CN111597202B - Battlefield situation information on-demand extraction method based on fractal theory - Google Patents
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Abstract
The invention discloses a fractal theory-based battlefield situation information on-demand extraction method and a fractal theory-based battlefield situation information on-demand extraction device, which relate to the technical field of equipment information systems and comprise the following steps of 1: compiling a demand description file according to the situation information demand description fractal framework; step 2: carrying out normalized constraint on the description content of the data source label segment by using a data source description rule; and step 3: carrying out normalized constraint on the description content of the screening condition label segment by using a screening condition description rule; and 4, step 4: the SQL sentences are assembled according to the rule of fractal frame nesting expansion, and the standardized generalized description language is adopted, so that the battlefield situation information extraction device of the fractal theory has high readability and expandability, is easy for non-developers to understand and apply, and can provide useful battlefield situation information which enables equipment operators to master the important situation information in the current environment in time under the condition of modern battlefield operation.
Description
Technical Field
The invention relates to the technical field of equipment information systems, in particular to a method and a device for extracting battlefield situation information on demand based on a fractal theory.
Background
The battlefield situation information is a set of complex and comprehensive information including the type, spatial position, motion track, signal parameters, working state, confrontation relationship, communication relationship and the like of our/enemy equipment, is usually stored and managed by a database of an equipment information system, and is inquired and acquired in real time by the information system through a database access interface SQL during the operation process of the equipment. Currently, there are two main methods for implementing:
one is to develop SQL codes in system software in a customized manner, and process the situation information extraction requirements determined during software development. The method has the advantages that the method directly operates the bottom database, and reduces extra calculation processing overhead; the method has the defects that the content of the extracted situation information is completely fixed, the new situation information extraction requirement can be only realized by a developer through modifying system codes, and the system adaptability is insufficient.
One is to develop situation information filtering query software, filter according to the friend or foe attributes, time conditions, region conditions and the like of situation information, configure filtering conditions through an interface by an equipment system operator, and convert the filtering conditions into SQL to execute query. Its advantage is that it has certain flexibility; the method has the defects that only the existing filtering conditions can be processed, and the new extraction requirement still needs to be realized by a developer through modifying system codes, so that the expandability is not strong.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: how to provide a method which can enable equipment operators to master important and useful battlefield situation information in the current environment in time under the conditions that modern battlefield operation equipment is complex and information is changeable, so as to make correct decisions and adapt to the situation extraction requirements of rapid change.
The invention provides a fractal theory-based battlefield situation information on-demand extraction method, which comprises the steps of
Step 1: compiling a demand description file according to the situation information demand description fractal framework;
step 2: carrying out normalized constraint on the description content of the data source label segment by using a data source description rule;
and step 3: carrying out normalized constraint on the description content of the screening condition label segment by using a screening condition description rule;
and 4, step 4: and assembling the SQL statement according to the rule of the fractal framework nested extension.
Furthermore, the situation information requirement description fractal framework comprises more than one data screening configuration tag, and each data screening configuration tag comprises a data source group tag and a screening condition tag;
each data source group label comprises more than one data source label, and a data screening configuration label is nested in each data source label;
nesting more than one screening condition tag or nesting data screening configuration tags in each screening condition tag;
one data screening configuration label corresponds to a single query process, and more than one data screening configuration labels serially form a query storage process.
Further, the step 2 specifically includes:
the attribute parameters of the data source group label comprise the number of data sources;
the data source label is nested in the data source group label, and the attribute parameters of the data source label comprise a data source type and a data source name;
the data source tag is nested in the data subset tag, and the attribute parameters comprise a data subset type and a subset data item number.
Further, the step 3 specifically includes:
the attribute parameters of the screening condition tags comprise screening types, operators, condition item names, condition table names, condition values and condition value types;
the system also comprises a condition value label, wherein the condition value label is nested in a screening condition label of which the screening type is not a composite condition, a data screening configuration label is embedded in the condition value label, and a return value of the data screening is used as a condition value.
