CN114168906B - Mapping geographic information data acquisition system based on cloud computing - Google Patents
Mapping geographic information data acquisition system based on cloud computing Download PDFInfo
- Publication number
- CN114168906B CN114168906B CN202210131683.9A CN202210131683A CN114168906B CN 114168906 B CN114168906 B CN 114168906B CN 202210131683 A CN202210131683 A CN 202210131683A CN 114168906 B CN114168906 B CN 114168906B
- Authority
- CN
- China
- Prior art keywords
- signal
- influence
- judgment
- mapping
- coefficient
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C15/00—Surveying instruments or accessories not provided for in groups G01C1/00 - G01C13/00
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Analysis (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Algebra (AREA)
- Remote Sensing (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of surveying and mapping geographic information, and aims to solve the problems that in the existing surveying and mapping geographic information acquisition system, the error of acquired data is difficult to be definitely pre-judged and analyzed, and the accuracy of data acquisition is difficult to be ensured, so that the accuracy of geographic surveying and mapping information acquisition cannot be ensured, and the development of a surveying and mapping geographic information system is hindered, in particular to a surveying and mapping geographic information data acquisition system based on cloud computing, which comprises a data acquisition unit, a data qualitative unit, a primary judgment unit, a secondary judgment unit and a comprehensive proofreading unit; according to the invention, errors affecting the geographic mapping information acquisition data are accurately pre-judged and analyzed from different angles and by using different methods, so that the accuracy of the geographic mapping information data acquisition is improved while the definite pre-judgment analysis of the error of the acquisition data is realized, and the efficient development and progress of a mapping geographic information system are promoted.
Description
Technical Field
The invention relates to the technical field of mapping geographic information, in particular to a mapping geographic information data acquisition system based on cloud computing.
Background
Mapping geographic information is information for describing the spatial position and distribution of various targets in the real world, is a general term for representing the inherent quantity, quality, distribution characteristics, numbers, characters, graphs and images of ground features and environments, and has spatial positioning characteristics, multiple attribute structural characteristics and temporal variation characteristics.
With the development of science and technology and the progress of human civilization, the utilization rate and the dependence of human work, life and construction activities on the mapping geographic information become stronger and stronger, and the mapping geographic information becomes one of the most important and basic information resources of human beings, so that the steps for mapping geographic information construction need to be increased, and the development requirements of the society are further met.
The construction process of surveying and mapping geographic information comprises data acquisition, processing, analysis, mapping, database establishment and the like, most of the existing surveying and mapping geographic information acquisition systems directly acquire geographic data information, and the data acquisition mode of the existing surveying and mapping geographic information acquisition systems is difficult to definitely predict and analyze the error of the acquired data and guarantee the accuracy of data acquisition, so that the accuracy of geographical surveying and mapping information acquisition cannot be guaranteed, and further the development and progress of a surveying and mapping geographic information system are greatly hindered.
In order to solve the above-mentioned drawbacks, a technical solution is now provided.
Disclosure of Invention
The invention aims to solve the problems that in the existing geographic information acquisition system for mapping, the error of the acquired data is difficult to be clearly pre-judged and analyzed, and the accuracy of data acquisition is difficult to be ensured, so that the accuracy of geographic mapping information acquisition cannot be ensured, and the development and progress of a geographic information system for mapping are hindered.
The purpose of the invention can be realized by the following technical scheme:
a surveying and mapping geographic information data acquisition system based on cloud computing comprises a data acquisition unit, a data qualitative unit, a primary judgment unit, a secondary judgment unit, a comprehensive proofreading unit, an inductive analysis unit and a display terminal;
the data acquisition unit is used for acquiring acquisition error factor information influencing the acquisition accuracy of geographic mapping and sending the acquisition error factor information to the data qualitative unit;
the data qualitative unit is used for carrying out data qualitative analysis processing on the received acquisition error factor information by class, respectively generating a skill comprehensive coefficient Jsw, an environment complex coefficient Hfz and an equipment fusion coefficient Sbr according to the data qualitative analysis processing, and respectively sending the data qualitative analysis processing to the primary judgment unit and the secondary judgment unit;
the preliminary judgment unit carries out preliminary qualitative analysis processing on various received data coefficients, generates a preliminary judgment influence minimum signal, a preliminary judgment influence medium signal and a preliminary judgment influence maximum signal according to the preliminary judgment coefficients, and sends the preliminary judgment influence minimum signal, the preliminary judgment influence medium signal and the preliminary judgment influence maximum signal to the comprehensive proofreading unit;
the secondary judgment unit is used for carrying out double judgment analysis processing on the received skill comprehensive coefficient Jsw, the environment complex coefficient Hfz and the equipment fusion coefficient Sbr, generating a secondary judgment influence minimum signal, a secondary judgment influence medium signal and a secondary judgment influence maximum signal according to the double judgment analysis processing, and sending the signals to the comprehensive proofreading unit;
the comprehensive proofreading unit is used for carrying out comprehensive judgment analysis processing on the received primary judgment influence type signal and secondary judgment influence type signal, generating a positive influence signal, an irrelevant influence signal and a general influence signal according to the comprehensive proofreading processing, and sending the positive influence signal, the irrelevant influence signal and the general influence signal to the inductive analysis unit;
the inductive analysis unit carries out inductive analysis processing on the received positive influence signal, irrelevant influence signal and general influence signal, and sends the inductive analysis processing to the display terminal in a text typeface mode for technical staff to refer and research.
