CN111861830B - Information cloud platform - Google Patents
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Abstract
The application discloses an information cloud platform, which comprises a data layer, a middle 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 middle layer is used for carrying out 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 information function according to the plurality of data sets. Through the information cloud platform, information efficiency can be improved, and the information department is helped to effectively respond to new challenges brought by new situations.
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 informative data, it has been for many years mainly focused on the overall resultant force in terms of data volume, storage capacity and processing capacity. However, the current information big data construction mode cannot be optimized continuously, a scale effect cannot be formed, the data value mining capability based on information service is not formed, the value of the data is not really embodied, and the main expression is that: insufficient convergence and fusion of external data resources, and the problems of undefined access target and poor feasibility exist; the innovation of the information mode is insufficient, the decision is scientific, the management is accurate, and the service high-efficiency degree is to be optimized; the construction of the data centers of all places of information departments is centralized, but not intensive, for example, the data centers supported by the cloud computing technology are established in disputes of all places of information departments, but the data centers are all combat in practice, the interoperability has a large problem, the real unification is not realized in management, the uniform 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, such as regional/county information, district level city and provincial level information, and unified big data value mining around multiple kinds of information departments is not formed yet. Therefore, the efficiency of information management is extremely low when the current information big data is applied.
Disclosure of Invention
The embodiment of the application provides an information cloud platform which is horizontally used for multi-class information departments, longitudinally used for each-class organization, and standardized processing is carried out on data through a data layer so as to meet data requirements in different application scenes, thereby being beneficial to improving information efficiency and helping the information departments to effectively cope with new challenges brought by new situations.
In a first aspect, an embodiment of the present application provides an information cloud platform, including a data layer, a middle 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 middle layer is used for carrying out 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 information function according to the plurality of data sets.
Optionally, the intelligence cloud platform further includes a data connector middleware and a communication proxy middleware, the raw data includes first raw data, second raw data and third raw data, and the data layer includes:
The primary library is used for acquiring the first primary data through the data connector middleware and the second primary data through the communication proxy middleware and storing the first primary data and the second primary data;
The private library is used for acquiring and storing the third initial data, wherein the third initial data is the initial data which is transmitted to the data layer through the data connector middleware or the communication proxy middleware;
The standard library is used for acquiring the first original data and the second original data from the original library, acquiring the third original data from the private library, carrying out 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 includes:
the data dictionary module is used for constructing data dictionary standards so as to realize unified management of standardized data;
The data resource registration module is used for registering 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 establishing a standardized data structure according to the data dictionary standard and the data table structure;
The data blood-edge relation module is used for establishing a data blood-edge relation according to the data dictionary standard and the standardized data structure so as to reduce the data dimension;
The first storage module is used for carrying out data standardization processing on the first original data, the second original data and the third original data according to the data dictionary standard, the standardization data structure and the data blood relationship to obtain standardization data and storing the standardization data.
Optionally, the data blood relationship includes a first hierarchical relationship, a second hierarchical relationship, a third hierarchical relationship and a fourth hierarchical relationship, where the first hierarchical relationship is a data owner, the second hierarchical relationship is a data warehouse, the third hierarchical relationship is a data table, and the fourth hierarchical relationship is a data field.
Optionally, the architecture of the data warehouse is a star connection network taking a fact table as a primary key, and taking an identity dimension table, a file dimension table, a relationship dimension table, a track dimension table, a monitoring entity list dimension table, an economic dimension table, an organization dimension table and an address dimension table as external keys, wherein the fact table comprises an index consisting of an identity, a file, a relationship, a track, a monitoring entity list, an economic, an organization and an address, the track dimension table comprises a personnel track dimension and an article track dimension, the address dimension table comprises an IP address library dimension and an address library dimension, and the file dimension comprises a personnel file dimension, a case file dimension, an article file dimension, a place file dimension, a group file dimension and an event file dimension.
