CN115115215A - Test information compiling method for accident risk value test - Google Patents

Abstract

The invention relates to a test information compiling method for accident risk value test, which comprises the following steps: s1, creating a first test information database (Q1) for performing the command target test, a second test information database (Q2) for performing the device test and a third test information database (Q3) for performing the environment test; s2, the test items of the second test information database (Q2) are provided to the control unit of at least one equipment related to training life in the designated area to be evaluated for the accident risk value in a data and signaling mode; the test items of the S3, the third test information database (Q3), while being provided in a data and signaling manner to the control unit of at least one training life related working equipment within the specified area to be assessed for the accident risk value, are also provided in a data and signaling manner in parallel to the control unit of at least one training life related monitoring device.

Description

Test information compiling method for accident risk value test

Technical Field

The invention relates to the technical field of group security management systems, in particular to a test information compiling method for an accident risk value test.

Background

In the information age, mobile internet, cloud computing, internet of things and big data technology are widely applied, and explosive growth of data and value expansion will have profound influence on future development of the group. Various targeted business tools can be used by group members in daily operation, training, life and learning processes, but the data are independent from each other, the data content and format are different greatly, and the quick perspective of comprehensive information cannot be realized even though the data are accumulated in a large amount over time. Therefore, it is urgent to establish a large data platform for each basic service accurately facing the community as soon as possible.

Patent document CN105357061A discloses an operation and maintenance monitoring and analyzing system based on large data flow processing technology, which includes: the monitoring end is used for obtaining monitoring data in the client and sending the monitoring data to the storage end; the storage end is used for storing a plurality of early warning processing rules, a plurality of data mining rules and historical records; the cache terminal synchronizes the early warning processing rules, the data mining rules and the history records stored in the storage terminal into the cache terminal according to a preset time interval, and receives the monitoring data stream sent by the monitoring terminal; the first processing group carries out early warning monitoring alarm analysis according to the early warning rule, the historical record and the monitoring data stream; and the second processing group performs data mining analysis according to the data mining rule, the historical records and the monitoring data stream, and outputs monitoring statistical analysis according to the analysis result.

Patent document No. CN111914004A discloses a academic early warning method, system and storage medium based on data mining algorithm, the method includes the following steps: converting the campus big data into the same data format by performing data cleaning on the campus big data; classifying the campus big data after data cleaning, and adding corresponding classification labels; performing principal component analysis on data with different classification labels to obtain a plurality of characteristic factors related to the achievement; inputting the obtained characteristic factors into a pre-trained score prediction model to predict the scores of each student; and if the score of the student is lower than the early warning value, marking the student as the academic early warning. According to the method, campus big data are fully mined in a data cleaning and principal component analysis mode, a data island is opened, and the academic situation of each student is comprehensively analyzed, so that a teacher can give out personalized teaching guidance opinions conveniently.

The prior art can only analyze a single-dimensional data source, and cannot analyze and manage multidimensional data which are independent from each other and have large data content and format differences, so that a collection scene of multidimensional data of a group can be fitted, a decision maker is helped to make reasonable suggestions and decisions for safety management of the group, daily equipment and facility management of the group and comprehensive quality management of an instruction target, and particularly, a behavior individual or a behavior unit is helped to perform targeted comprehensive evaluation and training planning.

Furthermore, on the one hand, due to the differences in understanding to the person skilled in the art; on the other hand, since the inventor has studied a lot of documents and patents when making the present invention, but the space is not limited to the details and contents listed in the above, however, the present invention is by no means free of the features of the prior art, but the present invention has been provided with all the features of the prior art, and the applicant reserves the right to increase the related prior art in the background.

Disclosure of Invention

Aiming at the defects of the prior art, the method can acquire all-around data from a plurality of third-party data acquisition systems of a big data network platform, and establishes different analysis models according to actual application requirements, fits business scenes, and provides scientific basis and rationalization suggestions for decision makers and daily work.

The technical scheme of the invention provides a test information compiling method for accident risk value test, which comprises the following steps: s1, creating a first test information database for performing instruction target test, a second test information database for performing equipment test and a third test information database for performing environment test; s2, providing the test items of the second test information database to a control unit of at least one device related to training life in the designated area of the accident risk value to be evaluated in a data and signaling mode; s3, the test items of the third test information database are provided in a data and signaling manner to the control unit of at least one training life related working equipment in the designated area to be evaluated for the accident risk value, and are also provided in a data and signaling manner to the control unit of at least one training life related monitoring device in parallel.

According to a preferred embodiment, step S1 includes at least the following operations:

s1.1, when the first test information database for the instruction target test is created, performing first screening on the instruction target in the specified area of the accident risk value to be evaluated, and selecting test information with a test result conforming to normal distribution to create the first test information database;

s1.2, performing secondary screening on the instruction target in the specified area of the accident risk value to be evaluated according to the equipment use condition of the instruction target during training on the test information in the first test information database, and selecting test information of which the test result conforms to normal distribution to create a second test information database;

s1.3, performing third screening on the instruction targets in the specified area of the accident risk value to be evaluated according to whether the instruction targets can be trained by using equipment under the specified environmental conditions, and selecting the test information with the test result conforming to normal distribution to create a third test information database.

According to a preferred embodiment, the first test information database is created as follows: at least seven dimensions of thought, safety skill, theory, physical training, daily management, teaching self-learning and special training are set after the accident risk value is analyzed, and a group of test information is set aiming at the seven dimensions respectively to form a first test information database.

According to a preferred embodiment, the seven-dimensional test information content includes specific items of test information and item profiles that classify and record behavior data of the instruction targets completing test items, and the instruction targets and item profile data thereof included in the first test information database are obtained by screening specified targets in the specified area to be evaluated for accident risk based on multiple kinds of behavior data of the instruction targets performed within a certain period of time, so as to screen out instruction targets that can complete test items in the specified area to be evaluated for accident risk.