Furthermore, the collected error factor information is used for representing factor information influencing the accuracy of the collected geographic mapping data in the geographic mapping process, and the collected error factor information comprises a technical level index, an environmental level index and an equipment level index, wherein the technical level index is used for representing comprehensive data information of each skill of each technician in the geographic mapping, and the technical level index comprises a job age, a job position grade and a skill depth, wherein the job age is used for representing the working time age data value of each worker engaged in the geographic mapping, the job position grade is used for representing the mapping job position grade data value of each worker engaged in the geographic mapping, and the skill depth is used for representing the depth data value mastered by each worker engaged in the geographic mapping on the mapping knowledge level;
the environment level index is used for representing comprehensive data information of the geographic mapping unit area terrain environment, meteorological environment and human environment, and comprises a terrain value, a meteorological value and a population value, wherein the terrain value is used for representing the data value of the number of terrain types contained in the unit area, the meteorological value is used for representing the data value of the weather environment change times of the unit area in a period of time, and the population value is used for representing the number value of floating population in the unit area;
the equipment level index is used for representing the performance data value of the completeness of the mapping work completed by various types of mapping equipment, and the equipment level index comprises an equipment unified quantity value, an equipment performance quantity value and an equipment category quantity value, wherein the equipment unified quantity value is used for representing the data quantity value of the unified height of the mapping mode of the mapping equipment in the geographic mapping industry, the equipment performance quantity value is used for representing the data quantity value of the equipment mapping performance grade, and the equipment category quantity value is used for representing a type of quantity value data of the mapping category size capable of being related to the mapping category size of various types of mapping equipment in the mapping work.
Further, the specific operation steps of the class-by-class data qualitative analysis processing are as follows:
s1: capturing a technical level index in the acquired error factor information in unit time, respectively calibrating the work age, position grade and skill depth in the technical level index as age, pos and sgs, normalizing the indexes, and obtaining a skill comprehensive coefficient Jsw according to a formula Jsw (age + pos + sgs), wherein a1, a2 and a3 are correction factor coefficients of the work age, position grade and skill depth, a3 & gt a1 & gt a3 & gt 0, and a1+ a2+ a3 & gt 1.3641;
s2: capturing an environment level index in the acquired error factor information in unit time, respectively calibrating a terrain value, a meteorological value and an population value of the environment level index into dxl, qxl and rwl, normalizing the dxl, qxl and rwl, and obtaining an environment complex coefficient Hfz according to a formula Hfz which is dxl + qxl + rwl, wherein b1, b2 and b3 are error factor coefficients of the terrain value, the meteorological value and the population value, b1 is greater than b2 is greater than b3 is greater than 0, and b1+ b2+ b3 is 0.8948;
s3: capturing an equipment level index in the acquired error factor information in unit time, respectively marking an equipment uniform value, an equipment performance value and an equipment category value of the equipment level index as stl, sxl and sfl, normalizing the equipment uniform value, the equipment performance value and the equipment category value, and obtaining an equipment fusion coefficient Sbr according to a formula Sbr ═ stl + sxl + sfl, wherein c1, c2 and c3 are normalization factor coefficients of the equipment uniform value, the equipment performance value and the equipment category value respectively, c2 > c1 > c3 > 0, and c1+ c2+ c3 ═ 5.7101.
Further, the specific operation steps of the preliminary qualitative analysis processing are as follows:
capturing a skill comprehensive coefficient Jsw, an environment complex coefficient Hfz and an equipment fusion coefficient Sbr in various data coefficients in unit time, comparing the skill comprehensive coefficient, the environment complex coefficient Hfz and the equipment fusion coefficient Sbr with corresponding preset reference values Yu1, Yu2 and Yu3 respectively, generating a technical-level qualified signal if Jsw is larger than or equal to Yu1, generating a technical-level unqualified signal if Jsw is smaller than Yu1, generating an environment-level qualified signal if Hfz is smaller than Yu2, generating an environment-level unqualified signal if Hfz is larger than or equal to Yu2, generating an equipment-level qualified signal if Sbr is larger than or equal to Yu3, and generating an equipment-level unqualified signal if Sbr is smaller than Yu 3;
respectively calibrating a technical level qualified signal and a technical level unqualified signal into symbols J-1 and J-2, respectively calibrating an environment level qualified signal and an environment level unqualified signal into symbols H-1 and H-2, and respectively calibrating an equipment level qualified signal and an equipment level unqualified signal into symbols S-1 and S-2;
simultaneously, judging calibration symbols J-1 or J-2, H-1 or H-2 and S-1 or S-2 of a technical level, an environment level and an equipment level in unit time are captured, intersection processing is carried out on various judging symbols, if J-1 n H-1 n S-1 is satisfied, an initial judging influence minimum signal is generated, if J-2 n H-2 n S-2 is satisfied, an initial judging influence maximum signal is generated, and under other conditions, initial judging influence medium signals are generated.