Further, the step 4 specifically includes:
step 4-1: starting parsing with the outermost data screening configuration tag as a starting point, converting the data screening configuration tag into a basic statement structure of "Select% 1 From% 2 Where% 3 is an alternative placeholder;
step 4-2: entering data screening configuration labels, analyzing data source group labels, replacing placeholder% 1 with "% 1-1,% 1-2,. > 10% 1-n", replacing placeholder% 2 with "% 2-1,% 2-2, > 2% 2-n" according to the number n of data sources, wherein n is a positive integer;
step 4-3: entering a data source group label, sequentially analyzing each data source label, and for the ith data source label, if the data source type is a native data source, replacing the placeholder% 2-i with 'Name', 'Name' is the corresponding data source Name; if the data source type is a combined data source, replacing the placeholder% 2-i with "(% 4) [ Name ]", [ Name ] is the corresponding data source Name,% 4 is a replaceable placeholder, and i is a positive integer;
step 4-4: entering an ith data source label, and replacing placeholder% 1-i with a data item contained in the data subset label; if the data source type is a combined data source, entering a nested data screening configuration label, replacing the placeholder% 4 with a statement obtained by converting the data screening configuration label, wherein i is a positive integer;
and 4-5: after the whole content of the data source group label is analyzed, a screening condition label is entered, if the type of the screening condition is not a compound condition, the placeholder% 3 is replaced by 'From ] - [ Key ] [ Operator ]% 5', From ] is a condition table name, [ Key ] is a condition item name, [ Operator ] is an Operator, and% 5 is an exchangeable placeholder; if the screening condition type is a composite class condition, replacing the placeholder% 3 with "% 3-1[ Operator ]% 3-2", [ Operator ] is a composite Operator AND OR OR,% 3-1,% 3-2 is an alternative placeholder;
and 4-6: for non-complex conditions, if the condition value type is a fixed value, replacing the placeholder% 5 with a condition value parameter; if the condition value type is a dynamic value, entering a data screening configuration tag nested in the condition value tag, and replacing the placeholder% 5 with a statement converted from the data screening configuration tag;
and 4-7: for the composite condition, replacing placeholder% 3-1 and placeholder% 3-2 with the statements obtained by nested screening condition label conversion respectively;
and 4-8: and recursively converting all the labels according to the rules until all the placeholders are replaced, and finishing the SQL statement assembly.
The application also provides a battlefield situation information on-demand extraction device based on the fractal theory, which comprises a data source analysis unit, a screening condition analysis unit, an SQL statement assembling and executing unit and a keyword dictionary unit;
the data source analysis unit is used for loading the data source description parameters into a weapon equipment system in a calculation module form, extracting the data source description parameters according to rules and converting the data source description parameters into character strings which can be processed by system software;
the screening condition analysis unit is used for loading the weapon equipment system in a form of a calculation module, extracting screening condition parameters according to rules and converting the screening condition parameters into character strings which can be processed by system software;
the SQL sentence assembling and executing unit is used for being installed in a weapon equipment system in a computing module mode, assembling analysis results of a data source and a screening condition into a correct SQL sentence according to SQL grammar rules, calling a database interface of the equipment information system for execution, and writing a return result into a specified position for the equipment information system or an operator to call or check;
the keyword dictionary unit is used for accessing the weapon equipment system in a plug-in device mode, and editing, loading, storing and inquiring the keyword dictionary data.
Furthermore, the data source analysis unit comprises a processor chip with high-speed computing capability and data source analysis software;
the screening condition analysis unit comprises a processor chip with high-speed computing capability and screening condition analysis software;
the SQL sentence assembling and executing unit comprises a processor chip with high-speed computing capability and SQL assembling and executing software;
the keyword dictionary unit comprises a data storage server and keyword dictionary management software.
Furthermore, the keyword dictionary unit is also used for describing the key value pairs and pressing key names in the requirement description template to replace real parameter names.
Furthermore, the keystroke value pair comprises a data item name, a database table name, an enumeration type parameter of a data source label and an enumeration type parameter of a screening condition label.