According to a preferred embodiment, the stimulus information is distributed, sorted and stored in an archive unit according to the type of the stimulus information and/or the format of the stimulus information, and the first test information database, the second test information database and the third test information database are used for screening the stimulus information stored in the archive unit for multiple times according to different screening conditions, so as to screen out an instruction target capable of completing a test item by using a specific device under a specified environment.

According to a preferred embodiment, the behavior data of the instruction target is mined by a first data mining analysis module, and the decision maker screens the multidimensional behavior data associated with the instruction target by a second data mining analysis module, and establishes a multidimensional behavior data model of the instruction target according to the preset weight configuration rule and the screened multidimensional behavior data in at least part of the time interval, so as to generate a comprehensive evaluation report of the instruction target by using the multidimensional behavior data model taking the preset weight configuration rule as a frame basis. The safety management system has the advantages that the safety management system can conveniently evaluate the comprehensive condition of the instruction target by utilizing various behavior data with low relevance of the instruction target in a group range through carrying out secondary classification and induction on the behavior data with different dimensions, so that a comprehensive evaluation report of the instruction target generated by the safety management system has the comprehensiveness of data, an evaluation result is fully supported by data, the evaluation accuracy is improved, the decision maker can conveniently conduct targeted guidance and planning on the instruction target (an individual behavior or a behavior unit) according to the comprehensive evaluation report, and the instruction target is promoted to rapidly determine a reasonable development direction.

According to a preferred embodiment, the second data mining analysis module can compare a comprehensive evaluation report generated by establishing a multi-dimensional behavior data model with a reference data model generated in advance by the security management system by using test data, so as to evaluate the behavior security of the instruction target; the second data mining analysis module generates prediction information of at least one behavior data associated with the comparison result or calls the prediction information of at least one behavior data associated with the comparison result into the database according to the comparison, so that the obtained prediction information is displayed by using the display terminal, and a decision maker conducts behavior safety management on the instruction target according to the prediction information.

According to a preferred embodiment, the multidimensional behavior data mined by the first data mining analysis module from the security management system is classified and recorded by using a plurality of archive units, wherein any archive unit independently records behavior data of a single dimension stored in the security management system, and the archive unit updates recorded behavior data thereof in real time; the archive unit also associates the behavior data recorded by the archive unit with the behavior individuals or behavior units which generate the behavior data.

According to a preferred embodiment, the mining of the multidimensional behavior data by the first data mining and analyzing module is to perform secondary classification and collection on the behavior data classified and recorded by the security management system by using the archive units in a dimension division manner, wherein the secondary classification and collection refers to reclassifying and collecting the behavior data recorded by different archive units according to different behavior individuals or behavior units associated with the behavior data.

According to a preferred embodiment, the second data mining analysis module screens behavior data associated with the instruction target from the behavior data mined by the first data mining analysis module and subjected to secondary classification and collection processing by using a plurality of kinds of single basic information of the instruction target as a retrieval basis, and the second data mining analysis module secondarily screens the screened behavior data associated with the instruction target according to a time interval determined by a decision maker.

According to a preferred embodiment, the preset weight configuration rule refers to a relational expression between behavior data of different dimensions and comprehensive quality data of the instruction target, which are input into the security management system in advance by a decision maker through the display terminal, so that the second data mining and analyzing module establishes a multidimensional behavior data model of the instruction target in a manner that the screened multidimensional behavior data associated with the instruction target in at least part of time intervals are filled into the relational expression.

According to a preferred embodiment, the decision maker determines the weights occupied by the behavior data of different dimensions according to the association degree of the behavior data of different dimensions, which can be monitored by the security management system, of the instruction target in at least a part of the time interval and the comprehensive quality data in the comprehensive assessment report to be generated, so that the decision maker can make a weight configuration rule that the weight values corresponding to the behavior data of different dimensions are different.

According to a preferred embodiment, the prediction information of the behavior data generated or retrieved by the second data mining analysis module is history data set information or pre-entered test data set information of the behavior individuals or behavior units recorded by the security management system.

Drawings

FIG. 1 is a schematic workflow diagram of a test information compiling method of a preferred accident risk value test according to the present invention;

FIG. 2 is a schematic diagram illustrating the steps of creating a database according to a preferred test information compiling method for an accident risk value test according to the present invention;

fig. 3 is a normal distribution diagram of test information of a preferred test information compiling method for an accident risk value test according to the present invention, which is satisfied when the test information is screened.

List of reference numerals

1: a file unit; 2: a first data mining analysis module; 3: a second data mining analysis module; 4: a display terminal; q1: a first test information database; q2: a second test information database; q3 third test information database.

Detailed Description

The following detailed description is made with reference to the accompanying drawings.

The test information compiling method of the accident risk value test can be in butt joint with other third-party data acquisition systems, so that the safety record data of the group (such as attendance, going out, returning to the team, going out, life system, boundary early warning, night patrol record and other data of group personnel) can be obtained, the activity file of the group can be established, and the safety management problem of the group can be analyzed. The invention can also comprehensively analyze abnormal distribution places, time periods, personnel and affiliated mechanisms of the safety data of various groups, and carry out early warning and reminding aiming at abnormal conditions. In addition, the emergency process management system can also provide emergency process management for decision makers of a group, the decision makers can set emergency processes (the processes comprise equipment (equipment) fixed points, personnel fixed points, manager login in place, material in place and the like) according to actual needs, and the emergency drilling process and results are pre-estimated by combining equipment facility data of vehicles, materials and the like of the group.