Optionally, the data owner includes a person, a case, a place, an event, and an article, and the first storage module includes:
the human dimension data pre-dimension reduction unit is used for carrying out data pre-dimension 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 pre-dimension reduction unit is used for carrying out data pre-dimension reduction on the first original data, the second original data and the third original data according to the case dimension to obtain a second target data set;
the location dimension data pre-dimension reduction unit is used for carrying out data pre-dimension reduction on the first original data, the second original data and the third original data according to the location dimension to obtain a third target data set;
The event dimension data pre-dimension reduction unit is used for carrying out data pre-dimension reduction on the first original data, the second original data and the third original data according to the event dimension to obtain a fourth target data set;
the article dimension data pre-dimension reduction unit is used for carrying out data pre-dimension reduction on the first original data, the second original data and the third original data according to the article dimension to obtain a fifth target data set.
Optionally, the first storage module further includes:
The first data normalization unit is used for performing data normalization processing on the data in the first target data set to obtain first normalized data;
The second data normalization unit is used for performing data normalization processing on the data in the second target data set to obtain second normalized data;
The third data normalization unit is used for performing data normalization processing on the data in the third target data set to obtain third normalized data;
a fourth data normalization unit, configured to perform data normalization processing on data in the fourth target data set, to obtain fourth normalized data;
And the fifth data normalization unit is used for performing data normalization processing on the data in the fifth target data set to obtain fifth normalized 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 from 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 configured to store the first normalized data according to the data dictionary standard, the normalized data structure, and the data blood-lineage relationship;
a second storage unit configured to store the second normalized data according to the data dictionary standard, the normalized data structure, and the data blood-edge relationship;
A third storage unit configured to store the third normalized data according to the data dictionary standard, the normalized data structure, and the data blood-lineage relationship;
A fourth storage unit configured to store the fourth normalized data according to the data dictionary standard, the normalized data structure, and the data blood-edge relationship;
And a fifth storage unit for storing the fifth standardized data according to the data dictionary standard, the standardized data structure and the data blood 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 a service layer of the information cloud platform described in the first aspect.
In a third aspect, an embodiment of the present application provides a server, where the server includes a data layer and an intermediate layer of the information cloud platform described in the first aspect.
It can be seen that the information cloud platform provided by the application comprises a data layer, a middle 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 middle layer is used for carrying out 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 information function according to the plurality of data sets. Therefore, the information cloud platform provided by the application is transversely used for multiple types of information departments, is longitudinally used for each level of organization, and performs standardized processing on data through a data layer so as to meet data requirements in different application scenes, thereby being beneficial to improving information efficiency and helping information effectively to bring new challenges to new situations.
These and other aspects of the application will be more readily apparent from the following description of the embodiments.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a software architecture of an information cloud platform according to an embodiment of the present application.
Fig. 2 is a hierarchical schematic diagram of a data blood relationship according to an embodiment of the present application.
Fig. 3 is a schematic 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 information cloud platform according to an embodiment of the present application.
Fig. 5 is a schematic hardware structure of an information cloud platform according to an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Referring to fig. 1, fig. 1 is a schematic diagram of a software architecture of an information cloud platform according to an embodiment of the present application. As shown in fig. 1, the intelligence cloud platform includes: the system comprises a data layer, a middle 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 middle layer is used for carrying out 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 information function according to the plurality of data sets.
For example, the data layer can acquire original data, and build 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 utilizing a data dictionary; the data standardization and standardized data are realized in the data layer, the data blood-edge relation is established through the data dictionary, the dimension of big data is reduced, and then the standard library for data storage is established through the data dictionary, the data resource registration and the data blood-edge relation, so that the purpose of data cleaning and conversion is achieved, and standardized service is provided for upper-layer business.
The middle layer performs dimension reduction processing on multi-source data based on understanding of information service, so that data modeling of dimensions such as 'people land things' is realized, namely, all data in a standard library are classified according to service requirement types, and a smaller data size is obtained.
The business layer realizes a preset information 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 at the client device, and the data layer and the middle layer are disposed at the server.
