
the personnel file comprises: the system comprises personnel basic information, a relationship of relatives, a social relationship, religion belief, case-involved information and interest specialties, wherein the personnel basic information comprises an identity card, a mobile phone number and a mobile phone card code of a person involved in stability;
The case library comprises: case basic information, case handling units, case involved personnel, case involved articles, case involved places and associated information;
the item archive comprises: article identification, article name, article type, article characteristics, case-related information of the article, article owner;
the site profile includes: the method comprises the following steps of (1) site category, site name, site administrative division, site detailed address, site longitude and latitude, and contact ways of site responsible persons and sites;
the relationship profile includes: mining the relationship among people, objects and the relationship between the people and the objects;
the person trajectory refers to a person trajectory profile of the unified identity dimension, comprising: people, time, place, event;
the item track comprises: vehicles, time, place and event, wherein the vehicles comprise buses and taxis;
the monitoring entity list refers to a list of all devices registered on the internet;
the economic archives refer to economic figures related to people, and include: shopping consumption, bank deposits, gas, water and electricity costs;
the organization mechanism includes: administrative institutions, enterprise institutions;
the group profile refers to a profile of a community of individuals;
The IP address library comprises: identification of the person or item, IP address visited, and time;
the event profile includes: event identification, occurrence place and event start-stop time;
the address library refers to an address library of individuals and groups with longitude and latitude.
Wherein the service layer comprises at least one of:
the first-known search module is used for providing the full-text retrieval capability for the data layer, wherein the first-known search module is specifically used for providing the full-text retrieval capability for a standard library and a private library of the data layer;
the figure image module is used for depicting a figure through basic attributes or labels, motion tracks, economic conditions and behavior interests of a person according to the information service to construct a figure image, wherein the motion tracks comprise frequent places, residence places and working places, and the economic conditions comprise credit;
the situation management and control module is used for carrying out statistical analysis, comparative analysis and trend prediction according to the situation of people, the situation of objects and the situation of places to realize the purposes of early warning and prediction;
the relationship analysis module is used for carrying out relationship analysis through six dimensions of blood margin, school margin, geography margin, affair margin, guest margin and random margin according to information business, wherein the blood margin refers to interpersonal relationship generated by marriage or fertility and comprises genealogy; the relationship between students refers to interpersonal relationship generated by education, including relationship between students and teachers and students; the geography relation refers to interpersonal relations generated by the region where the geography relation is located, such as a Country relation, a Country relation and the like; the event reason refers to the interpersonal relationship generated by the same event, such as customer relationship, interest relationship and the like; the passenger relationship refers to interpersonal relationship generated by the same consumption, such as high-speed rail passenger relationship and friend relationship; the random reason refers to an interpersonal relationship generated by uncertain random events;
The study and judgment analysis module is used for providing analysis of the same row, collision and association based on multiple data sources, performing data set operation through a user-defined rule and finding a target user;
the image detection module is used for providing an image searching function by an image, wherein the image detection module is specifically used for searching according to a human face or human face characteristics or human behavior characteristics and vehicle or vehicle characteristics;
the map studying and judging module is used for providing two-dimensional studying and judging based on multi-source data sources and taking a map as a support;
the comprehensive track module is used for presenting a multi-dimensional track according to the multi-source data source;
the set operation module is used for providing a user-defined rule, and calculating the rule through a rule engine to find a target user;
the study and judgment tool set module is used for providing a specialized analysis tool for case serial-parallel, call bill analysis and tourism analysis;
the control deployment management module is used for controlling the determined suspect and realizing early warning and notification through the early warning management module;
the early warning management module is used for receiving, processing, forwarding and pushing the discovery of each module of the platform, wherein the early warning has two pushing modes of a mobile terminal and a PC terminal;
the management and control module is used for providing rule customization service according to data standardization and the blood relationship of data, and realizing management and control, early warning and prediction of various personnel, places and articles, wherein the management and control module comprises a personnel management and control module, a place management and control module and an article management and control module;
The high-risk personnel management and control module is used for managing and controlling high-risk personnel;
and the file management module is used for managing basic files of personnel, places, articles, events and cases.
Wherein, intelligence cloud platform still includes: the basic service module is used for providing an algorithm for realizing a preset information function for each functional module of the service layer; the algorithm for realizing the preset intelligence function comprises the following steps: organization architecture, user management, role management, rights management, log management, message management, configuration management, data dictionary, data lineage. The basic service is a software package of each function module in the service layer and provides algorithm realization of each function of the service layer.
Wherein, intelligence cloud platform still includes: middleware for providing an algorithm for data normalization for the data layer; the middleware includes: the data asset module is used for displaying the data of the standard library; the uniform identity middleware is used for providing an algorithm for classifying the user data according to the identity for the data layer; and the view library middleware is used for providing an algorithm for carrying out data standardization on the video and/or image data for the data layer.