Example 1

The embodiment provides a test information compiling method for an accident risk value test, which comprises a file unit 1, a first data mining analysis module 2, a second data mining analysis module 3, a display terminal 4 and a plurality of data processing modules capable of supporting a security management system to perform security management of a group. The modules mentioned in this embodiment may refer to hardware, software, or a combined data processor capable of executing the relevant steps, and a method step corresponding to a certain module may also be split into multiple method steps and executed by multiple modules respectively. The preferred embodiments of the present invention are described in whole and/or in part in the context of other embodiments, which can supplement the present embodiment, without resulting in conflict or inconsistency.

According to a specific embodiment shown in fig. 1 to 3, a method for compiling test information for an accident risk value test includes the following steps:

s1, creating a first test information database Q1 for performing command target tests, a second test information database Q2 for performing equipment tests and a third test information database Q3 for performing environmental tests;

s1.1, when the first test information database Q1 for testing the command target is created, performing first screening on the command target in the designated area of the accident risk value to be evaluated, and selecting test information with the test result conforming to normal distribution to create the first test information database Q1;

s1.2, performing secondary screening on the instruction targets in the specified area of the accident risk value to be evaluated according to the equipment use condition of the instruction targets during training on the test information in the first test information database, and selecting test information of which the test result conforms to normal distribution to create a second test information database Q2;

s1.3, performing third screening on the instruction targets in the specified area of the accident risk value to be evaluated according to whether the instruction targets can be trained by using equipment under the specified environmental conditions, and selecting the test information with the test result conforming to normal distribution to create a third test information database Q3.

S2, the test items of the second test information database Q2 are provided to the control unit of at least one equipment related to training life in the designated area to be evaluated for the accident risk value in a data and signaling manner.

S3, the test items of the third test information database Q3, when being provided in a data and signaling manner to the control unit of at least one training life related working equipment within the designated area to be evaluated for the accident risk value, are also provided in a data and signaling manner to the control unit of at least one training life related monitoring device in parallel.

Preferably, the archive unit 1 classifies and records the behavior data of different dimensions of the group acquired by the security management system from other third-party data acquisition systems according to the different dimensions to which the behavior data belong, and stores the behavior data in different positions or storage subunits. The first data mining analysis module 2 can mine the behavior data of different dimensions belonging to different groups, individuals or equipment facilities from the behavior data of different dimensions stored by the archive unit 1 in a classified manner. The first data mining analysis module 2 can collect the mined behavior data into a data set after secondary classification in a mode of secondarily classifying the mined behavior data with different dimensions. The second data mining analysis module 3 can individually screen out all behavior data related to the instruction target in a specific time interval from the behavior data set summarized by the first data mining analysis module 2 by taking the basic information of the instruction target selected by the decision maker as a reference. The second data mining and analyzing module 3 can establish a multi-dimensional behavior data model which can be used for evaluating the comprehensive condition of the instruction target in a specific time interval by using the screened behavior data. Preferably, the multi-dimensional behavior data refers to behavior data of different dimensions. The decision maker can input the weight configuration rule which is the framework basis of the multi-dimensional behavior data model through the display terminal 4. The weight configuration rule can represent the association degree of the behavior data with different dimensions and the comprehensive condition of the instruction target, so that the weight occupied by the different behavior data when generating the comprehensive evaluation report of the instruction target is determined according to the influence degree of the different behavior data on the instruction target. According to the invention, through carrying out secondary classification and induction on the behavior data with different dimensions, a decision maker can conveniently evaluate the comprehensive condition of the instruction target by utilizing various behavior data with lower relevance of the instruction target in a group range, so that the comprehensive evaluation report of the instruction target generated by the safety management system has comprehensiveness of data, the evaluation result can be fully supported by the data, the evaluation accuracy is improved, the decision maker can conveniently conduct targeted guidance and planning on the instruction target (behavior individual or behavior unit) according to the comprehensive evaluation report, and the instruction target is promoted to rapidly determine a reasonable development direction. Preferably, the instruction target may be an individual or a unit of action, wherein the individual of action may be any one of a person, instructor or learner within the community.

Preferably, the first data mining and analyzing module 2 mines multidimensional behavior data stored in the security management system by means of automatic monitoring or data scanning. Preferably, the multidimensional behavior data mined by the first data mining and analyzing module 2 from the security management system is stored by the security management system in a classified manner by using a plurality of archive units 1. When the security management system acquires the behavior data from other third-party data acquisition systems, the behavior data are classified and stored according to different dimensionalities of the behavior data, so that multi-dimensional behavior data are formed, namely, the security management system performs classified storage according to different sources of the behavior data (representing terminal acquisition equipment of the third-party data acquisition system). For example, one archive unit 1 may store usage records of all persons using the same equipment or the same training field at different times. Preferably, the fact that the dimensions of the behavior data are different means that data association relations other than the time sequence relation do not exist among different behavior data due to the fact that terminal acquisition devices or training devices generating the behavior data are different. For example, there is no intuitive connection between usage data collected by a training field usage recording system and sleep condition data collected by a dormitory-installed sleep monitoring system. Preferably, any archive unit 1 stores only behavior data collected by one terminal collection device received by the security management system. The archive unit 1 updates its recorded behavior data in real time. Preferably, the terminal acquisition device acquiring the behavior data is used for acquiring the data in real time and continuously, so that the data transmitted to the security management system is continuously increased, and therefore, the archive unit 1 needs to continuously update or increase the stored behavior data. The behavior data stored in the archive unit 1 may be recorded by a plurality of individuals or units when using the same training device, so that the archive unit 1 further associates the behavior data generated by using the training device at different time points/time periods for different collected instruction targets with the basic information of the instruction target (behavior individual or behavior unit), so that the first data mining and analyzing module 2 mines the behavior data associated with the instruction target generated by different terminal collection devices according to the basic information of the instruction target. Preferably, the mining of the multidimensional behavior data by the first data mining and analyzing module 2 is to perform secondary classification and collection on the behavior data classified and recorded by the security management system by using the archive unit 1 in a dimension division manner. Preferably, the secondary classification and collection refers to reclassifying and summarizing the behavior data recorded by different archive units 1 according to the behavior individuals or behavior units associated with the behavior data. The behavior data recorded by the archive unit 1 is classified and stored according to different terminal acquisition devices. The first data mining analysis module 2 performs secondary classification processing on the behavior data mined from the archive unit 1 so that the behavior data of different dimensions are collected in different behavior individuals or subsets of behavior units according to different behavior individuals or behavior units associated with the behavior data, in order to facilitate the second data mining analysis module 3 to mine the behavior data of the specified instruction target. Preferably, the secondary classification and collection means that classified data are secondarily split according to different classification methods and are summarized into different data sets. Preferably, the quadratic summary data sets may be arranged according to the order of the acquisition times.