By adopting the technical scheme, the invention has the beneficial effects that: by adopting a standardized and generalized description language, the battlefield situation information extraction device of the fractal theory has high readability and expandability, is easy to understand and use by non-developers, can provide direct software support for data analysis, and is easy for system maintenance; in addition, the template framework has high universality, and can adapt to different situation information extraction requirements without modifying system codes, so that the development cost of the system is greatly reduced; and a new requirement description template can be dynamically added in the system operation stage, and the rapidly-changing battlefield situation requirements can be timely responded, so that the timeliness of acquiring the battlefield situation information by the equipment is improved.
Drawings
The invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a diagram of a situation information requirement description document template according to the present invention.
Detailed Description
All of the features disclosed in this specification, or all of the steps in any method or process so disclosed, may be combined in any combination, except combinations of features and/or steps that are mutually exclusive.
Any feature disclosed in this specification may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
Because the combat equipment in the modern battlefield is complex and the information is changeable, the equipment operator needs to master the important and useful battlefield situation information in the current environment in time to make a correct decision. Therefore, it is desirable for the equipment information system to provide the capability for battlefield situation extraction on demand. At present, a processing means is lacked to adapt to the rapidly changing situation extraction requirement.
Aiming at the current situation, the invention provides a method and a device for extracting battlefield situation information on demand based on a fractal theory, different situation extraction requirements are expressed according to a rule of a unified standard, and the device can analyze a corresponding structured description file and dynamically generate an SQL statement according to the rule without modifying codes. The invention has the characteristics of high expandability, easy understanding by non-developers, easy system maintenance and the like.
The invention mainly comprises the following steps: 1) a battlefield situation information on-demand extraction method based on a fractal theory comprises describing a fractal framework, a data source description rule, a screening condition description rule and an SQL statement assembly rule by situation information requirements; 2) a battlefield situation information on-demand extraction device based on a fractal theory comprises: the system comprises a data source analysis unit, a screening condition analysis unit, an SQL sentence assembling and executing unit and a keyword dictionary unit. The invention provides a set of complete operation device and file compiling specifications, which can be directly applied to an equipment information system, and can realize extraction of diversified situation information on a battlefield as required by compiling and loading different situation information requirement description files.
The situation information demand description fractal framework is used as a guide rule for writing a demand description file and is realized in a tag combination form of XML or JSON. A demand description framework is constructed based on a core principle of a fractal theory, namely a self-similarity principle and an iteration generation principle, and can be arbitrarily expanded by following the same description rule. A typical requirement description template is shown in figure 1.
A requirement description template may comprise a plurality of data screening configuration tags < DataFilter >, each data screening configuration tag < DataFilter > comprising a data source group tag < DataSrcSet > and a screening Condition tag < Condition >.
Each data source group tag < DataSrc > may comprise a plurality of data source tags < DataSrc >, and each data source tag < DataSrc > may be nested with a data screening configuration tag < DataFilter >.
Each screening Condition label < Condition > can be embedded with a plurality of screening Condition labels < Condition > and can also be embedded with a data screening configuration label < DataFilter >.
One data screening configuration tag < DataFilter > corresponds to a single query process. The plurality of data screening configuration tags < DataFilter > are serially organized into a query storage process.
The data source description rule carries out normalized constraint on the description content of the data source label segment, wherein:
(1) a data source group tag < DataSrcSet >, and the attribute parameters comprise: the number of data sources Count.
(2) A data source tag < DataSrc > nested in the data source group tag, and the attribute parameters comprise:
1) data source Type: including a native data source original, a combined data source issue, etc. The native data source represents that a corresponding data table exists in the equipment information system database and can be directly referred to. The combined data source represents that no data table directly corresponding to the combined data source exists in the equipment information system database, and a primary data source is introduced through nested data screening configuration < DataFilter > according to the requirement description rule of the patent, so that a temporary data table is constructed; according to the requirement description fractal framework of the patent invention, data screening configurations can be infinitely nested until all referenced data sources are finally from native data sources, and the constructed temporary data table meets the requirement.