Therefore, the information cloud platform provided by the application is transversely used for multiple types of information departments, is longitudinally used for each level of organization, and performs standardized processing on data through a data layer so as to meet data requirements in different application scenes, thereby being beneficial to improving information efficiency and helping the information departments to effectively cope with new challenges brought by new situations.
In one possible example, the intelligence cloud platform further includes a data connector middleware and a communication proxy middleware, the raw data including first raw data, second raw data, and third raw data, the data layer including:
The primary library is used for acquiring the first primary data through the data connector middleware and the second primary data through the communication proxy middleware and storing the first primary data and the second primary data;
The private library is used for acquiring and storing the third initial data, wherein the third initial data is the initial data which is transmitted to the data layer through the data connector middleware or the communication proxy middleware;
The standard library is used for acquiring the first original data and the second original data from the original library, acquiring the third original data from the private library, carrying out 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 data connector middleware is a data access product oriented to multiple data sources (third party platforms), and can realize access, cleaning and conversion of the multiple source data and enter the data layer. Specifically, the data layer comprises an original library and a standard library, and the data connector middleware is used for accessing data into the original library of the data layer so as 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 and water power system, a banking system, an internet platform and the like.
The communication proxy middleware is an access software product for collecting data of the sensing equipment, and achieves 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 a survey channel, a micro-activity, a social rehabilitation, a mobile intelligence platform (Police Mobile Platform, PMP), etc., to the data layer.
It should 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 autonomously by a citizen. With respect to private libraries, data is imported by the intelligence personnel, not through the data connector nor through the communication agent, but rather by themselves. The method is characterized in that the information cloud platform has a preset function, namely, a user uploads a data form, and then the data enters a private library for storage. Private libraries and public libraries (standard libraries) are parallel, so-called private libraries, because large amounts of data collected by people are unverified and can be confusing if unauthorized access to public libraries is made.
In this example, the data layer stores the acquired data in the original library and the private library 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 middle layer through the data interface, so that the acquired data can meet the data requirements in different application scenarios after being subjected to standardization processing, thereby being beneficial to improving the information efficiency 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 data dictionary standards so as to realize unified management of standardized data;
The data resource registration module is used for registering 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 establishing a standardized data structure according to the data dictionary standard and the data table structure;
The data blood-edge relation module is used for establishing a data blood-edge relation according to the data dictionary standard and the standardized data structure so as to reduce the data dimension;
The first storage module is used for carrying out data standardization processing on the first original data, the second original data and the third original data according to the data dictionary standard, the standardization data structure and the data blood relationship to obtain standardization data and storing the standardization data.
In this example, the data layer is specifically configured to perform data normalization processing on the original data through a data dictionary, a data resource registration, and a data blood relationship, so as to obtain the normalized data, and then store the normalized data in a standard library.
In one possible example, referring to fig. 2, the data blood relationship includes a first hierarchical relationship, a second hierarchical relationship, a third hierarchical relationship and a fourth hierarchical relationship, where the first hierarchical relationship is a data owner, the second hierarchical relationship is a data warehouse, the third hierarchical relationship is a data table, and the fourth hierarchical relationship is a data field.
The data blood relationship can be used for representing the generation, processing fusion, circulation and final extinction of the data, so that references are provided for data tracing, data value evaluation, data quality evaluation, data archiving and destruction.
In one possible example, please refer to fig. 3 together, the architecture of the data warehouse is a star connection network with a fact table as a primary key, and an identity dimension table, a archive dimension table, a relationship dimension table, a track dimension table, a monitoring entity list dimension table, an economic dimension table, an organization dimension table, and an address dimension table as external keys, where the fact table includes an index composed of an identity, a archive, a relationship, a track, a monitoring entity list, an economic, an organization, and an address, the track dimension table includes a personnel track dimension and an article track dimension, the address dimension table includes a personnel archive dimension, a case archive dimension, an article archive dimension, a location archive dimension, a group archive dimension, and an event archive dimension.