Preferably, the second data mining analysis module 3 filters the multidimensional behavior data associated with the instruction targets in response to the instructions of the decision maker. The second data mining analysis module 3 can retrieve the behavior data of various dimensions associated with the instruction target from the behavior data collected by the first data mining analysis module 2 in the secondary classification according to the basic information of the instruction target. Preferably, the second data mining and analyzing module 3 is further capable of establishing a multidimensional behavior data model of the instruction target according to a preset weight configuration rule and the screened multidimensional behavior data within a time interval, so that the second data mining and analyzing module 3 generates a comprehensive evaluation report of the instruction target by using the multidimensional behavior data model. Preferably, the decision maker determines the weights occupied by the behavior data of different dimensions according to the degree of association between the behavior data of different dimensions recorded by the security management system within a time interval by the instruction target and the comprehensive quality data of the comprehensive evaluation report to be generated, so that the decision maker can make a weight configuration rule that there is a difference between the weight values corresponding to the behavior data of different dimensions. The second data mining and analyzing module 3 further screens the retrieved behavior data by limiting the collection time of the behavior data, so as to obtain all the behavior data of the instruction target in a time interval. For example, the time and performance of training performed by all devices or venues used by an individual in a week, etc. Preferably, the length of the time interval is selectively made according to the needs of the decision maker. Preferably, the action individual can be any member in the community, and the action unit can be the whole community or a small group.

Preferably, the second data mining analysis module 3 can compare the comprehensive evaluation report generated by establishing the multidimensional behavior data model with a reference data model generated in advance by the security management system by using the test data, so as to realize evaluation of the behavior security of the instruction target. Preferably, the test data of the security management system may be a complete history data of other behavior individuals or behavior units, or may be reference data loaded from other systems, or may be standard reference data obtained by a big data analysis method. Preferably, the reference data model refers to a data model established by using the test data and the same weight configuration rule, and the comprehensive evaluation data and the comprehensive evaluation report output by the data model are the comprehensive evaluation reference data and the comprehensive evaluation reference report. Preferably, the decision maker can evaluate the safety of the behavior of the instruction target by comparing the comprehensive evaluation report generated by the behavior data of the instruction target with the comprehensive evaluation reference report. Preferably, the assessment of behavioral safety may refer to a judgment of whether or not the quality factors of daily work, training, life, learning, and physical condition of the instruction target are reasonable. Preferably, the second data mining and analyzing module 3 retrieves the prediction information of at least one behavior data associated with the comparison result from the database according to the comparison result, so that the obtained prediction information is displayed by using the display terminal 4, and a decision maker performs behavior safety management on the instruction target according to the prediction information.

Preferably, the forecast information of the behavior data called by the second data mining and analyzing module 3 is selected by the security management system from historical data set data or pre-entered test data set data of behavior individuals or behavior units recorded by the security management system. Preferably, the historical data set may be a complete historical data of other individual behaviors or units of behaviors. Preferably, the test data set is a summary of the aforementioned test data. Preferably, the second data mining and analyzing module 3 can compare the comprehensive evaluation report generated by establishing the multidimensional behavior data model with a reference data model generated in advance by the security management system by using the test data, so as to evaluate the behavior security of the instruction target. Preferably, the prediction information is that historical behavior data corresponding to behavior data of the instruction target in the historical data set is utilized to analyze and predict the real-time condition and the future behavior safety of the instruction target, so that a decision maker can conveniently conduct targeted guidance and planning on the instruction target (behavior individual or behavior unit) according to the comprehensive evaluation report, and the instruction target is promoted to rapidly determine a reasonable development direction.

The second data mining analysis module 3 screens out behavior data associated with the instruction target from the behavior data subjected to the secondary classification and collection processing by the first data mining analysis module 2 by using a plurality of kinds of single basic information of the instruction target as a retrieval basis, and the second data mining analysis module 3 performs secondary screening on the screened behavior data associated with the instruction target according to a time interval determined by a decision maker. Preferably, the single basic information may be the name, number and other basic information having a unique characteristic of the instruction target. Preferably, the preset weight configuration rule refers to a relational expression between behavior data of different dimensions of the security management system and comprehensive quality data of the instruction target, which is input by the decision maker through the display terminal 4 in advance, so that the second data mining analysis module 3 establishes the multidimensional behavior data model of the instruction target in a manner of filling the screened multidimensional behavior data associated with the instruction target within a time interval into the relational expression. The multidimensional behavior data model is constructed by combining a preset weight configuration rule with multidimensional behavior data, and a comprehensive evaluation report is directly generated according to the preset weight configuration rule and the multidimensional behavior data.

Example 2

This embodiment is a further improvement of embodiment 1, and repeated contents are not described again.