2) Data source Name: the name of the original data source is a database table name or an alias, and the name of the combined data source is self-defined.
(3) The data subset tag < selecteeset > is nested in the data source tag, and the attribute parameters comprise:
1) data subset Type: can be a full set all, an empty set none, a partial subset option, etc.; if the partial subset is adopted, the subset data item tag < SelectItem > is nested again, and the attribute parameter is the Name of the data item.
2) The number Count of the subset data items is valid only when the type of the subset is a partial subset.
The screening condition description rule carries out normalized constraint on the description content of the screening condition label segment, wherein:
(1) the screening Condition label < Condition >, the attribute parameters include:
1) screening Type: the method comprises the steps of non-screening condition none, comparison type condition match, range type condition range, set type condition set, matching type condition match, null type condition null and composite type condition multiplex; if the Condition is a composite Condition, then nesting and screening the Condition label < Condition >.
2) Operator: and matching with the screening type. The operators that can be matched by the comparison class condition include ═, >, < >, <! The name, <! <, | the! NOT, and a combination of NOT and the above operators, acting on a condition value; operators which can be matched by the range class condition comprise BETWEEN and NOBETWEEN and act on two condition values; operators which can be matched with the set class conditions comprise IN and NOT IN and act on a group of condition values; operators which can be matched by matching class conditions comprise LIKE and NOT LIKE and act on a condition value; operators which can be matched by NULL value class conditions comprise IS NULL and IS NOT NULL, and no condition value IS needed; operators which can be matched by the composite class condition comprise OR, AND, AND a plurality of screening condition labels can be nested.
3) Condition item name Key: data item names for conditional screening;
4) condition table name From: the data table name of the condition item for condition screening;
5) condition values: and the constant Value is used for screening the constant Value of the conditional operation, the Value represents the unique numerical Value matched by the monocular operation, and the Value left/Value right represents the boundary numerical Value matched by the binocular operation.
6) The condition value type ValueType: if the Value is a fixed Value type fixed, the condition Value parameter Value/Value left/Value right is valid; if the dynamic Value type is dynamic, the embedded condition Value tag < Value > is valid.
(2) The condition Value label < Value > is nested in the screening condition label of which the screening type is not a composite condition, the embedded data screening configuration label < dataFilter > is embedded in the condition Value label, and the return Value of the data screening is used as the condition Value.
The SQL statements are assembled according to rules of fractal framework nested extension, and the method specifically comprises the following steps:
(1) starting with the outermost data screening configuration tag < DataFilter >, the parsing converts < DataFilter > into a basic statement structure of "Select% 1 From% 2 Where% 1,% 2,% 3 is an alternative placeholder.
(2) Entering a data screening configuration tag < DataFilter >, parsing a data source group tag < DataSrcSet >, replacing placeholder% 1 with "% 1-1,% 1-2,. > 1-n", and placeholder% 2 with "% 2-1,% 2-2, > 2,% 2-n" according to the number of data sources n.
(3) And entering a data source group tag < DataSrcSet >, and sequentially analyzing each data source tag < DataSrc >. For the ith data source label, if the data source type is a native data source, replacing the placeholder% 2-i with 'Name'; if the data source type is a composed data source, the placeholder% 2-i is replaced with "(% 4) [ Name ]", [ Name ] is the corresponding data source Name, and% 4 is an replaceable placeholder.
(4) Entering an ith data source tag < DataSrc >, and replacing the placeholder% 1-i with a data item contained in the data subset tag < SelectionSet >; if the data source type is a combined data source, a nested data screening configuration label < DataFilter > is entered, and the placeholder% 4 is replaced by a statement obtained by the conversion of < DataFilter >.
(5) After the whole content of < DataSrcSet > is analyzed, a screening Condition label < Condition > is entered, if the type of the screening Condition is not a composite Condition, the placeholder% 3 is replaced by "[ From ] - [ Key ] [ Operator ]% 5", [ From ] is a Condition table name, [ Key ] is a Condition item name, [ Operator ] is an Operator, and "% 5 is an replaceable placeholder; if the filter condition type is a compound class condition, the placeholder% 3 is replaced with "% 3-1[ Operator ]% 3-2", [ Operator ] is a compound Operator AND OR OR,% 3-1,% 3-2 is an alternative placeholder.