Further, the specific operation steps of the double judgment analysis processing are as follows:
capturing a skill synthesis coefficient Jsw, an environment complexity coefficient Hfz and a device fusion coefficient Sbr in unit time according to a formula
Obtaining a double judgment coefficient Ecx, wherein e1, e2 and e3 are weight factor coefficients of a skill comprehensive coefficient Jsw, an environment complexity coefficient Hfz and a device fusion coefficient Sbr, respectively, e1 > e2 > e3 > 0, and e1+ e2+ e3 ═ 4.3012;
substituting the double-decision coefficient Ecx into a preset influence threshold Yu4 for comparison and analysis, generating a minimum signal of the double-decision influence if the double-decision coefficient Ecx is greater than the maximum value of the preset influence threshold Yu4, generating a medium signal of the double-decision influence if the double-decision coefficient Ecx is within a preset influence threshold Yu4, and generating a maximum signal of the double-decision influence if the double-decision coefficient Ecx is less than the minimum value of the preset influence threshold Yu 4.
Further, the specific operation steps of the comprehensive judgment analysis processing are as follows:
capturing a primary judgment influence minimum signal, a primary judgment influence medium signal and a primary judgment influence maximum signal in the primary judgment influence type signals, capturing a secondary judgment influence minimum signal, a secondary judgment influence medium signal and a secondary judgment influence maximum signal in the secondary judgment influence type signals, and carrying out comprehensive cross analysis on the two types of influence judgment signals;
if the signals to be simultaneously captured and integrated are respectively the initial judgment influence minimal signal and the secondary judgment influence minimal signal, an irrelevant influence signal is generated, if the signals to be simultaneously captured and integrated are respectively the initial judgment influence maximal signal and the secondary judgment influence maximal signal, a positive influence signal is generated, and otherwise, general influence signals are generated.
Further, the specific operation steps of the inductive analysis processing are as follows:
when a positive influence signal is received, generating a text typeface, namely 'a larger acquisition error phenomenon exists during data acquisition of mapping geographic information and the text typeface is extremely not beneficial to construction of subsequent mapping geographic information';
when an irrelevant influence signal is received, generating a text typeface, wherein the phenomenon of acquisition errors hardly exists during data acquisition of mapping geographic information, and the text typeface is extremely favorable for construction of subsequent mapping geographic information;
when receiving the general influence signal, generating the text word "there is certain collection error phenomenon during data collection of mapping geographic information, and is not favorable for the construction of the subsequent mapping geographic information".
Compared with the prior art, the invention has the beneficial effects that:
the method comprises the steps of accurately carrying out qualitative pre-judgment analysis on various error factor information influencing acquisition accuracy through symbolic calibration and normalization processing, and carrying out accurate pre-judgment analysis on errors influencing geographical mapping information acquisition data from different angles and by different methods according to the result of the qualitative pre-judgment analysis by using a numerical reference comparison mode and a formulaic processing and threshold substitution analysis mode, so that the accuracy of geographical mapping information data acquisition is improved while the definite pre-judgment analysis on the error of the acquisition data is realized, and the efficient development and progress of a geographical mapping information system are promoted.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a general block diagram of the system of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
as shown in fig. 1, a mapping geographic information data acquisition system based on cloud computing comprises a data acquisition unit, a data qualitative unit, a primary judgment unit, a secondary judgment unit, a comprehensive correction unit, an inductive analysis unit and a display terminal;
the data acquisition unit is used for acquiring acquisition error factor information influencing the acquisition accuracy of geographic mapping and sending the acquisition error factor information to the data qualitative unit;
wherein, the collected error factor information is used for representing factor information influencing the accuracy of the collected geographic mapping data in the geographic mapping process, and the collected error factor information comprises technical level indexes, environmental level indexes and equipment level indexes, wherein the technical level indexes are used for representing comprehensive data information of each skill of each technician in the geographic mapping, and the technical level indexes comprise a work age, a position grade and a skill depth, wherein the work age is used for representing the working time age data value of each worker engaged in the geographic mapping, the position grade is used for representing the mapping position grade data value of each worker engaged in the geographic mapping, the skill depth is used for representing the depth data value mastered by each worker engaged in the geographic mapping on the mapping knowledge level, and it needs to be explained that the larger the expression value of each factor index data in the technical level indexes is, the more the technical level index is higher, the less the influence on the data acquisition error of the surveying and mapping geographic information is, the more the construction of the following surveying and mapping geographic information is facilitated;
the environment level index is used for representing comprehensive data information of the complexity of a unit area topographic environment, a meteorological environment and a human environment where geographic mapping is located, and comprises a topographic magnitude value, a meteorological magnitude value and a population magnitude value, wherein the topographic magnitude value is used for representing a data value of the number of topographic types contained in the unit area where the geographic mapping is located, and it needs to be noted that the more the number of topographic types contained in the unit area is, the larger the expression value of the topographic magnitude value is, the more the influence on the data acquisition error of the geographic information of the geographic mapping is, and the unit area represents a spatial area with county level or regional level as a unit;
the weather magnitude is used for representing a data value of the number of weather environment changes of a unit area located in a period of time, and it should be noted that the weather environment changes refer to a phenomenon condition that the weather changes from one weather to another weather, such as from sunny to cloudy or from cloudy to sunny, and such a change mode is recorded as the number of changes, wherein the period of time is represented as a week time, and the unit area still represents a spatial area with a county level or a district level as a unit;
the population value is used for representing the quantity value of the floating population in the unit area, and it needs to be explained that the larger the expression value of the population value is, the more the floating population quantity in the unit area is, so that the larger the influence on the acquisition error of the mapping geographic information data is;