Please refer to fig. 5, fig. 5 is a schematic diagram of a hardware structure of an intelligence cloud platform according to an embodiment of the present disclosure. As shown in fig. 5, the intelligence cloud platform includes a client and a server. The client may be an electronic device with communication capability, and specifically may include various handheld devices, vehicle-mounted devices, wearable devices, computer devices or other processing devices connected to a wireless modem with wireless communication function, and various forms of User Equipment (UE), Mobile Station (MS), terminal device (terminal device), and the like; for example, the client may be an electronic device capable of running an application, such as a mobile intelligence dedicated terminal, a smart phone, a tablet computer, and an electronic book. The server may include a block link point server, a legacy server, a large storage system, a desktop computer, a laptop computer, a tablet computer, a palmtop computer, a smart phone, a portable digital player, a smart watch, a smart bracelet, and the like.
The server is provided with an intermediate layer and a data layer of the intelligence cloud platform.
The data layer is used for acquiring original data and performing data standardization processing on the original data to obtain standardized data. And the intermediate layer is used for performing data dimension reduction processing on the standardized data to obtain a plurality of data sets. The intelligence cloud platform can further comprise data connector middleware and communication agent middleware, and the data layer can comprise: an original library for acquiring the first original data through the data connector middleware and the second original data through the communication agent middleware, and storing the first original data and the second original data; the private library is used for acquiring and storing the third original data, and the third original data is the original data transmitted to the data layer through the data connector middleware or the communication agent middleware; the standard library is used for acquiring the first original data and the second original data from the original library, acquiring third original data from the private library, performing data standardization processing on the first original data, the second original data and the third original data to obtain standardized data, and storing the standardized data; and the data interface is used for transmitting the standardized data stored in the standard library to the middle layer.
The middle layer is used for performing data dimension reduction processing on the standardized data to obtain a plurality of data sets, wherein the plurality of data sets specifically comprise unified identities, personnel files, case files, article files, place files, relationship files, personnel tracks, article tracks, monitoring entity lists, economic files, organizations, group files, IP address libraries, event files and address libraries.
The client is provided with a service layer of the intelligence cloud platform, and the client realizes a preset intelligence function through the service layer. The business layer is a value application for a target client packaged by the server, and comprises a plurality of functional modules for realizing a plurality of preset information functions, and specifically comprises a priori search module, a figure portrait module, a situation management and control module, a relationship analysis module, a study and judgment analysis module, an image investigation module, a map study and judgment module, a comprehensive track module, an aggregation operation module, a study and judgment tool set module, a distribution and control management module, an early warning management module, a management and control module, a high-risk personnel management and control module and a file management module.
For example, in a specific personnel management and control application scenario, it is assumed that an intelligence person acquires the most basic information of a target user, such as a name and an identification number, and needs to determine whether the target user needs to perform personnel management and control. The method comprises the steps that an intelligence worker inputs a name and an identity card number of a target user in a worker control module of a client side, the worker control module obtains data of the target user from a corresponding data set (such as a worker file, a worker track and a case library) in a middle layer according to a corresponding relation with each data set in the middle layer, then the middle layer transmits the obtained data of the target user to the worker control module of a service layer of the client side, and the worker control module judges the target user according to the obtained data of the target user to determine whether the target user needs to be controlled or not. The data of the target user is the original data of the target user, which is acquired by a data layer from an internet bar system, a hotel system, a high-speed rail system, a gas-water electric system, a bank system, the internet and the like, or the original data of the target user, which is acquired by the data layer from a sensing device, a mobile information platform, a reconnaissance platform, a micro-activity platform, a social health agency guard platform and the like, is acquired by standardizing the data, storing the standardized data of the target user and then reducing the dimension of the data through an intermediate layer.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. An intelligence cloud platform is characterized by comprising a data layer, an intermediate layer and a service layer,
the data layer is used for acquiring original data and carrying out data standardization processing on the original data to obtain standardized data;
the intermediate layer is used for performing data dimension reduction processing on the standardized data to obtain a plurality of data sets;
and the service layer is used for realizing a preset intelligence function according to the plurality of data sets.
2. The intelligence cloud platform of claim 1, further comprising data connector middleware and communication agent middleware, the raw data comprising first raw data, second raw data, and third raw data, the data layer comprising:
An original library for acquiring the first original data through the data connector middleware and the second original data through the communication agent middleware, and storing the first original data and the second original data;
the private library is used for acquiring and storing the third original data, and the third original data is the original data transmitted to the data layer through the data connector middleware or the communication agent middleware;
the standard library is used for acquiring the first original data and the second original data from the original library, acquiring third original data from the private library, performing data standardization processing on the first original data, the second original data and the third original data to obtain standardized data, and storing the standardized data;
and the data interface is used for transmitting the standardized data stored in the standard library to the middle layer.