(6) For non-complex conditions, if the condition value type is a fixed value, replacing the placeholder% 5 with a condition value parameter; if the condition Value type is a dynamic Value, entering a data screening configuration tag < DataFilter > nested in a condition Value tag < Value >, and replacing a placeholder% 5 with a statement obtained by converting the < DataFilter >.
(7) For the composite Condition, placeholders% 3-1 and% 3-2 are replaced by statements converted from nested screening Condition tags < Condition > respectively.
(8) And recursively converting all the labels according to the rules until all the placeholders are replaced, and finishing the SQL statement assembly.
The data source analysis unit comprises a processor chip with high-speed computing capability and data source analysis software, can be loaded into a weapon equipment system in the form of a computing module, and extracts and converts data source description parameters into character strings which can be processed by the system software according to rules.
The screening condition analysis unit comprises a processor chip with high-speed computing capability and screening condition analysis software, can be installed in a weapon equipment system in a computing module mode, extracts screening condition parameters according to rules and converts the screening condition parameters into character strings capable of being processed by system software.
The SQL assembling and executing unit comprises a processor chip with high-speed computing capacity and SQL assembling and executing software, can be installed in a weapon equipment system in a computing module mode, assembles analysis results of a data source and a screening condition into correct SQL sentences according to SQL grammar rules, calls a database interface of the equipment information system to execute, and writes a return result into a designated position for the equipment information system or an operator to call or check.
The keyword dictionary unit comprises a data storage server and keyword dictionary management software, and can be accessed to a weapon equipment system in a plug-in device mode to edit, load, store and query the keyword dictionary data. The keyword dictionary is used for describing key value pairs such as data item names, database table names, enumeration type parameters of data source labels, enumeration type parameters of screening condition labels and the like, and the key names can be used for replacing real parameter names in a demand description template, so that unified updating and maintenance are facilitated.
The invention has the following advantages: the invention adopts standardized and generalized description language, has high readability and expandability, is easy for non-developers to understand and use, can provide direct software support for data analysis, and is easy for system maintenance;
the template framework designed by the invention has high universality, can adapt to different situation information extraction requirements without modifying system codes, and greatly reduces the system development cost;
the invention can dynamically add a new demand description template in the system operation stage, respond to rapidly changing battlefield situation demands in time and improve the timeliness of acquiring battlefield situation information by equipment.
Example 1
The scheme of the present invention is further illustrated in detail by the following examples: a certain reconnaissance equipment needs to acquire all situation information of early warning radar targets in a battlefield during operation. Because the reconnaissance equipment can reconceive and record a plurality of electromagnetic signal data on a battlefield, and the signal parameters of the early warning radar target are flexible and changeable, the early warning radar target is difficult to screen from a plurality of complex signal data by a fixed method, and other instant information is required to assist in screening.
The application number of the current early warning radar target is assumed to be 8 according to instant information, and the frequency range is 3000 MHz-3500 MHz. The method can be used for dynamically screening out early warning radar information in real time, and comprises the following steps:
step 1: according to the framework and the rules of the invention, the following requirement description template files are constructed:
the SQL engine execution statement is generated by template parsing as follows:
SELECTN_UNITTAGPARA.*FROM N_UNITTAGPARA,(SELECT DB_RB_BASICKNOWLEDGE.RTYPE FROM DB_RB_BASICKNOWLEDGE WHERE DB_RB_BASICKNOWLEDGE.OPR＝8)RadarTableWHERE(N_UNITTAGPARA.RTYPE IN(SELECTRadarTable.RTYPEFROMRadarTable))OR(N_UNITTAGPARA.RF BETWEEN 3000 AND 3500)；
through testing, the syntax of the SQL statement is completely correct and can be loaded and executed by a database.