the device level index is used for representing performance data values of the integrity of the mapping work completed by various mapping devices, and comprises a device uniform quantity value, a device performance quantity value and a device category quantity value, wherein the device uniform quantity value is used for representing a data quantity value of the mapping mode uniform height of the mapping devices in the geographic mapping industry, the device performance quantity value is used for representing a data quantity value of the device mapping performance grade, the device category quantity value is used for representing a class of quantity data of the mapping category size capable of being related to the mapping work of various mapping devices, and it needs to be noted that the larger the expression value of each factor index data in the device level index is, the higher the device level index is, and further, the smaller the influence on the mapping geographic information data acquisition error is, and the construction of the subsequent mapping geographic information is facilitated;
the data qualitative unit is used for carrying out data qualitative analysis processing on the received acquisition error factor information class by class, respectively generating a skill comprehensive coefficient Jsw, an environment complex coefficient Hfz and an equipment fusion coefficient Sbr according to the data qualitative analysis processing, and respectively sending the data qualitative analysis processing to the primary judgment unit and the secondary judgment unit;
the preliminary judging unit carries out preliminary qualitative analysis processing on the received various data coefficients, generates a preliminary judging influence minimum signal, a preliminary judging influence medium signal and a preliminary judging influence maximum signal according to the preliminary judging influence minimum signal, and sends the preliminary judging influence medium signal and the preliminary judging influence maximum signal to the comprehensive proofreading unit;
the secondary judgment unit is used for carrying out double judgment analysis processing on the received skill comprehensive coefficient Jsw, the environment complex coefficient Hfz and the equipment fusion coefficient Sbr, generating a secondary judgment influence minimum signal, a secondary judgment influence medium signal and a secondary judgment influence maximum signal according to the double judgment analysis processing, and sending the signals to the comprehensive proofreading unit;
the comprehensive proofreading unit is used for carrying out comprehensive judgment analysis processing on the received primary judgment influence type signal and secondary judgment influence type signal, generating a positive influence signal, an irrelevant influence signal and a general influence signal according to the comprehensive proofreading processing, and sending the positive influence signal, the irrelevant influence signal and the general influence signal to the inductive analysis unit;
the inductive analysis unit carries out inductive analysis processing on the received positive influence signal, irrelevant influence signal and general influence signal, and sends the signals to the display terminal in a text typeface mode for reference and research of technicians.
Example two:
as shown in fig. 1, a data acquisition unit is used for acquiring acquisition error factor information affecting the accuracy of geographic mapping acquisition and sending the information to a data qualitative unit;
when the data qualitative unit receives the acquisition error factor information, the data qualitative unit performs class-by-class data qualitative analysis processing according to the acquisition error factor information, and the specific operation steps are as follows:
s1: capturing a technical level index in the acquired error factor information in unit time, respectively calibrating the work age, position grade and skill depth in the technical level index as age, pos and sgs, normalizing the technical level index, and obtaining a skill comprehensive coefficient Jsw according to a formula Jsw-age + pos + sgs, wherein a1, a2 and a3 are correction factor coefficients of the work age, position grade and skill depth, a3 > a1 > a3 > 0, a1+ a2+ a 3-1.3641, and it needs to be noted that the correction factor coefficients are first factor coefficients for performing equalization correction on numerical errors occurring in formula calculation;
s2: capturing an environment level index in acquired error factor information in unit time, respectively calibrating a terrain value, a meteorological value and an population value of the environment level index into dxl, qxl and rwl, normalizing the dxl, qxl and rwl, and obtaining an environment complex coefficient Hfz according to a formula Hfz which is dxl + qxl + rwl, wherein b1, b2 and b3 are error factor coefficients of the terrain value, the meteorological value and the population value, b1 is more than b2 is more than b3 is more than 0, b1 is + b2+ b3 is 0.8948, and the error factor coefficients are used for expressing a class of factor coefficients of calculation errors generated by different units of various data;
s3: capturing an equipment level index in the acquired error factor information in unit time, respectively marking an equipment uniform value, an equipment performance value and an equipment category value of the equipment level index as stl, sxl and sfl, normalizing the equipment uniform value, the equipment performance value and the equipment category value, and obtaining an equipment fusion coefficient Sbr according to a formula Sbr ═ stl + sxl + sfl, wherein c1, c2 and c3 are normalization factor coefficients of the equipment uniform value, the equipment performance value and the equipment category value respectively, c2 > c1 > c3 > 0, c1+ c2+ c3 ═ 5.7101, and it needs to be stated that the normalization factor coefficients are used for unifying various data to the same level, so as to calculate and analyze a class factor coefficient;
s4: the generated skill comprehensive coefficient Jsw, the environment complex coefficient Hfz and the equipment fusion coefficient Sbr are sent to a primary judgment unit;
when the initial judgment unit receives various data coefficients, the initial qualitative analysis processing is carried out according to the data coefficients, and the specific operation steps are as follows:
capturing a skill comprehensive coefficient Jsw, an environment complex coefficient Hfz and an equipment fusion coefficient Sbr in various data coefficients in unit time, comparing the skill comprehensive coefficient, the environment complex coefficient Hfz and the equipment fusion coefficient Sbr with corresponding preset reference values Yu1, Yu2 and Yu3 respectively, generating a technical-level qualified signal if Jsw is larger than or equal to Yu1, generating a technical-level unqualified signal if Jsw is smaller than Yu1, generating an environment-level qualified signal if Hfz is smaller than Yu2, generating an environment-level unqualified signal if Hfz is larger than or equal to Yu2, generating an equipment-level qualified signal if Sbr is larger than or equal to Yu3, and generating an equipment-level unqualified signal if Sbr is smaller than Yu 3;
respectively calibrating a technical level qualified signal and a technical level unqualified signal into symbols J-1 and J-2, respectively calibrating an environment level qualified signal and an environment level unqualified signal into symbols H-1 and H-2, and respectively calibrating an equipment level qualified signal and an equipment level unqualified signal into symbols S-1 and S-2;
simultaneously capturing judgment calibration symbols J-1 or J-2, H-1 or H-2 and S-1 or S-2 of a technical level, an environment level and an equipment level in unit time, carrying out intersection processing on various judgment symbols, generating an initial judgment influence minimum signal if J-1 n H-1 n S-1 is equal to 1, generating an initial judgment influence maximum signal if J-2 n H-2 n S-2 is equal to 2, and generating initial judgment influence medium signals under other conditions;
and sending the generated initial judgment influence minimum signal, initial judgment influence medium signal and initial judgment influence maximum signal to the comprehensive proofreading unit.
