Big data-based case-related risk personnel label management analysis method and system

Abstract

The invention discloses a big data-based case-related risk personnel label management and analysis system, which is realized by relying on a data modeling platform and comprises a database table, according to the model function module, a front-end realizes a model registration interface, realizes butt joint of an addition, deletion and modification interface and a rear-end interface, and realizes a model function; function realization of a rear-end actual model, operation of a model table and data reading operation of a model detailed table; a label management module; and the label center application module is used for realizing multi-dimensional fuzzy data retrieval, querying data according to names or identity numbers, checking data according to label names, combining multiple labels to search data and combining multiple labels and multiple attributes to retrieve data. According to the method, the flexibility of data classification is improved, and configurability is provided for rapid data query; scientific and technological means are utilized to improve the case handling and checking efficiency of workers, and the ability and level of studying and judging public safety behaviors are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the field of public safety risk object prevention application technology, and more specifically to a method and system for risk personnel label management and analysis based on big data. Background Art

[0002] The management of people is an extremely important part of public security management, especially the public security management of persons involved in criminal cases. In actual applications, persons involved in criminal cases are roughly classified according to their personal information. The classification of data is relatively vague, and when searching, people who meet the conditions are searched based on the classified information. The query efficiency is relatively low, which affects the work efficiency of public security management. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for label management and analysis of risk personnel involved in cases based on big data, so as to improve the flexibility of data classification and the efficiency of staff query.

[0004] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0005] The label management and analysis system for personnel involved in cases with risks based on big data is implemented on the data modeling platform, including database tables; model function module: the front-end implements the model registration interface to implement the add, delete and modify interface to connect to the back-end interface to realize the functions of the model; the back-end actual model function implementation, the operation of the model table, and the data reading operation of the model detail table; label management module; label center application module: realize multi-dimensional fuzzy retrieval of data, realize data query based on name or ID number, realize data check based on label name, realize combination of multiple labels to search data, and multi-label + multi-attribute combination retrieval data; by establishing a dynamic classification mechanism model for personnel involved in cases, the data modeling platform is used to automatically classify the personnel generated every day into a model data according to the algorithm, create personnel labels and associate the labels with model codes, and transmit the generated model codes to the data processing platform. The data processing platform generates data and outputs data according to the structural requirements of the model object table. The output data includes model codes and sample data.

[0006] To further optimize the technical solution, the database tables include a base table, a model table, a model object table, a model detail table, a label category table, a label table, a label attribute association table and a dictionary table.

[0007] To further optimize the technical solution, the tag management module includes a tag directory and tag creation management; tag directory: realize the addition / deletion of the tag directory, and realize multi-level upper and lower hierarchies to distinguish different tag categories; tag creation management: realize the addition, deletion and modification of tags, and associate model coding and model coding of parallel attribute types.

[0008] The label management and analysis method of risk personnel involved in cases based on big data includes the following steps:

[0009] A. Create the model name, model code, model type and model description of the new main fields, and output the model detailed data;

[0010] B. Create new secondary fields: model name, model code, model type, and model description;

[0011] C. Create a new tag directory, category information code and category name;

[0012] D. Create a new tag, including the tag name, tag information code, tag category code, and tag description; at the same time, associate the attribute model and select the model of the attribute class;

[0013] E. The generated model code is transmitted to the data processing platform. The data processing platform generates data and outputs the data according to the structural requirements of the model object table. The output data must include the model code and sample data.

[0014] F. In the tag center, enter the tag name, enter the query information and search to find the corresponding tag data.

[0015] Due to the adoption of the above technical scheme, the technical progress achieved by the present invention is as follows.

[0016] The method and system for label management and analysis of risky persons involved in cases based on big data provided by the present invention are based on new technologies such as cloud computing and big data, make full use of the big data resource system, and flexibly and finely divide historical personnel data through data calculation and construction of output model operators based on historical cases for matching; improve the flexibility of data classification and provide configurability for rapid data query; use scientific and technological means to improve the efficiency of case handling and person investigation by staff, and comprehensively improve the ability and level of public security behavior analysis and judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is the work flow chart of the present invention. DETAILED DESCRIPTION

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] The label management and analysis system for risky persons involved in cases is based on big data and is implemented on a data modeling platform, including data tables, label management modules, model function modules and label center application modules.

[0020] The data tables include base table, model table, model object table, model detail table, label category table, label table, label attribute association table and dictionary table.

[0021] Base table: includes (main fields) object code, object name, (secondary fields) object address, object region, object contact number, and object gender.

[0022] Model table: used to store classified data fields including model code, model name, model type, model implementation logic, and model description.

[0023] Model object table: includes model code, object code, object name, and object value.

[0024] Model details table: includes model code, subject name, subject code, event time, location, event description, and remarks.

[0025] Tag category table: includes category information code, parent information code, and category name.

[0026] Label table: includes label information code, label name, label category code, label description, and model code.

[0027] Tag attribute association table: including tag information encoding and model encoding

[0028] Dictionary table: includes dictionary value, dictionary code, dictionary category, sorting, and status.

[0029] Model function module: the front-end implements the model registration interface, implements the add, delete and modify interface, and connects to the back-end interface to realize the model functions; the back-end actually implements the model functions, operates on the model table, and reads data from the model detail table.

[0030] Tag management module: includes tag directory and tag creation management.

[0031] Tag directory: implement the addition / deletion of tag directories and implement multiple levels to distinguish different tag categories.