While the foregoing description shows and describes a preferred embodiment of the invention, it is to be understood, as noted above, that the invention is not limited to the form disclosed herein, but is not intended to be exhaustive or to exclude other embodiments and may be used in various other combinations, modifications, and environments and may be modified within the scope of the inventive concept described herein by the above teachings or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (4)
1. A battlefield situation information on-demand extraction method based on a fractal theory is characterized by comprising the following steps: comprises that
Step 1: writing a demand description file according to a situation information demand description fractal framework, wherein the situation information demand description fractal framework is used as a guidance rule for writing the demand description file and is realized by adopting a tag combination form of XML or JSON;
step 2: carrying out normalized constraint on the description content of the data source label segment by using a data source description rule;
and step 3: carrying out normalized constraint on the description content of the screening condition label segment by using a screening condition description rule;
and 4, step 4: assembling SQL sentences according to rules of fractal frame nested extension, calling a database interface of the equipment information system to execute, and writing a return result into a specified position for the equipment information system or an operator to call or check;
the step 3 specifically includes:
the attribute parameters of the screening condition tags comprise screening types, operators, condition item names, condition table names, condition values and condition value types;
the system also comprises a condition value label, wherein the condition value label is nested in a screening condition label of which the screening type is not a composite condition, a data screening configuration label is embedded in the condition value label, and a return value of the data screening is used as a condition value.
2. The fractal theory-based battlefield situation information on-demand extraction method as claimed in claim 1, wherein:
the situation information demand description fractal framework comprises more than one data screening configuration tag, and each data screening configuration tag comprises a data source group tag and a screening condition tag;
each data source group label comprises more than one data source label, and a data screening configuration label is nested in each data source label;
nesting more than one screening condition tag or nested data screening configuration tags in each screening condition tag;
one data screening configuration tag corresponds to a single query process, and more than one data screening configuration tags form a query storage process in series.
3. The fractal theory-based battlefield situation information on-demand extraction method as claimed in claim 2, wherein: the step 2 specifically comprises:
the attribute parameters of the data source group labels comprise the number of data sources;
the data source label is nested in the data source group label, and the attribute parameters of the data source label comprise a data source type and a data source name;
the data source tag is nested in the data subset tag, and the attribute parameters comprise a data subset type and a subset data item number.
4. The fractal theory-based battlefield situation information on-demand extraction method as claimed in claim 1, wherein: the step 4 specifically includes:
step 4-1: starting parsing with the outermost data screening configuration tag as a starting point, converting the data screening configuration tag into a basic statement structure of "Select% 1 From% 2 Where% 3 is an alternative placeholder;
step 4-2: entering data screening configuration labels, analyzing data source group labels, replacing placeholder% 1 with "% 1-1,% 1-2,. > 10% 1-n", replacing placeholder% 2 with "% 2-1,% 2-2, > 2% 2-n" according to the number n of data sources, wherein n is a positive integer;
step 4-3: entering a data source group label, sequentially analyzing each data source label, and for the ith data source label, if the data source type is a native data source, replacing the placeholder% 2-i with 'Name', 'Name' is the corresponding data source Name; if the data source type is a combined data source, replacing the placeholder% 2-i with "(% 4) [ Name ]", [ Name ] is the corresponding data source Name,% 4 is a replaceable placeholder, and i is a positive integer;