Preferably, the test information compiling method for the accident risk value test can also be composed of a data comprehensive display module, a capability support module and a business topic application module. The comprehensive data display module can display the indexes and the dimensions of a plurality of service system data in a group in a classified mode, help an instruction target observe and analyze the data from different angles, and focus on the trend rule of the data. The data comprehensive display module can also display the calculation and analysis results of different models and perform cross viewing according to various dimensional conditions. In addition, the data comprehensive display module monitors and analyzes data associated with the data comprehensive display module by establishing an early warning index or model, so that an early warning mechanism based on data mining is established. When the real-time data reaches a preset limit value or is abnormal, the safety management system automatically gives an alarm, and simultaneously supports the limitation of set values such as an alarm threshold value, a confidence coefficient threshold value and the like. Preferably, the security management system supports a plurality of data screening modes, including field query, map selection, click selection and the like. Preferably, the capacity support module can perform data processing in a manner of reading data in all directions and summarizing the processed data. The capability support module can also carry out data interaction in a mode of selectively developing different types of data ports and feeding back operation results in real time. Preferably, the decision maker can also perform custom adjustment on the parameter weight of the application model established by each service related to the security management system, so that each model has a growth factor, and can correct the model according to the output result of the model.

Preferably, the business topic application module at least comprises flight personnel comprehensive capacity data application, personnel comprehensive quality data application, quality guarantee data application, equipment fault hidden danger data application, flight environment factor data application, flight safety risk data application, group safety management data application and comprehensive evaluation data application. Preferably, the comprehensive ability data application of the flight personnel is to comprehensively analyze various data of the instructor and the student to obtain personal advantages and disadvantages and comprehensive evaluation, and periodically give analysis results of development direction and missing and filling. Automatically generating the scoring conditions of individuals, units and whole groups; the flight crew integrated capability data application can also automatically generate the scoring for individuals, units, and entire groups.

Preferably, the personnel comprehensive quality data application comprises the steps of having custom examination questions and custom score weights; the examination system for respectively examining the instructor and the student automatically generates the individual and unit scoring conditions. The development direction and the defects of the student are analyzed through comprehensive data and historical data of the student such as thought, technology, safety skill, theory, physical training, daily management, teaching self-learning, special training and the like based on the examination. Preferably, the quality of guarantee data application analyzes single-item, multi-dimensional items of equipment (attendance rate, availability rate, maintenance quality, failure rate, etc.), reflects the comprehensive status of equipment (availability, historical data, etc.), and prompts instructors, team members in real time whether work training can be performed in a manner combined with work training scenarios. Preferably, the equipment fault hidden danger data application compares flight parameters, pilot feedback problems and guaranteed quality data, compares the data of the same type of equipment comprehensively, and analyzes the commonly occurring problems. Preferably, the flight environment factor data is used for modeling and analyzing meteorological information, geographic information, terrain information, electromagnetic interference, flight activities and field conditions before flight, and relevant prompts are provided for the day of flight after the flight crew selects the area, time and altitude. Preferably, the flight safety risk data application docking flight safety assessment plan implementation system and copying flight safety dynamic monitoring data, comprehensively analyzing the flight cycle of the flight camp, generating a safety assessment report and performing detailed analysis on each item. Preferably, the group security management data application performs real-time analysis on various data in the group security management aspect, finds out problems in a single group security aspect and trend conditions of development in a centralized stage, performs early warning on high-frequency and sudden problems, and comprehensively grasps the variation condition of unstable factors of the group. Preferably, the comprehensive evaluation data application analyzes and evaluates the comprehensive combat capability of the whole journey through various data analysis, and provides assistant decisions for training of the whole traveler, combat capability adjustment, treatment of various unstable factors, task execution and the like.

Preferably, the behavior data mining and retrieval operations of the first data mining analysis module 2 and the second data mining analysis module 3 are based on the text features of the retrieval request or the retrieval target, retrieval results based on the text features are obtained by using a retrieval engine and a machine learning model, and meanwhile, the method is combined with a text feature retrieval method to perform knowledge reasoning based on knowledge graph associated knowledge and based on a business target, and perform calculation based on graph calculation, machine learning and the like to obtain multi-dimensional intelligent retrieval of people. Preferably, the retrieval dimension includes basic information, skill information, qualification capability, technical capability, professional inclination, and the like. Preferably, the retrieval operation can also be based on personnel basic attributes, associated events (training, examination, simulation drilling and the like), associated equipment (piloted airplanes, training equipment and the like) conditions for reasoning and displaying personnel retrieval information further in a deep, multi-dimensional and visual mode.

Preferably, the safety management system establishes a personnel data label system through a developer holographic multidimensional portrait label system on the basis of a large data platform, integrates and calculates information such as basic information, ideas, technologies, safety skills, theories, physical training, daily management, teaching self-learning and training of various personnel to construct a personnel information holographic knowledge map, and combines an artificial intelligent natural language technology on the basis to realize automatic evaluation and labeling of the business quality, the technical ability, the safety skills, training results, teaching quality and the like of the personnel, form a 360-degree holographic portrait of the personnel, support personnel selection and management analysis of links such as training, examination training and simulated drilling, improve the training effect, and provide data support for long-term planning of training. The security management system is also provided with a label system construction closed loop, a label management closed loop, a personnel portrait closed loop, a label application closed loop and an effect evaluation label construction closed loop, wherein the label system construction closed loop, the personnel portrait closed loop, the label application closed loop and the effect evaluation label closed loop are required by personnel holographic portrait evaluation, and teaching training management is enabled through landing of a precise scene based on the label system. Preferably, the safety management system also develops the life cycle management processes of label creation, auditing, release, evaluation, deactivation and offline optimization, realizes the full life cycle management of the personnel label, and ensures the high efficiency, stability, practicability and effectiveness of the personnel label. According to the invention, through the life cycle management of the tags, the periodic evaluation and the version management of the tags are realized, the adaptability of the tags to various scene analyses is ensured, and the rapid landing of various tag operation organization architectures (strong matrix, balanced matrix and weak matrix) is supported.