3. The intelligence cloud platform of claim 2, wherein the criteria library comprises:
the data dictionary module is used for constructing a data dictionary standard so as to realize unified management of standardized data;
a data resource registration module, configured to register the first original data, the second original data, and the third original data to form a corresponding data table structure in the standard library, and establish a standardized data structure according to the data dictionary standard and the data table structure;
The data blood relationship module is used for establishing a data blood relationship according to the data dictionary standard and the standardized data structure so as to reduce data dimensionality;
the first storage module is configured to perform data normalization processing on the first raw data, the second raw data, and the third raw data according to the data dictionary standard, the normalized data structure, and the data blood relationship, to obtain normalized data, and store the normalized data.
4. The intelligence cloud platform of claim 3, wherein the data lineage relationships include a first hierarchical relationship that is a data owner, a second hierarchical relationship that is a data warehouse, a third hierarchical relationship that is a data table, and a fourth hierarchical relationship that is a data field.
5. The intelligence cloud platform of claim 4, wherein the data warehouse is configured as a star-connected network with fact tables as primary keys and identity dimension tables, archive dimension tables, relationship dimension tables, trajectory dimension tables, monitoring entity list dimension tables, economic dimension tables, organizational dimension tables, and address dimension tables as foreign keys, wherein the fact tables comprise indexes consisting of identities, archives, relationships, trajectories, monitoring entity lists, economies, organizations, and addresses, wherein the trajectory dimension tables comprise personnel trajectory dimensions and item trajectory dimensions, wherein the address dimension tables include IP address repository dimensions and address repository dimensions, and wherein the archive dimensions comprise personnel archive dimensions, case archive dimensions, item archive dimensions, site archives, group archives, and event archive dimensions.
6. The intelligence cloud platform of claim 5, wherein the data owners include people, cases, places, events, and items, the first storage module comprising:
the human dimension data dimension pre-reducing unit is used for performing data dimension pre-reduction on the first original data, the second original data and the third original data according to human dimensions to obtain a first target data set;
the case dimension data dimension pre-reducing unit is used for performing data dimension pre-reduction on the first original data, the second original data and the third original data according to case dimensions to obtain a second target data set;
a place dimension data dimension pre-reducing unit, configured to perform data dimension pre-reduction on the first original data, the second original data, and the third original data according to a place dimension to obtain a third target data set;
the event dimension data dimension pre-reducing unit is used for performing data dimension pre-reduction on the first original data, the second original data and the third original data according to an event dimension to obtain a fourth target data set;
and the article dimension data dimension pre-reducing unit is used for performing data dimension pre-reduction on the first original data, the second original data and the third original data according to article dimensions to obtain a fifth target data set.
7. The intelligence cloud platform of claim 6, wherein the first storage module further comprises:
the first data standardization unit is used for carrying out data standardization processing on the data in the first target data set to obtain first standardized data;
the second data standardization unit is used for carrying out data standardization processing on the data in the second target data set to obtain second standardized data;
the third data standardization unit is used for carrying out data standardization processing on the data in the third target data set to obtain third standardized data;
the fourth data standardization unit is used for carrying out data standardization processing on the data in the fourth target data set to obtain fourth standardized data;
and the fifth data standardization unit is used for carrying out data standardization processing on the data in the fifth target data set to obtain fifth standardized data.
8. The intelligence cloud platform of claim 7, wherein the normalizing the data in the first target data set to obtain first normalized data comprises:
acquiring data associated with a preset data field in the first target data set according to the preset data field;
And carrying out data standardization processing on the data associated with the preset data field according to a preset data standard to obtain standardized data of the preset data field.
9. The intelligence cloud platform of claim 8, wherein the first storage module further comprises:
a first storage unit for storing the first normalized data according to the data dictionary standard, the normalized data structure, and the data consanguinity relationship;
a second storage unit for storing the second normalized data according to the data dictionary standard, the normalized data structure, and the data consanguinity relationship;
a third storage unit for storing the third normalized data according to the data dictionary standard, the normalized data structure, and the data consanguinity relationship;
a fourth storage unit for storing the fourth normalized data according to the data dictionary standard, the normalized data structure, and the data consanguinity relationship;
a fifth storage unit for storing the fifth normalized data according to the data dictionary standard, the normalized data structure, and the data consanguinity relationship.
10. The intelligence cloud platform of any of claims 2-9, wherein the criteria library further comprises:
And the view library is used for acquiring original video data and original image data from the first original data, the second original data and the third original data, carrying out data standardization processing on the original video data and the original image data to obtain standardized video data and standardized image data, and storing the standardized video data and the standardized image data.
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