step 4-4: entering an ith data source label, and replacing placeholder% 1-i with a data item contained in the data subset label; if the data source type is a combined data source, entering a nested data screening configuration label, replacing the placeholder% 4 with a statement obtained by converting the data screening configuration label, wherein i is a positive integer;
and 4-5: after the whole content of the data source group label is analyzed, a screening condition label is entered, if the type of the screening condition is not a compound condition, the placeholder% 3 is replaced by 'From ] - [ Key ] [ Operator ]% 5', From ] is a condition table name, [ Key ] is a condition item name, [ Operator ] is an Operator, and% 5 is an exchangeable placeholder; if the screening condition type is a composite class condition, replacing the placeholder% 3 with "% 3-1[ Operator ]% 3-2", [ Operator ] is a composite Operator AND OR OR,% 3-1,% 3-2 is an alternative placeholder;
and 4-6: for non-complex conditions, if the condition value type is a fixed value, replacing the placeholder% 5 with a condition value parameter; if the condition value type is a dynamic value, entering a data screening configuration label nested in the condition value label, and replacing the placeholder% 5 with a statement converted from the data screening configuration label;
and 4-7: for the composite condition, replacing placeholder% 3-1 and placeholder% 3-2 with the statements obtained by nested screening condition label conversion respectively;
and 4-8: and recursively converting all the labels according to the rules until all the placeholders are replaced, and finishing the SQL statement assembly.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104363104A (en) * | 2014-09-29 | 2015-02-18 | 中国人民解放军总参谋部第五十四研究所 | User-requirement-oriented mass multivariate data situation displaying system and method |
| CN107005890A (en) * | 2014-11-25 | 2017-08-01 | 华为技术有限公司 | System and method for downlink machine-to-machine communication |
| CN107465571A (en) * | 2017-07-27 | 2017-12-12 | 中国人民解放军国防信息学院 | Tactical network simulated training background service flow generation method based on statistical property |
| CN107766650A (en) * | 2017-10-20 | 2018-03-06 | 中国民航大学 | The dynamic capability response avionics system vision simulation instrument implementation method of oriented mission |
| CN107944694A (en) * | 2017-11-21 | 2018-04-20 | 中国人民解放军陆军装甲兵学院 | A kind of equipment Safeguard operational process analysis method towards operational performance |
| CN109933669A (en) * | 2019-03-19 | 2019-06-25 | 南京大学 | A kind of matching process of situation of battlefield data label |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8655939B2 (en) * | 2007-01-05 | 2014-02-18 | Digital Doors, Inc. | Electromagnetic pulse (EMP) hardened information infrastructure with extractor, cloud dispersal, secure storage, content analysis and classification and method therefor |
| US11461785B2 (en) * | 2008-07-10 | 2022-10-04 | Ron M. Redlich | System and method to identify, classify and monetize information as an intangible asset and a production model based thereon |
| US9674124B1 (en) * | 2015-04-15 | 2017-06-06 | TJD Enterprises, Inc. | Methods and systems for dynamic execution of actions in response to communications events of one or more communications protocols |
| US10857457B2 (en) * | 2017-10-19 | 2020-12-08 | Daybreak Game Company Llc | Management and broadcasting of live multiplayer video game activity |
-
2020
- 2020-04-27 CN CN202010341596.7A patent/CN111597202B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104363104A (en) * | 2014-09-29 | 2015-02-18 | 中国人民解放军总参谋部第五十四研究所 | User-requirement-oriented mass multivariate data situation displaying system and method |
| CN107005890A (en) * | 2014-11-25 | 2017-08-01 | 华为技术有限公司 | System and method for downlink machine-to-machine communication |
| CN107465571A (en) * | 2017-07-27 | 2017-12-12 | 中国人民解放军国防信息学院 | Tactical network simulated training background service flow generation method based on statistical property |
| CN107766650A (en) * | 2017-10-20 | 2018-03-06 | 中国民航大学 | The dynamic capability response avionics system vision simulation instrument implementation method of oriented mission |
| CN107944694A (en) * | 2017-11-21 | 2018-04-20 | 中国人民解放军陆军装甲兵学院 | A kind of equipment Safeguard operational process analysis method towards operational performance |
| CN109933669A (en) * | 2019-03-19 | 2019-06-25 | 南京大学 | A kind of matching process of situation of battlefield data label |
Non-Patent Citations (2)
| Title |
|---|
| "COMPLEXITY AND THE LIMITS OF MODERN BATTLESPACE VISUALIZATION";Rousseau C C;《Canadian Military Journal》;20031231;第1-31页 * |
| "共用战场态势信息系统研究综述";侯锋 等;《测绘科学》;20071130;第17-21页 * |
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