Example three:
as shown in fig. 1, the data collection unit collects the collection error factor information affecting the accuracy of geographic mapping collection, and sends the information to the data qualitative unit;
when the data qualitative unit receives the acquisition error factor information, the data qualitative analysis processing is carried out class by class, and accordingly a skill comprehensive coefficient Jsw, an environment complex coefficient Hfz and an equipment fusion coefficient Sbr are respectively generated and subjected to secondary judgment;
when the secondary judgment unit receives the skill comprehensive coefficient Jsw, the environment complexity coefficient Hfz and the device fusion coefficient Sbr, the double judgment analysis processing is performed according to the received results, and the specific operation steps are as follows:
capturing a skill synthesis coefficient Jsw, an environment complexity coefficient Hfz and a device fusion coefficient Sbr in unit time according to a formula
Determining a double judgment coefficient Ecx, wherein e1, e2 and e3 are weight factor coefficients of a skill synthesis coefficient Jsw, an environment complexity coefficient Hfz and a device fusion coefficient Sbr, and e1 > e2 > e3 > 0, e1+ e2+ e3 is 4.3012, it should be noted that the weighting factor coefficient is used to indicate the importance degree of each item of data in the total amount, wherein, the skill synthesis coefficient Jsw and the device fusion coefficient Sbr are in inverse proportion to the environment complexity coefficient Hfz, that is, when the expression values of the skill synthesis coefficient Jsw and the device fusion coefficient Sbr are larger, the expression value of the environment complexity coefficient Hfz is smaller, the larger the expression value of the double judgment coefficient Ecx is, the larger the expression value of the double judgment coefficient Ecx is, and the smaller the influence of the acquired error on the acquisition accuracy of the surveying and mapping geographic information data is, the more beneficial the construction of the following surveying and mapping geographic information is;
substituting the double-decision coefficient Ecx into a preset influence threshold value Yu4 for comparison analysis, generating a minimum signal of the double-decision influence if the double-decision coefficient Ecx is greater than the maximum value of the preset influence threshold value Yu4, generating a medium signal of the double-decision influence if the double-decision coefficient Ecx is within a preset influence threshold value Yu4, and generating a maximum signal of the double-decision influence if the double-decision coefficient Ecx is less than the minimum value of the preset influence threshold value Yu 4;
and sending the generated second-judgment influence minimum signal, the generated second-judgment influence medium signal and the generated second-judgment influence maximum signal to the comprehensive proofreading unit.
Example four:
as shown in fig. 1, when the comprehensive calibration unit receives the primary judgment influence type signal and the secondary judgment influence type signal, the comprehensive judgment analysis processing is performed according to the signals, and the specific operation steps are as follows:
capturing a primary judgment influence minimum signal, a primary judgment influence medium signal and a primary judgment influence maximum signal in the primary judgment influence type signals, capturing a secondary judgment influence minimum signal, a secondary judgment influence medium signal and a secondary judgment influence maximum signal in the secondary judgment influence type signals, and carrying out comprehensive cross analysis on the two types of influence judgment signals;
if the signals which are simultaneously captured and integrated are respectively a primary judgment influence minimum signal and a secondary judgment influence minimum signal, generating an irrelevant influence signal, if the signals which are simultaneously captured and integrated are respectively a primary judgment influence maximum signal and a secondary judgment influence maximum signal, generating a positive influence signal, and otherwise, generating general influence signals;
sending the generated positive influence signal, irrelevant influence signal and general influence signal to an inductive analysis unit;
when the Danner analysis unit receives the positive influence signal, the irrelevant influence signal and the general influence signal, the induction analysis processing is carried out according to the positive influence signal, the irrelevant influence signal and the general influence signal, and the specific operation steps are as follows:
when a positive influence signal is received, generating a text sample, namely 'a phenomenon of larger acquisition error exists during data acquisition of mapping geographic information and the text sample is extremely not beneficial to construction of subsequent mapping geographic information';
when an irrelevant influence signal is received, generating a text typeface, wherein the phenomenon of acquisition errors hardly exists during data acquisition of mapping geographic information, and the text typeface is extremely favorable for construction of subsequent mapping geographic information;
when receiving the common influence signal, generating a text typeface, namely that certain acquisition error phenomenon exists during data acquisition of mapping geographic information and subsequent construction of mapping geographic information is not facilitated;
and the result of the inductive analysis is sent to a display terminal in a text typeface mode for technical staff to refer to and research.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions;
such as the formula:
collecting multiple groups of sample data and setting corresponding weight factor coefficient for each group of sample data by the technicians in the field; substituting the set weight factor coefficient and the acquired sample data into a formula, forming a ternary linear equation set by any three formulas, screening the calculated coefficients and taking the average value to obtain values of e1, e2 and e3 which are respectively 1.5015, 2.0471 and 0.7526;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and a corresponding weight factor coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relationship between the parameters and the quantized values is not affected.