Preferably, the invention can dynamically manage the label indexes. In particular, dynamic management of tag metrics includes traditional statistical analysis model management and big data analysis model management. The traditional statistical analysis model assembles the statistical result labels in a mode of constructing label indexes through multiple dimensions; the big data analysis model classifies and sorts the data characteristics by extracting various data characteristics describing the overall appearance of the personnel, so that a big data analysis label is formed. The invention realizes the dynamic management of the label indexes by defining the label rules (statistical analysis or calling a big data model). Preferably, the safety management system of the invention can also construct group pictures of personnel, show group label conditions of examiners, instructors and the like, realize rapid identification of group characteristics of the personnel, assist professional departments to formulate differentiated management strategies of different groups of the personnel, and guide to compile appropriate teaching, training and logistics support schemes. The individual portrait of the personnel is constructed based on the information such as qualification ability, technical ability, professional tendency and the like of the examinees or the trainees, the individual portrait of the personnel is constructed from the dimension of personnel management, and the personnel information is comprehensively displayed from specific grading evaluation indexes.

Preferably, the primary underlying data sources of the comprehensive assessment report of the present invention include instructor data information and student data information. The teacher data information comprises navigation knowledge scores, advanced teacher evaluation, the score condition of the students, special condition handling times, flight quality (scoring by the examiners and comprehensively obtaining flight parameters) and the like. The trainee data information comprises the aviation knowledge score, the survey score, the teacher evaluation, the flight score and the like. The invention can perform trend analysis aiming at students and teachers affiliated units (such as teaching groups, company and camp) and the like, and is convenient for decision makers to master the score change of the students, thereby providing data support for the decision makers to select the best teaching mode, strength, frequency, combination and the like. The safety management system of the invention also opens a data interface which can provide assistant decision information for other programs.

Preferably, the comprehensive quality data refers to the summary of information of thought, technology, safety skill, theory, physical training, daily management, teaching and self-learning, training and the like of each person. The method can perform unidirectional analysis and multidirectional comprehensive analysis on each flight crew, so that the outstanding capacity point and capacity short board of each flight crew are reflected, and a basis is provided for the culture planning of the comprehensive capacity of each flight crew; meanwhile, the invention also finds the advantages and short boards of the unit by means of historical data analysis and multi-dimensional analysis of the unit level.

Preferably, the sources of the data recorded in the archive unit 1 may also be thought information, technical information, safety skill information, theoretical information, physical training information, daily work and rest information, teaching and self-learning information, training information, attendance checking condition and group integrated management system data of each person. Preferably, the security management system provides a function of setting the weight of each parameter, so that the security management system can comb different attributes of people or organizations according to the weight configuration given by the decision maker, so that the display terminal 4 can display the following contents:

presenting the change trends of different abilities of people in the same professional direction and at different periods, and carrying out mutation reminding;

the embodiment of different abilities of the personnel in the same period is presented, and the advantages and disadvantages are prompted;

presenting the average performance of the mechanism in all aspects in the same period, and performing advantage and disadvantage prompt;

and presenting the change trend of the mechanism in different periods in the same working aspect, and carrying out sudden change reminding.

Example 3

This embodiment is a further improvement of embodiment 1, and repeated contents are not described again.

The safety management system is used for sorting and storing the personnel data, the equipment data and the environment data which are collected by different terminal collection equipment. Preferably, the real-time status of the person is preliminarily judged by analyzing the person data, and a judgment is made as to whether the person can have the task execution capability or can perform the intensive training according to the judgment result and the real-time person status detection data. Preferably, when analyzing the personnel data, the actual use condition of the equipment is judged by analyzing the equipment data, so that the equipment can meet the use requirement of the personnel while the personnel have corresponding capability. In addition, when appropriate personnel (instruction targets) are required to perform a particular task or exercise, the environment of the area in which the task or exercise is located also needs to be analyzed to further screen the people in the community.

Preferably, the personnel data, the equipment data and the environment data can be used as three types of screening conditions for screening personnel from a group, so that the best personnel capable of completing a specific task can be screened out or the success rate of different personnel for performing the specific task can be evaluated. Preferably, the personnel data can be basic information, ideas, technologies, safety skills, theories, physical training, daily management, teaching and self-learning, training and the like of various personnel. Preferably, the equipment data may be a type, a model, a purchase date, a purchase manner, a discard time limit, an expiration date, a use unit, a user, a custodian, a storage location, and memo information of the equipment. Preferably, the remark information may be used to record information such as maintenance and abnormal operation of the device. Preferably, the environmental data may be weather forecasts, geographic information, site conditions, obstacle distribution, electromagnetic interference, flight activity, etc. information for the flight area. When group personnel need to carry out flight training, the flight area weather forecast, geographic information, site conditions, obstacle distribution, electromagnetic interference, flight activities and other information are comprehensively collected, and flight area is comprehensively analyzed for flight environment, so that environmental factors possibly influencing flight implementation and flight safety are found out, basis is provided for pertinently providing preventive measures, implementation schemes and plan formulation, and the flight training system is beneficial to improving training quality and guaranteeing flight safety. Under the condition of analyzing the environment data, the recent training state of the personnel and the use state of the equipment are analyzed by utilizing the personnel data and the equipment data, so that whether different personnel in a group can participate in the flight mission or not is subjected to risk assessment, and a proper personnel is selected for carrying out the flight mission.

Preferably, the present embodiment further provides a security score processing method, including: and determining the historical capability index, the historical physiological index, the historical psychological index, the real-time psychological index and the real-time physiological index of the person to be evaluated according to the data of the person to be evaluated, and further obtaining the score of the person. And determining the historical performance index and the current performance index of the equipment to be evaluated according to the data of the equipment to be evaluated, thereby further obtaining the equipment score. And determining a task difficulty score associated with the to-be-evaluated person and the to-be-evaluated equipment according to the to-be-executed task data, the historical capability index of the to-be-evaluated person and the to-be-evaluated equipment data. And determining an environment score associated with the task to be executed and the equipment to be evaluated according to the environment data of the task to be executed, the equipment data to be evaluated and the task data to be executed. And determining the safety score of the to-be-executed task executed by the to-be-evaluated person under the environment condition of the to-be-executed task based on a preset scoring rule according to the person score, the task difficulty score and the environment score. And only when the safety score is higher than a preset safety threshold value, allowing the person to be evaluated to execute the task so as to ensure that the task to be executed can be safely executed by the person to be evaluated under the task execution environment.