In this example, the data warehouse after data standardization is a relational database with a star structure, so that the searching of the data warehouse is facilitated, and the efficiency of standardized data access is improved.
In one possible example, the data owner includes a person, a case, a place, an event, and an item, and the first storage module includes:
the human dimension data pre-dimension reduction unit is used for carrying out data pre-dimension 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 pre-dimension reduction unit is used for carrying out data pre-dimension reduction on the first original data, the second original data and the third original data according to the case dimension to obtain a second target data set;
the location dimension data pre-dimension reduction unit is used for carrying out data pre-dimension reduction on the first original data, the second original data and the third original data according to the location dimension to obtain a third target data set;
The event dimension data pre-dimension reduction unit is used for carrying out data pre-dimension reduction on the first original data, the second original data and the third original data according to the event dimension to obtain a fourth target data set;
the article dimension data pre-dimension reduction unit is used for carrying out data pre-dimension reduction on the first original data, the second original data and the third original data according to the article dimension to obtain a fifth target data set.
For example, if a trace is generated by a person's activity, then the pre-dimensionality of the data is reduced around the data generated by the "person". Thus, even if there are 1000 hundred million pieces of data per day, after the data is subjected to presupposition, it corresponds to 3000 tens of thousands of people in the city, and each person averages hundreds of pieces of data. Therefore, the data dimension reduction can be performed on the total data quantity, and 3000 thousands of people can be reasonably aggregated through an algorithm.
In this example, the dimension of the original data can be reduced in the total data amount by the data pre-dimension reduction, surrounding the human dimension, the case dimension, the place dimension, the event dimension and the object 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 includes:
The first data normalization unit is used for performing data normalization processing on the data in the first target data set to obtain first normalized data;
The second data normalization unit is used for performing data normalization processing on the data in the second target data set to obtain second normalized data;
The third data normalization unit is used for performing data normalization processing on the data in the third target data set to obtain third normalized data;
a fourth data normalization unit, configured to perform data normalization processing on data in the fourth target data set, to obtain fourth normalized data;
And the fifth data normalization unit is used for performing data normalization processing on the data in the fifth target data set to obtain fifth normalized data.
In this example, the data normalization processing may be performed on the data after the different data is subjected to the pre-dimensionality reduction, so as to obtain normalized data with different data dimensions.
In one possible example, 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 from 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 fields can be a plurality of preset data fields, so that a plurality of preset data standards are corresponding, and the plurality of preset data fields are in one-to-one correspondence with the plurality of preset data standards.
For example, hotel data are of a large variety, five-star hotel data are standardized, and three-star data are disordered. Then, the hotel data is also required to be uniformly managed for the information department, so that the data standardization is required. Specifically, for example, a field of an identity card (preset data field), a field of the identity card in the five-star hotel data is named ID1, a field of the identity card in the three-star hotel data is named ID2, but for the information department, the information department must perform processing, and the information department must collectively be named an ID (preset data standard), which is a data standardization process; for another example, the gender field (preset data field) is used for representing the male by 1, the female by 2 and the others by 3 in the five-star hotel data, the male by a, the female by B and the others by C in the three-star hotel data, and the data must be uniformly "known" for the information department, for example, the male by 11, the female by 12 and the others by 19 (preset data standard).
It should be noted that, the specific step of performing the data normalization process 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 may refer to the step of performing the data normalization process on the data in the first target data set, which is not described herein.
In this example, the original data of the associated different data standards are obtained through the data field and unified into one data standard, so that the data standardization is realized, the confusion of the data is reduced, and the data use efficiency is improved.
In one possible example, the first storage module further includes:
A first storage unit configured to store the first normalized data according to the data dictionary standard, the normalized data structure, and the data blood-lineage relationship;
a second storage unit configured to store the second normalized data according to the data dictionary standard, the normalized data structure, and the data blood-edge relationship;
A third storage unit configured to store the third normalized data according to the data dictionary standard, the normalized data structure, and the data blood-lineage relationship;
A fourth storage unit configured to store the fourth normalized data according to the data dictionary standard, the normalized data structure, and the data blood-edge relationship;
And a fifth storage unit for storing the fifth standardized data according to the data dictionary standard, the standardized data structure and the data blood relationship.