[0032] Tag creation management: implement the addition, deletion and modification of tags, and associate model coding with model coding of parallel attribute types.

[0033] Tag center application module: realize multi-dimensional fuzzy retrieval of data, realize data query based on name or ID number, realize data check based on tag name, realize data search by combining multiple tags, and retrieve data by combining multiple tags + multiple attributes.

[0034] By establishing a dynamic model of the mechanism for classifying persons involved in a case, the data modeling platform is used to automatically classify the personnel generated every day into a model data according to the algorithm, create personnel labels and associate the labels with the model codes, and pass the generated model codes to the data processing platform. The data processing platform generates data and outputs the data according to the structural requirements of the model object table. The output data includes model codes and sample data.

[0035] Based on big data, the label management and analysis method of risk personnel involved in cases, such as Figure 1 As shown, it includes the following steps:

[0036] A. Create the model name, model code, model type and model description of the new main fields, and output the model detailed data;

[0037] B. Create new secondary fields: model name, model code, model type, and model description;

[0038] C. Create a new tag directory, category information code and category name;

[0039] D. Create a new tag, including the tag name, tag information code, tag category code, and tag description; at the same time, associate the attribute model and select the model of the attribute class;

[0040] E. The generated model code is transmitted to the data processing platform. The data processing platform generates data and outputs the data according to the structural requirements of the model object table. The output data must include the model code and sample data.

[0041] F. In the tag center, enter the tag name, enter the query information and search to find the corresponding tag data.

[0042] The following is an example of creating a "provoking trouble" label.

[0043] 1) Create a new model named "Provoking Trouble", model code: MX202405160001, model type: label class, model description: people who have a record of provoking trouble in the past year, and output the model detailed data.

[0044] 2) Create a new model named "Number of arrests in the past year", model code: MX202405160002, model type: attribute class, model description: Number of suspect arrest registrations.

[0045] 3) Create a new model named "Time of first crime", model code: MX202405160003, model type: attribute class, model description: number of times the suspect was captured and registered.

[0046] 4) Create a new label directory: Category information code: BQML0001, Category name: People who provoke trouble.

[0047] 5) Create a new label: Label name: Personnel who cause trouble, label information code: BQ20240523001, label category code: BQML0001, label description: Personnel with a record of causing trouble, model code: MX202405160001; Associated attribute model: Select the model of the attribute class: "Number of times arrested in the past year", "Time of first crime".

[0048] 6) The generated model code is passed to the data processing platform. The data processing platform generates data and outputs the data according to the structural requirements of the model object table. The output data must include the model code. The sample data is as follows:

[0049] Model Encoding MX202405160001 Target person code 440102199908090000 Object Value yes

[0050] Insert into the model details table, the sample data is as follows:

[0051]

[0052] Insert model object table data, the example is as follows:

[0053] Model Encoding MX202405160002 Target person code 440102199908090000 Object Value 1 time

[0054] Insert into the model details table, the sample data is as follows:

[0055]

[0056]

[0057] Insert model object table data, the example is as follows:

[0058] Model Encoding MX202405160003 Target person code 440102199908090000 Object Value 2024-05-12

[0059] Insert into the model details table, the sample data is as follows:

[0060]

[0061] 7) In the tag center, enter the tag name: Zhang San, number of times arrested in the past year: 1 time, first crime time: 2024-05-12, click Search, and you can search for the corresponding tag data.

Claims

1. A label management and analysis system for personnel involved in risk cases based on big data, characterized by: Relying on the data modeling platform, including database tables; model function module: the front-end implements the model registration interface to implement the add, delete and modify interface to connect to the back-end interface to realize the model function; the back-end actual model function implementation, the operation of the model table, the data reading operation of the model detail table; label management module; Tag center application module: realize multi-dimensional fuzzy retrieval of data, realize query of data by name or ID number, realize check of data by tag name, realize combination of multiple tags to search data, and multi-tag + multi-attribute combination retrieval of data; By establishing a dynamic model of the mechanism for classifying persons involved in a case, the data modeling platform is used to automatically classify the personnel generated every day into a model data according to the algorithm, create personnel labels and associate the labels with the model codes, and pass the generated model codes to the data processing platform. The data processing platform generates data and outputs the data according to the structural requirements of the model object table. The output data includes model codes and sample data.

2. The label management and analysis system for risk personnel involved in cases based on big data according to claim 1 is characterized by: The database tables include a base table, a model table, a model object table, a model detail table, a tag category table, a tag table, a tag attribute association table and a dictionary table.

3. The label management and analysis system for risk personnel involved in cases based on big data according to claim 1 is characterized by: The tag management module includes a tag directory and tag creation management; tag directory: realizes the addition / deletion of the tag directory, and realizes multi-level upper and lower hierarchies to distinguish different tag categories; tag creation management: realizes the addition, deletion and modification of tags, and associates model coding and model coding of parallel attribute types.

4. A label management and analysis method for personnel involved in risk cases based on big data, characterized by: It includes the following steps: A. Create the model name, model code, model type and model description of the new main fields, and output the model detailed data; B. Create new secondary fields: model name, model code, model type, and model description; C. Create a new tag directory, category information code and category name; D. Create a new tag, including tag name, tag information code, tag category code, and tag description; At the same time, associate the attribute model and select the model of the attribute class; E. The generated model code is transmitted to the data processing platform. The data processing platform generates data and outputs the data according to the structural requirements of the model object table. The output data must include the model code and sample data. F. In the tag center, enter the tag name, enter the query information and search to find the corresponding tag data.

Images