The personnel data comprises data of basic information, ideas, technologies, safety skills, theories, physical training, historical evaluation scores, historical idea evaluation, historical psychological evaluation, historical task completion conditions, simulated theoretical examinations, simulated practical examinations, current physical evaluation and the like of personnel. Personnel data are managed in a closed loop mode through dynamic tags, and an accurate data tag system is established through tag creation, auditing, issuing, evaluation and deactivation. The label system is dynamically managed through indexes, and the label system comprises the following steps: constructing label indexes through multiple dimensions, and summarizing the label indexes into a statistical result label; and the big data analysis model extracts various data characteristics describing the overall appearance of the personnel, and classifies and sorts the data characteristics by combining with an analysis target to form a big data analysis label. By defining a label rule (statistic analysis or calling a big data model), updating frequency and starting scheduling of an updating task, the dynamic management of label indexes is realized. The method comprises the steps of constructing a group portrait of a person, realizing rapid identification of group characteristics of the person, assisting a professional department to make a differentiated management strategy of different groups of the person, constructing an individual portrait of the person based on information such as qualification capability, technical capability and professional tendency of the person, constructing the individual portrait of the person from the dimension of personnel management, and displaying the information of the person in all directions from specific grading evaluation indexes.

The data of the equipment to be evaluated comprises equipment use plan number, attendance number, fault number, personnel use plan number, attendance number, historical maintenance indexes, factory performance records, spare and accessory part replacement records, spare and accessory part performance records and the like. Wherein the equipment performance data is associated with at least one of temperature, humidity, wind speed, and air oxygen content.

Preferably, the environmental data includes regional weather forecasts, geographic information, site conditions, obstacle distributions, electromagnetic interference, flight activity. Aiming at various environmental data, the risk factors are presented in the forms of regional dyeing and the like by using the GIS, and the risk grade of the region is visually prompted. The weight can be changed according to the score of the personnel to be evaluated aiming at a single task, so that the same environmental factors present different danger levels to the personnel with different characteristics; and the risk level will change over time and as environmental factors change.

Preferably, after the task is completed, the task completion component is determined according to the task integrity condition, and the preset scoring rule is adjusted based on the comparison and matching between the task completion component and the safety score. For example, when the difference between the task completion score and the safety score is greater than a preset accuracy threshold, an early warning is given and related management personnel are informed to adjust and verify a preset rule. Personnel data and equipment data are updated based on the task completion component. Task completion includes completion time, motion accuracy, route accuracy, actual number of hazards during the task and hazard value for each hazard.

Preferably, the safety management system analyzes and evaluates safety risk points in a targeted manner by using the collected real-time equipment data (airplane use data and maintenance data) and real-time environment data, so as to find out potential safety hazards which may exist, and performs special evaluation on flight safety risks in a special period, a major mission and other important time periods, so that reduction of the potential safety hazards is facilitated.

Preferably, the flight safety analysis is to screen the possible hidden dangers of the flight mission by using the comprehensive flight record data, the equipment maintenance use record data, the flight personnel comprehensive numerical data, the flight environment of the flight and other data, so as to provide guarantee for the flight safety of the flight personnel. Preferably, the safety management system also has a preview function for risk assessment of the set flight activities. The specific safety management system provides risk conditions which may occur when personnel execute a specified flight project under assumed conditions according to personnel data, equipment data and existing flight environment data, so that risk simulation and prediction are performed on flight activities which are required to be executed by group personnel in important special items and important seasons.

Preferably, in use, the decision maker can select the subject of the airplane model, the flight personnel, the flight date, the flight area, the flight route and the high difficulty coefficient in the safety management system, and the safety management system gives the risk assessment value about the flight personnel performing the flight project at the specified time so as to be referred by the planning personnel. Preferably, the flight safety risk factor evaluation performed by the safety management system may be performed by using different analysis methods according to different selected reference data, and the reference functions of the finally output analysis results are different, so that the decision maker performs different flight safety risk factor evaluations using different analysis results. Preferably, the analysis method can be classified into lateral contrast, trend analysis, blood margin mining, and factor change simulation evaluation according to the difference of the reference data. Specifically, the transverse comparison refers to tracing the generation reasons of flight risks of the flight personnel in the flight process by using flight environment factor data, flight personnel comprehensive quality data, equipment guarantee condition data and flight parameter record data, and analyzing the reasons of the flight personnel with specific flight risks, so as to generate the possibility that various flight risks occur when the flight personnel fly. Specifically, the trend analysis is to draw a flight safety risk coefficient trend graph by using flight environment factor data, flight personnel comprehensive quality data, equipment guarantee condition data and flight parameter record data, so as to analyze and predict the change direction of the aircraft safety risk factor. Specifically, the blooding cut is to analyze the distribution situation of flight safety risk factors of the flight personnel by using flight environment factor data, flight personnel comprehensive quality data, equipment guarantee condition data, flight parameter recording data, execution situation of a safety assessment plan, itemized safety assessment values and assessment comprehensive scores, so as to assist in making a skill training scheme of the flight personnel. Specifically, the factor change simulation evaluation refers to the verification of the established model by using flight environment factor data, flight personnel comprehensive quality data, equipment guarantee condition data, flight parameter recording data, execution condition of a safety evaluation plan, subentry safety evaluation values and evaluation comprehensive scores, so that the growth condition of the flight personnel is estimated, and the risk simulation and prediction can be performed on the designated flight activities, so that the possible flight risk hidden danger is pre-warned, and a training direction beneficial to capacity improvement is provided for the flight personnel.