Therefore, in the example, the standardized data obtained by pre-dimension reduction and re-standardization of the data with different dimensions can be stored respectively, which is beneficial to improving the use efficiency of the standardized data.
In one possible example, the standard library further includes: 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.
In this example, the data layer performs data normalization processing on the acquired video and image data separately, and then stores the obtained normalized video data and normalized video data separately, which is beneficial to improving the application efficiency of the information cloud platform on the video and image data.
Referring to fig. 4, fig. 4 is a schematic diagram of a software architecture of another information cloud platform according to an embodiment of the present application. The intelligence cloud platform includes: the system comprises a data layer, a middle 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 middle layer is used for carrying out 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 information function according to the plurality of data sets.
The information cloud platform shown in fig. 4 is improved on the basis of the information cloud platform shown in fig. 1, and the same structure as that of fig. 1 in fig. 4 is described with reference to fig. 1.
In one possible example, the origin library includes:
The first integration module is used for carrying out data integration by acquiring data from an internet bar system, a hotel system, a high-speed rail system, a gas and water power system, a banking system and the internet through the data connector middleware to obtain first original data, and carrying out data integration by acquiring data from sensing equipment, a mobile information platform, a investigation communication platform, a micro-activity platform and a social security abstinence platform through the communication proxy middleware 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 integration module may further obtain data from a third party platform system other than an internet bar system, a hotel system, a high-speed rail system, a gas and water system, a banking system, and the internet through a data connector middleware, and the first integration module may further obtain data from another information platform other than a sensing device, a mobile information platform, a detection communication platform, a micro-activity platform, and a social health agency ring platform through a communication proxy middleware, which is only exemplary and not specifically limited in this application.
In one possible example, the first integration module includes:
The acquisition unit is used for acquiring data from an internet bar system, a hotel system, a high-speed rail system, a gas and water power system, a banking system and the internet through the data connector middleware, and acquiring data from sensing equipment, a mobile information platform, a investigation communication platform, a micro-activity platform and a social networking service abstinence platform through the communication proxy 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 and correspondingly obtaining a plurality of groups of data characteristics;
The comparison unit is used for comparing the characteristics of the plurality of groups of data and determining the similarity between the data of different data sources;
And the integration unit is used for integrating the acquired data according to the feature comparison result and the similarity between the data of different data sources.
The data characteristics comprise data types, data sizes, data contents, 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 (Eucledian 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 plurality of groups of data features is larger than the preset number, extracting data of one data source from the data of different data sources for integration; if the number of the same data features in the plurality of groups of data features is not more than a preset number, judging whether the similarity between the data of different data sources is more than a preset threshold value or not; if the similarity between the data of the different data sources is greater than a preset threshold, 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, all the data of the different data sources are extracted and integrated.
In this example, the first integration module in the original library may collect, sort, clean, and convert the data from different data sources and then load the data into a new data source storage, so as to provide data integration mode data of unified data view for data standardization of the standard library.
In one possible example, the private library includes:
the second integration module is used for acquiring structured data and unstructured data, and integrating 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, and convert the structured data and the unstructured data, and then load the collected structured data and the unstructured data into a new data source storage, so as to provide data in a data integration manner of a unified data view for data standardization of the standard library.
The middle layer is specifically configured to perform data dimension reduction processing according to at least one dimension to obtain a plurality of data sets: unified identity, personnel profile, case profile, item profile, venue profile, relationship profile, personnel track, item track, monitoring entity list, economic profile, organization, group profile, IP address library, event profile, address library.