When the method is used, various error factor information influencing the acquisition accuracy is accurately qualitatively pre-judged and analyzed by acquiring the acquisition error factor information influencing the acquisition accuracy of the geographic mapping data and utilizing a symbolic calibration and normalization processing mode;
according to the result of qualitative pre-judgment analysis, each item of collected error factor data is substituted into judgment analysis processing one by using a numerical reference comparison mode, and preliminary and direct judgment analysis is carried out on the data influenced by the errors collected by the geographic mapping information in a mode of signalization substitution output, symbol assignment calibration and collective calculation, so that the definite pre-judgment analysis on the error of the collected data is realized, a foundation is laid for ensuring the collection accuracy of the mapped geographic data, and the development and progress of a mapped geographic information system are promoted;
according to the result of qualitative pre-judgment analysis, a formulaic processing and threshold substitution analysis mode is utilized, secondary accurate judgment analysis is further carried out on error influence data acquired by the geographic mapping information, an integrated cross validation processing is carried out on a primary judgment influence type signal and a secondary judgment influence type signal in a comprehensive comparison judgment mode, a final judgment data signal for judging the accuracy of the geographic information acquisition of mapping is generated, and induction analysis is carried out in a text word mode;
by accurately predicting and analyzing errors affecting the geographic mapping information acquisition data from different angles and by using different methods, the accuracy of the geographic mapping information data acquisition is improved while the definite prediction and analysis of the error of the acquisition data is realized, and the efficient development and progress of a mapping geographic information system are promoted.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (5)
1. A mapping geographic information data acquisition system based on cloud computing is characterized by comprising a data acquisition unit, a data qualitative unit, a primary judgment unit, a secondary judgment unit, a comprehensive correction unit, an inductive analysis unit and a display terminal;
the data acquisition unit is used for acquiring acquisition error factor information influencing the acquisition accuracy of geographic mapping and sending the acquisition error factor information to the data qualitative unit;
the data qualitative unit is used for carrying out data qualitative analysis processing on the received acquisition error factor information by class, respectively generating a skill comprehensive coefficient Jsw, an environment complex coefficient Hfz and an equipment fusion coefficient Sbr according to the data qualitative analysis processing, and respectively sending the data qualitative analysis processing to the primary judgment unit and the secondary judgment unit;
the preliminary judgment unit carries out preliminary qualitative analysis processing on various received data coefficients, and the specific operation steps are as follows:
capturing a skill comprehensive coefficient Jsw, an environment complex coefficient Hfz and an equipment fusion coefficient Sbr in various data coefficients in unit time, comparing the skill comprehensive coefficient, the environment complex coefficient Hfz and the equipment fusion coefficient Sbr with corresponding preset reference values Yu1, Yu2 and Yu3 respectively, generating a technical-level qualified signal if Jsw is larger than or equal to Yu1, generating a technical-level unqualified signal if Jsw is smaller than Yu1, generating an environment-level qualified signal if Hfz is smaller than Yu2, generating an environment-level unqualified signal if Hfz is larger than or equal to Yu2, generating an equipment-level qualified signal if Sbr is larger than or equal to Yu3, and generating an equipment-level unqualified signal if Sbr is smaller than Yu 3;
respectively calibrating a technical level qualified signal and a technical level unqualified signal into symbols J-1 and J-2, respectively calibrating an environment level qualified signal and an environment level unqualified signal into symbols H-1 and H-2, and respectively calibrating an equipment level qualified signal and an equipment level unqualified signal into symbols S-1 and S-2;
simultaneously capturing judgment calibration symbols J-1 or J-2, H-1 or H-2 and S-1 or S-2 of a technical level, an environmental level and an equipment level in unit time, carrying out intersection processing on various judgment symbols, generating an initial judgment influence minimum signal if J-1 n H-1 n S-1=1, generating an initial judgment influence maximum signal if J-2 n H-2 n S-2=2 is satisfied, generating initial judgment influence medium signals under other conditions, and sending the generated initial judgment influence minimum signal, initial judgment influence medium signal and initial judgment influence maximum signal to a comprehensive proofreading unit;
the secondary judgment unit is used for performing double judgment analysis processing on the received skill comprehensive coefficient Jsw, the environment complex coefficient Hfz and the device fusion coefficient Sbr, and comprises the following specific operation steps:
capturing a skill synthesis coefficient Jsw, an environment complexity coefficient Hfz and a device fusion coefficient Sbr in unit time according to a formula
Obtaining a double judgment coefficient Ecx, wherein e1, e2 and e3 are weight factor coefficients of a skill comprehensive coefficient Jsw, an environment complexity coefficient Hfz and a device fusion coefficient Sbr, respectively, e1 > e2 > e3 > 0, and e1+ e2+ e3= 4.3012;