It should be noted that the above-mentioned embodiments are exemplary, and that those skilled in the art, having benefit of the present disclosure, may devise various arrangements that are within the scope of the present disclosure and that fall within the scope of the invention. It should be understood by those skilled in the art that the present specification and figures are illustrative only and are not limiting upon the claims. The scope of the invention is defined by the claims and their equivalents. Throughout this document, the features referred to as "preferably" are only an optional feature and should not be understood as necessarily requiring that such applicant reserves the right to disclaim or delete the associated preferred feature at any time.

Claims (10)

1. A test information compiling method for accident risk value test is characterized by comprising the following steps:

s1, creating a first test information database (Q1) for performing the command target test, a second test information database (Q2) for performing the device test and a third test information database (Q3) for performing the environment test;

s2, the test items of the second test information database (Q2) are provided to the control unit of at least one equipment related to training life in the designated area to be evaluated for the accident risk value in a data and signaling mode;

the test items of the S3, the third test information database (Q3), while being provided in a data and signaling manner to the control unit of at least one training life related working equipment within the specified area to be assessed for the accident risk value, are also provided in a data and signaling manner in parallel to the control unit of at least one training life related monitoring device.

2. The method for formulating test information for accident risk value test according to claim 1, wherein the step S1 comprises at least the following operations:

s1.1, when the first test information database (Q1) for testing the command targets is created, performing first screening on the command targets in the designated area of the accident risk value to be evaluated, and selecting test information with the test result conforming to normal distribution to create the first test information database (Q1);

s1.2, performing secondary screening on the instruction targets in the designated area of the accident risk value to be evaluated according to the equipment use conditions of the instruction targets during training on the test information in the first test information database (Q1), and selecting test information of which the test results accord with normal distribution to create a second test information database (Q2);

s1.3, screening the instruction targets in the designated area of the accident risk value to be evaluated for the third time according to whether the instruction targets can be trained by using equipment under the designated environmental conditions or not by using the test information in the second test information database (Q2), and selecting the test information of which the test result conforms to normal distribution to create a third test information database (Q3).

3. The test information preparation method of the accident risk value test according to claim 2, wherein the first test information database (Q1) is created as follows: at least seven dimensions of thought, safety skill, theory, physical training, daily management, teaching self-learning and special training are set after the accident risk value is analyzed, and a group of test information is respectively set aiming at the seven dimensions to form a first test information database (Q1).

4. The method for formulating test information for an accident risk value test according to claim 3, wherein the test information contents of seven dimensions include specific items of test information and item profiles in which behavior data of the test items are classified and recorded, and the instruction targets and item profile data thereof included in the first test information database (Q1) are screened for the specified targets in the specified area to be evaluated for accident risk based on a plurality of kinds of behavior data of the instruction targets in a certain period of time, thereby screening out the instruction targets capable of completing the test items in the specified area to be evaluated for accident risk.

5. The test information compilation method for accident risk value tests according to claim 4, characterized in that the stimulus information assignments are ordered and stored in an archive unit (1) in accordance with the type of stimulus information and/or the format of the stimulus information,

the first test information database (Q1), the second test information database (Q2) and the third test information database (Q3) screen the stimulus information stored in the archive unit (1) a plurality of times according to different screening conditions, thereby screening out a command target capable of completing a test project using a specific device under a specified environment.

6. The test information formulating method for accident risk value test according to claim 5, wherein the behavioral data of the order target is mined using a first data mining analysis module (2),

and the decision maker utilizes the second data mining analysis module (3) to screen the multidimensional behavior data associated with the instruction target, and establishes a multidimensional behavior data model of the instruction target through a preset weight configuration rule and the screened multidimensional behavior data in at least part of time intervals, so that a comprehensive evaluation report of the instruction target is generated by utilizing the multidimensional behavior data model taking the preset weight configuration rule as a frame basis.

7. The test information preparation method of the accident risk value test according to claim 6, wherein the second data mining analysis module (3) is capable of comparing a comprehensive evaluation report generated by establishing a multidimensional behavior data model with a reference data model generated in advance by the security management system using test data, thereby evaluating the behavior security of a command target;

the second data mining and analyzing module (3) generates prediction information of at least one behavior data associated with the comparison result or calls the prediction information of at least one behavior data associated with the comparison result into the database according to the comparison, so that the obtained prediction information is displayed by using the display terminal (4), and a decision maker performs behavior safety management on the instruction target according to the prediction information.

8. The method for formulating test information for accident risk value test according to claim 7, wherein the multidimensional behavior data mined by the first data mining and analyzing module (2) from the security management system is classified by a plurality of archive units (1), wherein,

any archive unit (1) records behavior data of a single dimension stored by the security management system independently, and the archive unit (1) updates the recorded behavior data in real time;

the archive unit (1) also associates the behavior data recorded therein with the behavior individuals or behavior units that generated the behavior data.

9. The method for compiling test information for accident risk value test according to claim 8, wherein the mining of the multidimensional behavior data by the first data mining and analyzing module (2) is to perform a secondary classification and collection of the behavior data classified and recorded by the security management system by using the archive unit (1) in a dimension division manner, wherein,

the secondary classification and collection refers to reclassifying and summarizing the behavior data recorded by different archive units (1) according to different behavior individuals or behavior units related to the behavior data.

10. The test information preparation method of an accident risk value test according to claim 9, wherein the second data mining analysis module (3) screens the behavior data associated with the instruction target from the behavior data mined and secondarily classified and collected by the first data mining analysis module (2) using a plurality of kinds of single basic information of the instruction target as a search basis, and the second data mining analysis module (3) secondarily screens the screened behavior data associated with the instruction target according to a time interval determined by a decision maker.

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