Specifically, the unified identity includes: real identity information, virtual identity information, electronic identity information;
The personnel profile includes: basic information of personnel, relatives, social relations, case related information and interests, wherein the basic information of the personnel comprises an identity card, a mobile phone number and a mobile phone card code of the personnel;
the case archive includes: case basic information, case handling units, case involving personnel, case involving articles, case involving places and associated information;
the article profile includes: item identification, item name, item category, item characteristics, item case related information, item owner;
the venue profile comprises: the location category, the location name, the location administrative division, the location detailed address, the location longitude and latitude, the location liability person and the location contact way;
the relationship profile includes: relationship between people, relationship between people and objects, relationship between objects and relationship mining;
The personnel track refers to the personnel track file with the unified identity dimension, and comprises the following steps: character, time, place, event;
the article track comprises: vehicles, time, places and events, wherein the vehicles comprise buses and taxis;
The monitoring entity list refers to a list of all devices registered on the internet;
the economic archive refers to economic class numbers related to people, and comprises: shopping consumption, bank deposit, gas water electricity cost;
the tissue mechanism comprises: administrative institutions and enterprise institutions;
The group profile refers to a profile of a community of individuals;
The IP address library includes: identification of a person or item, IP address visited, and time;
The event archive includes: event identification, occurrence place, event start-stop time;
The address library refers to an address library of individuals and groups with longitude and latitude.
Wherein the business layer comprises at least one of the following:
The first-known search module is used for providing the full-text retrieval capability of the data layer, wherein the first-known search module is specifically used for providing the full-text retrieval capability of a standard library and a private library of the data layer;
The figure image module is used for carrying out figure depiction according to the information service through basic attributes or labels of people, motion tracks, economic conditions and behavioral interests, and constructing figure images, wherein the motion tracks comprise places where people go frequently, residence places and workplaces, and the economic conditions comprise credits;
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 so as 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, earth margin, event margin, guest margin and follow margin according to the information service, wherein the blood margin refers to interpersonal relationship generated by marital or fertility and comprises a genealogy; the learning edge refers to interpersonal relationships generated by education, and comprises a classmate relationship, a teacher-student relationship and the like; the territory refers to an interpersonal relationship, such as a fellow villager, townsman or provincial relationship, nationality relationship and the like, generated due to the region where the territory is located; the event margin refers to interpersonal relationships generated by the same event, such as a client relationship, a benefit relationship and the like; the passenger margin refers to interpersonal relationship generated by the same consumption, such as high-speed railway passenger relationship and meal friend relationship; the trailing refers to the interpersonal relationship generated by uncertain random events;
the research analysis module is used for providing peer, collision and association analysis based on multiple data sources, carrying out data collection operation through a custom rule and finding out a target user;
the image detection module is used for providing a graph searching function, wherein the image detection module is specifically used for searching according to a human face or a human face characteristic or a human behavior characteristic, a vehicle or a vehicle characteristic;
The map research and judgment module is used for providing two-dimensional research and judgment based on multiple source data sources and taking a map as a base;
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 custom rule, calculating the rule through a rule engine and finding out a target user;
The research and judgment tool set module is used for providing specialized analysis tools for case serial-parallel, ticket analysis and travel industry analysis;
The distribution management module is used for distributing and controlling the determined suspects 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 is provided with 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 blood relationship of the data to realize 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, the information cloud platform further 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 information function comprises the following steps: organization architecture, user management, role management, rights management, log management, message management, configuration management, data dictionary, data blood-line. The basic service is a software package of each functional module in the service layer, and provides algorithm implementation of each function of the service layer.
Wherein, the information cloud platform further includes: middleware for providing data normalization algorithm for the data layer; the middleware includes: the data asset module is used for displaying the data of the standard library; the unified identity middleware is used for providing an algorithm for classifying user data according to identities for the data layer; and the view library middleware is used for providing an algorithm for data standardization of video and/or image data for the data layer.