substituting the double judgment coefficient Ecx into a preset influence threshold value Yu4 for comparison analysis, generating a minimum two-judgment influence signal if the double judgment coefficient Ecx is larger than the maximum value of the preset influence threshold value Yu4, generating a medium two-judgment influence signal if the double judgment coefficient Ecx is within a preset influence threshold value Yu4, generating a maximum two-judgment influence signal if the double judgment coefficient Ecx is smaller than the minimum value of the preset influence threshold value Yu4, and sending the generated minimum two-judgment influence signal, the generated medium two-judgment influence signal and the generated maximum two-judgment influence signal to a comprehensive proofreading unit;
the comprehensive proofreading unit is used for carrying out comprehensive judgment analysis processing on the received primary judgment influence type signal and secondary judgment influence type signal, generating a positive influence signal, an irrelevant influence signal and a general influence signal according to the comprehensive proofreading processing, and sending the positive influence signal, the irrelevant influence signal and the general influence signal to the inductive analysis unit;
the inductive analysis unit carries out inductive analysis processing on the received positive influence signal, irrelevant influence signal and general influence signal, and sends the inductive analysis processing to the display terminal in a text typeface mode for technical staff to refer and research.
2. The system for collecting data of geographic information for mapping based on cloud computing according to claim 1, wherein the collected error factor information is used to represent factor information affecting the accuracy of collected geographic mapping data in the process of geographic mapping, and the collected error factor information includes technical level indexes, environmental level indexes and equipment level indexes, wherein the technical level indexes are used to represent comprehensive data information of each skill of technicians in geographic mapping, and the technical level indexes include job age, job level and skill depth;
the environment level indexes are used for representing comprehensive data information of the complexity of the topographic environment, the meteorological environment and the human environment of the unit area where the geographical mapping is located, and comprise a topographic quantity value, a meteorological quantity value and a population quantity value;
the equipment level index is used for representing the performance data value of the completeness of the mapping work completed by various mapping equipment, and the equipment level index comprises an equipment uniform quantity value, an equipment performance quantity value and an equipment category quantity value.
3. The system for collecting data of mapping geographic information based on cloud computing according to claim 1, wherein the specific operation steps of the qualitative analysis and processing of data by category are as follows:
s1: capturing technical level indexes in error factor information collected in unit time, respectively calibrating the work age, position level and skill depth in the technical level indexes as age, pos and sgs, and carrying out normalization processing on the technical level indexes to obtain a skill comprehensive coefficient Jsw;
s2: capturing an environment level index in the collected error factor information in unit time, respectively marking a terrain value, a meteorological value and a population value of the environment level index as dxl, qxl and rwl, and normalizing the dxl, qxl and rwl to obtain an environment complexity coefficient Hfz;
s3: and capturing the equipment level indexes in the acquired error factor information in unit time, respectively marking the equipment unified quantity value, the equipment performance quantity value and the equipment category quantity value of the equipment level indexes as stl, sxl and sfl, and carrying out normalization processing on the values to obtain an equipment fusion coefficient Sbr.
4. The system for collecting data of mapping geographic information based on cloud computing according to claim 1, wherein the specific operation steps of the comprehensive decision analysis processing are as follows:
capturing a primary judgment influence minimum signal, a primary judgment influence medium signal and a primary judgment influence maximum signal in the primary judgment influence type signals, capturing a secondary judgment influence minimum signal, a secondary judgment influence medium signal and a secondary judgment influence maximum signal in the secondary judgment influence type signals, and carrying out comprehensive cross analysis on the two types of influence judgment signals;
if the signals to be simultaneously captured and integrated are respectively the initial judgment influence minimal signal and the secondary judgment influence minimal signal, an irrelevant influence signal is generated, if the signals to be simultaneously captured and integrated are respectively the initial judgment influence maximal signal and the secondary judgment influence maximal signal, a positive influence signal is generated, and otherwise, general influence signals are generated.
5. The cloud-computing-based surveying and mapping geographic information data acquisition system according to claim 1, wherein the specific operation steps of the inductive analysis processing are as follows:
when a positive influence signal is received, generating a text typeface, namely 'a larger acquisition error phenomenon exists during data acquisition of mapping geographic information and the text typeface is extremely not beneficial to construction of subsequent mapping geographic information';
when an irrelevant influence signal is received, generating a text typeface, wherein the phenomenon of acquisition errors hardly exists during data acquisition of mapping geographic information, and the text typeface is extremely favorable for construction of subsequent mapping geographic information;
when receiving the general influence signal, generating the text word "there is certain collection error phenomenon during data collection of mapping geographic information, and is not favorable for the construction of the subsequent mapping geographic information".