Referring to fig. 5, fig. 5 is a schematic hardware structure diagram of an information cloud platform according to an embodiment of the present application. 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 may specifically include various handheld devices with wireless communication functions, vehicle-mounted devices, wearable devices, computer devices or other processing devices connected to a wireless modem, and various forms of User Equipment (UE), mobile Station (MS), terminal devices (TERMINAL DEVICE), etc.; for example, the client may be an electronic device capable of running an application program, such as a mobile intelligence dedicated terminal, a smart phone, a tablet computer, an electronic book, and the like. The server may include a blockchain node server, a traditional server, a mass storage system, a desktop computer, a notebook computer, a tablet computer, a palm 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 information cloud platform.
The data layer is used for acquiring original data, and carrying out data standardization processing on the original data to obtain standardized data. And the middle layer is used for carrying out data dimension reduction processing on the standardized data to obtain a plurality of data sets. The intelligence cloud platform may further include a data connector middleware and a communication proxy middleware, and the data layer may include: the primary library is used for acquiring the first primary data through the data connector middleware and the second primary data through the communication proxy middleware and storing the first primary data and the second primary data; the private library is used for acquiring and storing the third initial data, wherein the third initial data is the initial data which is transmitted to the data layer through the data connector middleware or the communication proxy middleware; the standard library is used for acquiring the first original data and the second original data from the original library, acquiring the third original data from the private library, carrying out 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 carrying out data dimension reduction processing on the standardized data to obtain a plurality of data sets, wherein the data sets specifically comprise a unified identity, a personnel file, a case file, an article file, a place file, a relation file, a personnel track, an article track, a monitoring entity list, an economic file, an organization, a group file, an IP address library, an event file and an address library.
The client is provided with a service layer of the information cloud platform, and the client realizes a preset information function through the service layer. The business layer is a value application packaged by the server and applied to a target client, and comprises a plurality of functional modules for realizing a plurality of preset information functions, and particularly comprises a precedent searching module, a character portrait module, a situation management and control module, a relation analysis module, a research and judgment analysis module, an image investigation module, a map research and judgment module, a comprehensive track module, a set operation module, a research and judgment tool set module, a distribution 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 application scenario, it is assumed that an informative person obtains the most basic information of a target user, such as a name and an identification card number, and needs to confirm whether the target user needs personnel management. The information personnel input the name and the ID card number of the target user in a personnel management and control module of the client, the personnel management and control module can acquire the data of the target user from the corresponding data sets (such as personnel files, personnel tracks and case files) in the middle layer according to the corresponding relation with the data sets in the middle layer, then the middle layer transmits the acquired data of the target user to a personnel management and control module of a business layer of the client, and the personnel management and control module carries out research and judgment on the target user according to the acquired data of the target user to determine whether the target user needs to be managed and controlled. The data of the target user is the original data of the target user, which is obtained by a data layer from an internet bar system, a hotel system, a high-speed rail system, a gas water and electricity system, a banking system, the Internet and the like, or the original data of the target user, which is obtained by a sensing device, a mobile information platform, a detection communication platform, a micro-activity platform, a social networking service ring platform and the like, is obtained by data standardization, stored standardized data of the target user and dimension reduction by an intermediate layer.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
The foregoing has outlined rather broadly the more detailed description of embodiments of the application, wherein the principles and embodiments of the application are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (7)
1. An information cloud platform is characterized by comprising a data layer, a middle 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 middle layer is used for carrying out data dimension reduction processing on the standardized data to obtain a plurality of data sets;
The business layer is used for realizing a preset information function according to the plurality of data sets; the information cloud platform further comprises a data connector middleware and a communication proxy middleware, the original data comprises first original data, second original data and third original data, and the data layer comprises: the data connector middleware is a data access product facing a third party platform, can realize the access, cleaning and conversion of the data of the third party platform, is an access software product facing sensing equipment and used for acquiring data, configuring and managing the sensing equipment and the functions of data receiving, storing and distributing, and is also used for transmitting the data in the detection, micro-activity, social withdrawal, social recovery and mobile information platform to the data layer; the private library is used for acquiring and storing the third original data, wherein the third original data is the original data which is transmitted to the data layer through the data connector middleware or the communication proxy middleware, and the third original data is unverified data collected in folk; the standard library is used for acquiring the first original data and the second original data from the original library, acquiring the third original data from the private library, carrying out 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; the data interface is used for transmitting the standardized data stored in the standard library to the middle layer;
The standard library comprises: the data dictionary module is used for constructing data dictionary standards so as to realize unified management of standardized data; the data resource registration module is used for registering 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 establishing a standardized data structure according to the data dictionary standard and the data table structure; the data blood-edge relation module is used for establishing a data blood-edge relation according to the data dictionary standard and the standardized data structure so as to reduce the data dimension; the first storage module is used for carrying out data standardization processing on the first original data, the second original data and the third original data according to the data dictionary standard, the standardized data structure and the data blood relationship to obtain standardized data and storing the standardized data;
The data blood relationship comprises a first hierarchical relationship, a second hierarchical relationship, a third hierarchical relationship and a fourth hierarchical relationship, wherein the first hierarchical relationship is a data owner, the second hierarchical relationship is a data warehouse, the third hierarchical relationship is a data table, and the fourth hierarchical relationship is a data field.
2. The intelligence cloud platform of claim 1, wherein the data warehouse is configured as a star-connected network with fact tables as primary keys, identity dimension tables, archive dimension tables, relationship dimension tables, track dimension tables, monitoring entity list dimension tables, economic dimension tables, organization dimension tables, and address dimension tables as external keys, the fact tables including indexes consisting of identities, archives, relationships, tracks, monitoring entity lists, economic, organization, and addresses, the track dimension tables including personnel track dimensions and article track dimensions, the address dimension tables including personnel archive dimensions, case archive dimensions, article archive dimensions, venue archive dimensions, group archive dimensions, and event archive dimensions.
3. The intelligence cloud platform of claim 2, wherein said data owners comprise people, cases, places, events and items, said first storage module comprising:
the human dimension data pre-dimension reduction unit is used for carrying out data pre-dimension 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 pre-dimension reduction unit is used for carrying out data pre-dimension reduction on the first original data, the second original data and the third original data according to the case dimension to obtain a second target data set;
the location dimension data pre-dimension reduction unit is used for carrying out data pre-dimension reduction on the first original data, the second original data and the third original data according to the location dimension to obtain a third target data set;
The event dimension data pre-dimension reduction unit is used for carrying out data pre-dimension reduction on the first original data, the second original data and the third original data according to the event dimension to obtain a fourth target data set;
the article dimension data pre-dimension reduction unit is used for carrying out data pre-dimension reduction on the first original data, the second original data and the third original data according to the article dimension to obtain a fifth target data set.
4. The intelligence cloud platform of claim 3, wherein said first storage module further comprises:
The first data normalization unit is used for performing data normalization processing on the data in the first target data set to obtain first normalized data;
The second data normalization unit is used for performing data normalization processing on the data in the second target data set to obtain second normalized data;
The third data normalization unit is used for performing data normalization processing on the data in the third target data set to obtain third normalized data;
a fourth data normalization unit, configured to perform data normalization processing on data in the fourth target data set, to obtain fourth normalized data;
And the fifth data normalization unit is used for performing data normalization processing on the data in the fifth target data set to obtain fifth normalized data.
5. The intelligence cloud platform of claim 4, wherein 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 from 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.
6. The intelligence cloud platform of claim 5, wherein said first storage module further comprises:
A first storage unit configured to store the first normalized data according to the data dictionary standard, the normalized data structure, and the data blood-lineage relationship;
a second storage unit configured to store the second normalized data according to the data dictionary standard, the normalized data structure, and the data blood-edge relationship;
A third storage unit configured to store the third normalized data according to the data dictionary standard, the normalized data structure, and the data blood-lineage relationship;
A fourth storage unit configured to store the fourth normalized data according to the data dictionary standard, the normalized data structure, and the data blood-edge relationship;
And a fifth storage unit for storing the fifth standardized data according to the data dictionary standard, the standardized data structure and the data blood relationship.
7. The intelligence cloud platform of any of claims 1-6, wherein said 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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