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210131683.9A CN114168906B (en) | 2022-02-14 | 2022-02-14 | Mapping geographic information data acquisition system based on cloud computing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210131683.9A CN114168906B (en) | 2022-02-14 | 2022-02-14 | Mapping geographic information data acquisition system based on cloud computing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114168906A CN114168906A (en) | 2022-03-11 |
| CN114168906B true CN114168906B (en) | 2022-05-06 |
Family
ID=80489875
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210131683.9A Active CN114168906B (en) | 2022-02-14 | 2022-02-14 | Mapping geographic information data acquisition system based on cloud computing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114168906B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114454179B (en) * | 2022-03-18 | 2022-10-14 | 深圳威洛博机器人有限公司 | Automatic correction system and method for joints of intelligent robot hand |
| CN114741691A (en) * | 2022-05-05 | 2022-07-12 | 福州年科信息科技有限公司 | Computer software protection system based on cloud computing |
| CN114693281B (en) * | 2022-06-01 | 2022-09-02 | 山东志诚地理信息技术有限公司 | Engineering investigation information management system based on cloud platform |
| CN115062653B (en) * | 2022-06-09 | 2023-04-07 | 山东龙源电力工程有限公司 | Analysis maintenance system based on steam turbine of thermal power plant |
| CN114882025B (en) * | 2022-07-08 | 2022-09-06 | 山东一图百景信息技术有限公司 | Geographic mapping data acquisition and processing system based on big data |
| CN116302624B (en) * | 2023-05-12 | 2023-07-25 | 深圳市睿拓新科技有限公司 | Engineering project mapping data intelligent management system |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109241069A (en) * | 2018-08-23 | 2019-01-18 | 中南大学 | A kind of method and system that the road network based on track adaptive cluster quickly updates |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8612178B2 (en) * | 2005-05-18 | 2013-12-17 | Bae Systems Information And Electronic Systems Integration Inc. | Geolocation using high order statistics |
| CN108279428B (en) * | 2017-01-05 | 2020-10-16 | 武汉四维图新科技有限公司 | Map data evaluating device and system, data acquisition system, acquisition vehicle and acquisition base station |
| US20200034935A1 (en) * | 2018-07-24 | 2020-01-30 | MediaAgility Inc | System and method for intelligent subsidence risk analysis using gis data |
| CN109086935B (en) * | 2018-08-07 | 2021-03-30 | 中国地质环境监测院 | Regional geological disaster trend prediction method |
| CN112229587A (en) * | 2020-10-10 | 2021-01-15 | 哈尔滨工业大学 | Method for indirectly measuring dynamic deflection of high-speed rail bridge based on inclinometer |
-
2022
- 2022-02-14 CN CN202210131683.9A patent/CN114168906B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109241069A (en) * | 2018-08-23 | 2019-01-18 | 中南大学 | A kind of method and system that the road network based on track adaptive cluster quickly updates |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114168906A (en) | 2022-03-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114168906B (en) | Mapping geographic information data acquisition system based on cloud computing | |
| CN112308292B (en) | Method for drawing fire risk grade distribution map | |
| Lamb | Calibration of a conceptual rainfall‐runoff model for flood frequency estimation by continuous simulation | |
| CN110009226B (en) | Site pollution risk level evaluation and sensitive receptor influence identification system | |
| CN108241574A (en) | A kind of method and system analyzed based on test and management tool QC software test defect | |
| CN111951097A (en) | Enterprise credit risk assessment method, device, equipment and storage medium | |
| CN111401419A (en) | Improved RetinaNet-based employee dressing specification detection method | |
| CN115062675A (en) | Full-spectrum pollution tracing method based on neural network and cloud system | |
| CN116205310B (en) | Soil water content influence factor sensitive interval judging method based on interpretable integrated learning model | |
| CN114693281B (en) | Engineering investigation information management system based on cloud platform | |
| CN113360587B (en) | Land surveying and mapping equipment and method based on GIS technology | |
| CN115097796B (en) | Quality control system and method for simulating big data and correcting AQL value | |
| CN117538503A (en) | Real-time intelligent soil pollution monitoring system and method | |
| CN116229278B (en) | Method and system for detecting rust defect of vibration damper of power transmission line | |
| CN117114176A (en) | Land utilization change prediction method and system based on data analysis and machine learning | |
| CN116702588A (en) | Wind power plant key weather factor forecasting method and system based on multi-source data | |
| CN113344247B (en) | Deep learning-based power facility site selection prediction method and system | |
| CN113723871B (en) | Multi-source information-based current situation flood consistency processing method and system | |
| CN109101759A (en) | A kind of parameter identification method based on forward and reverse response phase method | |
| CN113822564A (en) | Flight plan minimum sample size confirmation method and device for airspace simulation analysis | |
| CN113222384A (en) | Town level analysis method based on improved entropy method | |
| CN112434750B (en) | Dam monitoring data development mode identification method based on convolutional neural network | |
| CN113297998B (en) | Method, device, equipment and storage medium for identifying territorial space planning problem | |
| Qian et al. | Equity Evaluation of Global Countries based on the Expanded Theil index and Factor Analysis | |
| CN117076454B (en) | Engineering quality acceptance form data structured storage method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |